Transformer impact fault positioning method and system based on distributed parameter inversion

Abstract

The invention discloses a transformer impact fault positioning method based on distributed parameter inversion calculation, and the method comprises the steps: carrying out the impact withstand voltage test of a transformer, collecting a voltage signal and a current signal of the transformer in real time, and calculating a transfer function; establishing a distribution parameter network of the transformer, performing inversion calculation on parameters in the distribution parameter network to obtain parameter values of the transformer equivalent distribution parameter network, and establishing a digital twinborn model of the transformer; simulating fault types of different positions of the transformer, and generating a transfer function data set under different node fault conditions; under the full-impact test voltage, a transfer function obtained through actual measurement is obtained; and evaluating the similarity of the faults at different positions, and determining a simulation fault position with the maximum similarity with the transfer function obtained by actual measurement. According to the method, the efficiency and accuracy of fault detection are effectively improved, the time and cost of fault diagnosis are reduced, and the method has important practical application value for maintenance and fault processing of the transformer.

Description

Technical Field

[0001] The present invention relates to the field of transformer detection, and in particular to a transformer impact fault locating method and system based on distributed parameter inversion. Background Art

[0002] As an important power transmission and transformation equipment in the power system, the health status of the transformer directly affects the stable operation of the power system. During the production, transportation, installation or long-term operation of the transformer, defects will inevitably occur. During normal operation, the defects may be further expanded under the action of impulse overvoltage and eventually develop into serious faults. The impulse withstand voltage test has the advantages of strict assessment of the longitudinal insulation of the equipment and easy discovery of potential quality problems of the equipment. It is an important means to assess the main insulation and longitudinal insulation level of the transformer.

[0003] The energy of the impulse voltage is small. When conducting an impulse voltage test, once a breakdown occurs, it cannot cause greater damage like the power frequency voltage test, and it is not easy to find the breakdown point. At present, it is difficult to identify small faults with local damage based on the voltage and current waveform diagram, and the fault location cannot be identified; at the same time, the transformer is compact and complex in structure, and the core and winding have a certain influence on signal propagation, which makes the local discharge positioning accuracy based on the spatial positioning algorithm low and difficult to carry out. If the fault location cannot be found, the transformer needs to be reinstalled and retested, which increases the test time and difficulty, and greatly wastes manpower and material resources. Based on this, the present application provides a transformer impulse fault positioning method based on distributed parameter inversion calculation, which uses an artificial intelligence algorithm to inversely calculate the transformer equivalent distributed parameter network, and simulates the faults at different positions of the equivalent network and compares them with the actual measurement results, thereby realizing the transformer impulse fault positioning. Summary of the invention

[0004] In order to solve the above technical problems, the purpose of the present invention is to provide a method for positioning partial discharge time difference inside transformer windings, which is used for accurate positioning of partial discharge inside transformer windings.

[0005] In order to achieve the above object, the present invention adopts the following scheme.

[0006] A transformer impulse fault location method based on distributed parameter inversion calculation, comprising:

[0007] Step 1, performing an impulse withstand voltage test on the transformer, and collecting the voltage signal and current signal of the transformer in real time;

[0008] Step 2, analyzing the voltage signal and the current signal to calculate the transfer function;

[0009] Step 3, establishing a distributed parameter network of the transformer based on the physical structure and electrical parameters of the transformer;

[0010] Step 4, performing inverse calculation on the parameters in the distributed parameter network of the transformer, obtaining the parameter values ​​of the distributed parameter network of the transformer, and establishing a digital twin model of the transformer;

[0011] Step 5: In the digital twin model of the transformer, each node of the distributed parameter network of the transformer simulates the fault type at different positions of the transformer to generate a transfer function data set under different node fault conditions;

[0012] Step 6, measuring the voltage and current waveforms of the transformer under the full impulse test voltage to obtain the transfer function actually measured;

[0013] Step 7, comparing the transfer function obtained by actual measurement with the transfer function data set under different node fault conditions, and obtaining similarity evaluation results of faults at different positions in the transfer function obtained by actual measurement and the transfer function data set under different node fault conditions;

[0014] Step 8: According to the similarity evaluation result, determine the simulated fault position with the greatest similarity to the transfer function obtained by actual measurement, and this position is the actual fault position of the transformer.

