Extension method of circuit breaker preventive test data sample and related device

Abstract

The application discloses a circuit breaker preventive test data sample expansion method and a related device, and the method comprises the following steps: obtaining preventive test data of each time since a circuit breaker to be evaluated is put into operation; screening a plurality of state parameters representing the state of a power system from the preventive test data; expanding the data of each test of each state parameter through a random disturbance method to obtain an expanded sample data set; calculating the uncertainty estimation value and the gain of the expanded sample data in the expanded sample data set; and generating expanded data of the expanded sample data according to the uncertainty estimation value and the gain, so that the technical problem that the prior art lacks a method for expanding the circuit breaker preventive test data sample and thus potential faults of the circuit breaker cannot be found is solved.

Description

Technical Field

[0001] This application relates to the field of power equipment monitoring technology, and in particular to a method and related apparatus for expanding the sample of preventive test data for circuit breakers. Background Technology

[0002] High-voltage circuit breakers are one of the most important electrical devices in the power grid. Conducting research on the health level or condition assessment of high-voltage circuit breakers is of great significance for improving the safety and stability of the power grid. Regularly shutting down existing high-voltage circuit breakers for preventative testing remains the primary measure to prevent accidents during operation.

[0003] Currently, the analysis of preventive test data for high-voltage circuit breakers lacks sample expansion, and the analysis methods mainly fall into three categories: The first method relies on whether the absolute value of the preventive test data exceeds the specified value to determine whether the high-voltage circuit breaker is abnormal. This method is a binary judgment based on "yes or no," effective for identifying obvious faults but ineffective for identifying latent faults. The second method plots the trend curve of the test data over time and observes whether there is a significant inflection point in the trend curve to determine whether the high-voltage circuit breaker is abnormal. This method is also effective for identifying obvious faults but ineffective for identifying latent faults. The third method uses a neural network algorithm to detect outliers in the test data of all high-voltage circuit breakers of the same model and batch as the one being evaluated, and determines whether the high-voltage circuit breaker is abnormal by checking for outliers. Compared to the first two methods, this method expands the data volume, but the data volume is still insufficient to support neural network algorithms based on big data analysis, remaining only at the theoretical stage and frequently failing in engineering practice.

[0004] Therefore, making full use of existing, limited preventive test data and employing certain mathematical methods to make explicit the subtle and hidden qualitative change trends contained in the preventive test data is very beneficial for timely detection of potential faults. This can minimize the probability of faults occurring in high-voltage circuit breakers during operation and improve power supply reliability. Summary of the Invention

[0005] This application provides a method and related apparatus for expanding the sample of preventive test data for circuit breakers, which solves the technical problem that the lack of a method for expanding the sample of preventive test data for circuit breakers in the prior art leads to the inability to detect potential faults in circuit breakers.

[0006] In view of this, the first aspect of this application provides a method for expanding a sample of preventive test data for circuit breakers, the method comprising:

[0007] Obtain data from all preventative tests conducted on the circuit breaker to be evaluated since its commissioning.

[0008] Several state parameters characterizing the power system state are selected from the aforementioned preventive test data;

[0009] The data for each state parameter in each trial are expanded using a random perturbation method to obtain an expanded sample dataset;

[0010] Calculate the uncertainty estimate and gain of the extended sample data in the extended sample dataset, and generate extended data of the extended sample data based on the uncertainty estimate and the gain.

[0011] Optionally, the step of expanding the data of each state parameter in each trial using a random perturbation method to obtain an expanded sample dataset specifically includes:

[0012] Based on the random perturbation formula, a random perturbation is added to the data of each state parameter in each trial to obtain an extended sample dataset;

[0013] The random perturbation formula is as follows:

[0014]

[0015] In the formula, Let be the estimated value of the k-th characteristic parameter obtained by the random perturbation method for the i-th time, k = 1, 2, ..., d, i = 1, 2, ..., n0; u is the number of extended sample data, u = 1, 2, ..., r; They are independent and identically distributed random variables, where the variance is... The selection is determined based on the actual application.

[0016] Optionally, the step of calculating the uncertainty estimate and gain of the extended sample data in the extended sample dataset, and generating extended data of the extended sample data based on the uncertainty estimate and the gain, specifically includes:

[0017] The initial uncertainty of the extended sample data is set, and the uncertainty estimate of the extended sample data is obtained by calculating it based on the uncertainty estimate calculation formula.

[0018] The gain is calculated based on the uncertainty estimate using the gain calculation formula, and the uncertainty estimate is updated based on the gain.

