Transformer winding partial discharge positioning method and device and electronic equipment

Abstract

This invention discloses a method, apparatus, and electronic device for locating partial discharge in transformer windings. The method includes: determining the actual energy ratio of the transformer winding; determining a discharge region and multiple candidate fault points within the discharge region based on the actual energy ratio; determining a first correlation coefficient between the first-end spectral transfer function and the first-end measured spectrum for each candidate fault point, and a second correlation coefficient between the last-end spectral transfer function and the last-end measured spectrum for each candidate fault point; and determining the partial discharge location result of the transformer winding based on the first and second correlation coefficients for each candidate fault point. This invention solves the technical problem of low accuracy in locating partial discharge sources in transformer windings in related technologies.

Description

Technical Field

[0001] This invention relates to the field of smart grids, and more specifically, to a method, apparatus, and electronic device for locating partial discharge in transformer windings. Background Technology

[0002] As a critical component of the power system, the safe and stable operation of transformers is essential for the reliability of the power network. Partial discharge (PD) activity is an important indicator for assessing the health status of transformer insulation systems, and can predict potential degradation or failure of insulation materials. Determining the specific location of partial discharge sources is of great significance for timely fault handling and preventative maintenance. However, existing PD location methods, such as ultrasonic or ultra-high frequency (UHF) detection methods, often suffer from low location accuracy, weak anti-interference capabilities, and limited real-time monitoring capabilities of transformer operating status, leading to low accuracy in locating partial discharge sources within transformer windings.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This invention provides a method, apparatus, and electronic device for locating partial discharge in transformer windings, thereby at least solving the technical problem of low accuracy in locating the location of partial discharge sources in transformer windings in related technologies.

[0005] According to one aspect of the present invention, a method for locating partial discharge in a transformer winding is provided, comprising: determining the actual energy ratio of the transformer winding, wherein the actual energy ratio is used to indicate the ratio between the measured energies of the first-end measurement point and the last-end measurement point of the transformer winding, and the measured energy is the energy of the partial discharge signal actually measured by a sensor device; determining a discharge region and a plurality of candidate fault points in the discharge region based on the actual energy ratio; determining a first correlation coefficient between the first-end spectral transfer function and the first-end measurement spectrum corresponding to each of the plurality of candidate fault points, and a second correlation coefficient between the last-end spectral transfer function and the last-end measurement spectrum corresponding to each of the plurality of candidate fault points, wherein the first-end spectral transfer function is used to indicate the frequency domain relationship of the current signal from the ground current source of the corresponding monitoring point to the first-end measurement point, the first-end measurement spectrum is used to indicate the spectrum of the partial discharge signal measured at the first-end measurement point, the last-end spectral transfer function is used to indicate the frequency domain relationship of the current signal from the ground current source of the corresponding monitoring point to the last-end measurement point, and the last-end measurement spectrum is used to indicate the spectrum of the partial discharge signal measured at the last-end measurement point; and determining the partial discharge location result of the transformer winding based on the first and second correlation coefficients corresponding to each of the plurality of candidate fault points.

[0006] According to another aspect of the present invention, a transformer winding partial discharge location device is also provided, comprising: an actual energy ratio determination module, configured to determine the actual energy ratio of the transformer winding, wherein the actual energy ratio is used to indicate the ratio between the measured energies of the first-end measurement point and the last-end measurement point of the transformer winding, and the measured energy is the energy of the partial discharge signal actually measured by a sensor device; a fault point determination module, configured to determine a discharge region and a plurality of candidate fault points in the discharge region based on the actual energy ratio; and a correlation coefficient determination module, configured to determine a first correlation coefficient between the first-end spectral transfer function and the first-end measured spectrum corresponding to each of the plurality of candidate fault points, and a plurality of candidate fault points. The second correlation coefficient between the end-point spectrum transfer function and the end-point measurement spectrum corresponding to each fault point is selected. The first-point spectrum transfer function is used to indicate the frequency domain relationship between the ground current source of the corresponding monitoring point and the current signal at the first-point measurement point. The first-point measurement spectrum is used to indicate the spectrum of the partial discharge signal measured at the first-point measurement point. The end-point spectrum transfer function is used to indicate the frequency domain relationship between the ground current source of the corresponding monitoring point and the current signal at the end-point measurement point. The end-point measurement spectrum is used to indicate the spectrum of the partial discharge signal measured at the end-point measurement point. The partial discharge location module is used to determine the partial discharge location result of the transformer winding based on the first correlation coefficient and the second correlation coefficient corresponding to each of the multiple candidate fault points.

[0007] According to another aspect of the present invention, a non-volatile storage medium is also provided, which stores a plurality of instructions adapted for a transformer winding partial discharge location method to be loaded by a processor and executed at any one of them.

[0008] According to another aspect of the present invention, an electronic device is also provided, including one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement any one of the transformer winding partial discharge location methods.

[0009] According to another aspect of the present invention, a computer program product is also provided, including a computer program that, when executed by a processor, implements the steps of any one of the transformer winding partial discharge location methods.