[0015] Optionally, in step 1, 50% of the impulse test voltage is applied to the transformer to perform an impulse withstand voltage test on the transformer.

[0016] Optionally, in step 5, two fault types of the transformer internal turn-to-turn breakdown and ground breakdown are simulated.

[0017] Optionally, in step 7, a calculation method of correlation coefficient and Euclidean distance is used to perform similarity evaluation.

[0018] A transformer impulse fault location system based on distributed parameter inversion calculation, the system comprising:

[0019] A signal acquisition unit, which is used to perform an impulse withstand voltage test on the transformer and collect voltage and current signals of the transformer in real time;

[0020] A signal processing unit, which is used to analyze the voltage signal and the current signal and calculate the transfer function;

[0021] A parameter calculation unit is used to establish a distributed parameter network of the transformer based on the physical structure and electrical parameters of the transformer; perform inverse calculation on the parameters in the distributed parameter network of the transformer to obtain parameter values ​​of the distributed parameter network of the transformer, and establish a digital twin model of the transformer;

[0022] A fault simulation unit, which is used to simulate the fault types at different positions of the transformer at each node of the distributed parameter network of the transformer in the digital twin model of the transformer, and generate a transfer function data set under different node fault conditions;

[0023] The fault location and diagnosis unit is used to measure the voltage and current waveforms of the transformer under the full impulse test voltage to obtain the transfer function actually measured at this time; compare the transfer function actually measured with the transfer function data set under different node fault conditions to obtain the similarity evaluation results of the transfer function actually measured and the faults at different positions in the transfer function data set under different node fault conditions; according to the similarity evaluation results, determine the simulated fault position with the greatest similarity to the transfer function actually measured, and this position is the actual fault position of the transformer.

[0024] Optionally, the signal acquisition unit includes a voltage sensor and a current sensor.

[0025] Optionally, the signal acquisition unit includes an automatic adjustment device for adapting to different test voltage levels.

[0026] Optionally, the parameter calculation unit includes an artificial intelligence algorithm library and a parameter optimization module.

[0027] Optionally, the fault simulation unit can automatically adjust the model fault location and network structure according to a preset fault mode.

[0028] Optionally, the fault location and diagnosis unit can locate and identify main longitudinal insulation faults based on the calculation results of the digital twin model and the actual measurement results.

[0029] Compared with the prior art, the present invention has the following beneficial technical effects:

[0030] By measuring the voltage and current waveforms of the transformer at 50% of the test voltage and calculating the transfer function, the transformer distributed parameter network is calculated using an artificial intelligence algorithm to establish a digital twin model of the tested transformer. By simulating the voltage, current and transfer function of different types of faults at different locations in the impact test in the digital twin model and comparing them with the actual measurement results, the simulated fault location with the greatest similarity to the measured transfer function is determined to achieve impact fault location. This effectively improves the efficiency and accuracy of fault detection, reduces the time and cost of fault diagnosis, and has important practical application value for transformer maintenance and fault handling. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The accompanying drawings illustrate exemplary embodiments of the present invention and together with the description serve to explain the principles of the present invention. These drawings are included to provide a further understanding of the present invention and are incorporated in and constitute a part of this specification.

[0032] Figure 1 A transformer impulse fault location method based on distributed parameter inversion calculation in an embodiment of the present invention;

[0033] Figure 2 2 is an example diagram of voltage and current signal waveforms according to an embodiment of the present invention;

[0034] Figure 3 This is an example diagram of a transformer equivalent network according to an embodiment of the present invention;

[0035] Figure 4 is an example diagram of transfer functions under different node injections according to an embodiment of the present invention;

[0036] Figure 5 This is an example diagram of the fault prediction result of the embodiment of the present invention. DETAILED DESCRIPTION

[0037] The following is combined with Figures 1 to 5 The present invention is further described in detail with reference to the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the relevant contents, rather than to limit the present invention. It is also necessary to explain that, for ease of description, only the parts related to the present invention are shown in the accompanying drawings.

