Power transformer winding insulation state monitoring method, device, equipment and medium

Abstract

The invention discloses a power transformer winding insulation state monitoring method, a power transformer winding insulation state monitoring device, power transformer winding insulation state monitoring equipment and a medium, and relates to the technical field of power system equipment state monitoring. And an improved particle swarm optimization algorithm is adopted to inversely calculate the insulation parameters of a plurality of windings of the transformer to the ground, so that the insulation state of each winding can be accurately evaluated. Compared with a traditional monitoring method, the method has the advantages that rich information contained in the common-mode leakage current can be fully utilized, the insulation degradation conditions of different windings can be accurately distinguished, more accurate insulation state evaluation is provided, and the safe operation level of the transformer is effectively improved. Moreover, multi-stage evaluation is carried out on the insulation state through a fuzzy comprehensive evaluation method, the insulation state can be comprehensively reflected, different severity degrees of the insulation state can be distinguished, the current state can be evaluated, the degradation trend can be predicted through dynamic indexes, and a scientific basis is provided for operation and maintenance decision making.

Description

Technical Field

[0001] The present application relates to the technical field of power system equipment status monitoring, and in particular to a method, device, equipment and medium for monitoring the insulation status of a power transformer winding. Background Art

[0002] Power transformers are critical equipment in power systems, and their safe and stable operation significantly impacts the reliability of the entire power grid. The insulation condition of transformer windings is a key factor in determining their safe operation. As transformers age, the winding insulation material gradually degrades due to various factors, including thermal aging, electrical stress, mechanical stress, and environmental factors. This can eventually lead to insulation breakdown and serious power outages. Consequently, insulation condition monitoring technology based on common-mode leakage current (CMLC) has gained increasing attention. CMLC refers to the current flowing into the earth through the transformer's grounding system and is closely related to insulation condition. As insulation performance degrades, CMLC increases accordingly.

[0003] In the related art, most monitoring methods based on common-mode leakage current directly measure the amplitude or phase of the leakage current. However, the applicant recognizes that existing monitoring methods often monitor the transformer as a whole and fail to fully utilize the rich information contained in the leakage current, making it impossible to accurately locate the specific location of insulation degradation, nor to promptly detect early signs of insulation degradation, resulting in poor real-time monitoring and low sensitivity, making it difficult to achieve accurate assessment and early warning of the insulation status. Summary of the Invention

[0004] In view of this, the present application provides a method, device, equipment and medium for monitoring the insulation status of power transformer windings. The main purpose is to solve the problem that existing monitoring methods cannot accurately locate the specific location of insulation degradation, nor can they promptly detect signs of early insulation degradation, resulting in poor real-time monitoring and low sensitivity, making it difficult to achieve accurate assessment and early warning of the insulation status.

[0005] According to a first aspect of the present application, a method for monitoring the insulation status of a power transformer winding is provided, the method comprising:

[0006] constructing a fully coupled network model of a transformer multi-conductor transmission line according to the transformer voltage spectrum characteristics, the physical structure of the transformer winding, and the electrical characteristics; extracting insulation state related parameters from the fully coupled network model of the transformer multi-conductor transmission line; and constructing an insulation parameter equivalent circuit model of the transformer using the insulation state related parameters;

[0007] Acquire real-time common-mode leakage current data collected by a common-mode leakage current multi-parameter measurement system, and process the real-time common-mode leakage current data using a fast Fourier transform algorithm to obtain key common-mode leakage current data;

[0008] Extracting insulation parameters from the transformer insulation parameter equivalent circuit model, and performing iterative inversion calculation on the insulation parameters and key data of the common-mode leakage current based on an improved particle swarm optimization algorithm to obtain target transformer insulation parameters;

[0009] The insulation state of the target transformer is evaluated based on the fuzzy comprehensive evaluation method to obtain the insulation state detection result of the transformer winding.

[0010] According to a second aspect of the present application, a device for monitoring the insulation condition of a power transformer winding is provided, the device comprising:

[0011] A construction module is used to construct a fully coupled network model of a transformer multi-conductor transmission line based on the transformer voltage spectrum characteristics, the physical structure of the transformer winding, and the electrical characteristics; extract insulation state related parameters from the fully coupled network model of the transformer multi-conductor transmission line; and construct an insulation parameter equivalent circuit model of the transformer using the insulation state related parameters;

[0012] An acquisition module is used to acquire real-time common-mode leakage current data collected based on a common-mode leakage current multi-parameter measurement system, and to process the real-time common-mode leakage current data using a fast Fourier transform algorithm to obtain key common-mode leakage current data;

[0013] A calculation module is used to extract insulation parameters from the transformer insulation parameter equivalent circuit model, and perform iterative inversion calculation on the insulation parameters and the common-mode leakage current key data based on an improved particle swarm optimization algorithm to obtain target transformer insulation parameters;

[0014] The evaluation module is used to evaluate the insulation status of the target transformer insulation parameters based on a fuzzy comprehensive evaluation method to obtain a transformer winding insulation status detection result.

[0015] According to a third aspect of the present application, a device is provided, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the methods described in the first aspect when executing the computer program.

[0016] According to a fourth aspect of the present application, a medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of any one of the methods in the first aspect are implemented.

[0017] By means of the above technical solution, the technical solution provided by the embodiment of the present application has at least the following advantages:

[0018] The present application provides a method, device, equipment and medium for monitoring the insulation status of power transformer windings. The present application establishes a detailed circuit model of the transformer and, based on the measurement data of the common-mode leakage current, uses an improved particle swarm optimization algorithm to inversely calculate the insulation parameters of multiple transformer windings to the ground, thereby achieving an accurate assessment of the insulation status of each winding. Compared with traditional monitoring methods, the present application can make full use of the rich information contained in the common-mode leakage current, accurately distinguish the insulation degradation of different windings, provide a more accurate insulation status assessment, and effectively improve the safe operation level of the transformer. Moreover, the present application uses a fuzzy comprehensive evaluation method to perform a multi-level assessment of the insulation status, which can comprehensively reflect the insulation status and distinguish the different severity levels of the insulation status. It can not only assess the current status, but also predict the degradation trend through dynamic indicators, providing a scientific basis for operation and maintenance decisions.

[0019] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present application. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0021] Figure 1 A schematic flow chart of a method for monitoring the insulation status of a power transformer winding provided in an embodiment of the present application is shown;

[0022] Figure 2A A schematic flow chart of another method for monitoring the insulation status of a power transformer winding provided in an embodiment of the present application is shown;

[0023] Figure 2B A partial schematic diagram of an initial circuit model provided by an embodiment of the present application is shown;

[0024] Figure 2C Another partial schematic diagram of the initial circuit model provided by an embodiment of the present application is shown;

[0025] Figure 2D A schematic diagram of an equivalent circuit model of transformer insulation parameters provided in an embodiment of the present application is shown;

[0026] Figure 3A A schematic diagram of a structure of a power transformer winding insulation state monitoring system provided by an embodiment of the present application is shown;

[0027] Figure 3B A schematic diagram of another structure of power transformer winding insulation status monitoring provided by an embodiment of the present application is shown;

[0028] Figure 4 A schematic diagram of the device structure of a device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0029] In the description of this application, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, "plurality" means two or more, unless otherwise specifically defined.

[0030] The following describes exemplary embodiments of the present application in more detail with reference to the accompanying drawings. Although exemplary embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present application and to fully convey the scope of the present application to those skilled in the art.

[0031] Traditional transformer insulation condition monitoring methods mainly include the following categories:

[0032] 1. Offline testing methods: Such as insulation resistance measurement, dielectric loss factor measurement, and partial discharge measurement. However, these methods require the transformer to be taken out of service, which not only affects power supply continuity but also increases equipment downtime and maintenance costs. Furthermore, offline testing only reflects the insulation condition at a specific point in time and cannot provide real-time monitoring and trend analysis of insulation conditions. 2. Dissolved Gas Analysis (DGA): This method analyzes the composition and content of dissolved gas in the transformer oil to determine whether the transformer is experiencing internal faults such as overheating and discharge. However, DGA suffers from a lag: by the time the gas content in the oil reaches a detectable level, the insulation may already be severely damaged. Furthermore, DGA primarily reflects the overall condition of the transformer and is difficult to pinpoint to specific winding locations. 3. Partial Discharge Detection: This method assesses insulation condition by detecting partial discharge signals generated by insulation defects. However, this method is susceptible to external electromagnetic interference, is complex to extract and process, and is insensitive to early-stage insulation degradation. 4. Frequency Response Analysis (FRA): This method analyzes the changes in the transformer's response characteristics at different frequencies to determine the mechanical deformation and displacement of the winding. However, FRA is difficult to effectively detect subtle changes in the early stages of insulation aging and requires specialized equipment and personnel. Each of these methods has limitations in practical application, particularly in detecting early stages of transformer winding insulation degradation, making it difficult to accurately assess and provide early warnings of insulation conditions. Therefore, a new method for real-time, sensitive, and accurate monitoring of transformer winding insulation conditions is urgently needed.

[0033] In recent years, insulation condition monitoring technology based on common-mode leakage current has gradually attracted attention. However, most existing common-mode leakage current-based monitoring methods directly measure the amplitude or phase of the leakage current, failing to fully utilize the rich information contained in the leakage current. It is also difficult to distinguish leakage current changes caused by insulation degradation from leakage current fluctuations caused by changes in normal operating conditions. In addition, traditional methods often monitor the transformer as a whole, making it difficult to distinguish the insulation conditions of different windings and accurately locate the specific location of insulation deterioration. This makes it difficult for maintenance personnel to carry out targeted preventive maintenance, reducing maintenance efficiency and increasing maintenance costs. At the same time, the transformer insulation system is a complex multi-parameter system, and monitoring a single parameter cannot fully reflect the insulation condition. For example, monitoring only the amplitude changes of the leakage current may ignore important information such as its spectral characteristics and phase relationships, resulting in incomplete and inaccurate monitoring results.

[0034] In general, existing transformer winding insulation condition monitoring methods suffer from poor real-time performance, low accuracy, and an inability to locate specific windings. To address these issues, this application proposes a power transformer winding insulation condition monitoring method based on common-mode leakage current inversion. Unlike traditional direct measurement methods, this method establishes a detailed circuit model of the transformer and, based on common-mode leakage current measurement data, inversely calculates the insulation parameters of the transformer's high- and low-voltage windings relative to ground, enabling accurate assessment of the insulation condition of each winding. By fully leveraging the rich information contained in the common-mode leakage current, it is possible to distinguish the insulation conditions of different windings and eliminate interference caused by varying operating conditions, thereby improving the accuracy and reliability of monitoring. Furthermore, the non-invasive online monitoring method eliminates the need to change the transformer's wiring configuration and does not affect normal transformer operation, significantly improving the practicality and operability of monitoring. By installing high-precision current sensors at the neutral point and casing grounding lines on the transformer's high- and low-voltage sides, combined with a multi-channel data acquisition system, real-time acquisition and processing of common-mode leakage current is achieved, providing basic data support for subsequent parameter inversion and condition assessment. In addition, the present application also innovatively adopts an improved particle swarm optimization algorithm to perform inverse calculation on the insulation parameters, and performs multi-level evaluation on the insulation status through a fuzzy comprehensive evaluation method, which can distinguish the different severity of the insulation status and provide a scientific basis for operation and maintenance decisions. Moreover, the present application also considers the influence of environmental and operating factors such as temperature, humidity, and load on the insulation parameters, and makes corrections by establishing a compensation model, thereby improving the accuracy and reliability of the evaluation results. The execution subject of the present application can be a condition monitoring system, which relies on the computing power of the server to provide services to users. The server can be an independent server, or it can provide cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and servers that provide basic cloud computing such as big data and artificial intelligence platforms, so that the condition monitoring system can accurately distinguish the insulation degradation of different windings, provide more accurate insulation status evaluation, and effectively improve the safe operation level of the transformer.

[0035] The present application provides a method for monitoring the insulation status of a power transformer winding. Figure 1 As shown, the method includes:

[0036] 101. A fully coupled network model of a transformer multi-conductor transmission line is constructed based on the transformer voltage spectrum characteristics, the physical structure of the transformer winding, and the electrical characteristics. Insulation state related parameters are extracted from the fully coupled network model of the transformer multi-conductor transmission line, and an equivalent circuit model of the transformer insulation parameters is constructed using the insulation state related parameters.

[0037] In the embodiment of the present application, the state monitoring system comprehensively considers the voltage spectrum characteristics, winding physical structure and electrical characteristics of the transformer, covers many factors, and can more comprehensively reflect the actual operation of the transformer. Compared with building a model based on a single factor, it is more accurate in describing the overall operating state of the transformer and will not miss key influencing factors. Moreover, based on the accurate physical structure and electrical characteristics of the winding, the constructed multi-conductor transmission line fully coupled network model is closely aligned with the actual physical condition of the transformer, laying an accurate foundation for subsequent parameter extraction and equivalent circuit model construction, and can accurately present the internal electromagnetic relationship of the transformer. Secondly, insulation state-related parameters are extracted from the multi-conductor transmission line fully coupled network model. These parameters are directly related to the insulation state, can accurately reflect the insulation condition of the transformer, provide core data for insulation state evaluation, avoid interference from irrelevant parameters, and focus on key indicators.

[0038] 102. Acquire real-time common-mode leakage current data collected by a common-mode leakage current multi-parameter measurement system, process the real-time common-mode leakage current data using a fast Fourier transform algorithm, and obtain key common-mode leakage current data.

