Residual charge voltage simulation analysis method and system and medium

Abstract

The invention discloses a residual charge voltage simulation analysis method, a system with the residual charge voltage simulation analysis method and a storage medium, and the residual charge voltage simulation analysis method uses measurement waveforms of a small number of tests for simulation. The problem of high-level VFTO breakdown fitting distortion caused by the characteristic of unbalanced breakdown frequency is solved, accurate evaluation of the last residual charge voltage of opening is achieved, and therefore a solution is provided for correctly judging the VFTO level of equipment under the condition that data is limited.

Description

Technical Field

[0001] The present invention relates to the technical field of overvoltage in power systems, and in particular to a residual charge voltage simulation analysis method, system and medium. Background Art

[0002] When the GIS disconnector is connected to the unloaded short bus, the switch contact gap will be repeatedly broken down, generating a very fast transient overvoltage (VFTO) with high amplitude and steepness, which poses a potential threat to the insulation of the GIS body and its connected primary equipment. The generation of VFTO is related to many factors inside the GIS, including bus topology, equipment parameters, operating conditions, etc. Among them, the residual charge voltage is one of the important factors affecting the VFTO level.

[0003] At present, the following three methods are mainly used to evaluate the residual voltage amplitude:

[0004] (1) The residual voltage standard value is simply taken as 1.0 as the initial condition for calculating overvoltage. This theoretical assumption of initial condition not only fails to consider the occurrence of peak residual voltage, but also has the error of taking a low-probability event as a necessary event.

[0005] (2) Simulation calculation and evaluation. The breakdown characteristics of the gap withstand voltage are theoretically calculated and then the residual voltage is simulated. However, for any actual GIS circuit, it is difficult to accurately obtain parameters such as the gap breakdown field strength, the disconnector opening operation speed, and the opening action completion time, which makes it difficult for the existing simulation calculation method to play a role in actual production applications.

[0006] (3) Experimental statistical analysis. The residual charge voltage characteristics are statistically analyzed by performing a large number of VFTO tests caused by disconnector operations, or the residual charge voltage simulation analysis is evaluated by combining the gap withstand voltage characteristics statistically calculated with measured data. However, in actual projects, due to the limitations of measurement conditions, it is impossible to perform multiple opening and closing tests, and often only a small amount of VFTO waveform data can be obtained, resulting in distorted statistical results. Summary of the invention

[0007] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, the present invention proposes a residual charge voltage simulation analysis method, which can solve the high-level VFTO breakdown fitting distortion problem caused by the unbalanced breakdown frequency characteristics, thereby realizing accurate evaluation of the residual charge voltage at the last time of the gate opening.

[0008] The present invention also proposes a system having the above-mentioned fusion method for dynamic measurement uncertainty evaluation.

[0009] The residual charge voltage simulation analysis method according to the first aspect of the present invention is characterized by comprising the following steps:

[0010] Screening data of ultra-fast transient overvoltage waveforms with high amplitude and high steepness, reducing the data volume to obtain waveform data after the data volume is reduced, and determining the breakdown moment of the waveform data after the data volume is reduced based on the singular value decomposition positioning technology;

[0011] Acquire the gap recovery voltage at the breakdown moment of the waveform data after the data amount is reduced, and obtain a scatter plot of the gap recovery voltage distribution over time;

[0012] The gap recovery voltage is split into a positive polarity breakdown voltage distribution over time scatter diagram and a negative polarity breakdown voltage distribution over time scatter diagram according to the distribution of the scatter diagram;

[0013] Using K-means clustering, the positive / negative polarity breakdown voltage samples are divided into majority class samples and minority class samples respectively;

[0014] The minority class samples are upsampled using the Random-SMOTE algorithm, and new samples generated by the upsampling are combined with the original breakdown samples into a new data set, and a linear regression fitting is performed on the new data set to obtain the breakdown characteristics;

[0015] Determining a gap voltage tolerance curve based on the breakdown characteristics, and obtaining a positive / negative polarity tolerance voltage based on the gap voltage tolerance curve;

[0016] Based on the gap recovery voltage, breakdown characteristics and positive / negative polarity withstand voltage, a high-amplitude and high-steepness ultra-fast transient overvoltage simulation is performed, and the distribution frequency of the residual voltage value at the last time of simulated opening is statistically analyzed.