[0010] In this embodiment of the invention, the actual energy ratio of the transformer winding is determined, wherein the actual energy ratio indicates the ratio between the measured energies at the beginning and end measurement points of the transformer winding, and the measured energy is the energy of the partial discharge signal actually measured by the sensor device; based on the actual energy ratio, a discharge region and multiple candidate fault points within the discharge region are determined; a first correlation coefficient between the beginning-end spectral transfer function and the beginning-end measured spectrum corresponding to each of the multiple candidate fault points is determined, and a second correlation coefficient between the end-end spectral transfer function and the end-end measured spectrum corresponding to each of the multiple candidate fault points is determined, wherein the beginning-end spectral transfer function indicates the frequency domain relationship between the current signal from the ground current source at the corresponding monitoring point to the beginning-end measurement point, and the beginning-end measured spectrum is used to indicate the frequency domain relationship between the current signal from the ground current source at the corresponding monitoring point to the beginning-end measurement point. The partial discharge signal spectrum measured at the first measurement point is used to indicate the frequency domain relationship between the ground current source at the corresponding monitoring point and the current signal at the last measurement point. The last measurement spectrum is used to indicate the partial discharge signal spectrum measured at the last measurement point. Based on the first correlation coefficient and the second correlation coefficient corresponding to each of the multiple candidate fault points, the partial discharge location result of the transformer winding is determined. This achieves the goal of accurately identifying specific candidate fault points from the initial discharge area determination by combining actual energy ratio analysis and spectrum correlation coefficient calculation. This improves the accuracy and reliability of partial discharge location in transformer windings and solves the technical problem of low location accuracy of partial discharge sources in transformer windings in related technologies. Attached Figure Description

[0011] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0012] Figure 1 This is a flowchart of a transformer winding partial discharge location method according to an embodiment of the present invention;

[0013] Figure 2 This is a flowchart of an optional transformer winding partial discharge location method according to an embodiment of the present invention;

[0014] Figure 3 This is a schematic diagram of the equivalent circuit network of an optional transformer winding according to an embodiment of the present invention;

[0015] Figure 4 This is a schematic diagram of a transformer winding partial discharge locating device according to an embodiment of the present invention. Detailed Implementation

[0016] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0017] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0018] According to an embodiment of the present invention, a method for locating partial discharge in a transformer winding is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0019] Figure 1 This is a flowchart of a transformer winding partial discharge location method according to an embodiment of the present invention, as shown below. Figure 1 As shown, the method includes the following steps:

[0020] Step S102: Determine the actual energy ratio of the transformer winding. The actual energy ratio is used to indicate the ratio between the measured energy at the beginning and end measurement points of the transformer winding. The measured energy is the energy of the partial discharge signal actually measured by the sensor device.

[0021] Optionally, in this step, partial discharge signals can be collected by sensor devices placed at the beginning and end of the transformer windings. These sensors can capture the current or voltage changes generated during partial discharge activity. Then, energy calculations are performed on these signals to obtain the measured energy at the beginning (primary side) and end (secondary side). The actual energy ratio is calculated by comparing the energy values ​​measured at both ends (i.e., the measured energy), reflecting the propagation and attenuation characteristics of the partial discharge signal in the windings. This ratio calculation provides crucial information for the preliminary location of the discharge area.

[0022] In one optional embodiment, determining the discharge region based on the actual energy ratio includes: constructing an equivalent circuit network of the transformer winding; determining the first-end spectral transfer function and the last-end spectral transfer function corresponding to each of the multiple monitoring points on the transformer winding based on the equivalent circuit network; constructing a fitted energy ratio curve based on the first-end spectral transfer function corresponding to each of the multiple monitoring points on the transformer winding, wherein the fitted energy ratio curve is used to indicate the correspondence between the monitoring points and the predicted energy ratio; and determining the discharge region based on the actual energy ratio and the fitted energy ratio curve.

[0023] Optionally, a theoretical model reflecting the electrical characteristics of the transformer winding—an equivalent circuit network—can be constructed. The equivalent circuit network may include, but is not limited to, electrical components such as resistors R, inductors L, and capacitors C, configured to match the frequency response characteristics of the transformer winding. This equivalent circuit network can be constructed by inverting and analyzing the frequency response data of the winding, with the aim of obtaining a model that can simulate the actual signal propagation characteristics of the winding. Based on the constructed equivalent circuit network, the frequency domain transfer function of the current signal from each monitoring point to the beginning and end of the winding can be calculated. The transfer function describes the frequency domain relationship between the system input and output; for partial discharge location, it reflects the signal propagation characteristics from the discharge source to the sensor. The beginning-end spectral transfer function and the end-end spectral transfer function are used to describe the frequency domain changes of the signal as it propagates from the discharge source to the beginning and end of the transformer winding, respectively. By calculating the energy ratio of the current signal at each monitoring point in the equivalent circuit network to the beginning of the winding, an energy ratio curve can be constructed. This curve depicts the relationship between the monitoring point location and the energy ratio, reflecting the energy attenuation characteristics of the partial discharge signal propagating in the winding. Finally, the energy ratio measured by the sensor equipment is compared with the fitted energy ratio curve constructed above. By finding the monitoring point where the actual energy ratio is closest to the energy ratio value in the fitted energy ratio curve, the winding region (i.e., the discharge region) where the partial discharge source is located can be preliminarily determined.

[0024] The above method integrates the principles of circuit theory, signal processing, and data analysis. It can, but is not limited to, obtain equivalent circuit parameters through intelligent optimization algorithms (such as genetic algorithms), and then accurately determine the discharge region by comparing and analyzing the simulation results of the equivalent network model with the actual measurement data. This process not only utilizes the energy ratio index but also considers the spectral characteristics, making the localization of partial discharge more accurate and providing a more powerful technical means for the maintenance and troubleshooting of power equipment.

[0025] Optionally, the specific discharge region can be determined based on the actual energy ratio and the fitted energy ratio curve as follows: The actual energy ratio is compared with the fitted energy ratio curve to find its corresponding position on the curve; based on the position of the actual energy ratio on the fitted energy ratio curve, its location within a specific segment of the curve is determined, thus defining the initial discharge region; further analysis of the energy ratio variation trend within the discharge region is conducted to identify multiple monitoring points whose energy ratio values ​​are closest to the actual energy ratio, serving as candidate fault points; through comparative analysis, the monitoring point with the highest matching degree between the energy ratio curve and the actual energy ratio within the region is selected, serving as the center point or key point of the discharge region, thereby more accurately determining the location of the discharge region. In this way, by meticulously comparing the actually measured energy ratio with the fitted energy ratio curve constructed based on the equivalent circuit network, the transformer winding portion where partial discharge activity occurs and its specific location can be effectively identified. This provides important reference points for subsequent precise discharge source location using the spectral transfer function, ensuring the accuracy and efficiency of the entire partial discharge location process.