[0019] The extended data is calculated based on the extended data calculation formula, according to the gain and the updated uncertainty estimate.

[0020] The formula for calculating the uncertainty estimate is as follows:

[0021]

[0022] In the formula, This is the estimated value of the uncertainty; The initial uncertainty is... q is the assumed environmental noise parameter matrix, and f is the system parameter matrix;

[0023] The gain calculation formula is as follows:

[0024]

[0025] In the formula, For the gain, Assume measurement noise;

[0026] The extended data calculation formula is as follows:

[0027]

[0028] In the formula, For the extended data, z is an estimate of the characteristic parameters. k These are measured values.

[0029] Optionally, when the circuit breaker to be evaluated is a high-voltage vacuum circuit breaker, the several state parameters specifically include:

[0030] Insulation resistance to ground, insulation resistance at the break point, leakage current, and circuit resistance; mechanical characteristic parameters include asynchrony of opening and closing, opening time, closing time, closing bounce time, average opening speed, average closing speed, and opening rebound amplitude.

[0031] A second aspect of this application provides an extended system for circuit breaker preventive test data samples, the system comprising:

[0032] The acquisition module is used to acquire preventive test data of the circuit breaker to be evaluated since it was put into operation.

[0033] A screening module is used to screen out several state parameters characterizing the state of the power system from the preventive test data;

[0034] The first extension module is used to extend the data of each state parameter in each trial by means of a random perturbation method to obtain an extended sample dataset;

[0035] The second extension module is used to calculate the uncertainty estimate and gain of the extended sample data in the extended sample dataset, and generate extended data of the extended sample data based on the uncertainty estimate and the gain.

[0036] Optionally, the first extension module is specifically used for:

[0037] Based on the random perturbation formula, a random perturbation is added to the data of each state parameter in each trial to obtain an extended sample dataset;

[0038] The random perturbation formula is as follows:

[0039]

[0040] In the formula, Let be the estimated value of the k-th characteristic parameter obtained by the random perturbation method for the i-th time, k = 1, 2, ..., d, i = 1, 2, ..., n0; u is the number of extended sample data, u = 1, 2, ..., r; They are independent and identically distributed random variables, where the variance is... The selection is determined based on the actual application.

[0041] Optionally, the second extension module is specifically used for:

[0042] The initial uncertainty of the extended sample data is set, and the uncertainty estimate of the extended sample data is obtained by calculating it based on the uncertainty estimate calculation formula.

[0043] The gain is calculated based on the uncertainty estimate using the gain calculation formula, and the uncertainty estimate is updated based on the gain.

[0044] The extended data is calculated based on the extended data calculation formula, according to the gain and the updated uncertainty estimate.

[0045] The formula for calculating the uncertainty estimate is as follows:

[0046]

[0047] In the formula, This is the estimated value of the uncertainty; The initial uncertainty is... q is the assumed environmental noise parameter matrix, and f is the system parameter matrix;

[0048] The gain calculation formula is as follows:

[0049]

[0050] In the formula, For the gain, Assume measurement noise;

[0051] The extended data calculation formula is as follows:

[0052]

[0053] In the formula, For the extended data, z is an estimate of the characteristic parameters. k These are measured values.

[0054] Optionally, when the circuit breaker to be evaluated is a high-voltage vacuum circuit breaker, the several state parameters specifically include:

[0055] Insulation resistance to ground, insulation resistance at the break point, leakage current, and circuit resistance; mechanical characteristic parameters include asynchrony of opening and closing, opening time, closing time, closing bounce time, average opening speed, average closing speed, and opening rebound amplitude.

[0056] A third aspect of this application provides an extension device for circuit breaker preventive test data samples, the device comprising a processor and a memory:

[0057] The memory is used to store program code and transmit the program code to the processor;

[0058] The processor is configured to execute, according to instructions in the program code, the steps of the method for expanding the circuit breaker preventive test data sample as described in the first aspect above.

[0059] A fourth aspect of this application provides a computer-readable storage medium for storing program code for executing the method for expanding circuit breaker preventive test data samples as described in the first aspect above.

[0060] As can be seen from the above technical solutions, this application has the following advantages:

[0061] This application provides a method for expanding a sample of preventive test data for a circuit breaker, comprising: acquiring preventive test data from each circuit breaker to be evaluated since its commissioning; selecting several state parameters characterizing the state of the power system from the preventive test data; expanding the data of each state parameter in each test using a random perturbation method to obtain an expanded sample dataset; calculating the uncertainty estimate and gain of the expanded sample data in the expanded sample dataset; and generating expanded data of the expanded sample data based on the uncertainty estimate and the gain.

