Transformer winding deformation on-line monitoring method based on impedance characteristic identification

Abstract

The invention discloses a transformer winding deformation on-line monitoring method based on impedance characteristic identification, and relates to the technical field of transformer winding parameter monitoring method.The transformer winding deformation on-line monitoring method comprises the steps that a T-shaped equivalent circuit of a transformer and the relation between input signals of the transformer and winding parameters are established; the method comprises the following steps: assuming that voltage excitation remains unchanged during normal operation of a transformer, when the temperature of a winding changes along with the change of a load connected to a secondary side and the environment temperature, other electrical quantities of the transformer all change, and acquiring the temperature change of the winding and real-time waveform data of voltages and currents at two ends of the transformer at corresponding moments; a winding resistance value and real-time waveform data of a sampling period are calculated according to winding temperature change, a phasor value is calculated, and parameters of a T-type equivalent circuit of the transformer are identified by using a recursive least square method based on a forgetting factor. On-line monitoring of the operation state of the transformer winding can be realized, the fault of the transformer winding can be found in time, the fault rate of the transformer is effectively reduced, and the service life is prolonged.

Description

Technical Field

[0001] The present invention relates to the technical field of transformer winding parameter monitoring methods, and in particular to an online transformer winding deformation monitoring method based on impedance feature identification. Background Art

[0002] Power transformers are crucial to ensuring the reliability of power systems. Power transformers operating in power grids will inevitably be impacted by various short-circuit faults, especially outlet or near-area short-circuits, which are more harmful to them. The huge electric force generated by the short-circuit current and the instantaneous rapid heating of the windings may cause the transformer windings to change in axial and radial dimensions, twist and bulge, or even inter-circuit faults. In addition, accidental collisions during transportation of transformers may also cause deformation of the windings and displacement of the body. Winding deformation has become the main cause of health hazards and failures of transformers.

[0003] At present, the commonly used transformer winding deformation detection methods include: short-circuit reactance method, low-voltage pulse method and frequency response analysis method, etc. These methods all require the transformer to be shut down for offline detection, and cannot monitor the transformer winding condition at any time and find faults in real time. Online monitoring and diagnosis that does not affect the operating status of the transformer is the development direction of the transformer monitoring system. Because the transformer short-circuit impedance and the inductance component in the impedance are related to the geometric size and relative position of the winding, the technology of analyzing the winding health status by online detection of the transformer short-circuit reactance change is gradually gaining attention. Summary of the invention

[0004] In view of the above-mentioned existing problems, the purpose of the present invention is to provide an online monitoring method for transformer winding deformation based on impedance feature identification, aiming to solve the technical problems that traditional transformer monitoring methods have low sensitivity and cannot timely capture small deformations of windings caused by load and environmental changes, and most existing transformer monitoring systems lack real-time data analysis capabilities for electrical quantities, resulting in potential problems being difficult to identify early.

[0005] In order to solve the above technical problems, an online monitoring method for transformer winding deformation based on impedance feature identification is proposed, including:

[0006] Establish a T-type equivalent circuit of the transformer and analyze the relationship between the transformer input signal and the winding parameters; collect the winding resistance value of the transformer top layer to calculate the oil temperature, and obtain the real-time waveform data of the voltage and current of the primary and secondary sides of the transformer; transform the T-type equivalent circuit into the standard form of least squares and perform discretization processing; use the processed results as input to identify the transformer parameters, set the over-limit conditions, and judge the deformation state of the winding.

[0007] As a preferred solution of the transformer winding deformation online monitoring method based on impedance feature identification described in the present invention, the T-type equivalent circuit includes establishing a T-type equivalent circuit of the transformer, listing the transformer phasor equation according to the T-type equivalent circuit, and establishing the relationship between each input signal of the transformer and the winding parameters, simplifying the phasor equation, and obtaining the impedance relationship between the primary side and the secondary side.

[0008] As a preferred solution of the transformer winding deformation online monitoring method based on impedance feature identification described in the present invention, the winding resistance value includes collecting the top oil temperature of the transformer, setting a cycle time limit, calculating the average winding temperature, using the oil temperature value at the set time to calculate the average winding temperature, and calculating the winding resistance value.