[0038] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other. The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0039] Unless otherwise specified, the exemplary embodiments / embodiments shown will be understood as exemplary features that provide various details of some ways in which the technical concept of the present invention can be implemented in practice. Therefore, unless otherwise specified, the features of the various embodiments / embodiments can be combined, separated, interchanged and / or rearranged without departing from the technical concept of the present invention.

[0040] The use of cross-hatching and / or shading in the accompanying drawings is generally used to make the boundaries between adjacent components clear. As such, unless otherwise specified, the presence or absence of cross-hatching or shading does not convey or indicate any preference or requirement for the specific materials, material properties, dimensions, proportions, commonalities between the components shown, and / or any other characteristics, attributes, properties, etc. of the components. In addition, in the accompanying drawings, the sizes and relative sizes of the components may be exaggerated for clarity and / or descriptive purposes. When the exemplary embodiments can be implemented differently, the specific process sequence can be performed in a different order than described. For example, two successively described processes can be performed substantially simultaneously or in an order opposite to the described order. In addition, the same figure numbers represent the same components.

[0041] When a component is referred to as being "on" or "over," "connected to," or "coupled to" another component, the component may be directly on, directly connected to, or directly coupled to the other component, or intervening components may be present. However, when a component is referred to as being "directly on," "directly connected to," or "directly coupled to" another component, there are no intervening components. For this purpose, the term "connected" may refer to a physical connection, an electrical connection, etc., with or without intervening components.

[0042] For descriptive purposes, the present invention may use spatially relative terms such as "below", "beneath", "under", "lower", "above", "upper", "above", "higher", and "side (e.g., as in "sidewall")" to describe the relationship of one component to another (other) component as shown in the accompanying drawings. The spatially relative terms are intended to encompass different orientations of the device in use, operation, and / or manufacture in addition to the orientation depicted in the accompanying drawings. For example, if the device in the accompanying drawings is turned over, components described as "below" or "beneath" other components or features would then be positioned "above" the other components or features. Thus, the exemplary term "below" can encompass both the "above" and "below" orientations. Furthermore, the device may be otherwise oriented (e.g., rotated 90 degrees or at other orientations), and the spatially relative descriptors used herein should be interpreted accordingly.

[0043] The terms used here are for the purpose of describing specific embodiments, and are not intended to be restrictive. As used here, unless the context clearly indicates otherwise, the singular forms "one (kind, person)" and "said (the)" are also intended to include plural forms. In addition, when the terms "comprise" and / or "include" and their variations are used in this specification, it is explained that there are stated features, integral bodies, steps, operations, parts, assemblies and / or their groups, but it is not excluded that there are or add one or more other features, integral bodies, steps, operations, parts, assemblies and / or their groups. It should also be noted that, as used here, the terms "substantially", "approximately" and other similar terms are used as approximate terms and not as degree terms, so that they are used to explain the inherent deviations of the measured values, calculated values ​​and / or the values ​​provided that will be recognized by those of ordinary skill in the art.

[0044] In one embodiment, the present invention provides a transformer impulse fault location method based on distributed parameter inversion calculation, comprising:

[0045] Step 1, performing an impulse withstand voltage test on the transformer, and collecting the voltage signal and current signal of the transformer in real time;

[0046] Step 2, analyzing the voltage signal and the current signal to calculate the transfer function;

[0047] Step 3, establishing a distributed parameter network of the transformer based on the physical structure and electrical parameters of the transformer;

[0048] Step 4, performing inverse calculation on the parameters in the distributed parameter network of the transformer, obtaining the parameter values ​​of the distributed parameter network of the transformer, and establishing a digital twin model of the transformer;

[0049] Step 5: In the digital twin model of the transformer, each node of the distributed parameter network of the transformer simulates the fault type at different positions of the transformer to generate a transfer function data set under different node fault conditions;

[0050] Step 6, measuring the voltage and current waveforms of the transformer under the full impulse test voltage to obtain the transfer function actually measured;

[0051] Step 7, comparing the transfer function obtained by actual measurement with the transfer function data set under different node fault conditions, and obtaining similarity evaluation results of faults at different positions in the transfer function obtained by actual measurement and the transfer function data set under different node fault conditions;

[0052] Step 8: According to the similarity evaluation result, determine the simulated fault position with the greatest similarity to the transfer function obtained by actual measurement, and this position is the actual fault position of the transformer.