[0017] The residual charge voltage simulation analysis method according to the embodiment of the present invention has at least the following beneficial effects: the present invention uses a small amount of experimental measurement waveforms for simulation, solves the high-level VFTO breakdown fitting distortion problem caused by the unbalanced breakdown frequency characteristics, and realizes accurate evaluation of the residual charge voltage at the last time of opening the gate, thereby providing a solution for correctly judging the VFTO level of the equipment when data is limited.

[0018] According to some embodiments of the present invention, in the step of screening the data of ultra-fast transient overvoltage waveforms with high amplitude and high steepness, reducing the data amount to obtain waveform data after the reduced data amount, and determining the breakdown moment of the waveform data after the reduced data amount based on the singular value decomposition positioning technology, a fixed step size n=1000 is used to screen the data to reduce the data amount.

[0019] According to some embodiments of the present invention, in the step of obtaining the gap recovery voltage at the breakdown moment of the waveform data after reducing the data amount and obtaining a scatter plot of the gap recovery voltage distribution over time, the gap recovery voltage calculation formula satisfies:

[0020] U G =U s -U L

[0021] Among them U G is the gap withstand voltage, U S is the voltage on the power supply side, U L is the load side voltage; where U L It is expressed by the power frequency voltage value at the end of the last transient breakdown.

[0022] According to some embodiments of the present invention, the step of using K-means clustering to divide the positive / negative polarity breakdown voltage samples into majority class samples and minority class samples respectively includes:

[0023] K objects are randomly selected as the initial cluster centers, and then the distance between each object and each seed cluster center is calculated, and each object is assigned to the cluster center closest to it to divide it into different categories.

[0024] According to some embodiments of the present invention, the step of upsampling the minority class samples using the Random-SMOTE algorithm includes:

[0025] For each minority class sample x, two samples y1 and y2 are randomly selected from the small class sample set, and a triangular area is formed with x, y1, and y2 as vertices. According to the upsampling factor n, N new minority class samples are randomly generated in the triangular area and added to the unbalanced data set to reduce the degree of imbalance.

[0026] According to some embodiments of the present invention, the step of synthesizing the new samples generated by upsampling and the original breakdown samples into a new data set, and performing linear regression fitting on the new data set to obtain the breakdown characteristics includes:

[0027] The new samples generated by upsampling and the original breakdown samples are combined into a new data set, and a linear regression fit is performed on the new data set;

[0028] The following univariate linear regression model is established as the breakdown characteristic:

[0029] U W =a+bt

[0030] Where a is the constant term and b is the regression coefficient.

[0031] The residual charge voltage simulation analysis system according to the second aspect of the present invention is characterized by comprising:

[0032] A data processing module, capable of screening data of ultra-fast transient overvoltage waveforms with high amplitude and high steepness, reducing the data volume to obtain waveform data after the data volume is reduced, and determining the breakdown moment of the waveform data after the data volume is reduced based on the singular value decomposition positioning technology;

[0033] A recovery voltage statistics module is capable of acquiring the gap recovery voltage at the breakdown moment of the waveform data after the data volume is reduced, and obtaining a scatter plot of the gap recovery voltage distribution over time;

[0034] A first recovery voltage decomposition module can decompose the gap recovery voltage into a positive polarity breakdown voltage distribution over time scatter diagram and a negative polarity breakdown voltage distribution over time scatter diagram according to the distribution of the scatter diagram;

[0035] The second recovery voltage decomposition module can use K-means clustering to divide the positive / negative polarity breakdown voltage samples into majority class samples and minority class samples respectively;