[0062] Compared with existing technologies, this application expands the sample of all preventive test data since the circuit breaker was put into operation, transforming small sample data into large sample data, thus obtaining more data that can be analyzed and utilized. This creates data conditions for big data analysis methods such as neural network algorithms and the construction of high-dimensional random matrices. It not only overcomes the deficiency of insufficient data in existing analysis methods but also fully considers the degree of influence and intrinsic correlation of multiple performance indicators on the overall health of high-voltage circuit breakers. Therefore, it solves the technical problem of existing technologies lacking a method for expanding the sample of preventive test data for circuit breakers, which leads to the inability to detect potential faults in circuit breakers. Attached Figure Description

[0063] Figure 1 This is a flowchart illustrating an embodiment of a method for expanding a sample of preventive test data for a circuit breaker, as provided in this application.

[0064] Figure 2 This is a schematic diagram of the structure of an extended system embodiment for a circuit breaker preventive test data sample provided in this application.

[0065] Figure 3a , 3b Figures 3 and 3c are schematic diagrams illustrating the three distributions of eigenvalues ​​in the annulus. Detailed Implementation

[0066] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0067] Please see Figure 1 The present application provides a method for expanding a sample of preventive test data for circuit breakers, comprising:

[0068] Step 101: Obtain the preventive test data of the circuit breaker to be evaluated since it was put into operation;

[0069] It should be noted that, specifically, the first step is to obtain the ledger information of the circuit breaker to be evaluated (such as high-voltage circuit breaker, high-voltage vacuum circuit breaker, etc.), and then obtain the preventive test data of the circuit breaker to be evaluated since it was put into operation.

[0070] Step 102: Select several state parameters that characterize the power system state from the preventive test data;

[0071] It should be noted that for any stationary system, the measurable state parameters of the system should remain essentially unchanged, with their test values ​​fluctuating only normally and randomly around the mean level, and this random fluctuation process is stationary. Therefore, new state parameter test data can be continuously generated based on the current state parameter test data, expanding the original state parameter test sample data. The expanded state parameter test sample data can still reflect the state of the system.

[0072] Suppose there are d parameters characterizing the system state, namely (p1, p2, ..., p...). k ,…,p d ).

[0073] Step 103: Expand the data of each state parameter in each trial using the random perturbation method to obtain an expanded sample dataset;

[0074] It should be noted that, assuming the i-th test data of the k-th original state parameter is... The expanded sample data obtained by adding random perturbation can be represented as:

[0075]

[0076] In the formula, Let be the estimated value of the k-th characteristic parameter obtained by the random perturbation method for the i-th time, k = 1, 2, ..., d, i = 1, 2, ..., n0; u is the number of extended sample data, u = 1, 2, ..., r. They are independent and identically distributed random variables, where the variance is... The selection is determined based on the actual application.

[0077] By calculating r times using equation (1), and expanding any test data for any original state parameter, the expanded sample dataset can be obtained as follows:

[0078] Due to the unique characteristics of high-voltage circuit breakers and their operation, their condition changes very slowly with increasing years of operation. Therefore, within the interval between two preventive test cycles (3 years per cycle), the high-voltage circuit breaker can be considered a stable system with its characteristic parameters remaining essentially unchanged. If characteristic parameter tests are conducted during this period, the test values ​​should fluctuate normally and randomly around the mean level. Therefore, the random disturbance method can be used to expand the characteristic parameter test sample data.

[0079] Taking the preventive test items of high-voltage vacuum circuit breakers as an example (other types of high-voltage circuit breakers can be referenced), electrical and mechanical characteristic parameters characterizing the health of high-voltage vacuum circuit breakers are selected as health assessment quantities. The electrical characteristic parameters include insulation resistance to ground (P1), break insulation resistance (P2), leakage current (P3), and loop resistance (P4); the mechanical characteristic parameters include asynchrony of opening (P5), asynchrony of closing (P6), opening time (P7), closing time (P8), closing bounce time (P9), and average opening speed (P1). 10 ), average closing speed (P) 11 ), circuit breaker rebound amplitude (P) 12 ).

[0080] Using the above-mentioned random perturbation method, any characteristic parameter P of the high-voltage vacuum circuit breaker is analyzed. k The test sample data (k = 1, 2, ..., 12) is expanded to obtain the expanded test sample data. for:

[0081]

[0082] In the formula, u = 1, 2, ..., r, where r is the number of expanded sample data; i = 1, 2, ..., n0, where n0 is the number of tests. The sample data is expanded to n0(r+1) data.