[0026] In one optional embodiment, constructing an equivalent circuit network for the transformer winding includes: obtaining a measured frequency response curve of the transformer winding, wherein the measured frequency response curve is used to indicate the amplitude and phase relationship of the change in current response from the excitation signal applied at the first measurement point to the last measurement point; determining target equivalent network parameters (i.e., optimal parameters) of the transformer winding based on the measured frequency response curve, wherein the target equivalent network parameters include at least the equivalent resistance, equivalent inductance, and equivalent capacitance of the transformer winding; and constructing an equivalent circuit network based on the target equivalent network parameters, wherein the equivalent circuit network is used to simulate the behavior of the transformer winding at different frequencies.

[0027] Optionally, firstly, the measured frequency response curve of the transformer winding needs to be obtained through experiments or field measurements. This curve is obtained by applying a series of excitation signals of different frequencies to the beginning of the winding and then measuring the change in current response amplitude and phase relationship at the end. The frequency response curve reflects the electrical characteristics of the winding at different frequencies, including signal attenuation and phase shift. Based on the measured frequency response curve, intelligent optimization algorithms (such as genetic algorithms) are used to determine the target equivalent network parameters, which include at least the equivalent resistance R, equivalent inductance L, and equivalent capacitance C of the winding. The intelligent optimization algorithm uses inversion calculations to find the parameter combination that best matches the measured frequency response curve, thereby obtaining the optimal equivalent circuit network parameters. This process is highly iterative and aims to simulate the real electrical behavior of the winding, especially its impedance characteristics at different frequencies. Once the target equivalent network parameters are determined, the equivalent circuit network of the winding can be constructed. This network consists of a series of R, L, and C elements connected in series and parallel, whose parameter values ​​reflect the electrical characteristics of the winding in the frequency domain. The establishment of the equivalent circuit network can not only simulate the behavior of the winding at different frequencies, but also be used for subsequent signal propagation characteristics and energy distribution analysis, providing a theoretical basis for partial discharge location.

[0028] The above methods enable accurate simulation of the electrical characteristics of transformer windings, especially their behavior in the frequency domain. This is crucial for subsequent signal transmission analysis based on equivalent circuit networks and for the precise location of partial discharge sources using energy ratio and spectral correlation. The constructed equivalent circuit network serves as the cornerstone of the entire positioning system, ensuring the accuracy and reliability of the positioning results.

[0029] In one optional embodiment, the target equivalent network parameters of the transformer winding are determined based on the measured frequency response curve, including: constructing an objective function, wherein the objective function is used to measure the degree of fit between the fitted frequency response curve and the measured frequency response curve, the degree of fit is inversely proportional to the function value of the objective function, and the fitted frequency response curve is obtained by fitting based on the equivalent network parameters of the transformer winding; optimizing the initial equivalent network parameters of the transformer winding with the goal of minimizing the function value of the objective function to obtain the target equivalent network parameters.

[0030] Optionally, the objective function is used to quantify the difference between the fitted frequency response curve and the measured frequency response curve, i.e., the degree of fit. The function value of the objective function is inversely proportional to the degree of fit; that is, the smaller the function value, the higher the degree of fit, meaning that the equivalent network parameters more closely reflect the electrical characteristics of the actual winding. The objective function can be constructed, but is not limited to, by comparing the amplitude and phase differences at various frequency points between the measured frequency response curve and the fitted frequency response curve simulated based on the equivalent network parameters. This difference can be measured by the sum of squared errors (such as the least squares method), correlation measures, or other statistical indicators to ensure that the optimal parameters found make the fitted curve as close as possible to the measured data. The optimization process can start with a set of initial equivalent network parameters, which can be set empirically or randomly generated. The goal of optimization is to adjust these parameters to minimize the function value of the objective function, i.e., to improve the degree of fit between the fitted frequency response curve and the measured frequency response curve. By constructing and optimizing the objective function, the equivalent network parameters that best reflect the electrical characteristics of the transformer winding can be intelligently determined based on the measured frequency response data. This process ensures the accuracy of the equivalent circuit network model, providing a solid foundation for subsequent signal transmission analysis and partial discharge localization, thereby effectively improving the reliability and accuracy of the localization results. The application of intelligent optimization algorithms not only accelerates the parameter search process but also improves the consistency between the fitted frequency response curve and the measured data.

[0031] In one alternative embodiment, constructing the objective function includes: constructing the objective function in the following manner:

[0032] ;

[0033] in, Represents the function value of the objective function; Used to measure the fitted frequency response curve obtained based on the equivalent network parameters to be optimized. Comparison with measured frequency response curve The differences between them; the equivalent network parameters to be optimized can be the initial equivalent network parameters or the intermediate equivalent network parameters obtained during the training and optimization process; Used to measure the fitted frequency response curve obtained based on the target equivalent network parameters. Comparison with measured frequency response curve The differences between them; Indicate the target equivalent network parameters; express The corresponding weights; express The corresponding weights; It is obtained in the following way:

[0034] ;

[0035] in, This represents the fitted frequency response curve obtained based on the equivalent network parameters to be optimized. Comparison with measured frequency response curve The correlation coefficient between them; This represents the fitted frequency response curve obtained based on the equivalent network parameters to be optimized. Comparison with measured frequency response curve The Euclidean distance between them; , These represent the corresponding weights; Adopted and Obtained in the same way.