[0036] The breakdown characteristic fitting module can use the Random-SMOTE algorithm to upsample the minority class samples, synthesize the new samples generated by the upsampling with the original breakdown samples into a new data set, and perform linear regression fitting on the new data set to obtain the breakdown characteristics;

[0037] A withstand voltage calculation module, capable of determining a gap voltage withstand curve based on the breakdown characteristics, and obtaining a positive / negative polarity withstand voltage based on the gap voltage withstand curve;

[0038] The comprehensive simulation module can perform high-amplitude and high-steepness ultra-fast transient overvoltage simulation based on the gap recovery voltage, breakdown characteristics and positive / negative polarity withstand voltage, and statistically simulate the distribution frequency of the last residual voltage value after the switch is opened.

[0039] According to some embodiments of the present invention, in the data processing module, a fixed step size n=1000 is used to filter the data to reduce the amount of data.

[0040] According to some embodiments of the present invention, in the recovery voltage statistics module, the gap recovery voltage calculation formula satisfies:

[0041] U G =U s -U L

[0042] Among them U G is the gap withstand voltage, U S is the voltage on the power supply side, U L is the load side voltage; where UL It is expressed by the power frequency voltage value at the end of the last transient breakdown.

[0043] According to some embodiments of the present invention, in the second recovery voltage decomposition module, K objects are randomly selected as initial cluster centers, and then the distance between each object and each seed cluster center is calculated, and each object is assigned to the cluster center closest to it to divide it into different categories.

[0044] According to some embodiments of the present invention, when the breakdown characteristic fitting module works, it includes:

[0045] For each minority class sample x, two samples y1 and y2 are randomly selected from the small class sample set, and a triangular area is formed with x, y1, and y2 as vertices. According to the upsampling factor n, N new minority class samples are randomly generated in the triangular area and added to the unbalanced data set to reduce the degree of imbalance.

[0046] According to some embodiments of the present invention, the breakdown characteristic fitting module further includes:

[0047] The new samples generated by upsampling and the original breakdown samples are combined into a new data set, and a linear regression fit is performed on the new data set;

[0048] The following univariate linear regression model is established as the breakdown characteristic:

[0049] U W =a+bt

[0050] Where a is the constant term and b is the regression coefficient.

[0051] According to a computer-readable storage medium of an embodiment of the third aspect of the present invention, the medium stores computer-executable instructions, and the computer-executable instructions are used to execute the above-mentioned residual charge voltage simulation analysis method.

[0052] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0054] Figure 1 A schematic diagram of the steps of the residual charge voltage simulation analysis method provided by an embodiment of the present invention;

[0055] Figure 2 A schematic diagram of the VFTO filtering waveform and the breakdown time positioning of the disconnector opening operation of the residual charge voltage simulation analysis method provided by the embodiment of the present invention;

[0056] Figure 3 A scatter plot of the gap recovery voltage distribution over time of the residual charge voltage simulation analysis method provided by an embodiment of the present invention;

[0057] Figure 4 A scatter plot of the positive polarity breakdown voltage distribution over time of the residual charge voltage simulation analysis method provided by an embodiment of the present invention;

[0058] Figure 5 A scatter plot of the negative polarity breakdown voltage distribution over time of the residual charge voltage simulation analysis method provided by an embodiment of the present invention;

[0059] Figure 6 A schematic diagram of using the elbow method for the negative polarity breakdown voltage in the residual charge voltage simulation analysis method provided by an embodiment of the present invention;

[0060] Figure 7 Schematic diagram of K-means clustering results of the residual charge voltage simulation analysis method provided in an embodiment of the present invention

[0061] Figure 8 A schematic diagram of upsampling using the Random-SMOTE algorithm of the residual charge voltage simulation analysis method provided by an embodiment of the present invention;

[0062] Fig. 9 A simulation flow chart of a repeated breakdown process of an isolating switch opening operation according to a residual charge voltage simulation analysis method provided in an embodiment of the present invention;

