An extended debye model equivalent circuit modeling method for oil-paper insulation equipment

Abstract

The application relates to an oil-paper insulation device extended Debye model equivalent circuit modeling method, which comprises the following steps: acquiring measured data including a test frequency and a dielectric response current amplitude; establishing an extended Debye model equivalent circuit topology structure and a mathematical relationship between the dielectric response current amplitude and extended Debye model parameters; performing logarithmic transformation on the dielectric response current amplitude under different frequencies, and performing smoothing and sample expansion by adopting local weighted regression; constructing a parameter identification objective function, introducing a weight function to weight errors of different frequency points, balancing fitting weights and improving full-band fitting effects; adopting an artificial bee algorithm to optimize and identify the extended Debye model parameters, so that the objective function is minimum; and constructing the extended Debye model equivalent circuit according to the optimal parameter set, so as to realize extended Debye model modeling of the oil-paper insulation device. The application can improve the stability and accuracy of model parameter identification, and enhance the engineering applicability and repeatability of the modeling result.

Description

Technical Field

[0001] This invention relates to the field of insulation condition monitoring technology for power equipment, specifically to a method for modeling the equivalent circuit of oil-paper insulated equipment based on the dielectric response current amplitude using the extended Debye model. This method is applicable to the insulation condition detection and life prediction of oil-paper insulated equipment such as oil-immersed power transformers, reactors, bushings, and instrument transformers. Background Technology

[0002] As a key piece of equipment for energy transmission and voltage transformation in the power grid, the operational reliability of power transformers directly affects the safety and stability of the power system. The oil-paper insulation system is the main insulation structure of the transformer. During long-term operation, under the coupled effects of multiple factors such as electric field stress, thermal stress, mechanical stress, and moisture, irreversible aging and deterioration processes occur. These include cellulose chain breakage, hydrolysis and thermal cracking, and oxidation of insulating oil to generate polar products. This leads to increased dielectric loss and decreased insulation strength, and in severe cases, may cause major accidents such as breakdown. Therefore, accurately assessing the aging state and remaining life of the transformer's oil-paper insulation is of significant engineering importance for developing reasonable maintenance strategies and reducing the risk of sudden failures.

[0003] Frequency domain dielectric spectroscopy (FDS) testing can acquire frequency domain response information of insulating media over a wide frequency band. It is characterized by rich information content and good repeatability of test results, and has become an important tool for the diagnosis and parameter evaluation of oil-paper insulation. To further reveal the polarization and relaxation mechanisms of oil-paper insulation and achieve quantitative modeling, engineering and academia often use the equivalent circuit of the extended Debye model to fit the FDS data. Geometric branches and multiple RC polarization branches are used to characterize the conductivity, geometric capacitance, and multiple relaxation processes of the medium, thereby achieving a parameterized description of the insulation state.

[0004] Currently, existing extended Debye modeling and parameter identification methods mostly rely on parameters such as the real and imaginary parts of complex capacitance (or complex dielectric constant) as the basis for fitting. Because the response current amplitude of oil-paper insulation is small and the testing time is long in the low-frequency range, the measurement process is easily affected by factors such as leakage current, environmental power frequency interference, temperature and humidity fluctuations, instrument drift, and phase measurement errors. This leads to significant deviations in the dielectric loss angle in the low-frequency range, which in turn affects the measurement results of the real and imaginary parts of the complex capacitance. These deviations are amplified during model parameter identification, resulting in problems such as fitting difficulties, poor parameter stability, sensitivity of identification results to initial values ​​or noise, and insufficient model repeatability. Even with the introduction of intelligent optimization methods such as genetic algorithms and particle swarm optimization, it is still difficult to fundamentally overcome the impact of low-frequency measurement deviations on modeling reliability when the objective function still highly depends on the low-frequency dielectric loss angle / phase information.