[0053] Optionally, in step 1, 50% of the impulse test voltage is applied to the transformer to perform an impulse withstand voltage test on the transformer.

[0054] Optionally, in step 5, two fault types of the transformer internal turn-to-turn breakdown and ground breakdown are simulated.

[0055] Optionally, in step 7, a calculation method of correlation coefficient and Euclidean distance is used to perform similarity evaluation.

[0056] In one embodiment, see Figure 1 The present invention provides a transformer impulse fault location method based on distributed parameter inversion calculation.

[0057] Step 1: Apply 50% of the impulse test voltage to the transformer, and use the voltage sensor and current sensor to collect the voltage and current signals of the transformer in real time, such as Figure 2 As shown;

[0058] During the acquisition process, key parameters such as the amplitude, rising edge, and falling edge of the voltage and current signals are recorded. Figure 2The signal parameters are: the wave head / wave tail time of the voltage signal is 1.22 / 51.12μs, the amplitude is 43.1kV, and the current signal amplitude is 87.1A.

[0059] Conduct experiments at lower voltages to obtain transfer functions and avoid transformer breakdown under standard impulse voltages. Select appropriate signal acquisition equipment, including: capacitive voltage divider, damage-indicating resistor, and data acquisition card. These devices must have sufficient accuracy (not less than 0.5%) to ensure the accuracy of signal acquisition.

[0060] In one embodiment, the voltage divider ratio of the capacitor voltage divider is 3000:1, the bandwidth of the data acquisition card is 70 MHz, and the sampling rate is 1 GS / s.

[0061] Step 2: Analyze the voltage signal and current signal using Fourier transform to calculate the transfer function. The formula is as follows:

[0062]

[0063] in, is a complex frequency variable, is the decay constant, is the angular frequency.

[0064] The transfer function reflects the electrical characteristics of the transformer at 50% of the impulse test voltage and is the key basis for fault simulation and location.

[0065] Step 3: Based on the physical structure and electrical parameters of the transformer, consider the resistance, capacitance, and inductance distribution parameters inside the transformer and establish the transformer distributed parameter network, such as Figure 3 As shown in the figure, each unit contains longitudinal capacitance Ki, capacitance to ground Ci, resistance Ri, inductance Li, mutual inductance Mij=Mji (i≠j) between units, and the network end is grounded, so it contains n non-grounded nodes and n RL series branches.

[0066] Step 4: Use an artificial intelligence algorithm, such as a genetic algorithm, a particle swarm optimization algorithm, or a gradient descent algorithm, to perform inverse calculations on the transfer function to obtain parameter values ​​of the transformer distributed parameter network; and establish a digital twin model of the transformer;

[0067] Taking genetic algorithm as an example, by calculating the equivalent network transfer function curve, aiming to infinitely approximate the measured transfer function curve and using the algorithm to iterate the equivalent network model, we can obtain Figure 3 The winding equivalent RLC network parameters are shown.

[0068] The measurement functions used are the Pearson correlation coefficient Ps that focuses on the difference in the shape of the entire curve and the Euclidean distance Ds that focuses on the difference in absolute amplitude, and the calculation formula is as follows.

[0069]

[0070]

[0071] Where xc and xm are the calculated and measured transfer function curve sequences, xc(i) and xm(i) are their values ​​at the i-th frequency point, and lx is the total number of points in the sequence. The value range of Ps is -1 ≤ Ps ≤ 1, and the value range of Ds is 0 ≤ Ds < +∞. The larger the Ps and the smaller the Ds, the better the agreement between the calculated and measured curves.