[0083] Step 104: Calculate the uncertainty estimate and gain of the extended sample data in the extended sample dataset, and generate the extended data of the extended sample data based on the uncertainty estimate and gain.

[0084] It should be noted that, based on the extended sample data obtained above, the sample size of the extended sample data is further expanded.

[0085] For ease of explanation later, the sample dataset will be expanded. Rewritten as The superscript j represents the expanded sample number index, j = 0, 2, ..., h-1. The sample number expansion steps are as follows:

[0086] 1) For the first raw data Calculate the corresponding row vector estimate. Right now:

[0087]

[0088] In the formula, f is the system parameter matrix, determined by the system's changing trend; g is the control matrix, determined by external conditions; u j For external influencing factors, j = 0, 1, ..., h-1, k = 1, 2, ..., d, i = 1, 2, ..., n0.

[0089] 2) The initial uncertainty is set. Begin by calculating the uncertainty estimate for the next step according to equation (4). Right now:

[0090]

[0091] In the formula, q is the assumed environmental noise parameter matrix; the initial uncertainty is...

[0092] 3) Based on uncertainty estimates Calculate gain Right now:

[0093]

[0094] In the formula, Assume measurement noise.

[0095] 4) Based on uncertainty estimates and gain Update uncertainty Right now:

[0096]

[0097] 5) Based on characteristic parameter estimates and gain Generate extended data Right now:

[0098]

[0099] In the formula, z k These are measured values.

[0100] For the extended sample dataset If we take r+1 data points and repeat steps 1) to 5) above h times, we can obtain h extended sample data points, thereby expanding the number of test data samples.

[0101] Taking the high-voltage vacuum circuit breaker as an example again, based on the expansion of the characteristic parameter test sample data in step 103, the above-mentioned sample number expansion method is used to further expand the sample data of any characteristic parameter to obtain the expanded characteristic parameter test sample. for:

[0102]

[0103] Where j = 1, 2, ..., h, and h is the number of expanded samples. The data of the characteristic parameter test samples are expanded to (h+1)×(r+1)n0.

[0104] Combined with the requirements of the health assessment of high-voltage vacuum circuit breakers, taking n0 = 3, r = 299, h = 119, the data of the characteristic parameter test samples can be expanded to 120×900, meeting the data requirements of big data analysis methods such as neural network algorithms and constructing high-dimensional random matrices.

[0105] A method for expanding the data samples of the preventive test of a circuit breaker provided in this embodiment first obtains the ledger information of the circuit breaker to be evaluated, and then obtains the data of历次 preventive tests since the circuit breaker to be evaluated was put into operation. Then, the sample data is expanded for the data to be analyzed, and then the number of samples of the data after the sample data expansion is expanded, so as to obtain data samples that can be effectively analyzed by big data analysis methods.

[0106] Taking the big data analysis method of constructing a high-maintenance random matrix and using the circular ratio of the circular theorem as the evaluation index as an example, the effectiveness of the sample expansion method is verified through an actual case.

[0107] The three pretest data of the opening time P7 of a certain operating high-voltage vacuum circuit breaker are 23.9ms, 24.38ms, and 25.25ms respectively. In this case, the influence of the test results of characteristic parameters is not considered, and the analysis is carried out only from this single factor of the opening time. The multi-factor analysis is similar. Judging from the absolute value of the data, the opening time is far less than the manufacturer's specified value of 36ms and is judged to be qualified. Judging from the trend of the data, although it has increased slightly, there is no obvious inflection point, so it is judged to be qualified.

[0108] Next, the data samples are expanded using the expansion method proposed in this invention, and then a high-dimensional 120×300-order random matrix is constructed using the expanded data samples. Due to space limitations, the specific data is not shown here. The distribution of its eigenvalues in the circle is as Figure 3a 、 Figure 3b 、 Figure 3c shown. Among them Figure 3a 、 Figure 3b 、 Figure 3c are the distribution of the eigenvalues of the corresponding pretest 1, pretest 2, and pretest 3 in the circle.