[0036] Optionally, designing the objective function is a crucial step in optimizing the equivalent network parameters. Its purpose is to find a set of parameters that makes the frequency response curve fitted based on these parameters match the measured frequency response curve as closely as possible. (Difference metric function) and Used to measure the difference between the fitted frequency response curve and the measured frequency response curve. This is the correlation coefficient, used to measure the similarity of the shapes of two curves. The correlation coefficient P reflects the linear correlation between two sets of data, with a value ranging from -1 to 1, where 1 indicates perfect correlation and 0 indicates no correlation. Euclidean distance. This method is used to calculate the difference in amplitude between two curves at various frequency points. It is an intuitive metric that reflects the overall degree of mismatch between the curves. During iterative optimization, the algorithm will try different equivalent network parameters. Calculate the corresponding And by adjusting the parameters to reduce The value. When a set of parameters is found. At that time, it will pass again. To evaluate the fit, we need to ensure that the optimization results not only converge during the iteration process, but also have a good fit with the experimental data.

[0037] Optionally, this embodiment constructs an objective function that comprehensively considers both curve shape similarity and amplitude differences to guide the optimization of equivalent network parameters. This objective function design allows the optimization process to simultaneously consider the curve's shape and amplitude, thereby effectively improving the matching degree between the fitted frequency response curve and the measured curve, ultimately obtaining a set of target equivalent network parameters that accurately reflect the electrical characteristics of the transformer windings. This approach not only improves the efficiency of parameter optimization but also ensures the model's accuracy, providing a solid foundation for subsequent signal analysis and partial discharge localization.

[0038] Optional, It can be obtained, but is not limited to, through the following methods:

[0039] ;

[0040] in, This indicates the frequency response curve measured. The sampling points obtained from the sampling (i.e., the measured frequencies) are represented by l, which represents the total number of measured frequencies obtained from the sampling of the measured frequency response curve. This indicates the fit from the frequency response curve. The sampled points (i.e., the fitting frequencies) are obtained.

[0041] It can be obtained, but is not limited to, through the following methods: .

[0042] In one optional embodiment, the initial equivalent network parameters of the transformer winding are optimized to obtain the target equivalent network parameters with the objective function value as the minimum. This includes: constructing an initial population, where each individual in the initial population represents a set of initial equivalent network parameters; determining the fitness value of each individual in the initial population, where the fitness value of each individual is obtained based on the objective function value; selecting parent individuals from the individuals included in the initial population using a roulette wheel selection algorithm based on the fitness value of each individual; performing a crossover operation based on the parent individuals to generate child individuals, and performing a mutation operation based on the individuals included in the initial population to obtain mutated individuals; updating the initial population based on the child individuals and mutated individuals, with the objective function value as the minimum; repeating the above operations until a predetermined termination condition is reached; and obtaining the target equivalent network parameters based on the individuals included in the updated initial population when the predetermined termination condition is reached.

[0043] Optionally, with the goal of minimizing the objective function value, the initial equivalent network parameters of the transformer winding are optimized by inverting the network parameters through an optimization algorithm to obtain the target equivalent network parameters. Specifically, the construction of the initial population is the starting point of the genetic algorithm, where each individual represents a different combination of initial equivalent network parameters. These parameter combinations can be randomly generated or preset based on some prior knowledge. The diversity of the initial population helps the genetic algorithm to explore extensively in the search space, increasing the probability of finding the optimal solution. For each individual in the initial population, its fitness value needs to be calculated. The fitness value is determined based on the objective function value; the smaller the objective function value, the higher the matching degree between the fitted frequency response curve and the measured frequency response curve, and the larger the corresponding fitness value. The fitness value reflects the health status of an individual in solving a specific optimization problem and is an important basis for selection, crossover, and mutation operations. The selection operation selects individuals with better performance from the current population for subsequent crossover and mutation. The roulette wheel selection algorithm assigns selection probabilities based on the individual's fitness value; individuals with higher fitness have a higher probability of being selected. This step ensures that superior individual traits are more likely to be inherited in offspring, thereby gradually improving the overall performance of the population. Crossover is the core genetic operation in genetic algorithms; it generates new offspring by combining the parameters of parent individuals. In this process, each offspring inherits some parameters from two parent individuals, forming new parameter combinations. Crossover increases population diversity and promotes exploration during parameter optimization. Mutation randomly changes certain parameters on selected individuals to generate new individuals. The purpose of mutation is to prevent the population from converging prematurely to a local optimum, ensuring the comprehensiveness and depth of the search. Updating the population refers to replacing or adding the new generation of individuals generated through crossover and mutation to the current population. The formation of the new population should be based on minimizing the objective function value; that is, retaining or prioritizing individuals that can reduce the objective function value to drive the algorithm towards the optimal solution. The above selection, crossover, mutation, and population update operations are repeated until predetermined termination conditions are met, such as reaching the maximum number of iterations, the objective function value being less than a certain threshold, or the optimal solution no longer improving in several consecutive iterations. This process allows the algorithm to progressively approach the optimal parameter combination, ultimately obtaining a set of target equivalent network parameters that minimize the objective function. When the termination condition is met, the individuals in the updated population are used to determine the target equivalent network parameters. The individual with the highest fitness value (i.e., the one that best minimizes the objective function) can be considered the optimal solution, and its parameter combination is the target equivalent network parameter.

[0044] The above method enables efficient searching for the optimal combination of equivalent network parameters to optimize the fitting of the frequency response curve, ensuring more accurate and reliable subsequent signal analysis and partial discharge localization results. This approach combines the advantages of intelligent algorithms, significantly improving the speed and accuracy of parameter optimization.

[0045] Step S104: Based on the actual energy ratio, determine the discharge region and multiple candidate fault points within the discharge region.

[0046] Optionally, in this step, based on the actual energy ratio, the region of the winding where the partial discharge source might be located can be inferred. There is a relationship between the energy ratio and the discharge location and winding characteristics; for example, the farther the discharge point is from the measurement point, the more significant the signal energy attenuation, leading to a change in the energy ratio. Using a pre-constructed equivalent network model, the observed energy ratio can be compared with the model prediction, thereby determining approximately which part of the winding the discharge activity occurred in.