[0009] As a preferred solution of the online monitoring method of transformer winding deformation based on impedance feature identification described in the present invention, the discretization processing includes discretizing the real-time waveform data collected at a set time according to the sampling frequency, transforming the established T-type equivalent circuit phasor equation into the standard form of least squares and discretizing it.

[0010] As a preferred solution of the transformer winding deformation online monitoring method based on impedance feature identification described in the present invention, the winding resistance value calculated from the temperature change and the voltage and current data after discretization are used as the input of the least square method to obtain the short-circuit reactance parameter:

[0011] K(k)=P(k-1)-S(k)[λ·I+S T (k)·P(k-1)S(k)] -1

[0012] R(k)=λ -1 P(k-1)-λ -1 (k)K(k)X T (k)P(k-1)

[0013] X(k)=X(k-1)+K(k)[Y(k)-S T (k)·X(k-1)]

[0014] Among them, k is the transformer ratio, u1 and u2 are the primary and secondary winding voltages, L1 and L2 are the primary and secondary side leakage inductances, i1 and i2 are the primary and secondary winding currents, R1 and R2 are the primary and secondary side resistances, K(k) and R(k) are the introduced process parameters, S, X, and Y are matrix parameters, and the λ forgetting factor is taken as =0.997.

[0015] As a preferred scheme of the online monitoring method of transformer winding deformation based on impedance feature identification described in the present invention, the judgment of whether the winding is deformed includes setting the short-circuit reactance horizontal ratio over-limit condition and the vertical ratio over-limit condition according to the industry standard DL / T1903-2018 to judge whether the winding is deformed.

[0016] The horizontal ratio over-limit condition is set as follows: the power transformer with a capacity of 100MVA or less and a voltage of 220kV or less is set as (X K.max -X K.min ) / X K.min ≤2.5%, power transformers with a capacity of 100MVA and above and a voltage of 220kV and above are set as (X K.max -X K.min ) / X K.min ≤2.0%;

[0017] Among them, X K.max is the maximum short-circuit reactance of each phase from high to low, X K.min It is the minimum value of the high-to-low short-circuit reactance of each phase A, B, and C.

[0018] The longitudinal ratio over-limit condition is set as follows: for power transformers with a capacity of 100MVA or less and a voltage of 220kV or less, the following conditions are set: Kt -X K0 ) / X K0 ≤2.0%, power transformers with a capacity of 100MVA and above and a voltage of 220kV and above are set as (X Kt -X K0 ) / X K0 ≤1.6%.

[0019] Among them, X Kt is the measured value of the short-circuit reactance of each gear on-line monitoring, X K0 is the measured X Kt The last offline test value or factory test value at the same gear, X K0 The first sampling value after the system is put into operation is also taken.

[0020] Another object of the present invention is to provide a system for online monitoring of transformer winding deformation based on impedance feature identification. The present invention solves the problem that it is difficult to capture tiny winding deformations caused by load changes or environmental factors. When the temperature changes greatly, the current technology does not respond promptly when monitoring temperature fluctuations. In terms of high-frequency sampling and complex data processing, traditional monitoring systems face performance bottlenecks and other problems.

[0021] As a preferred solution of the transformer winding deformation online monitoring system based on impedance feature identification described in the present invention, it is characterized by including a signal acquisition module, a data processing module, a parameter identification module, a judgment and alarm module, and a user interface module.

[0022] The signal acquisition module continuously collects the voltage, current and oil temperature data of the transformer at a high frequency, and sends the data to the data processing module; the data processing module discretizes and temperature compensates the data, calculates the current winding resistance value, and passes the result to the parameter identification module.

[0023] As a preferred solution of the transformer winding deformation online monitoring system based on impedance feature identification described in the present invention, the parameter identification module calculates the short-circuit reactance parameters based on the discretized data using the least squares method and the recursive least squares method, and transmits the parameters to the judgment and alarm module; the judgment and alarm module compares the identified short-circuit reactance parameters with the industry standard, analyzes whether the winding is deformed, and generates alarm information; the output results of the judgment and alarm module and the related monitoring data will be updated in real time to the user interface module for users to view and manage, and the user can set the monitoring parameters and thresholds through the interface.