[0063] Fig.10 A structural block diagram of a residual charge voltage simulation analysis system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0064] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0065] In the description of the present invention, it should be understood that descriptions involving orientations, such as up, down, front, back, left, right, etc., and orientations or positional relationships indicated are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0066] In the description of the present invention, "several" means one or more, "more" means more than two, "greater than", "less than", "exceed" etc. are understood as not including the number itself, and "above", "below", "within" etc. are understood as including the number itself. If there is a description of "first" or "second", it is only used for the purpose of distinguishing the technical features, and cannot be understood as indicating or implying the relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0067] In the description of the present invention, unless otherwise clearly defined, terms such as setting, installing, connecting, etc. should be understood in a broad sense, and technicians in the relevant technical field can reasonably determine the specific meanings of the above terms in the present invention in combination with the specific content of the technical solution.

[0068] Embodiment 1

[0069] In order to solve the problem of high requirements on the amount of experimental data in traditional residual charge voltage simulation, this application provides a residual charge voltage simulation analysis method based on a small amount of measured VFTO waveforms. Figure 1 As shown, the method includes:

[0070] Step S100, screening data of ultra-fast transient overvoltage waveforms with high amplitude and high steepness, reducing the data volume to obtain waveform data after the data volume is reduced, and determining the breakdown moment of the waveform data after the data volume is reduced based on the singular value decomposition positioning technology.

[0071] Since the measured VFTO waveform contains a large amount of discrete sequence data, it is difficult to locate the breakdown moment. In order to reduce the amount of calculation and save running time, a fixed step size of n = 1000 is used to filter the data to reduce the amount of data. Then the singular value decomposition (SVD) positioning technology is used to determine the breakdown moment. The extracted breakdown voltage waveform is shown in Figure 2 shown.

[0072] Step S200, obtaining the gap recovery voltage at the breakdown moment of the waveform data after the data amount is reduced, and obtaining a scatter plot of the gap recovery voltage distribution over time.

[0073] During the operation of the disconnector, the gap will have multiple breakdowns. The power supply side voltage and the load side voltage are obtained by analyzing the waveform, and the gap recovery voltage is obtained by calculating the difference between the two. An unbalanced data set of the gap recovery voltage changing with time in a single opening operation is obtained. The gap recovery voltage calculation formula is as follows:

[0074] U G =U S -U L (1)

[0075] Among them U Gis the gap withstand voltage, U S is the voltage on the power supply side, U L is the load side voltage. L It is expressed by the power frequency voltage value at the end of the last transient breakdown.

[0076] According to formula 1, the gap recovery voltage at the time of breakdown in the whole process is counted, and a scatter plot of the gap recovery voltage distribution over time is made, as shown in Figure 3 shown.

[0077] Step S300: split the gap recovery voltage into a positive polarity breakdown voltage distribution over time scatter diagram and a negative polarity breakdown voltage distribution over time scatter diagram according to the distribution of the scatter diagram.

[0078] According to the positive and negative axes of the voltage per unit value, the gap recovery voltage distribution over time scatter plot is divided into a positive polarity breakdown voltage distribution over time scatter plot and a negative polarity breakdown voltage distribution over time scatter plot for the next step. Taking the negative polarity breakdown voltage as an example, the positive and negative polarity breakdown voltage distribution over time scatter plots are shown in Figure 1. Figure 4 Figure 5 .

[0079] Step S400: Use K-means clustering to divide the positive / negative polarity breakdown voltage samples into majority class samples and minority class samples respectively.

[0080] K-means clustering is used to divide the positive and negative polarity breakdown voltage samples into majority class samples and minority class samples respectively. The principle is to randomly select K objects as the initial cluster centers, then calculate the distance between each object and each seed cluster center, and assign each object to the cluster center with the nearest distance to divide them into different categories.

[0081] Furthermore, the experimental operation provided in this application takes the negative polarity breakdown voltage of the opening process as an example, and the positive polarity breakdown voltage is also processed by the same method. The original sample of the negative polarity breakdown voltage of the opening process is analyzed by the elbow method, and it is found that when the K value is 3, the error sum of squares and category refinement are balanced, which is the actual cluster number of the current data set. Figure 6 Schematic diagram of the elbow method.