[0047] Step S106: Determine the first correlation coefficient between the first-end spectral transfer function and the first-end measurement spectrum corresponding to each of the multiple candidate fault points, and the second correlation coefficient between the last-end spectral transfer function and the last-end measurement spectrum corresponding to each of the multiple candidate fault points. The first-end spectral transfer function is used to indicate the frequency domain relationship between the ground current source of the corresponding monitoring point and the current signal at the first-end measurement point. The first-end measurement spectrum is used to indicate the spectrum of the partial discharge signal measured at the first-end measurement point. The last-end spectral transfer function is used to indicate the frequency domain relationship between the ground current source of the corresponding monitoring point and the current signal at the last-end measurement point. The last-end measurement spectrum is used to indicate the spectrum of the partial discharge signal measured at the last-end measurement point.

[0048] Optionally, the frequency domain transfer function curve shows the amplitude ratio and phase difference between the input and output currents at different frequencies, i.e., the signal transmission characteristics from the application point to the measurement point. In this step, after determining the discharge region, it is necessary to further pinpoint the discharge source. This step can be achieved by calculating the correlation coefficients between the signal spectra at the beginning and end points and the spectral transfer functions of different nodes in the equivalent network. The correlation coefficient is a statistic used to measure the degree of linear correlation between two sets of data, with a value ranging from -1 to 1; the closer to 1, the stronger the correlation. The first correlation coefficient between the spectral transfer function at the beginning point and the measured spectrum at the beginning point, and the second correlation coefficient between the spectral transfer function at the end point and the measured spectrum at the end point, respectively reflect the degree of matching between the dynamic characteristics of the partial discharge signal at the beginning and end points and the model prediction. By comparing the first and second correlation coefficients of different candidate fault points, the node whose signal spectrum best matches the model prediction can be identified, thereby determining the precise location of the discharge point.

[0049] Step S108: Based on the first correlation coefficient and the second correlation coefficient corresponding to each of the multiple candidate fault points, determine the partial discharge location result of the transformer winding.

[0050] Optionally, in this step, the correlation coefficients obtained above are comprehensively analyzed to determine the final partial discharge location result. Positioning the partial discharge source at a location with high correlation coefficients at both the beginning and end points indicates that the spectral characteristics of the discharge signal at both ends are closest to the model prediction, thus providing a more reliable location basis. This comprehensive evaluation can significantly improve the accuracy and reliability of partial discharge source location, providing strong support for the maintenance and fault prevention of power equipment.

[0051] In one optional embodiment, the partial discharge location result of the transformer winding is determined based on the first correlation coefficient and the second correlation coefficient corresponding to each of the multiple candidate fault points, including: calculating the average correlation coefficient corresponding to each of the multiple candidate fault points, wherein the average correlation coefficient is the average of the first correlation coefficient and the second correlation coefficient of the corresponding candidate fault point; and determining the candidate fault point with the average correlation coefficient greater than a preset threshold as the partial discharge location result of the transformer winding among the multiple candidate fault points.

[0052] Optionally, for each candidate fault point, a first correlation coefficient and a second correlation coefficient are calculated based on its location. The first correlation coefficient measures the similarity between the spectrum of the first-end signal and the current transfer function of the first-end candidate point in the equivalent network, while the second correlation coefficient measures the similarity between the spectrum of the last-end signal and the current transfer function of the last candidate point in the equivalent network. These correlation coefficients reflect the degree of agreement between the partial discharge signal and the response of the equivalent network at a specific location. The average correlation coefficient is the arithmetic mean of the first and second correlation coefficients for the corresponding candidate fault point. The purpose of calculating the average correlation coefficient is to comprehensively evaluate the similarity of the partial discharge signal at both ends of the transformer winding, ensuring the comprehensiveness and accuracy of the location results. By comparing the average correlation coefficient of each candidate fault point with a preset threshold, candidate points with an average correlation coefficient higher than the threshold can be selected. These points are considered more likely to be the actual location of the partial discharge. The preset threshold is set to exclude candidate points with low correlation and poor signal matching, thereby improving the accuracy of the location. It should be noted that the signal generated by the partial discharge has a high correlation with the response signal of the equivalent network near its actual location, while the correlation of the signal decreases significantly at locations far from the discharge point. Therefore, only those points with an average correlation coefficient higher than a certain threshold are identified as potential locations for partial discharge.

[0053] By employing the above methods, the most likely location of partial discharge can be further screened and confirmed based on the initial location, improving the accuracy and reliability of the localization. This approach utilizes the principles of signal processing and statistical analysis. By calculating the average correlation coefficient and comparing it with a threshold, signal analysis can be effectively combined with the winding structure characteristics to significantly improve the accuracy of partial discharge localization in transformer windings.

[0054] It should be noted that, considering the low accuracy of the initial partial discharge location, due to unavoidable parameter errors in the equivalent circuit network (RLC network) obtained by inversion, but the actual partial discharge point is definitely located near the initially located partial discharge point, the frequency domain transfer function curves (i.e., the node current transfer functions) of the nodes (a, b, c…) near the initial partial discharge point in the equivalent network are calculated sequentially. Simultaneously, the spectra of the first and last signals when the transformer under test experiences partial discharge are calculated. Furthermore, the correlation coefficients between the first-end spectrum and the current transfer functions of the aforementioned nearby nodes (a, b, c…) are calculated respectively. , …; calculate the correlation coefficients between the terminal spectrum and the current transfer function of the nearby nodes (a, b, c…). , Wait. The final partial discharge location was determined to be... The largest node (i is the node index).

[0055] Through the above steps S102 to S108, the goal of accurately identifying specific candidate fault points from the initial discharge area determination can be achieved by combining actual energy ratio analysis and spectrum correlation coefficient calculation. This improves the accuracy and reliability of partial discharge location in transformer windings and solves the technical problem of low location accuracy of partial discharge sources in transformer windings in related technologies.