[0005] Therefore, a new method for modeling the equivalent circuit of the extended Debye model is urgently needed: while maintaining an effective characterization of the multipolar relaxation process of oil-paper insulation, the method should minimize the dependence on low-frequency dielectric loss angle or phase information, prioritize the use of features with higher measurement stability to construct the relationship between model parameters and test data, and improve the consistency of fitting across the entire frequency band through reasonable data preprocessing and objective function design, thereby achieving stable and accurate identification of extended Debye model parameters and reliable construction of the equivalent circuit. Summary of the Invention

[0006] This invention aims to address the difficulties in modeling the equivalent circuit of the extended Debye model for transformer oil-paper insulation, caused by significant measurement errors in the imaginary part of the complex capacitance and the dielectric loss angle in the low-frequency band, as well as the poor stability and reliability of parameter identification results. It provides a method for modeling the equivalent circuit of the extended Debye model for oil-paper insulation equipment based on the dielectric response current amplitude. This method does not rely on the dielectric loss angle or phase information, which has significant errors in the low-frequency band. Instead, it uses the amplitude of the sinusoidal current of the dielectric response obtained from frequency domain dielectric spectrum testing as the core feature quantity. It utilizes the inherent mechanism between the amplitude of the dielectric response current and the polarization / conductivity characteristics of the oil-paper insulation system to establish a mathematical relationship between the amplitude of the dielectric response current and the parameters of the extended Debye model. Combined with data preprocessing and optimized identification strategies, it achieves effective modeling of the equivalent circuit of the extended Debye model, thereby improving the stability and accuracy of model parameter identification and enhancing the engineering applicability and repeatability of the modeling results.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: a method for modeling the equivalent circuit of an extended Debye model for oil-paper insulation equipment, comprising the following steps:

[0008] Step S1: Perform frequency domain dielectric spectrum testing on the oil-paper insulation equipment to obtain measured data including the test frequency f and the dielectric response current amplitude I;

[0009] Step S2: Establish the equivalent circuit topology of the extended Debye model, and under frequency domain conditions, combine the physical relationship of the dielectric response current to establish the mathematical relationship between the amplitude I of the dielectric response current and the parameters of the extended Debye model;

[0010] Step S3: Perform logarithmic transformation on the amplitude I of the dielectric response current at different frequencies, and use local weighted regression for smoothing and sample expansion; construct the parameter identification objective function in the logarithmic coordinate system, and introduce a weight function to weight the error at different frequency points in order to balance the fitting weight and improve the fitting effect across the entire frequency band.

[0011] Step S4: Use the artificial hummingbird algorithm to optimize and identify the parameters of the extended Debye model so as to minimize the objective function; construct the equivalent circuit of the extended Debye model based on the obtained optimal parameter set to realize the extended Debye model modeling of the oil-paper insulation equipment.

[0012] Further, in step S1, the dielectric response current is the current response generated after a sinusoidal excitation voltage is applied to the oil-paper insulation device under test during the frequency domain dielectric spectrum test. When the test frequency is f, the dielectric response current is a sinusoidal alternating current with frequency f, and its phasor is obtained by vector synthesis of the loss current component in phase with the excitation voltage and the capacitive current component leading the excitation voltage by 90°. The amplitude I of the dielectric response current is the measured amplitude of the sinusoidal current with frequency f, which is equivalent to the modulus of the dielectric response current phasor.

[0013] Further, in step S2, the extended Debye model equivalent circuit is composed of a geometric equivalent circuit and a polarization equivalent circuit; wherein, the geometric equivalent circuit is composed of an insulation resistance R g and geometric capacitance C g The polarization equivalent circuit is composed of N RC series polarization branches connected in parallel, where N is a natural number; each RC series polarization branch includes a polarization resistor R. pi With a polarization capacitor C pi And the polarization resistor R pi and the polarization capacitor C pi Connected in series, where i = 1,2, …, N.

[0014] Furthermore, in step S2, the mathematical relationship between the dielectric response current amplitude I and the extended Debye model parameters is specifically as follows:

[0015]

[0016] Where, τ i Let ω be the time constant of the i-th polarization branch, U be the voltage amplitude applied across the dielectric, and ω be the angular frequency.

[0017] Furthermore, in step S3, local weighted regression is used for smoothing and sample augmentation, specifically as follows:

[0018] In the logarithmic frequency-logarithmic current amplitude coordinate system, at any frequency point f k A local sample set is formed by selecting several data points in its neighborhood around the center, and then sorting the local sample set according to the frequency point f. k Distance is weighted and weighted least squares fitting is performed to obtain the frequency points f. k The corresponding smoothed estimate is obtained by repeating the above local weighted fitting process for each frequency point to generate a continuous smooth curve, and then interpolating at preset frequency intervals on the smooth curve to generate expanded samples, thereby obtaining the expanded dielectric response current amplitude I. L The dataset.