[0024] A computer device includes a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of the online monitoring method for transformer winding deformation based on impedance feature identification are implemented.

[0025] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of the online monitoring method for transformer winding deformation based on impedance feature identification are implemented.

[0026] Beneficial effects of the present invention: By establishing a T-type equivalent circuit and phasor equation of the transformer, combined with the voltage, current and oil temperature data collected in real time, the present invention can monitor the operating status of the transformer in real time, ensure that the winding deformation problem is discovered in time, and avoid the potential risk of damage to the equipment; by establishing a T-type equivalent circuit and phasor equation of the transformer, combined with the voltage, current and oil temperature data collected in real time, the operating status of the transformer can be monitored in real time, ensure that the winding deformation problem is discovered in time, and avoid the potential risk of damage to the equipment; the sampling frequency reaches 2500Hz, which can capture the rapid changes of electrical quantities, ensure the system's responsiveness to transient events, and greatly improve the timeliness of monitoring data; by converting the T-type equivalent circuit into the standard form of the least squares method and performing discretization processing, it is possible to effectively extract key parameters from complex time domain signals; the recursive least squares method based on the forgetting factor can dynamically adjust the parameter identification process to adapt to the operating status of the transformer under different load conditions, thereby improving the system's adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work, among which:

[0028] Figure 1 An overall flow chart of an online transformer winding deformation monitoring method based on impedance feature identification provided by an embodiment of the present invention.

[0029] Figure 2 A T-type equivalent circuit diagram of a main transformer of an online monitoring method for transformer winding deformation based on impedance feature identification provided by an embodiment of the present invention.

[0030] Figure 3 A diagram of a 3D simulation model of a main transformer of an online monitoring method for transformer winding deformation based on impedance feature identification provided by an embodiment of the present invention.

[0031] Figure 4 A mesh division diagram of a main transformer 3D simulation model of an online transformer winding deformation monitoring method based on impedance feature identification provided by an embodiment of the present invention.

[0032] Figure 5 A diagram of the short-circuit impedance identification result of phase A of normal winding operation data of an online monitoring method for transformer winding deformation based on impedance feature identification provided by an embodiment of the present invention.

[0033] Figure 6A graph of the A-phase short-circuit impedance identification error percentage of normal winding operation data of an online monitoring method for transformer winding deformation based on impedance feature identification provided by an embodiment of the present invention.

[0034] Figure 7 A simulation model diagram of the deformation of the high-voltage side of phase A of a transformer is provided in accordance with an online monitoring method of transformer winding deformation based on impedance feature identification according to an embodiment of the present invention.

[0035] Figure 8 A system solution module diagram of a transformer winding deformation online monitoring system based on impedance feature identification provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0036] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0037] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0038] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it an embodiment that is mutually exclusive with other embodiments, either individually or selectively.

[0039] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0040] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0041] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0042] Example 1, reference Figure 1-Figure 2 , which is the first embodiment of the present invention, provides an online monitoring method for transformer winding deformation based on impedance feature identification, comprising:

[0043] S1: Establish a T-type equivalent circuit of the transformer and analyze the relationship between the transformer input signal and the winding parameters.

[0044] Furthermore, a T-type equivalent circuit of the transformer is established, and the transformer phasor equation is listed according to the T-type equivalent circuit. At the same time, the relationship between each input signal of the transformer and the winding parameters is established. The transformer phasor equation is simplified to:

[0045]

[0046] in, are the transformer primary side voltage and current, They are the voltage and current after secondary side conversion, Z1 is the primary side impedance, and Z'2 is the impedance converted from the secondary side to the primary side.

[0047] It should be noted that the real-time waveform data of the primary and secondary voltages and currents of the transformer are collected and recorded at different times t1 and t2, the sampling frequency of the acquisition board is set to 2500 Hz, and the length of the real-time waveform data is ten cycles T.

[0048] In a 50Hz system, one cycle is 20ms.

[0049] In a 60Hz system, one cycle is 16.7ms.

[0050] S2: Collect the transformer top layer oil temperature calculation winding resistance value, and obtain the real-time waveform data of the voltage and current of the primary and secondary sides of the transformer.