[0072] Constructing the initial form of the objective function f 1, that is

[0073]

[0074] Among them, α and β are non-negative weight coefficients, and their values ​​are determined according to the size relationship between Ps and Ds and the optimization effect.

[0075]

[0076] Where xH(fi) is the value of the transfer function at each frequency measurement point fi, i is the sequence index, and lx is the total number of frequency points included in the curve.

[0077] According to the circuit principle, at the frequency fi, the circuit network matrix equation is written with the node voltage vector U and the branch current vector I as independent variables, and the calculation formula at this frequency can be obtained as follows:

[0078]

[0079] Among them, Z is the branch impedance matrix, Y is the node admittance matrix, and their expressions are shown as follows:

[0080]

[0081]

[0082] and is the incidence matrix, and its expression is:

[0083]

[0084] The subscripts in brackets represent the elements of the matrix or vector at the corresponding positions; MD represents the matrix formed by removing the first and last two rows and the first and last two columns of the matrix M; M* represents the column vector composed of the elements of the first column of MD; U represents a column vector composed of an element 1 and an element 0 added to the first and last ends of the vector UD, respectively, to reflect the position information of the input source and the ground terminal.

[0085] In this step, the optimization algorithm is used to find the best parameters so that the difference between the transfer function predicted by the model and the actual measured transfer function is minimized.

[0086] Step 5, simulating two types of faults, turn-to-turn breakdown and ground breakdown, at each node of the transformer distributed parameter network, measuring the voltage waveform at the head end and the current waveform at the end end, and generating a set of transfer functions under different node fault conditions; considering the influence of two different types of faults, turn-to-turn breakdown and ground breakdown, on the transfer function;

[0087] By changing the model parameters, the changes in the electrical characteristics of the transformer under different fault conditions are simulated.

[0088] Step 6: Obtain electrical characteristic data under actual fault conditions and obtain a transfer function obtained by actual measurement;

[0089] Apply 100% impulse test voltage to the transformer, collect the voltage signal and current signal of the transformer again, and calculate the transfer function at this time. The transfer function at this time is under the condition of breakdown fault.

[0090] Step 7: Determine the similarity of the fault location through quantitative analysis, and measure its similarity through numerical characteristic values, including: Pearson Correlation Coefficient (PCC), AbsoulteSum of Logarithmic Error (ALSE), Standard Deviation (SD), Euclidean Metric. The calculation formula is as follows:

[0091] Correlation coefficient:

[0092]

[0093] Logarithmic absolute sum of errors:

[0094]

[0095] Where Xi and Yi are the amplitudes of the amplitude-frequency characteristic curve at frequency point i when the winding is normal and faulty, and N is the total number of frequency points.

[0096] Standard Deviation:

[0097]

[0098] Euclidean distance:

[0099]

[0100] The transfer functions obtained by actual measurement are compared with the transfer function set obtained by fault simulation to evaluate the similarity between the measured transfer function and the simulated transfer function. A set of transfer function curves are shown in Figure 4 As shown in the figure, the frequency response curve of the transfer function obtained after simulating fault signals at different positions, the amplitude and waveform have changed, and the signal characteristics of the transfer function are analyzed. At the same time, the correlation coefficient between the transfer function and the transfer function under normal conditions, the absolute sum of the logarithm of the error, and the waveform characteristics of the amplitude-frequency curve are considered to analyze the relationship between the position. Compared with normal conditions, the measured transfer function curve will be offset after a fault occurs at different positions.

[0101] Step 8: According to the similarity evaluation result, the simulated fault position with the greatest similarity to the measured transfer function is determined as the actual fault position of the transformer, such as Figure 5 As shown, the theoretical position and the predicted result can be obtained. The theoretical position is a straight line, and the predicted result has an error within 15%, which can provide a basis for further maintenance and troubleshooting.