[0019] Furthermore, in step S3, the weighting function is constructed using the reciprocal of the relative dielectric response current amplitude, where the relative dielectric response current amplitude is the expanded dielectric response current amplitude I at each angular frequency ω. L (ω) and I within the test frequency band L The ratio of minimum values; the expression for the weighting function is:

[0020] w(ω)=min(I L ) / I L (ω)

[0021] Where w(ω) represents the weighting function at angular frequency ω.

[0022] Furthermore, in step S3, the constructed objective function is used to measure the difference between the dielectric response current amplitude calculated by the extended Debye model and the expanded dielectric response current amplitude I. L The difference between them is used as the optimization objective for parameter identification of the extended Debye model, with the minimum of the difference being taken as the objective function; wherein, the objective function is established in a logarithmic coordinate system, and the input includes the amplitude of the dielectric response current I after logarithmic transformation and local weighted regression processing. L (ω) and the predicted dielectric response current amplitude calculated from the parameters of the extended Debye model. Furthermore, the objective function introduces a frequency-related weighting function w(ω) to weight the error contributions at different frequency points, thereby balancing the fitting weights of high and low frequency bands and improving the consistency of fitting across the entire frequency band, thus obtaining identification results with better global fitting performance and higher parameter stability; the expression of the objective function is:

[0023]

[0024] Where J represents the objective function.

[0025] Further, in step S4, the artificial hummingbird algorithm is used to optimize and identify the parameters of the extended Debye model. Specifically, the set of parameters to be identified in the extended Debye model is constructed into the position vector of a hummingbird individual, and the search boundary is set according to the physical value range of each parameter. The constructed objective function is used as the fitness function, and the foraging search mechanism in the artificial hummingbird algorithm is used to update the position of the hummingbird individual to achieve a dynamic balance between global search and local exploitation. During the iteration process, the current best individual and its fitness are retained and updated. When the preset termination condition is met, the optimal parameter set is output, and the equivalent circuit of the extended Debye model is constructed with the optimal parameter set to achieve stable identification and accurate modeling of the model parameters.

[0026] The present invention also provides an extended Debye model equivalent circuit modeling system for oil-paper insulation equipment, including a memory, a processor, and computer program instructions stored in the memory and executable by the processor. When the processor executes the computer program instructions, it can implement the above-mentioned method.

[0027] The present invention also provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the above-described method.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] 1) Reduce the impact of low-frequency phase / loss angle errors: This invention uses the dielectric response current amplitude as the core modeling quantity to avoid dependence on dielectric loss angle or phase information with large errors in the low-frequency band, thereby reducing the impact of low-frequency measurement deviations on parameter identification results from the source.

[0030] 2) Higher parameter identification stability and better consistency of full-band fitting: This invention introduces logarithmic transformation, LOESS smoothing and sample expansion, and uses a frequency-related weighting function to balance the error contribution of different frequency points, making the identification process less sensitive to noise and initial values, while effectively improving the problem of unbalanced fitting in high and low frequency bands and improving the overall fitting effect of the full-band.

[0031] 3) Convenient implementation and strong engineering applicability: This invention uses the current amplitude data that can be directly obtained from conventional FDS testing as input, without the need for additional complex phase calibration or loss angle correction, making it easy to promote and apply in laboratory and field testing conditions. Attached Figure Description

[0032] Figure 1 This is a flowchart of the method for modeling the equivalent circuit of the extended Debye model for oil-paper insulation equipment provided in this embodiment of the invention;

[0033] Figure 2 This is a topology diagram of the equivalent circuit of the extended Debye model in an embodiment of the present invention;

[0034] Figure 3 This is the final fitting effect diagram of an embodiment of the present invention. Detailed Implementation

[0035] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0036] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0037] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0038] like Figure 1 As shown in the figure, this embodiment provides a method for modeling the equivalent circuit of an extended Debye model for oil-paper insulation equipment, and its specific implementation steps are as follows.