[0056] Based on the above embodiments and optional embodiments, the present invention proposes an optional implementation method. Figure 2 This is a flowchart of an optional transformer winding partial discharge location method according to an embodiment of the present invention, such as... Figure 2 As shown, the method includes:

[0057] S1, Winding Frequency Response Analysis and Measurement: Before the partial discharge test, the frequency response curves of the transformer windings at the beginning and end are measured to obtain the measured frequency response curves. For example, a sweep source can be applied to the beginning of the transformer windings to collect the frequency response curve of the current at the end of the transformer windings. The driving point impedance that best reflects the inherent oscillation frequency characteristics of the windings in the frequency response curve is used as the curve to be fitted.

[0058] S2, Equivalent Circuit Network Modeling: Based on the winding frequency response curve (i.e., the measured frequency response curve), construct an equivalent trapezoidal circuit network of the winding (hereinafter referred to as the equivalent circuit network), and use an intelligent optimization algorithm to invert the network parameters; during the inversion process, the number of transformer model elements, resistance R, inductance L, capacitance C, and other parameters should be determined by the actual transformer, for example, Figure 3 This is a schematic diagram of the equivalent circuit network of an optional transformer winding according to an embodiment of the present invention. Let be the winding node voltage corresponding to node i. Let be the current flowing between the winding nodes corresponding to node i. The distributed longitudinal capacitance per unit length of the winding corresponding to node i. Let be the capacitance to ground per unit length of the winding corresponding to node i. Let be the resistance per unit length of the winding corresponding to node i. Let be the inductance per unit length of the winding corresponding to node i. Based on the actual transformer configuration of 8 units, and considering the non-uniform longitudinal characteristics of the winding during calculation, since the special winding section of this type of winding generally does not exceed half the total winding length, K for the first four units is solved separately, while the current coupling coefficient K for the last four units is solved uniformly; L for all units is solved separately, while R and C are solved uniformly, resulting in a total of 15 parameters to be solved. The iterative solution process for these 15 parameters can be accelerated using a genetic algorithm, which has a strong global search capability in the search space. Through the genetic operations of multiple individuals, it can extensively explore the search space to find the globally optimal solution or a near-optimal solution. This global search capability makes the genetic algorithm highly efficient and accurate when dealing with complex problems.

[0059] S3, Simulation calculation of node current transfer function: Calculate the spectrum transfer function curve of the current from the ground current source to the winding start measurement point and end measurement point of each node (i.e. each monitoring point on the transformer), hereinafter referred to as node current transfer function (specifically refers to the frequency domain relationship of the current signal from the ground current source at the node to the measurement point (start measurement point or end measurement point), including the start current transfer function and the end current transfer function.

[0060] S4, Fitting the Energy Ratio Curve: A smooth energy ratio curve is fitted using the node current transfer function to obtain the fitted energy ratio curve. Considering the low accuracy of the initial partial discharge location, due to unavoidable parameter errors in the equivalent circuit network (RLC network) obtained through inversion, but assuming the actual partial discharge point is located near the initially located partial discharge point, the frequency domain transfer function curves (i.e., node current transfer functions) of the nodes (a, b, c…) near the initial partial discharge point in the equivalent network are calculated sequentially. Simultaneously, the spectra of the first and last signals when the transformer under test experiences partial discharge are calculated. Furthermore, the correlation coefficients between the first-end spectrum and the node current transfer functions of the aforementioned nearby nodes (a, b, c…) are calculated respectively. , …; Calculate the correlation coefficients between the terminal spectrum and the current transfer function of the nearby nodes (a, b, c…). , Wait. The final partial discharge location was determined to be... The largest node (i is the node index).

[0061] S5, Energy Ratio Calculation: Calculate the partial discharge signal energy collected by sensors at both ends of the equivalent network. Based on the energy ratio formula, calculate the position from the partial discharge source to the first-end sensor to determine the discharge region. Specifically, apply the same pulsed current source to each independent node of the RLC equivalent circuit network, simultaneously collect the current at the beginning and end of the RLC network, calculate the energy of the beginning and end currents, and then compare them. Plot an energy ratio curve, with the horizontal axis representing the node position and the vertical axis representing the logarithm of the energy ratio. Measure the energy ratio at the beginning and end of the transformer under test during partial discharge. Use the energy ratio curve to find the corresponding partial discharge node position to achieve preliminary partial discharge localization and preliminarily determine the discharge region.

[0062] S6. Initially determine that the discharge region is between nodes a and b. Calculate the correlation coefficients of the head current transfer function of the equivalent network at node a and the transformer head spectrum (i.e., the head measurement spectrum), the correlation coefficients of the tail current transfer function of the equivalent network at node a and the transformer tail spectrum (tail measurement spectrum), the correlation coefficients of the head current transfer function of the equivalent network at node b and the transformer head spectrum, and the correlation coefficients of the tail current transfer function of the equivalent network at node b and the transformer tail spectrum.

[0063] S7, the criteria for precise positioning are: the mean of the correlation coefficients at the beginning and end of node a, and the end with the larger mean of the correlation coefficients at the beginning and end of node b.

[0064] It should be noted that this embodiment utilizes spectral inversion to obtain the specific parameters of the transformer's equivalent network; relevant algorithms are introduced into the inversion process to accelerate the convergence process; after preliminary partial discharge localization, the location of the partial discharge source is further accurately located by combining the transfer function and spectral correlation coefficient. A distributed parameter network is constructed through inversion to obtain the true energy ratio curve, and the winding discharge region is determined by repeatedly measuring the energy ratio (time domain) of the first and last signals, and further accurately located by combining spectral similarity (frequency domain).