[0039] In an embodiment of the present application, the status monitoring system uses a common-mode leakage current multi-parameter measurement system to collect data, which can obtain common-mode leakage current information in multiple dimensions, which is more comprehensive than a single parameter measurement. Different parameters can reflect the insulation status, electromagnetic interference and other conditions of the equipment from different angles, which helps to analyze the operating status of the equipment more accurately. Moreover, the collection of real-time data can capture the dynamic changes of the common-mode leakage current in a timely manner. During the operation of the equipment, its electrical performance can be monitored in real time, and it can respond quickly to sudden faults or abnormalities, meeting the timeliness requirements of online monitoring. Secondly, the Fast Fourier Transform (FFT) algorithm can convert the real-time data of the common-mode leakage current in the time domain into the frequency domain, quickly extract key data such as the harmonic components of the leakage current and their phases, greatly shorten the data processing time, and provide accurate data for the subsequent insulation parameter inversion and insulation status evaluation based on harmonic analysis, which helps to more accurately judge the insulation condition of the transformer.

[0040] 103. Insulation parameters are extracted from the transformer insulation parameter equivalent circuit model. Based on the improved particle swarm optimization algorithm, the insulation parameters and common-mode leakage current key data are iteratively inverted and calculated to obtain the target transformer insulation parameters.

[0041] In an embodiment of the present application, the state monitoring system extracts insulation parameters from the transformer insulation parameter equivalent circuit model. Since the transformer insulation parameter equivalent circuit model is specially constructed based on the transformer characteristics, it can accurately reflect the insulation-related electrical characteristics, making the extracted parameters highly targeted and closely related to the actual insulation state of the transformer, providing a reliable data basis for subsequent analysis. Moreover, the equivalent circuit model comprehensively considers multiple factors, and the extracted insulation parameters cover many aspects such as insulation resistance and capacitance, which can fully reflect the characteristics of the insulation system and avoid the one-sidedness of single parameter evaluation. Secondly, the improved particle swarm optimization algorithm has an efficient global search capability and can quickly find the parameter combination that best matches the insulation parameters and common-mode leakage current key data in a complex parameter space. Compared with traditional algorithms, the iteration efficiency is higher and the optimal solution is approached faster. Through iterative inversion calculation, the insulation parameters are continuously adjusted so that the calculation results of the equivalent circuit model at all harmonic frequency points are highly consistent with the measured common-mode leakage current data in amplitude and phase, thereby improving the accuracy of insulation parameter calculation and more accurately quantifying the degree of insulation degradation. In addition, the improved algorithm can optimize the design for the inversion problem of transformer insulation parameters, can better adapt to transformers with different operating conditions and structural parameters, can effectively solve the insulation parameters in a variety of situations, and has a wider range of applications.

[0042] 104. The insulation parameters of the target transformer are evaluated based on the fuzzy comprehensive evaluation method to obtain the insulation status detection results of the transformer winding.

[0043] In the embodiment of the present application, the insulation state of the transformer is affected by many factors, such as temperature, humidity, degree of aging, etc., which are inherently fuzzy and uncertain. The fuzzy comprehensive evaluation method is based on fuzzy mathematics theory and can effectively process this type of uncertain information, which is more in line with the actual characteristics of the insulation state of the transformer. For example, the degree of insulation aging is difficult to define with precise numerical values, and the fuzzy comprehensive evaluation method can be reasonably evaluated through concepts such as membership. At the same time, the fuzzy comprehensive evaluation method can use multiple insulation parameters as evaluation factors, comprehensively consider the weights of each factor, and comprehensively evaluate the insulation state, avoiding the one-sidedness of a single parameter evaluation, and improving the accuracy and reliability of the insulation state evaluation. Therefore, the present application realizes accurate regional monitoring of the insulation state of each winding of the transformer by establishing an equivalent circuit model of the transformer insulation parameters and applying common-mode leakage current inversion technology. Compared with traditional monitoring methods, it can accurately distinguish the insulation degradation of different windings, provide more accurate insulation state evaluation, and effectively improve the safe operation level of the transformer.

[0044] The embodiment of the present application provides a method for monitoring the insulation status of a power transformer winding. Compared with the prior art, the embodiment of the present application establishes a detailed circuit model of the transformer and, based on the measurement data of the common-mode leakage current, uses an improved particle swarm optimization algorithm to inversely calculate the insulation parameters of multiple transformer windings to the ground, thereby achieving an accurate assessment of the insulation status of each winding. Compared with traditional monitoring methods, the present application can make full use of the rich information contained in the common-mode leakage current, accurately distinguish the insulation degradation of different windings, provide a more accurate insulation status assessment, and effectively improve the safe operation level of the transformer. Moreover, the present application uses a fuzzy comprehensive evaluation method to perform a multi-level assessment of the insulation status, which can comprehensively reflect the insulation status and distinguish the different severity levels of the insulation status. It can not only assess the current status, but also predict the degradation trend through dynamic indicators, providing a scientific basis for operation and maintenance decisions.

[0045] Furthermore, as a refinement and expansion of the specific implementation of the above embodiment, in order to fully illustrate the specific implementation process of this embodiment, the embodiment of the present application provides another method for monitoring the insulation status of a power transformer winding, such as Figure 2A As shown, the method includes:

[0046] 201. A fully coupled network model of a transformer multi-conductor transmission line is constructed based on the transformer voltage spectrum characteristics, transformer winding physical structure and electrical characteristics.

[0047] In the embodiment of the present application, for the transformer, it is first necessary to understand the spectrum characteristics of its operating voltage. r The harmonic amplitude of the symmetrical pulse-width modulated (PWM) voltage can be expressed as a specific formula. This step is very critical because the spectral characteristics of the PWM waveform determine the distribution of electrical stress on the insulation system. For example, for a transformer driven by a wide bandgap device, the rise time can be as low as 100ns, so the spectral bandwidth can reach several MHz, so the impact of these high-frequency harmonics on the insulation state must be considered. For a transformer with a fundamental frequency of f1 and a rise time of f r The harmonic amplitude formula of the symmetrical PWM voltage is as follows:

[0048] Formula 1:

[0049] The spectrum bandwidth can be determined by its angular frequency f c Expressed as follows:

[0050] Formula 2:

[0051] Where f1 represents the fundamental frequency of the PWM voltage, t r Indicates the rise time of PWM voltage, h indicates the harmonic number, Indicates the amplitude of the fundamental wave of the PWM voltage, It represents the amplitude of the hth harmonic, that is, the peak value of the harmonic voltage with a frequency of h times the fundamental frequency, f c Indicates the spectrum bandwidth of the PWM voltage signal.

[0052] When building a complete transformer multi-conductor transmission line fully coupled network model, it is necessary to represent each winding turn as a basic unit with distributed parameters. Among them, the current and voltage can be assumed to be uniformly distributed within the turn. Taking a simplified 3:3 turn transformer as an example, the following is established: Figure 2B and Figure 2C The initial circuit model shown, Figure 2C yes Figure 2B The omitted part in Figure 2B and Figure 2C Together, they form a complete initial circuit model. By defining the voltage vector, current vector, and source voltage vector, and establishing a multi-conductor transmission line equation in the frequency domain, which includes the correlation matrix K, the resistance matrix R, the inductance matrix L, and the capacitance matrix C, the electrical characteristics of the transformer under high-frequency conditions can be fully described.

[0053] Specifically, the condition monitoring system constructs an initial circuit model based on the transformer voltage spectrum characteristics, the transformer winding physical structure and electrical characteristics, as shown in the following formula 3:

[0054] Formula 3:

[0055]

[0056] V0=HV g ,

[0057] I0=TV g ,

[0058] Where K represents the correlation matrix, R represents the resistance matrix, L represents the inductance matrix, C represents the capacitance matrix, and j represents the imaginary unit. ω represents the angular frequency in radians per second, and its relationship with the frequency f is ω = 2πf, V represents the voltage vector, I represents the current vector, V g represents the source voltage vector, n v Represents the total number of voltage nodes, v1 represents the voltage value of the first voltage node, v2 represents the voltage value of the second voltage node, v4 represents the voltage value of the third voltage node, v5 represents the voltage value of the fourth voltage node, and n irepresents the total number of current nodes, i1 represents the current value of the first current node, i2 represents the current value of the second current node, i3 represents the current value of the third current node, i4 represents the current value of the fourth current node, i5 represents the current value of the fifth current node, i6 represents the current value of the sixth current node, and n g represents the total number of coupling groups, v HV,1 Represents the voltage value of the first high-voltage side coupling group, v HV,2 Indicates the voltage value of the second high-voltage side coupling group, v LV,1 Represents the voltage value of the first low-voltage side coupling group, v LV,2 represents the voltage value of the second low-voltage side coupling group, V0 represents the input voltage vector, I0 represents the input current vector, T represents the first matrix, and H represents the second matrix.

[0059] To obtain accurate model parameters, the condition monitoring system utilizes 3D finite element simulation. Calculating parameters R and L requires solving the magnetostatic field equations, taking into account the complex permeability of the core, homogenization of the stranded conductors, and frequency-dependent effects. Calculating parameter C requires solving the electrostatic field equations, taking into account the core grounding condition and assuming that the dielectric constant remains constant over the considered frequency range. The K, T, and H matrices are related to the direct connection structure of the transformer windings.

[0060] First, the condition monitoring system uses 3D finite element simulation to obtain the target model parameters of the initial circuit model. The target model parameters include the target correlation matrix K, the target resistance matrix R, the target inductance matrix L, and the target capacitance matrix C. The specific process is as follows:

[0061] Target Interaction Matrix K Calculation Method: The target interaction matrix K is obtained by analyzing the actual physical connection topology of the transformer windings. For each current branch and node, the matrix elements are set to +1 (inflow), -1 (outflow), or 0 (disconnection), depending on the direction of current flow. The target interaction matrix K can be obtained directly from the circuit diagram without requiring a field solution; it is completely determined by the transformer winding connection structure.

[0062] Calculation Method for the Target Resistance Matrix R: The target resistance matrix R is obtained by solving the magnetostatic field equations using the 3D finite element method. For the winding resistance, the complete resistance matrix is ​​obtained by applying a unit current excitation to each conductor. The calculation process takes into account the conductivity of the conductor material, the frequency-dependent skin effect, and the homogenization of the twisted conductor. The magnetostatic field equations are shown in Equation 4 below:

[0063] Formula 4:

[0064] Among them, Formula 4 is a partial differential equation describing the magnetic vector potential A in the electromagnetic field, which is commonly used in eddy current or time-harmonic electromagnetic field problems. represents the gradient operator, μ represents the magnetic permeability, A represents the magnetic vector potential, j represents the imaginary unit, ω represents the angular frequency, σ represents the conductivity, and J0 represents the source current density.

[0065] For the insulation equivalent resistance, the dielectric loss angle is considered and the corresponding relative dielectric constant and loss angle are assigned to each insulating material. During the calculation, a unit voltage is applied to the target winding, and the other conductors and the magnetic core are set to ground. The electrostatic field distribution is then solved, and the equivalent resistance is obtained by calculating the power loss. This method can accurately reflect the spatial distribution and loss characteristics of the insulation material and is particularly suitable for evaluating insulation aging and localized degradation. The loss angle distribution can be adjusted according to the measured data during the calculation to simulate different degrees of insulation degradation. The electrostatic field equation is as follows:

[0066] Formula 5:

[0067] in, represents the gradient / divergence operator, represents the gradient of the electric potential V, corresponding to the electric field strength, The negative sign is implicit in the definition of the electric field, Represents a vector field Take the divergence, that is, the divergence of the electric displacement vector D = εE, where ε represents the dielectric constant and V represents the electric potential.

[0068] Calculation method for the target inductance matrix L: The target inductance matrix L is obtained by solving the magnetostatic field equations using a 3D finite element method. It is obtained by applying a unit current to each conductor and calculating the magnetic field energy storage. The calculation takes into account the complex permeability of the core (μ = μ′ - jμ″) and its frequency dependence to accurately reflect the magnetic field distribution at high frequencies.

[0069] Calculation Method for the Target Capacitance Matrix C: The target capacitance matrix C is obtained by solving the electrostatic field equation using a 3D finite element method. The unit voltage method is used to apply a unit voltage to each node in turn and calculate the electrostatic field energy. The calculation takes into account the dielectric constant of the insulating material and the grounding condition of the core. The dielectric constant is assumed to be constant within the considered frequency range.

[0070] Next, the condition monitoring system uses an electrostatic field analysis method to calculate the capacitive coupling coefficient between the source voltage vector and the voltage vector, resulting in the target first matrix T. This target first matrix T describes the relationship between the node voltage and the source voltage, primarily determined by the capacitive coupling between each node and the voltage source. Through electrostatic field analysis, a unit voltage is applied to each source voltage terminal, and the capacitive coupling coefficient with each node is calculated to form the complete target first matrix T. The element T{i,j} of the target first matrix T represents the capacitive coupling coefficient between the i-th node and the j-th source voltage.

[0071] Then, the condition monitoring system obtains the circuit topology of the initial circuit model, determines the connection relationship between the source voltage vector and the current vector based on the circuit topology, and obtains the target second matrix H. The target second matrix H describes the direct relationship between the source voltage and the branch current, and is determined by analyzing the connection relationship between the transformer winding and the external voltage source. For each branch and each voltage source, the matrix elements are set to +1 (same direction), -1 (opposite direction) or 0 (no association) according to their relative directions. The target second matrix H can be determined directly from the circuit topology without the need for field solution. Based on the above process, the condition monitoring system uses the target model parameters, the target first matrix and the target second matrix to adjust the initial circuit model to obtain a fully coupled network model of the transformer multi-conductor transmission line.