[0039] 1. Acquisition of frequency domain dielectric spectrum data

[0040] Frequency domain dielectric spectroscopy (FDS) tests were performed on the oil-paper insulation equipment to obtain measured data, including the test frequency f and the dielectric response current amplitude I.

[0041] The dielectric response current is the current response generated when a sinusoidal excitation voltage is applied to the paper-insulated device under test during frequency domain dielectric spectrum testing. At the test frequency f, the dielectric response current is a sinusoidal alternating current with frequency f, and its phasor is obtained by vector synthesis of the loss current component in phase with the excitation voltage and the capacitive current component leading the excitation voltage by 90°. The amplitude I of the dielectric response current is the measured amplitude of the sinusoidal current with frequency f, which is equivalent to the magnitude of the dielectric response current phasor.

[0042] 2. Construction of the mathematical expression of the extended Debye model in the frequency domain

[0043] Based on the mechanistic relationship between dielectric response current and the polarization / conductivity characteristics of oil-paper insulation, a mechanism is established as follows: Figure 2 The equivalent circuit topology of the extended Debye model is shown, and a mathematical relationship between the amplitude I of the dielectric response current and the parameters of the extended Debye model is established under frequency domain conditions, taking into account the physical relationship of the dielectric response current.

[0044] Specifically, the extended Debye model equivalent circuit consists of a geometric equivalent circuit and a polarization equivalent circuit. The geometric equivalent circuit is composed of the insulation resistance R... g and geometric capacitance C g The polarization equivalent circuit is composed of N RC series polarization branches connected in parallel, where N is a natural number. Each RC series polarization branch includes a polarization resistor R. pi With a polarization capacitor C pi And polarization resistance R pi and polarization capacitor C pi Connected in series, where i = 1, 2, …, N.

[0045] The mathematical relationship between the dielectric response current amplitude I and the parameters of the extended Debye model is as follows:

[0046]

[0047] in:

[0048] ω is the angular frequency, and ω = 2πf, where f is the test frequency;

[0049] U is the amplitude of the AC excitation voltage applied across the insulation system;

[0050] τ i Let τ be the time constant under the i-th polarization branch, and τ i =R pi C pi ;

[0051] R pi Let be the polarization resistance of the i-th polarization branch;

[0052] C pi Let be the polarization capacitance of the i-th polarization branch;

[0053] R g The insulation resistance of the insulation system;

[0054] C g The geometric capacitance of an insulating system is mainly determined by the insulating structure.

[0055] N is the proposed number of polarization branches, where N is a natural number and i = 1, 2, …, N.

[0056] 3. Data preprocessing and objective function construction

[0057] Logarithmically transform the dielectric response current amplitude I at different frequencies, and use Locally Weighted Regression (LOESS) for smoothing and sample expansion. Construct a parameter identification objective function in logarithmic coordinate system, and introduce a weight function to weight the errors at different frequency points in order to balance the fitting weights and improve the fitting effect across the entire frequency band.

[0058] To improve the fitting stability and global consistency of dielectric response current amplitude data over a wide frequency band, this invention preprocesses the measured dielectric response current amplitude data and constructs an objective function for identifying parameters of the extended Debye model based on this data.

[0059] First, perform a logarithmic transformation on I corresponding to each frequency point f to obtain log... 10 (I) This approach aims to compress the magnitude differences between data in different frequency bands and enhance the identifiability of low-frequency features. Subsequently, Locally Weighted Regression (LOESS) is used to smooth and augment the logarithmic domain data, resulting in the augmented dataset I.L Specifically, for any location x0 to be estimated, a local sample set Ɲ(x0) is constructed by selecting four samples in its neighborhood, and a second-order local polynomial is fitted using weighted least squares:

[0060]

[0061] Among them, K i (x0) represents the distance-related local weights, using the Tricube kernel function:

[0062]

[0063] in, The neighborhood scale is used. This yields the smoothed estimate at x0:

[0064]

[0065] A continuous smooth curve is obtained by repeating the above local fitting for each x0. Expanded samples are generated by interpolation at preset logarithmic frequency intervals on the smooth curve, thereby obtaining a smoother and denser logarithmic domain dataset log. 10 (I L ).