[0082] Taking K as the true cluster number 3, K-means clustering was used to find that the breakdown samples with negative breakdown voltage at the end of the gate opening period were minority samples. The clustering results are shown in Figure 7 .

[0083] Step S500: Use the Random-SMOTE algorithm to upsample the minority class samples, synthesize the new samples generated by the upsampling and the original breakdown samples into a new data set, and perform linear regression fitting on the new data set to obtain breakdown characteristics.

[0084] The Random-SMOTE algorithm is used to upsample the minority class samples. The upsampling method uses the Random-SMOTE algorithm. For each minority class sample x, two samples y1 and y2 are randomly selected from the small class sample set. A triangular area is formed with x, y1, and y2 as vertices. According to the upsampling factor n, N new minority class samples are randomly generated in the triangular area and added to the unbalanced data set to reduce the degree of imbalance. The specific formula for generating minority class samples is as follows. The principle is as follows Figure 8 shown.

[0085] Reference Figure 8 The sampling diagram is specifically divided into:

[0086] (1) Perform random linear interpolation between y1 and y2 to generate N temporary samples tj (j = 1, 2, ... N);

[0087] t j =y1+rand(0,1)*(y2-y1),j=1,2,…,N (2)

[0088] (2) Perform random linear interpolation between tj and x to construct new small class samples pj (j = 1, 2, ... N)

[0089] p j =x+rand(0,1)*(t j -x),j=1,2,…,N (3)

[0090] Among them: rand(0,1) represents a random number in the interval (0,1).

[0091] Step S600: determining a gap voltage tolerance curve based on the breakdown characteristic, and obtaining a positive / negative polarity tolerance voltage based on the gap voltage tolerance curve.

[0092] The new samples generated by upsampling are combined with the original breakdown samples into a new data set, and a linear regression fit is performed on the new data set. The linear regression model is a classic statistical model, and the application scenario of this model is to predict a continuous numerical variable (dependent variable) based on a known variable (independent variable).

[0093] Based on the existing breakdown voltage theory, the insulation withstand voltage UW of the disconnector contact gap is approximately linearly related to the gap length d. Since it is difficult to directly observe the change process of the contact gap distance d in the experiment, the change of the contact gap distance can be reflected by the switch operation time t. Therefore, when it is assumed that the disconnector action is a uniform motion, the contact gap breakdown voltage can be seen to change linearly with the contact action time, and the following univariate linear regression model is established:

[0094] U W=a+bt (4)

[0095] Where a is the constant term and b is the regression coefficient.

[0096] The present invention uses the least square method to process the test data to obtain the gap breakdown characteristics, that is, the positive polarity breakdown voltage curve U W+ With negative polarity breakdown voltage U W- .

[0097] Step S700: Based on the gap recovery voltage, breakdown characteristics and positive / negative polarity withstand voltage, high-amplitude and high-steepness ultra-fast transient overvoltage simulation is performed, and the distribution frequency of the last residual voltage value after the simulated opening is statistically analyzed.

[0098] The simulation process of repeated breakdown process of disconnector opening operation is as follows Fig. 9 shown.

[0099] The present invention uses matlab for simulation, and the opening phase is randomly selected from 0° to 360° during the simulation. L Equal to the power supply side voltage U S During the disconnect switch opening operation, the power supply voltage U S Power frequency changes.

[0100] Gap insulation withstand voltage U W The time-varying curve is obtained from the breakdown characteristics. The gap recovery voltage UG is the difference between the power supply voltage and the load side voltage. G >U W+ When U G W- When the arc is extinguished, the two ends of the break are at the same potential, and the US value is assigned to U L , single breakdown ends, where U L The power frequency voltage value at the end of the last transient breakdown remains constant until the next breakdown occurs. This process is repeated until the opening action is completed and the repeated breakdown process ends.