[0051] Furthermore, the voltage excitation of the transformer is assumed to remain unchanged during normal operation. When the winding temperature changes with the load connected to the secondary side and the ambient temperature, the electrical quantity of the transformer changes, and the real-time waveform data of the winding temperature change and the voltage and current at both ends of the transformer at the corresponding moment are collected.

[0052] It should be noted that the transformer top oil temperature is collected, and the winding average temperature is calculated according to the national standard GB / T15164-1994. The winding average temperature is calculated using the oil temperature values ​​at t1 and t2 respectively. The formula for the winding resistance value calculated from the winding average temperature is:

[0053] R1(T r1 2) = R1(T r1 1)[1+α(T r1 2-T r1 1)]

[0054] R2(T r2 2) = R2(T r2 1)[1+α(T r2 2-T r2 1)]

[0055] Where α is the temperature coefficient of the conductor, which is 0.00393; T r1 1 and T r2 1 represents the average temperature of the primary and secondary windings at time t1, T r1 2 and T r2 2 represent the average temperatures of the primary and secondary windings at time t2 respectively.

[0056] It should also be noted that the real-time waveform data of the primary and secondary voltages and currents collected at times t1 and t2 are discretized according to the sampling frequency. The discretization method of voltage and current is:

[0057]

[0058] Among them, k is the transformer ratio, u1 and u2 are the primary and secondary winding voltages, L1 and L2 are the primary and secondary side leakage inductances, i1 and i2 are the primary and secondary winding currents, and R1 and R2 are the primary and secondary side resistances.

[0059] S3: Transform the T-type equivalent circuit into the standard form of least squares and perform discretization.

[0060] Furthermore, the established T-type equivalent circuit phasor equation is transformed into the standard form of least squares, where k is the transformer ratio:

[0061]

[0062] The standard form of the least squares method is discretized, and the least squares identification equation after discretization is:

[0063] [S] n×4 [X] 4×1 =[Y] n×1

[0064] in:

[0065]

[0066] Among them, S, X, and Y are matrix parameters.

[0067] S4: Use the processed results as input to identify transformer parameters, set over-limit conditions, and determine the winding deformation state.

[0068] Furthermore, the winding resistance value inferred from the temperature change and the voltage and current data after discretization are used as the input of the least square method to identify the remaining transformer parameters;

[0069] The implementation steps of the recursive least squares method based on the forgetting factor are:

[0070]

[0071] R(k)=λ -1 P(k-1)-λ -1 (k)K(k)X T (k)P(k-1)

[0072] X(k)=X(k-1)+K(k)[Y(k)-S T (k)·X(k-1)]

[0073] Among them, K(k) and R(k) are the introduced process parameters, and the λ forgetting factor is taken as = 0.997.

[0074] It should be noted that the over-limit conditions of the horizontal and vertical ratios of short-circuit reactance are set according to the industry standard DL / T1903-2018 to determine whether the winding is deformed;

[0075] Short-circuit reactance ratio over-limit setting: The power transformer with capacity of 100MVA and below and voltage of 220kV and below is set as follows:

[0076] (X K.max -X K.min ) / X K.min ≤2.5%

[0077] The power transformers with a capacity of 100MVA and above and a voltage of 220kV and above are set as follows:

[0078] (X K.max -X K.min ) / X K.min ≤2.0%

[0079] Among them, X K.max is the maximum short-circuit reactance of each phase from high to low, X K.min It is the minimum value of high-to-low short-circuit reactance of each phase A, B, and C;

[0080] Short-circuit reactance ratio over-limit setting: The power transformer with capacity of 100MVA and below and voltage of 220kV and below is set as follows:

[0081] (X Kt -X K0 ) / X K0 ≤2.0%

[0082] The power transformers with a capacity of 100MVA and above and a voltage of 220kV and above are set as follows:

[0083] (X Kt -X K0 ) / X K0 ≤1.6%

[0084] Among them, X Kt is the measured value of the short-circuit reactance of each gear on-line monitoring, X K0 is the measured X Kt The last offline test value or factory test value at the same gear, X K0 The first sampling value after the system is put into operation can also be taken.

[0085] At the same time, the judgment results, alarm information and real-time monitoring data are fed back to the user interface, and the display information is updated. The user can view the status through the interface, set monitoring parameters and thresholds according to needs, and send adjustment instructions to the signal acquisition module.