[0072] Extract insulation state related parameters from the fully coupled network model of the transformer multi-conductor transmission line, and use the insulation state related parameters to construct an equivalent circuit model of the transformer insulation parameters.

[0073] In the embodiment of the present application, key parameters related to insulation status monitoring need to be extracted from the complete multi-conductor transmission line fully coupled network model, including the capacitance and resistance of the high-voltage side to the ground, the capacitance and resistance of the low-voltage side to the ground, and the coupling capacitance between the high-voltage side and the low-voltage side. Based on these parameters, a method is established as follows: Figure 2D The simplified insulation equivalent circuit model shown, i.e., the transformer insulation parameter equivalent circuit model, defines the calculation methods for diagonal elements and off-diagonal elements, and ultimately obtains the equivalent impedance matrix, which directly reflects the insulation status of each transformer winding.

[0074] Specifically, the condition monitoring system extracts insulation state-related parameters from the fully coupled network model of the transformer's multi-conductor transmission line. The insulation state-related parameters include multiple high-voltage side-to-ground insulation capacitances, multiple high-voltage side-to-ground insulation resistances, multiple low-voltage side-to-ground insulation capacitances, multiple low-voltage side-to-ground insulation resistances, and multiple coupling capacitances between the high-voltage side and the low-voltage side. The insulation state-related parameters are then used to determine an insulation equivalent impedance matrix. The insulation equivalent impedance matrix includes multiple first diagonal elements and multiple first off-diagonal elements. The first diagonal element is as shown in the following formula 6:

[0075] Formula 6:

[0076] The first off-diagonal element is as follows:

[0077] Formula 7:

[0078] Among them, Z ii represents the first diagonal element, R i Indicates the insulation resistance of winding i to ground, in Ω, C iIt represents the insulation capacitance of winding i to ground in F, j represents the imaginary unit, ω represents the angular frequency in rad / s, ω=2πf, f represents the operating frequency, such as 50Hz or PWM harmonic frequency, Z ij represents the first off-diagonal element, C ij It represents the coupling capacitance between winding i and winding j, with the unit of F. Finally, the insulation equivalent impedance matrix is ​​used to construct the transformer insulation parameter equivalent circuit model.

[0079] Calculate the insulation equivalent impedance matrix Z. In terms of physical meaning mapping: the diagonal elements of the Z matrix represent the insulation impedance of each winding to ground, and the off-diagonal elements reflect the coupling characteristics between windings. In terms of inversion algorithm requirements: by measuring common-mode leakage current and voltage, combined with the following formula 8, the insulation parameters in the Z matrix can be inverted to achieve quantitative assessment of the insulation status. In terms of engineering practicality: the complete model is too complex, and the Z matrix simplifies it into a lumped parameter model to reduce computational complexity while retaining key physical properties, making it suitable for real-time monitoring.

[0080] It should be noted that the technical route of first establishing a complete multi-conductor transmission line fully coupled network model and then simplifying it is of great significance: the complete model provides a solid theoretical basis, ensuring a complete description of physical phenomena, especially the electromagnetic field distribution under high-frequency PWM drive; at the same time, it serves as a reference standard to verify the accuracy of the simplified model; more importantly, the parameters in the simplified model are systematically extracted from the complete model rather than directly estimated. This top-down parameter acquisition method ensures the theoretical rationality and practicality of the monitoring. In addition, the complete model provides the necessary theoretical basis for precise regional monitoring and parameter adjustment under different working conditions, which is a key to the realization of this application. This "first complete, then simple" approach ensures that the monitoring technology has both rigorous theoretical support and practical engineering value.

[0081] In the process of establishing a simplified insulation equivalent circuit model, the PWM waveform spectrum characteristics serve as a basic condition to affect the electromagnetic field calculation of the complete model. Specifically, the PWM spectrum characteristics define the high-frequency harmonic distribution, guide the frequency domain calculation of the inductance and capacitance matrix in the multi-conductor transmission line model, and ensure that the complete model covers the electrical stress distribution of the actual working conditions. The key parameters of the simplified model, such as the resistance and capacitance of the high-voltage side to the ground, and the resistance and capacitance of the low-voltage side to the ground, are directly extracted from the complete model. These parameters already imply the influence of the PWM spectrum, such as the high-frequency electric field distribution correction capacitor parameters. Therefore, the construction of the simplified model is directly based on the parameters extracted from the complete model, without the need for additional correction through the PWM spectrum. The high-frequency characteristics are internalized into the circuit model through the equivalent impedance matrix to form Figure 2D The engineering practical equivalent circuit shown.

[0082] Furthermore, in order to conduct effective condition monitoring, a baseline value for the insulation parameters must be established. This requires initial parameter measurements of the transformer in the no-load state at normal operating temperature and recording of environmental conditions. To ensure the reliability of the baseline value, measurement data from at least 7 days in different time periods should be collected. After eliminating obvious outliers, the parameter mean is calculated as the baseline value, and the parameter standard deviation is calculated for subsequent judgment of the significance of the change. It should be noted that the insulation parameter baseline value plays a core reference role in the establishment of the equivalent circuit model: first, it provides the model with initial parameters under normal conditions, such as the high-voltage side to ground resistance R at no-load. H , low voltage side to ground capacitance C L , can define the "healthy baseline" of the equivalent circuit; secondly, combine long-term monitoring data to determine the normal fluctuation range of parameters, such as R H ±3σ is used as the threshold for judging insulation degradation. In addition, the benchmark value provides a reference value for environmental factor compensation, which can eliminate external interference. Finally, as the quantitative starting point for multi-level status assessment, it supports the precise calculation and trend prediction of indicators such as the insulation resistance change rate.

[0083] 203. Establish a multi-parameter measurement system for common-mode leakage current.

[0084] In the embodiments of this application, the multi-parameter common-mode leakage current measurement system uses a non-invasive online monitoring method. This method does not require changes to the transformer wiring and does not affect the normal operation of the transformer, greatly improving the practicality and operability of the monitoring. By installing high-precision current sensors at the neutral point and casing grounding line on the high- and low-voltage sides of the transformer, combined with a multi-channel data acquisition system, real-time collection and processing of common-mode leakage current is achieved, providing basic data support for subsequent parameter inversion and status assessment.

[0085] Hardware Configuration for the Common-Mode Leakage Current Multi-Parameter Measurement System: High-quality hardware configuration is the foundation for accurate measurements. Therefore, a high-precision Hall Effect current sensor is used, with an accuracy of 0.01 mA and a frequency response range of 10 Hz to 10 kHz, capable of capturing high-frequency common-mode leakage current signals. The voltage sensor should have an accuracy of 0.1% and a sampling frequency of at least 10 kHz. The data acquisition system utilizes a 16-bit or higher resolution analog-to-digital converter (ADC) with a sampling rate of 20 kHz to ensure sufficient signal detail. An industrial-grade data acquisition controller is used, achieving time accuracy better than 1 ms.

[0086] Installation Location of the Common-Mode Leakage Current Multi-Parameter Measurement System: The sensor installation location directly impacts the measurement results. Therefore, current sensors are installed on the neutral grounding wire on the high-voltage side, the neutral grounding wire on the low-voltage side, and the transformer casing grounding wire. These locations fully capture the common-mode leakage current in all parts of the transformer. Voltage sensors are installed at the high-voltage side phase voltage measurement points, the low-voltage side phase voltage measurement points, and the neutral-to-ground potential measurement point to obtain comprehensive voltage reference signals.

[0087] Anti-interference measures for the common-mode leakage current multi-parameter measurement system: Because common-mode leakage current signals are weak and susceptible to external interference, effective anti-interference measures must be implemented. Therefore, signal transmission utilizes twisted-pair shielded cable with the shield grounded at a single point to avoid ground loops. For power supply processing, a power supply filter is installed to suppress power supply interference, and an isolated power supply is used. For signal conditioning, a low-pass filter with a cutoff frequency set to 5kHz is used before A / D conversion. A differential input method is used to reduce common-mode interference, and appropriate gain is set to ensure that the signal reaches 80% of the ADC's range, improving conversion accuracy.

[0088] By adopting high-precision sensors and advanced signal processing technology, the real-time performance and sensitivity of the monitoring system can be significantly improved, and tiny changes in insulation parameters can be captured, enabling timely detection of early signs of insulation degradation, providing strong guarantees for the safe operation of transformers, effectively extending the service life of equipment, and reducing operation and maintenance costs and safety risks.

[0089] 204. Acquire real-time common-mode leakage current data collected by a common-mode leakage current multi-parameter measurement system, process the real-time common-mode leakage current data using a fast Fourier transform algorithm, and obtain key common-mode leakage current data.

[0090] In the embodiment of the present application, the original signal obtained needs to be properly processed before it can be used for subsequent analysis. Signal processing includes collecting the fundamental amplitude and phase of each winding voltage as a reference quantity, sampling the common-mode leakage current in real time, and recording its amplitude, phase and waveform. In terms of spectrum analysis, the harmonic components of the leakage current and their phases are extracted through the FFT algorithm, and the phase difference between the leakage current and the winding voltage is calculated, focusing on the amplitude and phase of key frequency points such as the fundamental wave, switching frequency and its harmonics. In terms of data synchronization, the time synchronization of the voltage and current measurement values ​​is ensured, all collected data are marked with timestamps, and data with different sampling rates are processed through interpolation algorithms. The specific process is as follows:

[0091] First, the data preprocessing stage is performed. The winding voltage sampling frequency and the common-mode leakage current sampling frequency are determined based on the common-mode leakage current real-time data, and the winding voltage sampling frequency and the common-mode leakage current sampling frequency are compared. If the comparison determines that the winding voltage sampling frequency and the common-mode leakage current sampling frequency are inconsistent, an interpolation algorithm is obtained and used to interpolate and align the winding voltage data and the common-mode leakage current data in the common-mode leakage current real-time data to obtain the target winding voltage data and target common-mode leakage current data. The common-mode leakage current real-time data is timestamped and includes winding voltage data, common-mode leakage current data, and environmental parameters. The target winding voltage data includes the fundamental amplitude and phase of multiple winding voltages, and the target common-mode leakage current data includes the amplitude, phase, and waveform of multiple common-mode leakage current signals.

[0092] Interpolation algorithms are primarily used to address the timeline asynchrony problem caused by different sampling rates for voltage and current signals. Specifically, when voltage and current signals cannot be directly aligned due to sampling rate differences, methods such as cubic spline interpolation are used to interpolate the low-sampling-rate signal (such as voltage) to the time points of the high-sampling rate, generating a time series fully synchronized with the current signal. This step relies on high-precision timestamps (e.g., 1ms) applied in real time during acquisition to ensure the spatiotemporal consistency of all data, providing accurate input for subsequent harmonic extraction, phase difference calculation, and parameter inversion. Timestamps are applied in real time during the data acquisition process. Specifically, when the sensor captures the instantaneous value of voltage, current, or environmental parameters (such as temperature, humidity, and load), the data acquisition card (DAQ) immediately assigns a high-precision timestamp (with accuracy better than 1ms) to each data point. This real-time stamping mechanism avoids phase calculation errors caused by time deviation during subsequent processing and provides a time reference for the interpolation algorithm, ultimately ensuring the accuracy of harmonic analysis and parameter inversion.

[0093] Next, in the FFT analysis phase, the condition monitoring system uses a fast Fourier transform (FFT) algorithm to perform harmonic analysis on the target common-mode leakage current data, obtaining target harmonic phase data and target harmonic amplitude data corresponding to each harmonic frequency. The target harmonic phase data includes multiple common-mode leakage current harmonic phases, and the target harmonic amplitude data includes multiple common-mode leakage current harmonic amplitudes. The FFT algorithm is also used to perform harmonic analysis on the target winding voltage data, obtaining specified harmonic phase data corresponding to each harmonic frequency. The specified harmonic phase data includes multiple winding voltage harmonic phases.

[0094] Subsequently, in the phase difference calculation stage, the difference between the harmonic phase of the leakage current and the harmonic phase of the corresponding winding voltage at the same frequency point is calculated. In the presence of multiple harmonic frequencies and multiple winding voltages, the phase difference calculation must follow the same frequency and same winding pairing principle. The specific process is: for each harmonic frequency, determine the target harmonic phase data and the specified harmonic phase data corresponding to the harmonic frequency, obtain multiple winding voltage harmonic phases in the specified harmonic phase data, determine the common-mode leakage current harmonic phase corresponding to each winding voltage harmonic phase in the target harmonic phase data, calculate the phase difference between each winding voltage harmonic phase and the common-mode leakage current harmonic phase corresponding to each winding voltage harmonic phase, and obtain multiple phase differences corresponding to the harmonic frequency, which can ensure the regional analysis of the insulation status of the high-voltage and low-voltage windings.