[0102] In one embodiment, the present invention provides a transformer impulse fault location system based on distributed parameter inversion calculation, the system comprising:

[0103] A signal acquisition unit, which is used to perform an impulse withstand voltage test on the transformer and collect voltage and current signals of the transformer in real time;

[0104] A signal processing unit, which is used to analyze the voltage signal and the current signal and calculate the transfer function;

[0105] A parameter calculation unit is used to establish a distributed parameter network of the transformer based on the physical structure and electrical parameters of the transformer; perform inverse calculation on the parameters in the distributed parameter network of the transformer to obtain parameter values ​​of the distributed parameter network of the transformer, and establish a digital twin model of the transformer;

[0106] A fault simulation unit, which is used to simulate the fault types at different positions of the transformer at each node of the distributed parameter network of the transformer in the digital twin model of the transformer, and generate a transfer function data set under different node fault conditions;

[0107] The fault location and diagnosis unit is used to measure the voltage and current waveforms of the transformer under the full impulse test voltage to obtain the transfer function actually measured at this time; compare the transfer function actually measured with the transfer function data set under different node fault conditions to obtain the similarity evaluation results of the transfer function actually measured and the faults at different positions in the transfer function data set under different node fault conditions; according to the similarity evaluation results, determine the simulated fault position with the greatest similarity to the transfer function actually measured, and this position is the actual fault position of the transformer.

[0108] The signal acquisition unit includes a voltage sensor and a current sensor.

[0109] The signal acquisition unit includes an automatic adjustment device for adapting to different test voltage levels.

[0110] The parameter calculation unit includes an artificial intelligence algorithm library and a parameter optimization module.

[0111] The fault simulation unit can automatically adjust the model fault location and network structure according to the preset fault mode.

[0112] The fault location and diagnosis unit can locate and identify main longitudinal insulation faults based on the calculation results of the digital twin model and the actual measurement results.

[0113] In one embodiment, the present invention further provides a transformer impulse fault location system based on distributed parameter inversion calculation, comprising:

[0114] A signal acquisition unit, used for acquiring voltage and current signals of the transformer;

[0115] A signal processing unit, used for performing a preprocessing operation of the signal;

[0116] Parameter calculation unit, used to establish a digital twin model of the transformer equivalent distributed parameter network;

[0117] a fault simulation unit, used to simulate fault conditions in the model;

[0118] The fault location and diagnosis unit is used to generate a fault diagnosis report.

[0119] In one embodiment, the system includes: a signal acquisition unit, a signal processing unit, a parameter calculation unit, a fault simulation unit, a fault location and diagnosis unit, and a user interface.

[0120] The signal acquisition unit, composed of high-precision voltage sensors and current sensors, collects voltage and current signals during the transformer impulse voltage test. The unit also includes an automatic adjustment device for adapting to different test voltage levels to ensure the accuracy and reliability of signal acquisition.

[0121] The signal processing unit is responsible for performing signal preprocessing operations, including filtering, denoising, signal amplification and digital processing. This unit can process signals quickly and accurately, providing high-quality raw data for subsequent parameter calculations.

[0122] The parameter calculation unit is used to establish a digital twin model of the transformer equivalent distributed parameter network. This unit uses an artificial intelligence algorithm library and a parameter optimization module to intelligently optimize the model parameters through intelligent algorithms to achieve accurate simulation of the internal state of the transformer.

[0123] The fault simulation unit can automatically adjust the model fault location and network structure according to the preset fault mode. This unit generates a set of transfer functions under fault conditions by simulating fault conditions such as turn-to-turn breakdown and ground breakdown inside the transformer, providing a reference for fault location.

[0124] The fault location and diagnosis unit locates and identifies the main longitudinal insulation fault based on the calculation results of the digital twin model and the actual measurement results. This unit uses advanced data analysis technology to evaluate the similarity between the measured transfer function and the simulated transfer function by calculating the correlation coefficient and Euclidean distance, thereby achieving rapid and accurate positioning of the fault location.

[0125] The user interface provides an intuitive operating environment that allows users to configure test parameters, start and stop tests, and view test results. The interface also provides the function of generating fault diagnosis reports, including fault type, location, severity and repair suggestions.

[0126] In the description of this specification, the description with reference to the terms "one embodiment / method", "some embodiments / methods", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment / method or example are included in at least one embodiment / method or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment / method or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments / methods or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments / methods or examples described in this specification and the features of the different embodiments / methods or examples, unless they are contradictory.