[0066] Based on this, an objective function is constructed in logarithmic coordinates to measure the magnitude I of the predicted dielectric response current calculated by the extended Debye model. 拟合 with I L The difference between them is used as the optimization objective to identify the parameters, and the minimum of this difference is taken as the parameter identification objective. The expression of the objective function is:

[0067]

[0068] Where J represents the objective function.

[0069] Meanwhile, to balance the contribution of different frequency points to the fitting error, a frequency-related weighting function is introduced to weight the error term. The weighting function is constructed using the reciprocal of the amplitude of the dielectric response current.

[0070] w(ω)=min(I L ) / I L (ω)

[0071] Where w(ω) represents the weighting function at angular frequency ω.

[0072] 4. Extended Debye Model Parameter Identification and Equivalent Circuit Construction

[0073] The Artificial Hummingbird Algorithm (AHA) is used to optimize and identify the parameters of the extended Debye model to minimize the objective function. Based on the obtained optimal parameter set, the equivalent circuit of the extended Debye model is constructed to realize the extended Debye model modeling of the oil-paper insulation equipment.

[0074] After completing data preprocessing and objective function construction, this invention uses AHA to optimize and identify the equivalent circuit parameters of the extended Debye model, and constructs the equivalent circuit accordingly, as detailed below.

[0075] First, the parameters to be identified in the extended Debye model are combined into a parameter vector as the position representation of the hummingbird individual. The parameter vector includes the geometric branch parameters R. g C g And the parameters R of the N polarization branches pi C pi .

[0076] Secondly, the constructed objective function is used as the fitness function. AHA iteratively updates the individual position by simulating the foraging behavior of hummingbirds. During the iteration process, it comprehensively adopts search mechanisms such as directional foraging, regional foraging, and migratory foraging to achieve a dynamic balance between global search and local development. In each iteration, the individual optimal solution and the global optimal solution are updated according to the fitness value, and the current optimal parameter set is retained to avoid the loss of good solutions.

[0077] When the preset termination condition is met, the globally optimal parameter set {R} is output. g C g ,R p1 C p1 ,…,R pN C pN Finally, based on the optimal parameter set, an equivalent circuit of the extended Debye model is constructed, and the dielectric response current amplitude and complex capacitance response curve are calculated using the equivalent circuit. These are then compared and verified with measured data, thereby achieving reliable modeling of the extended Debye model for oil-paper insulation equipment.

[0078] 5. Description of Experimental Sample Preparation and Testing

[0079] To verify the effectiveness of the method proposed in this invention, this embodiment prepares transformer oil paper insulation samples with different aging degrees under laboratory conditions and conducts frequency domain dielectric spectrum tests to obtain the data required for modeling, as detailed below.

[0080] (1) Sample preparation: Weidmann T4 transformer insulating paperboard conforming to IEC 60641 standard was selected as the solid insulating material, with a thickness of 1 mm; No. 25 Karamay naphthenic mineral insulating oil was selected as the liquid insulating material. First, the insulating paperboard was placed in a vacuum drying oven at 105 ℃ for 48 h to remove initial moisture and control the moisture content to below 0.5%; at the same time, the insulating oil was subjected to vacuum degassing treatment. Subsequently, under vacuum conditions of 50 Pa and temperature of 130 ℃, the dried insulating paperboard was immersed in the insulating oil for 48 h to obtain an oil-paper insulation model sample in its initial state (aging time of 0 d).

[0081] (2) Accelerated thermal aging: The thermal aging process of oil-paper insulation under long-term operation conditions of transformer was simulated by constant temperature accelerated aging method. The initial oil-paper insulation samples were placed in a vacuum oven at 130 ℃ for accelerated thermal aging, and samples were taken at 10 days, 20 days and 30 days of aging. The unaged samples were used as the control group (0 days). Finally, four groups of oil-paper insulation samples with different aging degrees were prepared.

[0082] (3) Frequency Domain Dielectric Spectroscopy Test: The frequency domain dielectric spectrum of the above four groups of samples was tested using a DIRANA dielectric response analyzer. A three-electrode test system was used, with an excitation voltage of 200 V peak value; the test frequency range was 10... -3 ~10 3 The test frequency was controlled at a constant temperature of 35 ℃. During the test, data such as the real part of the complex capacitance, the imaginary part of the complex capacitance, and the amplitude of the dielectric response current were acquired for each sample at different frequencies.