[0101] According to the above breakdown process, the whole process VFTO simulation is carried out, the number of opening action simulations is set to 100 times, and the distribution frequency of the residual voltage value at the last time of the simulated opening is counted.

[0102] Embodiment 2

[0103] Another aspect of the present invention provides a residual charge voltage simulation analysis system, such as Fig.10 As shown, the system 20 includes:

[0104] ​The data processing module 201 can screen the data of the ultra-fast transient overvoltage waveform with high amplitude and high steepness, reduce the data volume to obtain the waveform data after the data volume is reduced, and determine the breakdown time of the waveform data after the data volume is reduced based on the singular value decomposition positioning technology;

[0105] The recovery voltage statistics module 202 can obtain the gap recovery voltage at the breakdown moment of the waveform data after the data volume is reduced, and obtain a scatter plot of the gap recovery voltage distribution over time;

[0106] The first recovery voltage decomposition module 203 can decompose the gap recovery voltage into a positive polarity breakdown voltage distribution over time scatter diagram and a negative polarity breakdown voltage distribution over time scatter diagram according to the distribution of the scatter diagram;

[0107] The second recovery voltage decomposition module 204 can use K-means clustering to divide the positive / negative polarity breakdown voltage samples into majority class samples and minority class samples respectively;

[0108] The breakdown characteristic fitting module 205 can use the Random-SMOTE algorithm to upsample the minority class samples, synthesize the new samples generated by the upsampling with the original breakdown samples into a new data set, and perform linear regression fitting on the new data set to obtain the breakdown characteristics;

[0109] A withstand voltage calculation module 206 is capable of determining a gap voltage withstand curve based on the breakdown characteristics, and obtaining a positive / negative polarity withstand voltage based on the gap voltage withstand curve;

[0110] The comprehensive simulation module 207 can perform high-amplitude and high-steepness ultra-fast transient overvoltage simulation based on the gap recovery voltage, breakdown characteristics and positive / negative polarity withstand voltage, and statistically simulate the distribution frequency of the last residual voltage value after the switch is opened.

[0111] Furthermore, in the second recovery voltage decomposition module 204, K objects are randomly selected as initial cluster centers, and then the distance between each object and each seed cluster center is calculated, and each object is assigned to the cluster center with the closest distance to divide into different categories.

[0112] Furthermore, in the recovery voltage statistics module 202, the gap recovery voltage calculation formula satisfies:

[0113] U G =U S -U L

[0114] Among them U G is the gap withstand voltage, U S is the power supply voltage, U L is the load side voltage; where U LIt is expressed by the power frequency voltage value at the end of the last transient breakdown.

[0115] Furthermore, in the data processing module 201, a fixed step size n=1000 is used to filter the data to reduce the amount of data.

[0116] Furthermore, when the breakdown characteristic fitting module 205 works, it includes:

[0117] For each minority class sample x, two samples y1 and y2 are randomly selected from the small class sample set, and a triangular area is formed with x, y1, and y2 as vertices. According to the upsampling factor n, N new minority class samples are randomly generated in the triangular area and added to the unbalanced data set to reduce the degree of imbalance.

[0118] Furthermore, the breakdown characteristic fitting module 205 also includes:

[0119] The new samples generated by upsampling and the original breakdown samples are combined into a new data set, and a linear regression fit is performed on the new data set;

[0120] The following univariate linear regression model is established as the breakdown characteristic:

[0121] U w =a+bt

[0122] Where a is the constant term and b is the regression coefficient.

[0123] Another aspect of the present invention provides a computer-readable storage medium storing computer-executable instructions for executing the above-mentioned Figure 1 The residual charge voltage simulation analysis method shown.