[0065] This embodiment also provides a transformer winding partial discharge locating device, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the terms "module" and "device" can refer to a combination of software and / or hardware that performs a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0066] According to an embodiment of the present invention, an apparatus embodiment for implementing the above-described transformer winding partial discharge location method is also provided. Figure 4 This is a schematic diagram of a transformer winding partial discharge locating device according to an embodiment of the present invention, as shown below. Figure 4 As shown, the above-mentioned transformer winding partial discharge location device includes: an actual energy ratio determination module 400, a fault point determination module 402, a correlation coefficient determination module 404, and a partial discharge location module 406, wherein:

[0067] The actual energy ratio determination module 400 is used to determine the actual energy ratio of the transformer winding. The actual energy ratio is used to indicate the ratio between the measured energy at the beginning and end measurement points of the transformer winding. The measured energy is the energy of the partial discharge signal actually measured by the sensor device.

[0068] The fault point determination module 402 is connected to the actual energy ratio determination module 400 and is used to determine the discharge area and multiple candidate fault points in the discharge area based on the actual energy ratio.

[0069] The correlation coefficient determination module 404, connected to the fault point determination module 402, is used to determine the first correlation coefficient between the first-end spectrum transfer function and the first-end measurement spectrum corresponding to each of the multiple candidate fault points, and the second correlation coefficient between the last-end spectrum transfer function and the last-end measurement spectrum corresponding to each of the multiple candidate fault points. The first-end spectrum transfer function is used to indicate the frequency domain relationship between the ground current source of the corresponding monitoring point and the current signal at the first-end measurement point, the first-end measurement spectrum is used to indicate the spectrum of the partial discharge signal measured at the first-end measurement point, the last-end spectrum transfer function is used to indicate the frequency domain relationship between the ground current source of the corresponding monitoring point and the current signal at the last-end measurement point, and the last-end measurement spectrum is used to indicate the spectrum of the partial discharge signal measured at the last-end measurement point.

[0070] The partial discharge location module 406 is connected to the correlation coefficient determination module 404 and is used to determine the partial discharge location result of the transformer winding based on the first correlation coefficient and the second correlation coefficient corresponding to each of the multiple candidate fault points.

[0071] It should be noted that the above modules can be implemented by software or hardware. For example, for the latter, it can be implemented in the following ways: the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0072] It should be noted that the aforementioned actual energy ratio determination module 400, fault point determination module 402, correlation coefficient determination module 404, and partial discharge location module 406 correspond to steps S102 to S108 in the embodiments. The instances and application scenarios implemented by the above modules and their corresponding steps are the same, but are not limited to the content disclosed in the above embodiments. It should be noted that the above modules, as part of the device, can run in a computer terminal.

[0073] It should be noted that the optional or preferred implementation methods of this embodiment can be found in the relevant descriptions in the embodiments, and will not be repeated here.

[0074] The aforementioned transformer winding partial discharge location device may also include a processor and a memory. The aforementioned actual energy ratio determination module 400, fault point determination module 402, correlation coefficient determination module 404, partial discharge location module 406, etc., are all stored in the memory as program modules. The processor executes the aforementioned program modules stored in the memory to realize the corresponding functions.

[0075] The processor contains a core that retrieves the corresponding program modules from memory. One or more cores may be configured. Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory includes at least one memory chip.

[0076] According to an embodiment of this application, an embodiment of a non-volatile storage medium is also provided. Optionally, in this embodiment, the non-volatile storage medium includes a stored program, wherein, when the program is running, it controls the device containing the non-volatile storage medium to execute any of the transformer winding partial discharge location methods.

[0077] Optionally, in this embodiment, the non-volatile storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals, and the non-volatile storage medium includes stored programs.

[0078] According to an embodiment of this application, an embodiment of a processor is also provided. Optionally, in this embodiment, the processor is used to run a program, wherein the program executes any of the above-described transformer winding partial discharge location methods.

[0079] According to an embodiment of this application, an embodiment of a computer program product is also provided, which, when executed on a data processing device, is adapted to execute a program that initializes the transformer winding partial discharge location method steps described above.

[0080] This invention provides an electronic device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of any of the above-described transformer winding partial discharge location methods.

[0081] The order of the above embodiments of the present invention is merely for description and does not represent the superiority or inferiority of the embodiments.

[0082] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0083] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of modules described above can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between modules, and may be electrical or other forms.

[0084] The modules described above as separate components may or may not be physically separate. Similarly, the components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple modules. Some or all of the modules can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0085] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0086] If the aforementioned integrated modules are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable non-volatile storage medium. Based on this understanding, the technical solution of this invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a non-volatile storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned non-volatile storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0087] The above are merely preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for locating partial discharge in a transformer winding, characterized in that, include: The actual energy ratio of the transformer winding is determined, wherein the actual energy ratio is used to indicate the ratio between the measured energy at the beginning measurement point and the end measurement point of the transformer winding, and the measured energy is the energy of the partial discharge signal actually measured by the sensor device; Based on the actual energy ratio, the discharge region and multiple candidate fault points in the discharge region are determined. A first correlation coefficient is determined between the first-end spectral transfer function and the first-end measured spectrum corresponding to each of the plurality of candidate fault points, and a second correlation coefficient is determined between the last-end spectral transfer function and the last-end measured spectrum corresponding to each of the plurality of candidate fault points. The first-end spectral transfer function is used to indicate the frequency domain relationship between the ground current source of the corresponding monitoring point and the current signal at the first-end measurement point. The first-end measured spectrum is used to indicate the spectrum of the partial discharge signal measured at the first-end measurement point. The last-end spectral transfer function is used to indicate the frequency domain relationship between the ground current source of the corresponding monitoring point and the current signal at the last-end measurement point. The last-end measured spectrum is used to indicate the spectrum of the partial discharge signal measured at the last-end measurement point. Based on the first correlation coefficient and the second correlation coefficient corresponding to each of the multiple candidate fault points, the partial discharge location result of the transformer winding is determined.