[0083] 6. Identification of equivalent circuit parameters for the extended Debye model

[0084] This embodiment sets up multiple aging time points to prepare oil-paper insulation samples with different aging degrees, and conducts frequency domain dielectric spectroscopy tests on each to verify the stability and consistency of the method of the present invention. For ease of explanation, only one set of samples corresponding to one aging time point (20 days of aging) is selected as an example for demonstration. The test and modeling results of the other samples are consistent with or similar to this example. Based on the above method, the original test data of the 20-day aged samples are processed according to... Figure 2 The process described above involves processing and parameter identification to obtain the parameters of each branch of the extended Debye model equivalent circuit, as shown in Table 1. Further, the extended Debye model equivalent circuit is constructed based on the parameters listed in Table 1, and the real and imaginary part curves of the complex capacitor are reconstructed accordingly. The reconstructed results are compared with the measured complex capacitor data to verify the effectiveness of the parameter identification results and the modeling method.

[0085] Table 1. Equivalent Circuit Parameters of Transformer Extended Debye Model

[0086]

[0087] Note: Resistance parameters are in GΩ, and capacitance parameters are in nF.

[0088] Figure 3 The original measured FDS samples and fitted curves for a 20-day sample are shown. Firstly, the figure shows that the real and imaginary parts of the complex capacitance reconstructed from the equivalent circuit parameters of the extended Debye model obtained by fitting the dielectric response current have a high degree of agreement with the measured values. Secondly, the fitted values ​​show that the parameters obtained by the AHA optimization algorithm conform to the general rules of the extended Debye model for oil-paper insulation, and the time constants of different branches are all within a reasonable range, indicating the effectiveness of the method presented in this invention.

[0089] This embodiment also provides an extended Debye model equivalent circuit modeling system for oil-paper insulation equipment, including a memory, a processor, and computer program instructions stored in the memory and executable by the processor. When the processor executes the computer program instructions, it can implement the above-described method.

[0090] This embodiment also provides a computer-readable storage medium storing computer program instructions that, when executed by a processor, implement the above-described method.

[0091] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0092] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0093] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0094] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0095] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for modeling the equivalent circuit of an extended Debye model for oil-paper insulated equipment, characterized in that, Includes the following steps: Step S1: Perform frequency domain dielectric spectrum testing on the oil-paper insulation equipment to obtain measured data including the test frequency f and the dielectric response current amplitude I; Step S2: Establish the equivalent circuit topology of the extended Debye model, and under frequency domain conditions, combine the physical relationship of the dielectric response current to establish the mathematical relationship between the amplitude I of the dielectric response current and the parameters of the extended Debye model; Step S3: Perform logarithmic transformation on the amplitude I of the dielectric response current at different frequencies, and use local weighted regression for smoothing and sample expansion; construct the parameter identification objective function in the logarithmic coordinate system, and introduce a weight function to weight the error at different frequency points in order to balance the fitting weight and improve the fitting effect across the entire frequency band. Step S4: Use the artificial hummingbird algorithm to optimize and identify the parameters of the extended Debye model so as to minimize the objective function; construct the equivalent circuit of the extended Debye model based on the obtained optimal parameter set to realize the extended Debye model modeling of the oil-paper insulation equipment.

2. The method for modeling the equivalent circuit of an extended Debye model for oil-paper insulation equipment according to claim 1, characterized in that, In step S1, the dielectric response current is the current response generated after a sinusoidal excitation voltage is applied to the oil-paper insulation device under test during the frequency domain dielectric spectrum test. When the test frequency is f, the dielectric response current is a sinusoidal alternating current with frequency f, and its phasor is obtained by vector synthesis of the loss current component in phase with the excitation voltage and the capacitive current component leading the excitation voltage by 90°. The amplitude I of the dielectric response current is the measured amplitude of the sinusoidal current with frequency f, which is equivalent to the modulus of the dielectric response current phasor.