[0124] The embodiment of the present application uses K-means clustering to distinguish between the majority class and the minority class. Since the VFTO level of the minority class is the most significant and poses a serious threat, upsampling is performed on the minority class, so that when generating new samples, more attention is paid to the minority class samples with significant VFTO levels to avoid generating invalid samples. Using the Random-SMOTE algorithm, random interpolation is performed in the triangular area formed by the minority class sample point and the two neighboring points. The synthetic sample is no longer generated only on the line connecting the two sample points. The generated new sample has some features of the three adjacent original samples at the same time, and still has the features of the minority class sample. The distribution area is expanded, solving the problem of easy overfitting. The performance in evaluating the distribution and maximum value of the residual voltage at the end of the opening of the gate is better than the traditional linear regression model without adding resampling technology, which is conducive to accurately evaluating the VFTO level of the equipment and reducing the occurrence of faults.

[0125] The device embodiments described above are only illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0126] It will be appreciated by those skilled in the art that all or some of the steps and systems in the disclosed method above may be implemented as software, firmware, hardware and appropriate combinations thereof. Some physical components or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor or a microprocessor, or may be implemented as hardware, or may be implemented as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or a non-transitory medium) and a communication medium (or a temporary medium). As known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, disk storage or other magnetic storage devices, or any other medium that may be used to store desired information and may be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media typically embodies computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0127] The above is a specific description of the preferred implementation of the present application, but the present application is not limited to the above-mentioned implementation mode. Technical personnel familiar with the field can also make various equivalent deformations or substitutions without violating the spirit of the present application. These equivalent deformations or substitutions are all included in the scope defined by the claims of the present application.

Claims

1. A residual charge voltage simulation analysis method, characterized in that: The following steps are involved: Screening data of ultra-fast transient overvoltage waveforms with high amplitude and high steepness, reducing the data volume to obtain waveform data after the data volume is reduced, and determining the breakdown moment of the waveform data after the data volume is reduced based on the singular value decomposition positioning technology; Acquire the gap recovery voltage at the breakdown moment of the waveform data after the data amount is reduced, and obtain a scatter plot of the gap recovery voltage distribution over time; The gap recovery voltage is split into a positive polarity breakdown voltage distribution over time scatter diagram and a negative polarity breakdown voltage distribution over time scatter diagram according to the distribution of the scatter diagram; Using K-means clustering, the positive / negative polarity breakdown voltage samples are divided into majority class samples and minority class samples respectively; The minority class samples are upsampled using the Random-SMOTE algorithm, and new samples generated by the upsampling are combined with the original breakdown samples into a new data set, and a linear regression fitting is performed on the new data set to obtain the breakdown characteristics; Determining a gap voltage tolerance curve based on the breakdown characteristics, and obtaining a positive / negative polarity tolerance voltage based on the gap voltage tolerance curve; Based on the gap recovery voltage, breakdown characteristics and positive / negative polarity withstand voltage, a high-amplitude and high-steepness ultra-fast transient overvoltage simulation is performed, and the distribution frequency of the residual voltage value at the last time of simulated opening is statistically analyzed.

2. The method according to claim 1, characterized in that: In the step of screening the data of the ultra-fast transient overvoltage waveform with high amplitude and high steepness, reducing the data amount to obtain the waveform data after the data amount is reduced, and determining the breakdown moment of the waveform data after the data amount is reduced based on the singular value decomposition positioning technology, a fixed step size n=1000 is used to screen the data to reduce the data amount.

3. The method according to claim 1, characterized in that In the step of obtaining the gap recovery voltage at the breakdown moment of the waveform data after reducing the data volume and obtaining a scatter plot of the gap recovery voltage distribution over time, the gap recovery voltage calculation formula satisfies: IN G =U S -IN L Among them U G is the gap withstand voltage, U S is the voltage on the power supply side, U L is the load side voltage; where U L It is expressed by the power frequency voltage value at the end of the last transient breakdown.

4. The method according to claim 1, characterized in that: The step of using K-means clustering to divide the positive / negative polarity breakdown voltage samples into majority class samples and minority class samples respectively includes: K objects are randomly selected as the initial cluster centers, and then the distance between each object and each seed cluster center is calculated, and each object is assigned to the cluster center closest to it to divide it into different categories.