[0086] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0087] Example 2, reference Figure 3-Figure 7 , which is an embodiment of the present invention, provides an online monitoring method for transformer winding deformation based on impedance feature identification. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through experiments.

[0088] As attached Figure 3According to the parameters of the actual transformer with model SZ11-50000 / 110, a simulation model was established using finite element simulation software. The transformer capacity is 50000kVA, the no-load loss is 26.11kW, the load loss is 163.94kW, the short-circuit impedance percentage is 16.19%, and the corresponding leakage inductance is 0.1186H.

[0089] As attached Figure 5 The short-circuit impedance identification result diagram of phase A winding with normal winding operation data shows that short-circuit impedance identification is performed on the transformer operation data when no winding deformation occurs. The red dotted line is the actual short-circuit impedance percentage of 16.19, and the blue solid line is the identification result.

[0090] As attached Figure 6 Normal winding operation data A phase winding short circuit impedance identification error percentage diagram is attached Figure 5 The error percentage between the identification result and the actual short-circuit impedance percentage is 16.19.

[0091] As attached Figure 7 The simulation model diagram of the deformation of the high-voltage side winding of phase A of the transformer is shown. Through the finite element simulation model, the deformation of the transformer winding is simulated by taking phase A as an example. The specific simulation method is to shorten the upper end of the phase A winding by 2%.

[0092] As attached Figure 8 The transformer A phase high voltage side winding deformation short circuit impedance identification result diagram, for the attached Figure 7 The operating data obtained after deformation is used for short-circuit impedance identification. The red dotted line is the actual short-circuit impedance percentage of 16.19, and the blue solid line is the identification result. It can accurately reflect the changes in short-circuit impedance after deformation.

[0093] Embodiment 3, the third embodiment of the present invention, is different from the first two embodiments in that:

[0094] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program codes.

[0095] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0096] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0097] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0098] Example 4, reference Figure 8 , which is the fourth embodiment of the present invention, provides an online monitoring system for transformer winding deformation based on impedance feature identification, including a signal acquisition module 10, a data processing module 20, a parameter identification module 30, a judgment and alarm module 40, and a user interface module 50.

[0099] The signal acquisition module 10 continuously monitors the primary and secondary voltages and currents of the transformer, as well as the real-time data of the top oil temperature data at a frequency of 2500Hz, and transmits the real-time data in the form of digital signals to the data processing module 20 after acquisition; the data processing module 20 receives the real-time data signal, performs discretization processing, calculates the resistance value of the winding, and packages the result together with the temperature-related information, and transmits it to the parameter identification module 30 in a standardized format of the least squares method; the parameter identification module 30 calculates the short-circuit reactance parameters in real time according to the received voltage, current and resistance values ​​by the least squares method and the recursive least squares method, and transmits the identification results to the judgment and alarm module 40; after receiving these short-circuit reactance parameters, the judgment and alarm module 40 analyzes them according to industry standards, judges whether the winding is deformed, and generates corresponding alarm information, and at the same time, the judgment result, alarm information and real-time monitoring data are fed back to the user interface module 50; the user interface module 50 will update the display information, the user can view the status through the interface, set the monitoring parameters and thresholds according to the needs, and send adjustment instructions to the signal acquisition module.

[0100] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A transformer winding deformation online monitoring method based on impedance feature identification, characterized in that: include, Establish the T-type equivalent circuit of the transformer and analyze the relationship between the transformer input signal and the winding parameters; Collect the transformer top layer oil temperature calculation winding resistance value, and obtain the real-time waveform data of the transformer primary and secondary voltage and current; Transform the T-type equivalent circuit into the standard form of least squares and perform discretization; The processed results are used as input to identify transformer parameters, set over-limit conditions, and determine the winding deformation state.

2. The method for online monitoring of transformer winding deformation based on impedance feature identification according to claim 1, characterized in that: The T-type equivalent circuit includes establishing a T-type equivalent circuit of the transformer, listing the transformer phasor equation according to the T-type equivalent circuit, establishing the relationship between each input signal of the transformer and the winding parameters, simplifying the phasor equation, and obtaining the impedance relationship between the primary side and the secondary side.