3. The method for modeling the equivalent circuit of an extended Debye model for oil-paper insulated equipment according to claim 1, characterized in that, In step S2, the extended Debye model equivalent circuit is composed of a geometric equivalent circuit and a polarization equivalent circuit; wherein, the geometric equivalent circuit is composed of an insulation resistance R. g and geometric capacitance C g The polarization equivalent circuit is composed of N RC series polarization branches connected in parallel, where N is a natural number; each RC series polarization branch includes a polarization resistor R. pi With a polarization capacitor C pi And the polarization resistor R pi and the polarization capacitor C pi Connected in series, where i = 1, 2, …, N.

4. The method for modeling the equivalent circuit of an extended Debye model for oil-paper insulated equipment according to claim 3, characterized in that, In step S2, the mathematical relationship between the dielectric response current amplitude I and the parameters of the extended Debye model is as follows: Where, τ i Let ω be the time constant of the i-th polarization branch, U be the voltage amplitude applied across the dielectric, and ω be the angular frequency.

5. The method for modeling the equivalent circuit of an extended Debye model for oil-paper insulated equipment according to claim 1, characterized in that, In step S3, local weighted regression is used for smoothing and sample augmentation, specifically as follows: In the logarithmic frequency-logarithmic current amplitude coordinate system, at any frequency point f k A local sample set is formed by selecting several data points in its neighborhood around the center, and then sorting the local sample set according to the frequency point f. k Distance is weighted and weighted least squares fitting is performed to obtain the frequency points f. k The corresponding smoothed estimate is obtained by repeating the above local weighted fitting process for each frequency point to generate a continuous smooth curve, and then interpolating at preset frequency intervals on the smooth curve to generate expanded samples, thereby obtaining the expanded dielectric response current amplitude I. L The dataset.

6. The method for modeling the equivalent circuit of an extended Debye model for oil-paper insulated equipment according to claim 5, characterized in that, In step S3, the weighting function is constructed using the reciprocal of the relative dielectric response current amplitude, where the relative dielectric response current amplitude is the expanded dielectric response current amplitude I at each angular frequency ω. L (ω) and I within the test frequency band L The ratio of minimum values; the expression for the weighting function is: w(ω)=min(I L ) / I L (oh) Where w(ω) represents the weighting function at angular frequency ω.

7. The method for modeling the equivalent circuit of an extended Debye model for oil-paper insulated equipment according to claim 6, characterized in that, In step S3, the constructed objective function is used to measure the difference between the dielectric response current amplitude calculated by the extended Debye model and the expanded dielectric response current amplitude I. L The difference between them is used as the optimization objective for parameter identification of the extended Debye model, with the minimum of the difference being taken as the objective function; wherein, the objective function is established in a logarithmic coordinate system, and the input includes the amplitude of the dielectric response current I after logarithmic transformation and local weighted regression processing. L (ω) and the predicted dielectric response current amplitude calculated from the parameters of the extended Debye model. Furthermore, the objective function introduces a frequency-related weighting function w(ω) to weight the error contributions at different frequency points, thereby balancing the fitting weights of high and low frequency bands and improving the consistency of fitting across the entire frequency band, thus obtaining identification results with better global fitting performance and higher parameter stability; the expression of the objective function is: Where J represents the objective function.

8. The method for modeling the equivalent circuit of an extended Debye model for oil-paper insulated equipment according to claim 1, characterized in that, In step S4, the artificial hummingbird algorithm is used to optimize and identify the parameters of the extended Debye model. Specifically, the set of parameters to be identified in the extended Debye model is constructed into the position vector of the hummingbird individual, and the search boundary is set according to the physical value range of each parameter. The constructed objective function is used as the fitness function, and the foraging search mechanism in the artificial hummingbird algorithm is used to update the position of the hummingbird individual in order to achieve a dynamic balance between global search and local development. During the iteration process, the current best individual and its fitness are retained and updated. When the preset termination condition is met, the optimal parameter set is output, and the equivalent circuit of the extended Debye model is constructed with the optimal parameter set to achieve stable identification and accurate modeling of the model parameters.

9. A system for modeling the equivalent circuit of an extended Debye model for oil-paper insulation equipment, characterized in that, It includes a memory, a processor, and computer program instructions stored in the memory and executable by the processor, wherein when the processor executes the computer program instructions, it can implement the method as described in any one of claims 1-8.

10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by a processor, the method described in any one of claims 1-8 is implemented.

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