5. The method according to claim 1, characterized in that The step of upsampling the minority class samples using the Random-SMOTE algorithm includes: For each minority class sample x, two samples y1 and y2 are randomly selected from the small class sample set, and a triangular area is formed with x, y1, and y2 as vertices. According to the upsampling factor n, N new minority class samples are randomly generated in the triangular area and added to the unbalanced data set to reduce the degree of imbalance.

6. The method according to claim 1, characterized in that The step of synthesizing the new samples generated by upsampling and the original breakdown samples into a new data set, and performing linear regression fitting on the new data set to obtain the breakdown characteristics includes: The new samples generated by upsampling and the original breakdown samples are combined into a new data set, and a linear regression fit is performed on the new data set; The following univariate linear regression model is established as the breakdown characteristic: And W =a+bt Where a is the constant term and b is the regression coefficient.

7. A residual charge voltage simulation analysis system, characterized in that: include: A data processing module is capable of screening data of ultra-fast transient overvoltage waveforms with high amplitude and high steepness, reducing the data volume to obtain waveform data after the data volume is reduced, and determining the breakdown moment of the waveform data after the data volume is reduced based on the singular value decomposition positioning technology; A recovery voltage statistics module is capable of acquiring the gap recovery voltage at the breakdown moment of the waveform data after the data volume is reduced, and obtaining a scatter plot of the gap recovery voltage distribution over time; A first recovery voltage decomposition module can decompose the gap recovery voltage into a positive polarity breakdown voltage distribution over time scatter diagram and a negative polarity breakdown voltage distribution over time scatter diagram according to the distribution of the scatter diagram; The second recovery voltage decomposition module can use K-means clustering to divide the positive / negative polarity breakdown voltage samples into majority class samples and minority class samples respectively; The breakdown characteristic fitting module can use the Random-SMOTE algorithm to upsample the minority class samples, synthesize the new samples generated by the upsampling with the original breakdown samples into a new data set, and perform linear regression fitting on the new data set to obtain the breakdown characteristics; A withstand voltage calculation module, capable of determining a gap voltage withstand curve based on the breakdown characteristics, and obtaining a positive / negative polarity withstand voltage based on the gap voltage withstand curve; The comprehensive simulation module can perform high-amplitude and high-steepness ultra-fast transient overvoltage simulation based on the gap recovery voltage, breakdown characteristics and positive / negative polarity withstand voltage, and statistically simulate the distribution frequency of the residual voltage value at the last time of the simulated opening.

8. The system according to claim 7, characterized in that In the data processing module, a fixed step size n=1000 is used to screen the data to reduce the data volume.

9. The system according to claim 7, characterized in that In the recovery voltage statistics module, the gap recovery voltage calculation formula satisfies: IN G =U S -IN L Among them U G is the gap withstand voltage, U S is the voltage on the power supply side, U L is the load side voltage; where U L It is expressed by the power frequency voltage value at the end of the last transient breakdown.

10. The system according to claim 7, characterized in that In the second recovery voltage decomposition module, K objects are randomly selected as initial cluster centers, and then the distance between each object and each seed cluster center is calculated, and each object is assigned to the cluster center closest to it to divide it into different categories.

11. The system according to claim 7, characterized in that When the breakdown characteristic fitting module is working, it includes: For each minority class sample x, two samples y1 and y2 are randomly selected from the small class sample set, and a triangular area is formed with x, y1, and y2 as vertices. According to the upsampling factor n, N new minority class samples are randomly generated in the triangular area and added to the unbalanced data set to reduce the degree of imbalance.

12. The system according to claim 7, characterized in that The breakdown characteristic fitting module also includes: The new samples generated by upsampling and the original breakdown samples are combined into a new data set, and a linear regression fit is performed on the new data set; The following univariate linear regression model is established as the breakdown characteristic: And W =a+bt Where a is the constant term and b is the regression coefficient.

13. A computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are used to execute the method according to any one of claims 1 to 7.

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