[0095] Finally, the data integration stage is used to generate key common-mode leakage current data using environmental parameters, target winding voltage data, target common-mode leakage current data, target harmonic phase data corresponding to each harmonic frequency, target harmonic amplitude data corresponding to each harmonic frequency, specified harmonic phase data corresponding to each harmonic frequency, and multiple phase differences corresponding to each harmonic frequency. For example, the processed data includes the amplitude of each harmonic of the leakage current (e.g., fundamental 5mA, 10kHz harmonic 0.2mA) and its corresponding phase (e.g., fundamental 30°, 10kHz harmonic 150°), the phase of each winding voltage at the same harmonic frequency point (e.g., high-voltage side fundamental 0°, 10kHz harmonic 120°), the phase difference at each frequency point (e.g., fundamental Δθ = 30°, 10kHz harmonic Δθ = 30°), synchronously recorded environmental parameters such as temperature, humidity, and load current, high-precision timestamps (1ms level) to ensure multi-channel synchronization, and alignment through interpolation algorithms (e.g., interpolating 1kHz voltage to 10kHz to align with current). These data are directly input into the inversion algorithm for inversion to obtain insulation parameters. After environmental compensation correction, fuzzy comprehensive evaluation can be used to output regional insulation status levels, realizing precise monitoring at the winding level.

[0096] For multi-harmonic scenarios, phase difference data is independently generated for each frequency point (fundamental, switching frequency, and integer multiples thereof), ultimately forming a phase difference matrix with frequency and winding dimensions. This data, along with harmonic amplitudes and environmental parameters (temperature, humidity, and load), is fed into the inversion algorithm. Combined with timestamps (1ms accuracy) and aligned synchronized waveform data, this ensures the temporal and spatial consistency of the parameter inversion, ultimately enabling precise insulation condition assessment across multiple regions and frequency bands. For example, a 10kHz phase difference on the high-voltage side often indicates capacitor degradation, while an increased fundamental phase difference on the low-voltage side indicates decreased resistance.

[0097] Extract insulation parameters from the transformer insulation parameter equivalent circuit model, and perform iterative inversion calculation on insulation parameters and common-mode leakage current key data based on the improved particle swarm optimization algorithm to obtain the target transformer insulation parameters.

[0098] In the embodiment of the present application, before parameter inversion, it is necessary to establish a mathematical relationship between common-mode leakage current and insulation parameters. Specifically, the leakage current vector and winding voltage vector are obtained from the common-mode leakage current key data, and the admittance matrix is ​​obtained from the transformer insulation parameter equivalent circuit model. The leakage current vector, winding voltage vector, and admittance matrix are used to establish a relationship equation, such as the following formula 8:

[0099] Formula 8: I L =V·Y,

[0100] Among them, I L represents the leakage current vector, V represents the winding voltage vector, and Y represents the admittance matrix. The admittance matrix includes multiple second diagonal elements and multiple second non-diagonal elements. The elements of the admittance matrix are determined by the insulation capacitance and resistance. The diagonal elements represent the insulation characteristics of each winding to the ground, and the non-diagonal elements represent the coupling relationship between the windings. The second diagonal elements are as shown in the following formula 9:

[0101] Formula 9:

[0102] The second off-diagonal element is as shown in the following formula 10:

[0103] Formula 10: Y ij =-jωC ij ,

[0104] Among them, Y ii represents the second diagonal element, R i Indicates the insulation resistance of winding i to ground, in Ω, C i It represents the insulation capacitance of winding i to ground, in F, j represents the imaginary unit, ω represents the angular frequency, jωC i represents the capacitive admittance term, Y ij represents the second off-diagonal element, C ij Represents the coupling capacitance between winding i and winding j, in F, -jωC ij represents the negative capacitive admittance term.

[0105] The core of parameter inversion is to construct an appropriate objective function. First, an error function is defined at a specific frequency, representing the difference between the measured leakage current and the theoretical leakage current calculated using the model. Next, an objective function is constructed by weighting the errors at different frequencies, with particular emphasis on the matching degree at key frequencies such as the fundamental frequency, the switching frequency, and its harmonics. The optimization goal is to find the parameter set that minimizes this objective function.

[0106] Specifically, the target harmonic phase data and target harmonic amplitude data corresponding to each harmonic frequency are obtained from the common-mode leakage current key data, and the theoretical leakage current vector data is calculated based on the transformer insulation parameter equivalent circuit model. Then, the error function of the improved particle swarm optimization algorithm is determined, and the objective function is determined based on the error function, as shown in the following formula 11:

[0107] Formula 11: E(ω) = |U measured (ω)-I calculated (ω)|,

[0108] minF=∑w(ω)|I measured (ω)-I calculated (ω)| 2 ,

[0109] Among them, E(ω) represents the error function, minF represents the objective function, and I measured (ω) represents the target harmonic phase data and target harmonic amplitude data at the frequency ω, that is, the leakage current vector actually measured at the frequency ω, which contains the amplitude and phase information of the frequency point. calculated (ω) represents the theoretical leakage current vector data, and w(ω) represents the weight function. It should be noted that different weights are assigned according to the importance of different frequencies.

[0110] To effectively determine the optimal parameters, an improved particle swarm optimization algorithm was employed. Initial values ​​were set based on typical transformer parameters, and reasonable parameter constraints were set. The algorithm was implemented using 20 particles, with a maximum of 80 iterations. The inertia weight was linearly decreased from 0.9 to 0.4, and the learning factor was set to 2.0. The optimization process involved initializing particle positions and velocities, calculating the objective function value, updating the optimal position, calculating the new velocity and position, and checking convergence conditions until the optimal solution was found or the maximum number of iterations was reached.

[0111] Specifically, the maximum number of iterations, the linear change of the inertia weight, and the preset learning factor corresponding to the improved particle swarm optimization algorithm are obtained. Based on the improved particle swarm optimization algorithm, the maximum number of iterations, the linear change of the inertia weight, the preset learning factor, the objective function, and the relationship equation are used to iteratively calculate the key data of the common-mode leakage current and the theoretical leakage current vector data until the optimal solution is found or the maximum number of iterations is reached, and the quantized values ​​of the insulation parameters of each winding of the transformer are obtained, wherein the quantized values ​​of the insulation parameters of each winding of the transformer include the specified insulation resistance of multiple windings to the ground, the specified insulation capacitance of multiple windings to the ground, and the specified coupling capacitance between multiple windings. Among them, in the stage of calculating the new speed and position, the speed update formula is as follows Formula 12:

[0112] Formula 12:

[0113] in, Indicates that particle i is in the dth dimension (parameter dimension, such as the high-voltage side resistance R H , capacitor C H w represents the inertia weight (e.g., initial value 0.9, linearly decreasing to 0.4), which controls the tendency of the particle to maintain its original speed. The larger the value, the stronger the global search capability; the smaller the value, the higher the local convergence accuracy. represents the speed of particle i in the kth iteration on the dth dimension. c1 represents the individual learning factor (usually set to 2.0), which adjusts the particle to its historical optimal position p id The step size of the movement. r1 represents a random number in the interval [0, 1], which increases the randomness of the search and avoids premature convergence of the algorithm. id represents the historical optimal position of particle i in the dth dimension (such as the best R H value). c2 represents the group learning factor (usually set to 2.0), which adjusts the particles to the global optimal position p gd The step length of the movement. r2 represents a random number in the interval [0, 1], which together with r1 introduces random perturbations. gd Represents the global optimal position of the entire particle swarm in the dth dimension (such as the best R among all particles H value). Indicates the current position of particle i at the kth iteration in the dth dimension (such as the R obtained by the current inversion H The position update formula is as follows:

[0114] Formula 13:

[0115] in, represents the position of particle i at the k+1th iteration in the dth dimension (such as the updated R H value). represents the position of particle i at the kth iteration in the dth dimension. Represents the new speed calculated by Formula 12.

[0116] Environmental factors can affect insulation parameters during field measurements, so appropriate compensation is required. Regarding the impact of temperature and humidity, the ambient temperature and humidity are measured, a compensation coefficient model is established, and parameter corrections are made based on the degree to which the temperature and humidity deviate from standard conditions. Regarding the impact of load, the load current during measurement is recorded, a load correction coefficient is established, and the inversion parameters are further adjusted to ensure consistent evaluation results under different operating conditions. Specifically, environmental parameters are obtained from the common-mode leakage current key data, and the temperature compensation coefficient and humidity compensation coefficient are calculated using the environmental parameters, as shown in the following formula 14:

[0117] Formula 14: kT =1+α(T-20),

[0118] k H =1+β(H-50),

[0119] Among them, k T represents the temperature compensation coefficient, α represents the temperature coefficient, which is -0.02°C, T represents the ambient temperature in the environmental parameters, k H The temperature and humidity compensation coefficients are then used to modify the insulation parameters of each transformer winding to obtain the target transformer insulation parameters, as shown in the following formula 15:

[0120] Formula 15:

[0121] C i_corrected =C i ′k T k H ,

[0122] Among them, R i_corrected Indicates the target insulation resistance of winding i to ground in the target transformer insulation parameter, C i_corrected Indicates the target insulation capacitance of winding i to ground in the target transformer insulation parameter, k T Indicates the temperature compensation coefficient, k H Represents the humidity compensation coefficient, R i ′ represents the specified insulation resistance of winding i to ground in the quantified value of the insulation parameter of each transformer winding, C i ′ represents the specified insulation capacitance of winding i to ground in the quantified value of the insulation parameter of each winding of the transformer. For example, when the temperature rises and causes R H When the inversion value is low, the corrected R H_corrected The value will be restored to the equivalent value at the reference temperature to ensure that the insulation status assessment is not affected by environmental fluctuations and truly reflects the degree of material degradation (such as moisture and aging).

[0123] The optimal solution obtained by the parameter inversion algorithm represents the precise quantitative value of the insulation parameters of each transformer winding, including the high-voltage side ground resistance R H and capacitor C H , low voltage side to ground resistance R L and capacitor C L , and the inter-winding coupling capacitance C HL After being corrected by environmental compensation, these parameters directly reflect the actual state of the insulation material. For example, R H A drop (e.g. from 1GΩ to 0.6GΩ) indicates that the insulation is damp or aging, C HAbnormal increase (such as from 300pF to 450pF) may be caused by dielectric delamination or partial discharge. HL Fluctuations reflect insulation degradation between windings. The optimal solution minimizes the difference between the measured leakage current and the model-calculated value (i.e., the objective function in Equation 1), ensuring that the amplitude and phase of the equivalent circuit at all harmonic frequencies (e.g., fundamental frequency 50 Hz, switching frequency 10 kHz) optimally match the measured data. This accurately quantifies the degree of insulation degradation, supporting regional status assessments (e.g., high-voltage side "warning"), trend predictions (e.g., a 5% monthly resistance drop), and multi-level alarms (normal / caution / warning / danger), providing a core basis for transformer insulation health management.

[0124] 206. Use the target transformer insulation parameters to process each winding separately to obtain the insulation resistance change rate, resistance change slope, insulation capacitance change rate, capacitance change slope, insulation power factor, equivalent time constant, target coupling capacitance change rate and target coupling capacitance change slope corresponding to each winding.

[0125] In the embodiments of the present application, multiple evaluation indicators are established to comprehensively evaluate the insulation status. Basic evaluation indicators include the insulation resistance change rate, insulation capacitance change rate, insulation power factor, equivalent time constant, and inter-winding coupling capacitance change rate. These indicators directly reflect the static characteristics of the insulation status. Dynamic evaluation indicators include the resistance change slope, capacitance change slope, and inter-winding coupling capacitance change slope. These indicators can reflect the trend and speed of insulation degradation and are of great significance for early detection of potential problems.

[0126] Specifically, the condition monitoring system obtains a reference resistance value and a reference capacitance value for each winding, obtains a target insulation resistance and a target insulation capacitance of the winding to ground from the target transformer insulation parameters, obtains the target insulation resistance of the previous month corresponding to the target insulation resistance, and the target insulation capacitance of the previous month corresponding to the target insulation capacitance, and calculates using the reference resistance value, the reference capacitance value, the target insulation resistance and the target insulation capacitance of the winding to ground, the target insulation resistance of the previous month corresponding to the target insulation resistance, and the target insulation capacitance of the previous month corresponding to the target insulation capacitance to obtain the insulation resistance change rate, resistance change slope, insulation capacitance change rate, capacitance change slope, insulation power factor, and equivalent time constant corresponding to the winding, as shown in the following formula 16:

[0127] Formula 16:

[0128] ΔR i ′=ΔR i -ΔR i′ ,

[0129]

[0130] ΔC i ′=ΔC i -ΔC i′ ,

[0131]

[0132] τ i =R i_corrected ·C i_corrected ,

[0133] Where, ΔR i Indicates the rate of change of insulation resistance corresponding to winding i, ΔR i ′ represents the resistance change slope corresponding to winding i, ΔC i Indicates the rate of change of insulation capacitance corresponding to winding i, ΔC i ′ represents the capacitance change slope corresponding to winding i, tanδ i Indicates the insulation power factor corresponding to winding i, τ i Represents the equivalent time constant corresponding to winding i, R i_xorrected represents the target insulation resistance of winding i to ground, R baseline Indicates the reference resistance value, C i_corrected represents the target insulation capacitance of winding i to ground, C baseline Indicates the reference capacitance value, tanδ i Indicates the insulation power factor corresponding to winding i, δ indicates the loss angle, ω indicates the angular frequency, ω=2πf, τ i Represents the equivalent time constant corresponding to winding i, ΔR i′ Indicates the target insulation resistance of winding i in the previous month, ΔC i′ Indicates the target insulation capacitance of winding i in the previous month.