[0127] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0128] It should be understood by those skilled in the art that the above embodiments are only for the purpose of clearly illustrating the present invention, and are not intended to limit the scope of the present invention. For those skilled in the art, other changes or modifications may be made based on the above disclosure, and these changes or modifications are still within the scope of the present invention.

Claims

1. A transformer impulse fault location method based on distributed parameter inversion calculation, characterized in that: include: Step 1, performing an impulse withstand voltage test on the transformer, and collecting the voltage signal and current signal of the transformer in real time; Step 2, analyzing the voltage signal and the current signal to calculate the transfer function; Step 3, establishing a distributed parameter network of the transformer based on the physical structure and electrical parameters of the transformer; Step 4, performing inverse calculation on the parameters in the distributed parameter network of the transformer, obtaining the parameter values ​​of the distributed parameter network of the transformer, and establishing a digital twin model of the transformer; Step 5: In the digital twin model of the transformer, each node of the distributed parameter network of the transformer simulates the fault type at different positions of the transformer to generate a transfer function data set under different node fault conditions; Step 6, measuring the voltage and current waveforms of the transformer under the full impulse test voltage to obtain the transfer function actually measured; Step 7, comparing the transfer function obtained by actual measurement with the transfer function data set under different node fault conditions, and obtaining similarity evaluation results of faults at different positions in the transfer function obtained by actual measurement and the transfer function data set under different node fault conditions; Step 8: According to the similarity evaluation result, determine the simulated fault position with the greatest similarity to the transfer function obtained by actual measurement, and this position is the actual fault position of the transformer.

2. The method according to claim 1, characterized in that Preferably, in step 1, 50% of the impulse test voltage is applied to the transformer to perform an impulse withstand voltage test on the transformer.

3. The method according to claim 1, characterized in that: In step 5, two fault types of the transformer, namely, internal turn-to-turn breakdown and ground breakdown, are simulated.

4. The method according to claim 1, characterized in that: In step 7, the similarity evaluation is performed using the calculation method of correlation coefficient and Euclidean distance.

5. A transformer impact fault location system based on distributed parameter inversion calculation, characterized in that: The system comprises: A signal acquisition unit, which is used to perform an impulse withstand voltage test on the transformer and collect voltage and current signals of the transformer in real time; A signal processing unit, which is used to analyze the voltage signal and the current signal and calculate the transfer function; A parameter calculation unit is used to establish a distributed parameter network of the transformer based on the physical structure and electrical parameters of the transformer; perform inverse calculation on the parameters in the distributed parameter network of the transformer to obtain parameter values ​​of the distributed parameter network of the transformer, and establish a digital twin model of the transformer; A fault simulation unit, which is used to simulate the fault types at different positions of the transformer at each node of the distributed parameter network of the transformer in the digital twin model of the transformer, and generate a transfer function data set under different node fault conditions; The fault location and diagnosis unit is used to measure the voltage and current waveforms of the transformer under the full impulse test voltage to obtain the transfer function actually measured at this time; compare the transfer function actually measured with the transfer function data set under different node fault conditions to obtain the similarity evaluation results of the transfer function actually measured and the faults at different positions in the transfer function data set under different node fault conditions; according to the similarity evaluation results, determine the simulated fault position with the greatest similarity to the transfer function actually measured, and this position is the actual fault position of the transformer.

6. The system according to claim 5, characterized in that The signal acquisition unit includes a voltage sensor and a current sensor.

7. The system according to claim 5, characterized in that The signal acquisition unit includes an automatic adjustment device for adapting to different test voltage levels.

8. The system according to claim 5, characterized in that The parameter calculation unit includes an artificial intelligence algorithm library and a parameter optimization module.

9. The system according to claim 5, characterized in that The fault simulation unit can automatically adjust the model fault location and network structure according to the preset fault mode.

10. The system according to claim 5, characterized in that The fault location and diagnosis unit can locate and identify main longitudinal insulation faults based on the calculation results of the digital twin model and the actual measurement results.

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