3. The method for online monitoring of transformer winding deformation based on impedance feature identification according to claim 2, characterized in that: The winding resistance value includes collecting the top oil temperature of the transformer, setting a cycle time limit, calculating the average winding temperature, using the oil temperature value at the set time to calculate the average winding temperature, and calculating the winding resistance value.

4. The method for online monitoring of transformer winding deformation based on impedance feature identification according to claim 3, characterized in that: The discretization processing includes discretizing the real-time waveform data collected at a set time according to the sampling frequency, transforming the established T-type equivalent circuit phasor equation into a standard form of least squares and discretizing it.

5. The method for online monitoring of transformer winding deformation based on impedance feature identification according to claim 4, characterized in that: The winding resistance value calculated from the temperature change and the voltage and current data after discretization are used as the input of the least squares method to obtain the short-circuit reactance parameters: K(k)=P(k-1)-S(k)[λ·I+S T (k)·P(k-1)S(k)] -1 R(k)=λ -1 P(k-1)-λ -1 (k)K(k)X T (k)P(k-1) X(k)=X(k-1)+K(k)[Y(k)-S T (k)·X(k-1)] Among them, k is the transformer ratio, u1 and u2 are the primary and secondary winding voltages, L1 and L2 are the primary and secondary side leakage inductances, i1 and i2 are the primary and secondary winding currents, R1 and R2 are the primary and secondary side resistances, K(k) and R(k) are the introduced process parameters, S, X, and Y are matrix parameters, and the λ forgetting factor is taken as =0.

997.

6. The method for online monitoring of transformer winding deformation based on impedance feature identification according to claim 5, characterized in that: The determining whether the winding is deformed includes setting a short-circuit reactance horizontal ratio over-limit condition and a vertical ratio over-limit condition according to the industry standard DL / T1903-2018 to determine whether the winding is deformed; The horizontal ratio over-limit condition is set as follows: the power transformer with a capacity of 100MVA or less and a voltage of 220kV or less is set as (X K.max -X K.min ) / X K.min ≤2.5%, power transformers with a capacity of 100MVA and above and a voltage of 220kV and above are set as (X K.max -X K.min ) / X K.min ≤2.0%; Among them, X K.max is the maximum short-circuit reactance of each phase from high to low, X K.min It is the minimum value of high-to-low short-circuit reactance of each phase A, B, and C; The longitudinal ratio over-limit condition is set as follows: for power transformers with a capacity of 100MVA or less and a voltage of 220kV or less, the following conditions are set: Kt -X K0 ) / X K0 ≤2.0%, power transformers with a capacity of 100MVA and above and a voltage of 220kV and above are set as (X Kt -X K0 ) / X K0 ≤1.6%; Among them, X Kt is the measured value of the short-circuit reactance of each gear on-line monitoring, X K0 is the measured X Kt The last offline test value or factory test value at the same gear, X K0 The first sampling value after the system is put into operation is also taken.

7. A system using the transformer winding deformation online monitoring method based on impedance feature identification as claimed in any one of claims 1 to 7, characterized in that: It includes signal acquisition module, data processing module, parameter identification module, judgment and alarm module, and user interface module; The signal acquisition module continuously collects the voltage, current and oil temperature data of the transformer at a high frequency, and sends the data to the data processing module; the data processing module discretizes and temperature compensates the data, calculates the current winding resistance value, and passes the result to the parameter identification module.

8. The transformer winding deformation online monitoring system based on impedance feature identification according to claim 6, characterized in that: The parameter identification module calculates the short-circuit reactance parameters based on the discretized data using the least squares method and the recursive least squares method, and transmits the parameters to the judgment and alarm module; the judgment and alarm module compares the identified short-circuit reactance parameters with the industry standards, analyzes whether the winding is deformed, and generates alarm information; the output results of the judgment and alarm module and related monitoring data will be updated in real time to the user interface module for users to view and manage, and the user can set monitoring parameters and thresholds through the interface.

9. A computer 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 a method for online monitoring of transformer winding deformation based on impedance feature identification according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a transformer winding deformation online monitoring method based on impedance feature identification according to any one of claims 1 to 7 are implemented.

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