[0134] Subsequently, multiple target inter-winding coupling capacitances associated with the windings are obtained from the target transformer insulation parameters, along with a reference coupling capacitance value corresponding to each inter-winding target coupling capacitance and the target inter-winding coupling capacitance from the previous month corresponding to each inter-winding target coupling capacitance. Calculations are performed using the multiple target inter-winding coupling capacitances, the reference coupling capacitance value corresponding to each inter-winding target coupling capacitance, and the target inter-winding coupling capacitance from the previous month corresponding to each inter-winding target coupling capacitance to obtain multiple inter-winding coupling capacitance change rates and multiple inter-winding coupling capacitance change slopes, as shown in the following formula 17:

[0135] Formula 17:

[0136] ΔC ij ′=ΔC ij -ΔC ij′ ,

[0137] Where, ΔC ij Indicates the rate of change of the inter-winding coupling capacitance between winding i and winding j, ΔC ij ′ represents the slope of the inter-winding coupling capacitance between winding i and winding j, C ij_baseline Indicates the reference coupling capacitance between winding i and winding j, C ij represents the target inter-winding coupling capacitance between winding i and winding j, ΔC ijj It represents the target inter-winding coupling capacitance between winding i and winding j in the previous month. Winding j is coupled to winding i.

[0138] Considering that there are multiple coupling capacitances between a winding i and multiple other windings, a weighted average method is used to process these multi-valued parameters. Specifically, the weighted average method is used to calculate the change rate of the coupling capacitance between multiple windings and the change slope of the coupling capacitance between multiple windings to obtain the target coupling capacitance change rate and target coupling capacitance change slope corresponding to the winding, as shown in the following formula 18:

[0139] Formula 18:

[0140]

[0141] Where, ΔC i_coupled Indicates the target coupling capacitance change rate corresponding to winding i, ΔC i_coupled ′ represents the target coupling capacitance change slope corresponding to winding i, ΔC ij Indicates the rate of change of the inter-winding coupling capacitance between winding i and winding j, ΔC ij ′ represents the slope of the inter-winding coupling capacitance between winding i and winding j, w ij Represents the weight coefficient of the coupling capacitance between winding i and winding j. Among them, for the coupling capacitance between high and low voltage windings, w ij =0.6; for the coupling capacitance between windings of the same voltage level, w ij =0.3; for the coupling capacitance between the auxiliary winding, w ij = 0.1. A weighted average method is used to process the coupling capacitance parameters between a single winding and multiple windings, avoiding the information loss that might result from selecting a single parameter. Finally, each winding is processed separately to obtain the corresponding insulation resistance change rate, resistance change slope, insulation capacitance change rate, capacitance change slope, insulation power factor, equivalent time constant, target coupling capacitance change rate, and target coupling capacitance change slope. By calculating the combined coupling capacitance change rate and change slope for each winding, the insulation status between that winding and all other windings can be fully reflected.

[0142] 207. Establish preset multi-level insulation status assessment standards.

[0143] In the embodiment of the present application, a multi-level insulation state evaluation standard is established based on the evaluation index. For each winding i, its insulation state is evaluated according to the following multi-level insulation state evaluation standard:

[0144] Normal state: |ΔR i |<8%,|ΔC i |<5%,tanδ i <0.5%,|ΔR i ′|<0.5% / month,|ΔC i ′|<0.4% / month,|ΔC i_coupled |<6%,|ΔC i_coupled ′|<0.4% / month;

[0145] Note the status:

[0146] 8%≤|ΔR i |<25%,5%≤|ΔC i |<15%,0.5%≤tanδ i <1%,0.5% / month≤|ΔR i ′|<1% / month,0.4% / month≤|ΔC i ′|<0.8% / month, 6%≤|ΔC i_coupled |<18%,0.4% / month≤|ΔC i_coupled ′|<0.8% / month;

[0147] Warning status:

[0148] 25%≤|ΔR i |<45%,15%≤|ΔC i |<30%,1%≤tanδ i <2%,1% / month≤|ΔR i ′|<2% / month,0.8% / month≤|ΔC i ′|<1.8% / month, 18%≤|ΔC i_coupled |<35%,0.8% / month≤|ΔC i_coupled ′|<1.8% / month;

[0149] Dangerous state: 45% ≤ |ΔR i |,30%≤|ΔC i |,2%≤tanδ i ,2% / month≤|ΔR i ′|,1.8% / month≤|ΔC i ′|,35%≤|ΔC i_coupled |,1.8% / month≤|ΔC i_coupled ′|.

[0150] The normal state corresponds to minor parameter changes and a lower power factor, and the system can operate normally without special attention; the caution state indicates that the insulation parameters have changed significantly but have not yet affected safe operation, and it is recommended to increase the monitoring frequency; the warning state means that the insulation performance has significantly deteriorated, which may affect the life of the equipment, and inspection and maintenance should be arranged; the dangerous state corresponds to serious insulation degradation, which poses a safety risk and requires immediate measures such as reducing the load or shutting down for maintenance.

[0151] 208. Based on the fuzzy comprehensive evaluation method, according to the preset multi-level insulation status evaluation standard, as well as the insulation resistance change rate, resistance change slope, insulation capacitance change rate, capacitance change slope, insulation power factor, equivalent time constant, target coupling capacitance change rate and target coupling capacitance change slope corresponding to each winding, the insulation status of each winding is evaluated to obtain the insulation evaluation status of each winding.

[0152] In the embodiments of the present application, since a single indicator cannot fully reflect the insulation status, a fuzzy comprehensive evaluation method is used to perform a multi-indicator fusion assessment. First, an evaluation factor set is established, including static and dynamic characteristic indicators. Then, a comment set is established, and a weight vector is determined to weight the importance of each indicator under different operating conditions. Then, a membership function is established to describe the degree to which each indicator belongs to each comment. Finally, a comprehensive evaluation calculation is performed to obtain a comprehensive evaluation result of the insulation status. Specifically, the status monitoring system generates a comment set based on preset multi-level insulation status evaluation standards, where the comment set includes normal status, caution status, warning status, and dangerous status.

[0153] For each winding, the condition monitoring system uses the corresponding insulation resistance change rate, resistance change slope, insulation capacitance change rate, capacitance change slope, insulation power factor, equivalent time constant, target coupling capacitance change rate and target coupling capacitance change slope as the winding evaluation factor set, that is, U = {ΔR i ,ΔC i ,tanδ i ,τ i ,ΔR i ′,ΔC i ′,ΔC i_coupled ,ΔC i_coupled Then, based on the fuzzy comprehensive evaluation method, a single-factor evaluation matrix is ​​constructed according to the preset multi-level insulation status evaluation standard and the evaluation factor set. The weight vector included in the fuzzy comprehensive evaluation method is obtained, and the weight vector and the single-factor evaluation matrix are used to calculate and determine the comprehensive evaluation result vector. The insulation evaluation status of the winding is determined based on the comprehensive evaluation result vector, as shown in the following formula 19:

[0154] Formula 19: B i =A·M i ,

[0155] Among them, B i Represents the comprehensive evaluation result vector corresponding to winding i, M i represents the single factor evaluation matrix corresponding to winding i, and represents the weight vector A={0.22,0.18,0.15,0.10,0.10,0.08,0.10,0.07}, M i The element M in i,jk Represents the degree of membership of factor j of winding i to comment k. By constructing a set of evaluation factors for each winding and performing fuzzy comprehensive evaluation, it is possible to accurately evaluate the insulation status of each winding. This application provides a quantitative evaluation method for insulation parameters, establishing multiple evaluation indicators including the insulation resistance change rate, insulation capacitance change rate, insulation power factor, and equivalent time constant, and integrating multiple indicators through fuzzy comprehensive evaluation to comprehensively reflect the insulation status. This method can not only evaluate the current status, but also predict the degradation trend through dynamic indicators, providing a scientific basis for preventive maintenance decisions.

[0156] 209. Using the insulation evaluation status of the plurality of windings as the transformer winding insulation status detection result.

[0157] In this embodiment of the present application, the condition monitoring system uses the insulation assessment status of multiple windings as the transformer winding insulation status detection result. The evaluation results of all windings are comprehensively considered, and according to the "short board effect" principle, the worst state is taken as the overall transformer condition judgment result, and the location of the problematic winding is marked.

[0158] Because transformer operating conditions can affect the accuracy of insulation condition assessments, appropriate corrections are required. Regarding load impact, the weighting of each indicator is adjusted based on the load factor, prioritizing capacitance changes under light loads and resistance changes under heavy loads. Regarding aging impact, the impact of transformer operating time on baseline values ​​is considered. As service life increases, the baseline values ​​for resistance and capacitance are appropriately adjusted. Regarding temperature cycle impact, daily and annual temperature differences are calculated, and a temperature fluctuation correction factor is established to dynamically adjust the assessment thresholds.

[0159] Specifically, the service life of the transformer is obtained, and the reference resistance value and the reference capacitance value are corrected using the service life of the transformer, as shown in the following formula 20:

[0160] Formula 20: R baseline_adjusted =R baseline (1-0.015t),

[0161] C baseline_adjusted =C baseline (1+0.008t),

[0162] Among them, R baseline_adjusted Indicates the corrected reference resistance value, which is used to dynamically adapt to the impact of equipment aging on parameters, Cbaseline_adjusted Represents the corrected reference capacitance value, which is used to dynamically match the natural change of capacitance over time, R baseline Indicates the reference resistance value, C baseline = represents the baseline capacitance value, t represents the transformer's age, i.e., the equipment's operating time, 0.015 represents the aging attenuation coefficient, representing the linear attenuation rate of the baseline value over time (i.e., 1.5% attenuation per year), and 0.008 represents the average annual capacitance growth rate (i.e., 0.8% increase per year), reflecting the long-term effects of material aging or ambient humidity accumulation. The temperature compensation coefficient and load correction coefficient are obtained and used to correct the baseline coupling capacitance value corresponding to the target coupling capacitance between each winding, as shown in the following formula 21:

[0163] Formula 21: C ij_baseline_adjusted =C ij_baseline (1+K T ×K I ),

[0164] Among them, C ij_baseline_adjusted Represents the corrected reference coupling capacitance value between winding i and winding j, C ij_baseline Indicates the reference coupling capacitance between winding i and winding j, K T Indicates the temperature compensation coefficient, K I Represents the load correction coefficient. Then obtain the daily temperature fluctuation value and the annual temperature cumulative change value, and use the daily temperature fluctuation value and the annual temperature cumulative change value to calculate the temperature fluctuation correction coefficient, as shown in the following formula 22:

[0165] Formula 22: k T_var =1+0.01(ΔT day -0.1ΔT year ),

[0166] Among them, k T_var Indicates the temperature fluctuation correction coefficient, which is used to quantify the correction factor of temperature fluctuation on equipment parameters (such as insulation resistance and capacitance), ΔT day Indicates daily temperature fluctuation value, ΔT year Represents the cumulative annual temperature change. 0.01 and 0.1 are weighting factors, controlling the contribution of daily and annual fluctuations to the overall temperature, respectively. The temperature fluctuation correction factor is used to adjust the pre-set multi-level insulation status assessment standard.

[0167] Then, the current transformer load rate is obtained and the current transformer load rate is detected. When the detection determines that the current transformer load rate is in a light load state, the first weight vector corresponding to the light load state is obtained, and the weight vector of the fuzzy comprehensive evaluation method is adjusted to the first weight vector, that is, when it is in a light load (L<30%) state, it is adjusted to the first weight vector {0.20, 0.16, 0.15, 0.10, 0.08, 0.08, 0.15, 0.08}; when the detection determines that the current transformer load rate is in a heavy load state, the second weight vector corresponding to the heavy load state is obtained, and the weight vector of the fuzzy comprehensive evaluation method is adjusted to the second weight vector, that is, when it is in a heavy load (L>70%) state, it is adjusted to the second weight vector {0.24, 0.18, 0.15, 0.10, 0.12, 0.08, 0.07, 0.06}.

[0168] This application breaks through the limitation of traditional common-mode leakage current monitoring methods that it is difficult to distinguish between insulation degradation and changes in normal operating conditions. Through environmental factor compensation and operating condition correction, it achieves effective compensation for factors such as temperature, humidity, and load changes. Especially in working environments with large temperature cycle fluctuations, it can accurately identify true signs of insulation degradation and avoid false alarms and missed alarms.

[0169] The embodiment of the present application provides a method for monitoring the insulation status of a power transformer winding. Compared with the prior art, the embodiment of the present application establishes a detailed circuit model of the transformer and, based on the measurement data of the common-mode leakage current, uses an improved particle swarm optimization algorithm to inversely calculate the insulation parameters of multiple transformer windings to the ground, thereby achieving an accurate assessment of the insulation status of each winding. Compared with traditional monitoring methods, the present application can make full use of the rich information contained in the common-mode leakage current, accurately distinguish the insulation degradation of different windings, provide a more accurate insulation status assessment, and effectively improve the safe operation level of the transformer. Moreover, the present application uses a fuzzy comprehensive evaluation method to perform a multi-level assessment of the insulation status, which can comprehensively reflect the insulation status and distinguish the different severity levels of the insulation status. It can not only assess the current status, but also predict the degradation trend through dynamic indicators, providing a scientific basis for operation and maintenance decisions.

[0170] Further, as Figure 1 The specific implementation of the method, the embodiment of the present application provides a power transformer winding insulation state monitoring device, such as Figure 3A As shown, the device includes: a construction module 301, an acquisition module 302, a calculation module 303 and an evaluation module 304.