2. The method according to claim 1, characterized in that, Determining the discharge region based on the actual energy ratio includes: Construct the equivalent circuit network of the transformer winding; Based on the equivalent circuit network, the first-end spectral transfer function and the last-end spectral transfer function corresponding to each of the multiple monitoring points on the transformer winding are determined. Based on the first-end spectral transfer function corresponding to each of the multiple monitoring points on the transformer winding, a fitted energy ratio curve is constructed, wherein the fitted energy ratio curve is used to indicate the correspondence between the monitoring points and the predicted energy ratio. The discharge region is determined based on the actual energy ratio and the fitted energy ratio curve.

3. The method according to claim 2, characterized in that, The construction of the equivalent circuit network of the transformer winding includes: Obtain the measured frequency response curve of the transformer winding, wherein the measured frequency response curve is used to indicate the variation amplitude and phase relationship of the current response from the excitation signal applied at the first end measurement point to the last end measurement point; Based on the measured frequency response curve, the target equivalent network parameters of the transformer winding are determined, wherein the target equivalent network parameters include at least the equivalent resistance, equivalent inductance, and equivalent capacitance of the transformer winding. Based on the target equivalent network parameters, the equivalent circuit network is constructed, wherein the equivalent circuit network is used to simulate the behavior of the transformer winding at different frequencies.

4. The method according to claim 3, characterized in that, The determination of the target equivalent network parameters of the transformer winding based on the measured frequency response curve includes: Construct an objective function, wherein the objective function is used to measure the degree of fit between the fitted frequency response curve and the measured frequency response curve, the degree of fit is inversely proportional to the function value of the objective function, and the fitted frequency response curve is obtained based on the equivalent network parameters of the transformer winding; With the goal of minimizing the function value of the objective function, the initial equivalent network parameters of the transformer winding are optimized to obtain the target equivalent network parameters.

5. The method according to claim 4, characterized in that, The objective function to be constructed includes: The objective function is constructed as follows: ； in, This represents the function value of the objective function; Used to measure the fitted frequency response curve obtained based on the equivalent network parameters to be optimized. Compared with the measured frequency response curve The differences between them; Used to measure the fitted frequency response curve obtained based on the target equivalent network parameters. Compared with the measured frequency response curve The differences between them; Indicates the target equivalent network parameters; express The corresponding weights; express The corresponding weights; It is obtained in the following way: ； in, This represents the fitted frequency response curve obtained based on the equivalent network parameters to be optimized. Compared with the measured frequency response curve The correlation coefficient between them; This represents the fitted frequency response curve obtained based on the equivalent network parameters to be optimized. Compared with the measured frequency response curve The Euclidean distance between them; , These represent the corresponding weights; Adopted and Obtained in the same way.

6. The method according to claim 4, characterized in that, The optimization of the initial equivalent network parameters of the transformer winding, with the objective of minimizing the function value of the target function, to obtain the target equivalent network parameters includes: Construct an initial population, wherein each individual in the initial population is a set of initial equivalent network parameters; Determine the fitness value of each individual in the initial population, wherein the fitness value of each individual is obtained based on the function value of the objective function; Based on the fitness value of each individual, a roulette wheel selection algorithm is used to select parent individuals from the individuals included in the initial population; Crossover is performed on the parent individuals to generate offspring individuals, and mutation is performed on the individuals included in the initial population to obtain mutated individuals; Based on the offspring individuals and the mutated individuals, the initial population is updated with the objective function value being minimized; Repeat the above operations until a predetermined termination condition is reached; based on the individuals included in the updated initial population when the predetermined termination condition is reached, the target equivalent network parameters are obtained.

7. The method according to any one of claims 1 to 6, characterized in that, The determination of the partial discharge location result of the transformer winding based on the first correlation coefficient and the second correlation coefficient corresponding to each of the multiple candidate fault points includes: Calculate the average correlation coefficient corresponding to each of the plurality of candidate fault points, wherein the average correlation coefficient is the average of the first correlation coefficient and the second correlation coefficient of the corresponding candidate fault points; Among the multiple candidate fault points, the candidate fault point with an average correlation coefficient greater than a preset threshold is determined as the partial discharge location result of the transformer winding.

8. A transformer winding partial discharge locating device, characterized in that, include: The actual energy ratio determination module is used to determine the actual energy ratio of the transformer winding, wherein the actual energy ratio is used to indicate the ratio between the measured energy at the beginning measurement point and the end measurement point of the transformer winding, and the measured energy is the energy of the partial discharge signal actually measured by the sensor device; The fault point determination module is used to determine the discharge region and multiple candidate fault points in the discharge region based on the actual energy ratio. The correlation coefficient determination module is used to determine the first correlation coefficient between the first-end spectral transfer function and the first-end measurement spectrum corresponding to each of the plurality of candidate fault points, and the second correlation coefficient between the last-end spectral transfer function and the last-end measurement spectrum corresponding to each of the plurality of candidate fault points. The first-end spectral transfer function indicates the frequency domain relationship between the ground current source of the corresponding monitoring point and the current signal at the first-end measurement point. The first-end measurement spectrum indicates the spectrum of the partial discharge signal measured at the first-end measurement point. The last-end spectral transfer function indicates the frequency domain relationship between the ground current source of the corresponding monitoring point and the current signal at the last-end measurement point. The last-end measurement spectrum indicates the spectrum of the partial discharge signal measured at the last-end measurement point. The partial discharge location module is used to determine the partial discharge location result of the transformer winding based on the first correlation coefficient and the second correlation coefficient corresponding to each of the multiple candidate fault points.

9. A non-volatile storage medium, characterized in that, The non-volatile storage medium stores multiple instructions, which are adapted to be loaded by a processor and executed by the transformer winding partial discharge location method according to any one of claims 1 to 7.

10. An electronic device, characterized in that, It includes one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the transformer winding partial discharge location method according to any one of claims 1 to 7.

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