[0171] A construction module 301 is configured to construct a fully coupled network model of a transformer multi-conductor transmission line based on the transformer voltage spectrum characteristics, the physical structure of the transformer winding, and the electrical characteristics; extract insulation state-related parameters from the fully coupled network model of the transformer multi-conductor transmission line; and construct an insulation parameter equivalent circuit model of the transformer using the insulation state-related parameters.

[0172] An acquisition module 302 is configured to acquire real-time common-mode leakage current data collected by a common-mode leakage current multi-parameter measurement system, and process the real-time common-mode leakage current data using a fast Fourier transform algorithm to obtain key common-mode leakage current data.

[0173] A calculation module 303 is configured to extract insulation parameters from the transformer insulation parameter equivalent circuit model, and perform iterative inversion calculation on the insulation parameters and the common-mode leakage current key data based on an improved particle swarm optimization algorithm to obtain target transformer insulation parameters;

[0174] The evaluation module 304 is configured to perform insulation status evaluation on the insulation parameters of the target transformer based on a fuzzy comprehensive evaluation method to obtain a transformer winding insulation status detection result.

[0175] In a specific application scenario, the construction module 301 is used to construct an initial circuit model based on the transformer voltage spectrum characteristics, the transformer winding physical structure and electrical characteristics.

[0176]

[0177] V0=HV g ,

[0178] I0=TV g ,

[0179] Where K represents the correlation matrix, R represents the resistance matrix, L represents the inductance matrix, C represents the capacitance matrix, j represents the imaginary unit, ω represents the angular frequency, V represents the voltage vector, I represents the current vector, V g represents the source voltage vector, n v represents the total number of voltage nodes, v1 represents the voltage value of the first voltage node, v2 represents the voltage value of the second voltage node, v4 represents the voltage value of the third voltage node, n5 represents the voltage value of the fourth voltage node, and n i represents the total number of current nodes, i1 represents the current value of the first current node, i2 represents the current value of the second current node, i3 represents the current value of the third current node, i4 represents the current value of the fourth current node, i5 represents the current value of the fifth current node, i6 represents the current value of the sixth current node, and n g represents the total number of coupling groups, v HV,1 Indicates the voltage value of the first high-voltage side coupling group, vHV,2 Indicates the voltage value of the second high-voltage side coupling group, v LV,1 Represents the voltage value of the first low-voltage side coupling group, v LV,2 Represents the voltage value of the second low-voltage side coupling group, V0 represents the input voltage vector, I0 represents the input current vector, T represents the first matrix, and H represents the second matrix; obtains a 3D finite element simulation method, and adopts the 3D finite element simulation method to obtain the target model parameters of the initial circuit model, and the target model parameters include a target correlation matrix, a target resistance matrix, a target inductance matrix and a target capacitance matrix; obtains an electrostatic field analysis method, and adopts the electrostatic field analysis method to calculate the capacitive coupling coefficient between the source voltage vector and the voltage vector to obtain a target first matrix, and obtains a circuit topology structure of the initial circuit model, and determines the connection relationship between the source voltage vector and the current vector according to the circuit topology structure, and obtains Target second matrix; using the target model parameters, the target first matrix and the target second matrix to adjust the initial circuit model to obtain the transformer multi-conductor transmission line fully coupled network model; extracting the insulation state related parameters from the transformer multi-conductor transmission line fully coupled network model, the insulation state related parameters including multiple high-voltage side to ground insulation capacitances, multiple high-voltage side to ground insulation resistances, multiple low-voltage side to ground insulation capacitances, multiple low-voltage side to ground insulation resistances, multiple coupling capacitances between the high-voltage side and the low-voltage side; using the insulation state related parameters to determine the insulation equivalent impedance matrix, the insulation equivalent impedance matrix including multiple first diagonal elements and multiple first non-diagonal elements, wherein,

[0180] The first diagonal elements are:

[0181]

[0182] First off-diagonal element:

[0183]

[0184] Among them, Z ii represents the first diagonal element, R i Indicates the insulation resistance of winding i to ground, C i represents the insulation capacitance of winding i to ground, j represents the imaginary unit, ω represents the angular frequency, and Z ij represents the first off-diagonal element, C ij represents the coupling capacitance between winding i and winding j; and the insulation equivalent impedance matrix is ​​used to construct the transformer insulation parameter equivalent circuit model.

[0185] In a specific application scenario, the acquisition module 302 is used to determine the winding voltage sampling frequency and the common-mode leakage current sampling frequency according to the common-mode leakage current real-time data, and compare the winding voltage sampling frequency and the common-mode leakage current sampling frequency; if the comparison determines that the winding voltage sampling frequency and the common-mode leakage current sampling frequency are inconsistent, then obtain an interpolation algorithm, and use the interpolation algorithm to perform interpolation alignment processing on the winding voltage data and the common-mode leakage current data in the common-mode leakage current real-time data to obtain target winding voltage data and target common-mode leakage current data, wherein the common-mode leakage current is The real-time current data is timestamped. The common-mode leakage current real-time data includes the winding voltage data, the common-mode leakage current data, and environmental parameters. The target winding voltage data includes the fundamental amplitude and phase of multiple winding voltages. The target common-mode leakage current data includes the amplitude, phase, and waveform of multiple common-mode leakage current signals. The fast Fourier transform algorithm is used to perform harmonic analysis on the target common-mode leakage current data to obtain target harmonic phase data and target harmonic amplitude data corresponding to each harmonic frequency. The target harmonic phase data includes multiple common-mode leakage current harmonic phases. The target harmonic amplitude data includes multiple common-mode leakage current harmonic amplitudes; the fast Fourier transform algorithm is used to perform harmonic analysis on the target winding voltage data to obtain specified harmonic phase data corresponding to each harmonic frequency, and the specified harmonic phase data includes multiple winding voltage harmonic phases; for each harmonic frequency, the target harmonic phase data and specified harmonic phase data corresponding to the harmonic frequency are determined, multiple winding voltage harmonic phases are obtained from the specified harmonic phase data, and the corresponding winding voltage harmonic phase of each winding voltage harmonic phase is determined in the target harmonic phase data. Common-mode leakage current harmonic phase, respectively calculate the phase difference between each of the winding voltage harmonic phases and the common-mode leakage current harmonic phase corresponding to each of the winding voltage harmonic phases, and obtain multiple phase differences corresponding to the harmonic frequency; use the environmental parameters, the target winding voltage data, the target common-mode leakage current data, the target harmonic phase data corresponding to each of the harmonic frequencies, the target harmonic amplitude data corresponding to each of the harmonic frequencies, the specified harmonic phase data corresponding to each of the harmonic frequencies, and the multiple phase differences corresponding to each of the harmonic frequencies to generate the common-mode leakage current key data.

[0186] In a specific application scenario, the calculation module 303 is used to obtain the leakage current vector and the winding voltage vector from the common-mode leakage current key data, obtain the admittance matrix from the transformer insulation parameter equivalent circuit model, and establish a relationship equation using the leakage current vector, the winding voltage vector, and the admittance matrix.

[0187] I L =V·Y,

[0188] Among them, I Lrepresents the leakage current vector, V represents the winding voltage vector, and Y represents the admittance matrix, which includes a plurality of second diagonal elements and a plurality of second non-diagonal elements, wherein,

[0189] The second diagonal elements are:

[0190]

[0191] The second off-diagonal element is:

[0192] Y ij =-jωC ij ,

[0193] Among them, Y ii represents the second diagonal element, R i Indicates the insulation resistance of winding i to ground, C i represents the insulation capacitance of winding i to ground, j represents the imaginary unit, ω represents the angular frequency, jωC i represents the capacitive admittance term, Y ij represents the second off-diagonal element, C ij Represents the coupling capacitance between winding i and winding j, -jωC ij represents a negative capacitive admittance term; obtaining target harmonic phase data corresponding to each harmonic frequency and target harmonic amplitude data corresponding to each harmonic frequency from the common-mode leakage current key data, and calculating theoretical leakage current vector data based on the transformer insulation parameter equivalent circuit model; determining an error function of the improved particle swarm optimization algorithm, and determining an objective function based on the error function,

[0194] E(ω)=|I measured (ω)-I calcuIated (ω)|,

[0195] minF=Σw(ω)|I measured (ω)-I calculated (ω)| 2 ,

[0196] Wherein, E(ω) represents the error function, minF represents the objective function, and I measured (ω) represents the target harmonic phase data and target harmonic amplitude data at frequency ω, I calculated(ω) represents the theoretical leakage current vector data, w(ω) represents the weight function; obtain the maximum number of iterations, the linear change of the inertia weight, and the preset learning factor corresponding to the improved particle swarm optimization algorithm; based on the improved particle swarm optimization algorithm, use the maximum number of iterations, the linear change of the inertia weight, the preset learning factor, the objective function, and the relationship equation to iteratively calculate the common-mode leakage current key data and the theoretical leakage current vector data until the optimal solution is found or the maximum number of iterations is reached, and the quantized values ​​of the insulation parameters of each winding of the transformer are obtained, the quantized values ​​of the insulation parameters of each winding of the transformer including the specified insulation resistance of multiple windings to ground, the specified insulation capacitance of the multiple windings to ground, and the specified coupling capacitance between multiple windings; obtain environmental parameters from the common-mode leakage current key data, and use the environmental parameters to calculate the temperature compensation coefficient and the humidity compensation coefficient,

[0197] k T =1+α(T-20),

[0198] k H =1+β(H-50),

[0199] Among them, k T represents the temperature compensation coefficient, α represents the temperature coefficient, T represents the ambient temperature in the environmental parameters, k H represents the humidity compensation coefficient, β represents the humidity coefficient, and H represents the ambient humidity in the environmental parameters; the temperature compensation coefficient and the humidity compensation coefficient are used to perform parameter correction on the quantized values ​​of the insulation parameters of each winding of the transformer to obtain the target transformer insulation parameters,

[0200]

[0201] C i_corrected =C i ′k T k H ,

[0202] Among them, R i_corrected Indicates the target insulation resistance of winding i to ground in the target transformer insulation parameter, C i_corrected represents the target insulation capacitance of winding i to ground in the target transformer insulation parameter, k T Indicates the temperature compensation coefficient, k H Represents the humidity compensation coefficient, R i ′ represents the specified insulation resistance of winding i to ground in the quantified value of the insulation parameter of each winding of the transformer, C i ' represents the specified insulation capacitance of winding i to ground in the quantified value of the insulation parameter of each winding of the transformer.

[0203] In a specific application scenario, the evaluation module 304 is used to obtain a reference resistance value and a reference capacitance value for each winding, obtain the target insulation resistance and target insulation capacitance of the winding to ground in the target transformer insulation parameters, obtain the target insulation resistance in the previous month corresponding to the target insulation resistance, and the target insulation capacitance in the previous month corresponding to the target insulation capacitance, and use the reference resistance value, the reference capacitance value, the target insulation resistance and target insulation capacitance of the winding to ground, the target insulation resistance in the previous month corresponding to the target insulation resistance, and the target insulation capacitance in the previous month corresponding to the target insulation capacitance to perform calculations to obtain the insulation resistance change rate, resistance change slope, insulation capacitance change rate, capacitance change slope, insulation power factor, and equivalent time constant corresponding to the winding.

[0204]

[0205] ΔR i ′=ΔR i -ΔR i′ ,

[0206]

[0207] ΔC i ′=ΔC i -ΔC i′ ,

[0208]

[0209] τ i =R i_corrected ·C i_corrected ,

[0210] Where, ΔR i Indicates the rate of change of insulation resistance corresponding to winding i, ΔR i ′ represents the resistance change slope corresponding to winding i, ΔC i Indicates the rate of change of insulation capacitance corresponding to winding i, ΔC i ′ represents the capacitance change slope corresponding to winding i, tanδ i Indicates the insulation power factor corresponding to winding i, τ i Represents the equivalent time constant corresponding to winding i, R i_corrected represents the target insulation resistance of winding i to ground, R baseline Indicates the reference resistance value, C i_corrected represents the target insulation capacitance of winding i to ground, C baseline represents the reference capacitance value, tanδ i represents the insulation power factor corresponding to winding i, ω represents the angular frequency, τ i Represents the equivalent time constant corresponding to winding i, ΔRi′ Indicates the target insulation resistance of winding i in the previous month, ΔC i′ represents the target insulation capacitance of winding i in the previous month; obtaining multiple inter-winding target coupling capacitances related to the winding from the target transformer insulation parameter, obtaining a reference coupling capacitance value corresponding to each of the inter-winding target coupling capacitances, and the inter-winding target coupling capacitance corresponding to each of the inter-winding target coupling capacitances in the previous month; using the multiple inter-winding target coupling capacitances, the reference coupling capacitance value corresponding to each of the inter-winding target coupling capacitances, and the inter-winding target coupling capacitance corresponding to each of the inter-winding target coupling capacitances in the previous month to perform calculations, obtaining multiple inter-winding coupling capacitance change rates and multiple inter-winding coupling capacitance change slopes,

[0211]

[0212] ΔC ij ′=ΔC ij -ΔC ij′ ,

[0213] Where, ΔC ij Indicates the rate of change of the inter-winding coupling capacitance between winding i and winding j, ΔC ij ′ represents the slope of the inter-winding coupling capacitance between winding i and winding j, C ij_baseline Indicates the reference coupling capacitance between winding i and winding j, C ij represents the target inter-winding coupling capacitance between winding i and winding j, ΔC ij′ represents the target inter-winding coupling capacitance between winding i and winding j in the previous month, where winding j represents the winding that is coupled with winding i; a weighted average method is obtained, and the weighted average method is used to calculate the multiple inter-winding coupling capacitance change rates and the multiple inter-winding coupling capacitance change slopes to obtain the target coupling capacitance change rates and target coupling capacitance change slopes corresponding to the windings.

[0214]

[0215] Where, ΔC i_coupled Indicates the target coupling capacitance change rate corresponding to winding i, ΔC i_coupled ′ represents the target coupling capacitance change slope corresponding to winding i, ΔC ij Indicates the rate of change of the inter-winding coupling capacitance between winding i and winding j, ΔC ij ′ represents the slope of the inter-winding coupling capacitance between winding i and winding j, w ijRepresents the coupling capacitance weight coefficient between winding i and winding j; process each of the windings separately to obtain the insulation resistance change rate, resistance change slope, insulation capacitance change rate, capacitance change slope, insulation power factor, equivalent time constant, target coupling capacitance change rate and target coupling capacitance change slope corresponding to each winding; obtain a preset multi-level insulation state evaluation standard, based on the fuzzy comprehensive evaluation method, according to the preset multi-level insulation state evaluation standard, and the insulation resistance change rate, resistance change slope, insulation capacitance change rate, capacitance change slope, insulation power factor, equivalent time constant, target coupling capacitance change rate and target coupling capacitance change slope corresponding to each winding, perform insulation state evaluation on each winding to obtain the insulation evaluation state of each winding; use the insulation evaluation states of the multiple windings as the insulation state detection results of the transformer windings.

[0216] In a specific application scenario, the evaluation module 304 is used to generate a comment set based on the preset multi-level insulation status evaluation standard, and the comment set includes normal state, attention state, warning state, and dangerous state; for each winding, the insulation resistance change rate, resistance change slope, insulation capacitance change rate, capacitance change slope, insulation power factor, equivalent time constant, target coupling capacitance change rate, and target coupling capacitance change slope corresponding to the winding are used as the evaluation factor set of the winding; based on the fuzzy comprehensive evaluation method, a single factor evaluation matrix is ​​constructed according to the preset multi-level insulation status evaluation standard and the evaluation factor set; the weight vector included in the fuzzy comprehensive evaluation method is obtained, and the weight vector and the single factor evaluation matrix are used to calculate and determine the comprehensive evaluation result vector, and the insulation evaluation status of the winding is determined according to the comprehensive evaluation result vector.

[0217] In specific application scenarios, such as Figure 3B As shown, the device further includes: a correction module 305.

[0218] The correction module 305 is used to obtain the service life of the transformer and use the service life of the transformer to correct the reference resistance value and the reference capacitance value.

[0219] R baseline_adjusted =R baseline (1-0.015t),

[0220] C baseline_adjusted =C baseline (1+0.008t),

[0221] Among them, R baseline_adjusted Indicates the corrected reference resistance value, C baseline_adjusted Represents the corrected reference capacitance value, R baseline Indicates the reference resistance value, Cbaseline represents the reference capacitance value, t represents the service life of the transformer; obtaining a temperature compensation coefficient and a load correction coefficient, and using the temperature compensation coefficient and the load correction coefficient to correct the reference coupling capacitance value corresponding to each target coupling capacitance between the windings,

[0222] C ij_baseline_adjusted =C ij_baseline (1+K T ×K I ),

[0223] Among them, C ij_baseline_adjusted Represents the corrected reference coupling capacitance value between winding i and winding j, C ij_baseline Indicates the reference coupling capacitance between winding i and winding j, K T represents the temperature compensation coefficient, K I Represents the load correction coefficient; obtain the daily temperature fluctuation value and the annual temperature cumulative change value, and calculate the temperature fluctuation correction coefficient using the daily temperature fluctuation value and the annual temperature cumulative change value,

[0224] k T_var =1+0.01(ΔT day -0.1ΔT year ),

[0225] Among them, k T_var represents the temperature fluctuation correction coefficient, ΔT day Indicates the daily temperature fluctuation value, ΔT year Indicates the annual temperature cumulative change value; uses the temperature fluctuation correction coefficient to adjust the preset multi-level insulation status evaluation standard; obtains the current transformer load rate, and detects the current transformer load rate; when the detection determines that the current transformer load rate belongs to a light load state, obtains a first weight vector corresponding to the light load state, and adjusts the weight vector of the fuzzy comprehensive evaluation method to the first weight vector; when the detection determines that the current transformer load rate belongs to a heavy load state, obtains a second weight vector corresponding to the heavy load state, and adjusts the weight vector of the fuzzy comprehensive evaluation method to the second weight vector.

[0226] The embodiment of the present application provides a device. Compared with the prior art, the embodiment of the present application establishes a detailed circuit model of the transformer and, based on the measurement data of the common-mode leakage current, uses an improved particle swarm optimization algorithm to inversely calculate the insulation parameters of multiple windings of the transformer to the ground, thereby achieving an accurate assessment of the insulation status of each winding. Compared with traditional monitoring methods, the present application can make full use of the rich information contained in the common-mode leakage current, accurately distinguish the insulation degradation of different windings, provide a more accurate insulation status assessment, and effectively improve the safe operation level of the transformer. Moreover, the present application uses a fuzzy comprehensive evaluation method to perform a multi-level assessment of the insulation status, which can comprehensively reflect the insulation status and distinguish the different severity levels of the insulation status. It can not only evaluate the current status, but also predict the degradation trend through dynamic indicators, providing a scientific basis for operation and maintenance decisions.

[0227] It should be noted that for other corresponding descriptions of the functional units involved in the power transformer winding insulation state monitoring device provided in the embodiment of the present application, reference can be made to Figure 1 and Figures 2A to 2D The corresponding description in is not repeated here. The user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0228] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification. The above-mentioned embodiments only express several implementation methods of the present application. The description is relatively specific and detailed, but it cannot be understood as a limitation on the scope of the patent of this application. It should be pointed out that for ordinary technicians in this field, without departing from the concept of this application, several variations and improvements can be made, which all fall within the scope of protection of this application. Therefore, the scope of protection of this application should be based on the attached claims.

[0229] In an exemplary embodiment, see Figure 4 , also provided is a device comprising a bus, a processor, a memory, and a communication interface, and may also include an input / output interface and a display device, wherein the various functional units can communicate with each other via the bus. The memory stores a computer program, and the processor is configured to execute the program stored in the memory to perform the power transformer winding insulation condition monitoring method described in the above embodiment. A medium stores a computer program, which, when executed by the processor, implements the steps of the power transformer winding insulation condition monitoring method described above.

[0230] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present application can be implemented through hardware or by means of software plus a necessary general hardware platform. Based on this understanding, the technical solution of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each implementation scenario of the present application.

[0231] Those skilled in the art will understand that the accompanying drawings are only schematic diagrams of a preferred implementation scenario, and the modules or processes in the accompanying drawings are not necessarily necessary for the implementation of this application. Those skilled in the art will understand that the modules in the devices in the implementation scenario can be distributed in the devices of the implementation scenario according to the description of the implementation scenario, or can be changed accordingly and located in one or more devices different from this implementation scenario. The modules of the above-mentioned implementation scenarios can be combined into one module, or can be further split into multiple sub-modules. The above-mentioned serial numbers of this application are only for description and do not represent the pros and cons of the implementation scenarios. Disclosed above are only a few specific implementation scenarios of this application, but this application is not limited thereto, and any changes that can be thought of by those skilled in the art should fall within the scope of protection of this application.

Claims

1. A method for monitoring the insulation status of a power transformer winding, characterized in that: include: constructing a fully coupled network model of a transformer multi-conductor transmission line according to the transformer voltage spectrum characteristics, the physical structure of the transformer winding, and the electrical characteristics; extracting insulation state related parameters from the fully coupled network model of the transformer multi-conductor transmission line; and constructing an insulation parameter equivalent circuit model of the transformer using the insulation state related parameters; Acquire real-time common-mode leakage current data collected by a common-mode leakage current multi-parameter measurement system, and process the real-time common-mode leakage current data using a fast Fourier transform algorithm to obtain key common-mode leakage current data; Extracting insulation parameters from the transformer insulation parameter equivalent circuit model, and performing iterative inversion calculation on the insulation parameters and key data of the common-mode leakage current based on an improved particle swarm optimization algorithm to obtain target transformer insulation parameters; The insulation state of the target transformer is evaluated based on the fuzzy comprehensive evaluation method to obtain the insulation state detection result of the transformer winding.

2. The method according to claim 1, characterized in that The method comprises: constructing a transformer multi-conductor transmission line fully coupled network model based on the transformer voltage spectrum characteristics, the transformer winding physical structure and the electrical characteristics; extracting insulation state related parameters from the transformer multi-conductor transmission line fully coupled network model; and constructing a transformer insulation parameter equivalent circuit model using the insulation state related parameters. An initial circuit model is constructed based on the transformer voltage spectrum characteristics, the transformer winding physical structure and electrical characteristics, V0=HV g , I0=TV g , Where K represents the correlation matrix, R represents the resistance matrix, L represents the inductance matrix, C represents the capacitance matrix, j represents the imaginary unit, ω represents the angular frequency, V represents the voltage vector, I represents the current vector, V g represents the source voltage vector, n v Represents the total number of voltage nodes, v1 represents the voltage value of the first voltage node, v2 represents the voltage value of the second voltage node, v4 represents the voltage value of the third voltage node, v5 represents the voltage value of the fourth voltage node, and n i represents the total number of current nodes, i1 represents the current value of the first current node, i2 represents the current value of the second current node, i3 represents the current value of the third current node, i4 represents the current value of the fourth current node, i5 represents the current value of the fifth current node, i6 represents the current value of the sixth current node, and n g represents the total number of coupling groups, v HV,1 Represents the voltage value of the first high-voltage side coupling group, v HV,2 Indicates the voltage value of the second high-voltage side coupling group, v LV,1 Represents the voltage value of the first low-voltage side coupling group, v LV,2 represents the voltage value of the second low-voltage side coupling group, V0 represents the input voltage vector, I0 represents the input current vector, T represents the first matrix, and H represents the second matrix; Obtaining a 3D finite element simulation method, and using the 3D finite element simulation method to obtain target model parameters of the initial circuit model, the target model parameters including a target correlation matrix, a target resistance matrix, a target inductance matrix, and a target capacitance matrix; obtaining an electrostatic field analysis method, calculating a capacitive coupling coefficient between the source voltage vector and the voltage vector using the electrostatic field analysis method to obtain a target first matrix, and obtaining a circuit topology structure of the initial circuit model, determining a connection relationship between the source voltage vector and the current vector based on the circuit topology structure to obtain a target second matrix; The initial circuit model is adjusted using the target model parameters, the target first matrix, and the target second matrix to obtain the transformer multi-conductor transmission line fully coupled network model; Extracting the insulation state related parameters from the transformer multi-conductor transmission line fully coupled network model, the insulation state related parameters including multiple high-voltage side to ground insulation capacitances, multiple high-voltage side to ground insulation resistances, multiple low-voltage side to ground insulation capacitances, multiple low-voltage side to ground insulation resistances, and multiple coupling capacitances between the high-voltage side and the low-voltage side; The insulation state related parameters are used to determine an insulation equivalent impedance matrix, wherein the insulation equivalent impedance matrix includes a plurality of first diagonal elements and a plurality of first non-diagonal elements, wherein: The first diagonal elements are: First off-diagonal element: Among them, Z ii represents the first diagonal element, R i Indicates the insulation resistance of winding i to ground, C i represents the insulation capacitance of winding i to ground, j represents the imaginary unit, ω represents the angular frequency, and Z ij represents the first off-diagonal element, C ij Represents the coupling capacitance between winding i and winding j; The insulation equivalent impedance matrix is ​​used to construct an equivalent circuit model of the transformer insulation parameters.

3. The method according to claim 1, characterized in that The fast Fourier transform algorithm is used to process the real-time data of the common-mode leakage current to obtain key data of the common-mode leakage current, including: Determining a winding voltage sampling frequency and a common-mode leakage current sampling frequency according to the common-mode leakage current real-time data, and comparing the winding voltage sampling frequency and the common-mode leakage current sampling frequency; If the comparison determines that the winding voltage sampling frequency and the common-mode leakage current sampling frequency are inconsistent, an interpolation algorithm is obtained, and the interpolation algorithm is used to perform interpolation alignment processing on the winding voltage data and the common-mode leakage current data in the common-mode leakage current real-time data to obtain target winding voltage data and target common-mode leakage current data, wherein the common-mode leakage current real-time data is timestamped, the common-mode leakage current real-time data includes the winding voltage data, the common-mode leakage current data and environmental parameters, the target winding voltage data includes the fundamental amplitude and phase of multiple winding voltages, and the target common-mode leakage current data includes the amplitude, phase and waveform of multiple common-mode leakage current signals; Performing harmonic analysis on the target common-mode leakage current data using the fast Fourier transform algorithm to obtain target harmonic phase data and target harmonic amplitude data corresponding to each harmonic frequency, wherein the target harmonic phase data includes multiple common-mode leakage current harmonic phases, and the target harmonic amplitude data includes multiple common-mode leakage current harmonic amplitudes; Performing harmonic analysis on the target winding voltage data using the fast Fourier transform algorithm to obtain designated harmonic phase data corresponding to each harmonic frequency, the designated harmonic phase data including a plurality of winding voltage harmonic phases; For each of the harmonic frequencies, target harmonic phase data and designated harmonic phase data corresponding to the harmonic frequency are determined, multiple winding voltage harmonic phases are obtained from the designated harmonic phase data, a common-mode leakage current harmonic phase corresponding to each of the winding voltage harmonic phases is determined from the target harmonic phase data, and a phase difference between each of the winding voltage harmonic phases and the common-mode leakage current harmonic phase corresponding to each of the winding voltage harmonic phases is respectively calculated to obtain multiple phase differences corresponding to the harmonic frequency; The common-mode leakage current key data is generated using the environmental parameters, the target winding voltage data, the target common-mode leakage current data, the target harmonic phase data corresponding to each harmonic frequency, the target harmonic amplitude data corresponding to each harmonic frequency, the specified harmonic phase data corresponding to each harmonic frequency, and multiple phase differences corresponding to each harmonic frequency.

4. The method according to claim 1, wherein Extracting insulation parameters from the transformer insulation parameter equivalent circuit model, and performing iterative inversion calculation on the insulation parameters and the common-mode leakage current key data based on an improved particle swarm optimization algorithm to obtain target transformer insulation parameters include: Obtaining a leakage current vector and a winding voltage vector from the common-mode leakage current key data, obtaining an admittance matrix from the transformer insulation parameter equivalent circuit model, and establishing a relationship equation using the leakage current vector, the winding voltage vector, and the admittance matrix. I L =V·Y, Among them, I L represents the leakage current vector, V represents the winding voltage vector, and Y represents the admittance matrix, which includes a plurality of second diagonal elements and a plurality of second non-diagonal elements, wherein, The second diagonal elements are: The second off-diagonal element is: Y ij =-jωC ij , Among them, Y ii represents the second diagonal element, R i Indicates the insulation resistance of winding i to ground, C i represents the insulation capacitance of winding i to ground, j represents the imaginary unit, ω represents the angular frequency, jωC i represents the capacitive admittance term, Y ij represents the second off-diagonal element, C ij Represents the coupling capacitance between winding i and winding j, -jωC ij represents the negative capacitive admittance term; Obtaining target harmonic phase data corresponding to each harmonic frequency and target harmonic amplitude data corresponding to each harmonic frequency from the common-mode leakage current key data, and calculating theoretical leakage current vector data based on the transformer insulation parameter equivalent circuit model; Determine the error function of the improved particle swarm optimization algorithm, and determine the objective function according to the error function, E(ω)=|I measured (ω)-I calculated (ω)|, minF=∑w(ω)|I measured (ω)-I calculated (oh)| 2 , Wherein, E(ω) represents the error function, minF represents the objective function, and I measured (ω) represents the target harmonic phase data and target harmonic amplitude data at frequency ω, I calculated (ω) represents the theoretical leakage current vector data, and w(ω) represents the weight function; Obtaining the maximum number of iterations, the linear change of the inertia weight, and the preset learning factor corresponding to the improved particle swarm optimization algorithm; Based on the improved particle swarm optimization algorithm, the common-mode leakage current key data and the theoretical leakage current vector data are iteratively calculated using the maximum number of iterations, the linear change of the inertia weight, the preset learning factor, the objective function, and the relationship equation until an optimal solution is found or the maximum number of iterations is reached, thereby obtaining quantized values ​​of insulation parameters of each winding of the transformer, wherein the quantized values ​​of insulation parameters of each winding of the transformer include specified insulation resistances of multiple windings to ground, specified insulation capacitances of the multiple windings to ground, and specified coupling capacitances between multiple windings; Obtaining environmental parameters from the common-mode leakage current key data, and calculating a temperature compensation coefficient and a humidity compensation coefficient using the environmental parameters, k T =1+α(T-20), k H =1+β(H-50), Among them, k T represents the temperature compensation coefficient, α represents the temperature coefficient, T represents the ambient temperature in the environmental parameters, k H represents the humidity compensation coefficient, β represents the humidity coefficient, and H represents the ambient humidity in the environmental parameters; The temperature compensation coefficient and the humidity compensation coefficient are used to perform parameter correction on the quantized values ​​of the insulation parameters of each winding of the transformer to obtain the target transformer insulation parameters. C i_corrected =C′ i k T k H , Among them, R i_corrected Indicates the target insulation resistance of winding i to ground in the target transformer insulation parameter, C i_corrected represents the target insulation capacitance of winding i to ground in the target transformer insulation parameter, k T Indicates the temperature compensation coefficient, k H Represents the humidity compensation coefficient, R′ i Indicates the specified insulation resistance of winding i to ground in the quantified value of the insulation parameter of each winding of the transformer, C′ i Indicates the specified insulation capacitance of winding i to ground in the quantified value of the insulation parameter of each winding of the transformer.

5. The method according to claim 1, wherein The insulation state evaluation of the target transformer insulation parameters based on the fuzzy comprehensive evaluation method to obtain the transformer winding insulation state detection result includes: For each of the windings, obtain a reference resistance value and a reference capacitance value, obtain the target insulation resistance and target insulation capacitance of the winding to ground from the target transformer insulation parameters, obtain the target insulation resistance of the previous month corresponding to the target insulation resistance, and the target insulation capacitance of the previous month corresponding to the target insulation capacitance, and calculate using the reference resistance value, the reference capacitance value, the target insulation resistance and target insulation capacitance of the winding to ground, the target insulation resistance of the previous month corresponding to the target insulation resistance, and the target insulation capacitance of the previous month corresponding to the target insulation capacitance to obtain the insulation resistance change rate, resistance change slope, insulation capacitance change rate, capacitance change slope, insulation power factor, and equivalent time constant corresponding to the winding. ΔR i ′=ΔR i -ΔR i′ , ΔC i ′=ΔC i -ΔC i′ , t i =R i_corrected ·C i_corrected , Where, ΔR i Indicates the rate of change of insulation resistance corresponding to winding i, ΔR i ′ represents the resistance change slope corresponding to winding i, ΔC i Indicates the rate of change of insulation capacitance corresponding to winding i, ΔC i ′ represents the capacitance change slope corresponding to winding i, tanδ i Indicates the insulation power factor corresponding to winding i, τ i Represents the equivalent time constant corresponding to winding i, R i_corrected represents the target insulation resistance of winding i to ground, R baseline Indicates the reference resistance value, C i_corrected represents the target insulation capacitance of winding i to ground, C baseline represents the reference capacitance value, tanδ i represents the insulation power factor corresponding to winding i, ω represents the angular frequency, τ i Represents the equivalent time constant corresponding to winding i, ΔR i′ Indicates the target insulation resistance of winding i in the previous month, ΔC i′ represents the target insulation capacitance of winding i in the previous month; Obtaining a plurality of inter-winding target coupling capacitances associated with the winding from the target transformer insulation parameters, obtaining a reference coupling capacitance value corresponding to each of the inter-winding target coupling capacitances, and an inter-winding target coupling capacitance corresponding to each of the inter-winding target coupling capacitances in the previous month; Calculation is performed using the multiple inter-winding target coupling capacitances, the reference coupling capacitance value corresponding to each of the inter-winding target coupling capacitances, and the inter-winding target coupling capacitance corresponding to each of the inter-winding target coupling capacitances in the previous month to obtain multiple inter-winding coupling capacitance change rates and multiple inter-winding coupling capacitance change slopes. ΔC ij ′=ΔC ij -ΔC ij′ , Where, ΔC ij Indicates the rate of change of the inter-winding coupling capacitance between winding i and winding j, ΔC ij ′ represents the slope of the inter-winding coupling capacitance between winding i and winding j, C ij_baseline Indicates the reference coupling capacitance between winding i and winding j, C ij represents the target inter-winding coupling capacitance between winding i and winding j, ΔC ij′ represents the target inter-winding coupling capacitance between winding i and winding j in the previous month, where winding j represents the winding that is coupled with winding i; Obtain a weighted average method, and use the weighted average method to calculate the multiple inter-winding coupling capacitance change rates and the multiple inter-winding coupling capacitance change slopes to obtain the target coupling capacitance change rates and target coupling capacitance change slopes corresponding to the windings. Where, ΔC i_coupled Indicates the target coupling capacitance change rate corresponding to winding i, ΔC i_coupled ′ represents the target coupling capacitance change slope corresponding to winding i, ΔC ij Indicates the rate of change of the inter-winding coupling capacitance between winding i and winding j, ΔC ij ′ represents the slope of the inter-winding coupling capacitance between winding i and winding j, w ij Represents the coupling capacitance weight coefficient between winding i and winding j; Processing each of the windings separately to obtain the insulation resistance change rate, resistance change slope, insulation capacitance change rate, capacitance change slope, insulation power factor, equivalent time constant, target coupling capacitance change rate, and target coupling capacitance change slope corresponding to each winding; Obtain a preset multi-level insulation state evaluation standard, and based on the fuzzy comprehensive evaluation method, perform an insulation state evaluation on each winding according to the preset multi-level insulation state evaluation standard, and the insulation resistance change rate, resistance change slope, insulation capacitance change rate, capacitance change slope, insulation power factor, equivalent time constant, target coupling capacitance change rate, and target coupling capacitance change slope corresponding to each winding, to obtain an insulation evaluation state of each winding; The insulation evaluation status of the plurality of windings is used as the transformer winding insulation status detection result.

6. The method according to claim 5, characterized in that Based on the fuzzy comprehensive evaluation method, according to the preset multi-level insulation state evaluation standard, and the insulation resistance change rate, resistance change slope, insulation capacitance change rate, capacitance change slope, insulation power factor, equivalent time constant, target coupling capacitance change rate and target coupling capacitance change slope corresponding to each winding, the insulation state of each winding is evaluated to obtain the insulation evaluation state of each winding, including: Generate a comment set according to the preset multi-level insulation status evaluation standard, the comment set including normal state, attention state, warning state, and dangerous state; For each winding, the insulation resistance change rate, resistance change slope, insulation capacitance change rate, capacitance change slope, insulation power factor, equivalent time constant, target coupling capacitance change rate, and target coupling capacitance change slope corresponding to the winding are used as an evaluation factor set for the winding; Based on the fuzzy comprehensive evaluation method, a single factor evaluation matrix is ​​constructed according to the preset multi-level insulation status evaluation standard and the evaluation factor set; A weight vector included in the fuzzy comprehensive evaluation method is obtained, the weight vector and the single factor evaluation matrix are used to calculate and determine a comprehensive evaluation result vector, and the insulation evaluation status of the winding is determined according to the comprehensive evaluation result vector.

7. The method according to claim 5, characterized in that The method further comprises: Obtaining the service life of the transformer, and using the service life of the transformer to correct the reference resistance value and the reference capacitance value, R baseline_adjusted =R baseline (1-0.015t), C baseline_adjusted =C baseline (1+0.008t), Among them, R baseline_adjusted Indicates the corrected reference resistance value, C baseline_adjusted Represents the corrected reference capacitance value, R baseline Indicates the reference resistance value, C baseline represents the reference capacitance value, and t represents the service life of the transformer; Obtaining a temperature compensation coefficient and a load correction coefficient, and using the temperature compensation coefficient and the load correction coefficient to correct the reference coupling capacitance value corresponding to each target coupling capacitance between the windings, C ij_baseline_adjusted =C ij_baseline (1+K T ×K I ), Among them, C ij_baseline_adjusted Represents the corrected reference coupling capacitance value between winding i and winding j, C ij_baseline Indicates the reference coupling capacitance between winding i and winding j, K T represents the temperature compensation coefficient, K I represents the load correction factor; Obtaining daily temperature fluctuation values ​​and annual temperature cumulative change values, and calculating a temperature fluctuation correction coefficient using the daily temperature fluctuation values ​​and the annual temperature cumulative change values, k T_var =1+0.01(ΔT day -0.1ΔT year ), Among them, k T_var represents the temperature fluctuation correction coefficient, ΔT day Indicates the daily temperature fluctuation value, ΔT year Indicates the cumulative change in temperature over the year; Adjusting the preset multi-level insulation status evaluation standard using the temperature fluctuation correction coefficient; Obtaining a current transformer load factor and detecting the current transformer load factor; When it is determined that the current transformer load rate is in a light load state, obtaining a first weight vector corresponding to the light load state, and adjusting the weight vector of the fuzzy comprehensive evaluation method to the first weight vector; When it is detected that the current transformer load rate belongs to an overload state, a second weight vector corresponding to the overload state is obtained, and the weight vector of the fuzzy comprehensive evaluation method is adjusted to the second weight vector.

8. A device for monitoring the insulation status of a power transformer winding, characterized in that: include: A construction module is used to construct a fully coupled network model of a transformer multi-conductor transmission line based on the transformer voltage spectrum characteristics, the physical structure of the transformer winding, and the electrical characteristics; extract insulation state related parameters from the fully coupled network model of the transformer multi-conductor transmission line; and construct an insulation parameter equivalent circuit model of the transformer using the insulation state related parameters; An acquisition module is used to acquire real-time common-mode leakage current data collected based on a common-mode leakage current multi-parameter measurement system, and to process the real-time common-mode leakage current data using a fast Fourier transform algorithm to obtain key common-mode leakage current data; A calculation module is used to extract insulation parameters from the transformer insulation parameter equivalent circuit model, and perform iterative inversion calculation on the insulation parameters and the common-mode leakage current key data based on an improved particle swarm optimization algorithm to obtain target transformer insulation parameters; The evaluation module is used to evaluate the insulation status of the target transformer insulation parameters based on a fuzzy comprehensive evaluation method to obtain a transformer winding insulation status detection result.

9. A device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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