[0109] ]>As can be seen from 3a, the eigenvalues of the covariance matrix of the high-dimensional random matrix of the first preventive test data of the opening time are basically distributed within the circle, meeting the circular theorem; as Figure 3b can be seen, the eigenvalues of the opening time test data of the second preventive test data are also basically distributed within the circle. As Figure 3c can be seen, the eigenvalues of the opening time test data of the second preventive test data are distributed outside the circle and concentrated towards the center. The eigenvalue ratios are c = 0.25 ≥ c p=0.05 indicates that the tripping time test data has undergone a substantial change rather than random fluctuations, indicating that the high-voltage vacuum circuit breaker has a hidden defect in its health and needs to be repaired or replaced.

[0110] The above describes a method for expanding circuit breaker preventive test data samples in the embodiments of this application. The following describes a system for expanding circuit breaker preventive test data samples in the embodiments of this application.

[0111] Please see Figure 2 An extended system for circuit breaker preventive test data samples provided in this application embodiment includes:

[0112] The acquisition module 201 is used to acquire the preventive test data of the circuit breaker to be evaluated since it was put into operation.

[0113] The screening module 202 is used to screen out several state parameters characterizing the state of the power system from the preventive test data;

[0114] The first extension module 203 is used to extend the data of each state parameter in each trial by means of random perturbation method to obtain an extended sample dataset;

[0115] The second extension module 204 is used to calculate the uncertainty estimate and gain of the extended sample data in the extended sample dataset, and to generate extended data of the extended sample data based on the uncertainty estimate and gain.

[0116] Furthermore, this application embodiment also provides an expansion device for circuit breaker preventive test data samples, the device including a processor and a memory:

[0117] The memory is used to store program code and transmit the program code to the processor;

[0118] The processor is used to execute the method for expanding the circuit breaker preventive test data sample as described in the above method embodiment according to the instructions in the program code.

[0119] Furthermore, this application embodiment also provides a computer-readable storage medium for storing program code for executing the method for expanding circuit breaker preventive test data samples as described in the above method embodiment.

[0120] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the system and modules described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0121] The terms "first," "second," "third," "fourth," etc., used in this application's specification and the aforementioned drawings 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 this application described herein can be implemented, for example, in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover 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.

[0122] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0123] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units 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 an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0124] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0125] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0126] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, 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 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 described in the various embodiments of this application. The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0127] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for expanding a sample of preventive test data for circuit breakers, characterized in that, include: Obtain data from all preventative tests conducted on the circuit breaker to be evaluated since its commissioning. Several state parameters characterizing the power system state are selected from the aforementioned preventive test data; The data for each state parameter in each trial are expanded using a random perturbation method to obtain an expanded sample dataset; Calculate the uncertainty estimate and gain of the extended sample data in the extended sample dataset, and generate extended data of the extended sample data based on the uncertainty estimate and the gain; The process of expanding the data of each state parameter in each trial using a random perturbation method to obtain an expanded sample dataset specifically includes: Based on the random perturbation formula, a random perturbation is added to the data of each state parameter in each trial to obtain an extended sample dataset; The random perturbation formula is as follows: ； In the formula, To obtain the first by the random perturbation method The characteristic parameter is the first The estimated value of the second time. , ; To expand the number of sample data, ; They are independent and identically distributed random variables, where the variance is... The selection is determined based on the actual application.

2. The method for expanding the sample of preventive test data for circuit breakers according to claim 1, characterized in that, The calculation of the uncertainty estimate and gain of the extended sample data in the extended sample dataset, and the generation of extended data of the extended sample data based on the uncertainty estimate and the gain, specifically includes: The initial uncertainty of the extended sample data is set, and the uncertainty estimate of the extended sample data is obtained by calculating it based on the uncertainty estimate calculation formula. The gain is calculated based on the uncertainty estimate using the gain calculation formula, and the uncertainty estimate is updated based on the gain. The extended data is calculated based on the extended data calculation formula, according to the gain and the updated uncertainty estimate. The formula for calculating the uncertainty estimate is as follows: ； In the formula, The uncertainty estimate is... The initial uncertainty is... , Assuming an environmental noise parameter matrix, This is the system parameter matrix; The gain calculation formula is as follows: ； In the formula, For the gain, Assume measurement noise; The extended data calculation formula is as follows: ; In the formula, For the extended data, These are the estimated values ​​of the characteristic parameters. These are measured values.

3. The method for expanding the sample of preventive test data for circuit breakers according to claim 1, characterized in that, When the circuit breaker to be evaluated is a high-voltage vacuum circuit breaker, several state parameters specifically include: Insulation resistance to ground, insulation resistance at the break point, leakage current, and circuit resistance; mechanical characteristic parameters include asynchrony of opening and closing, opening time, closing time, closing bounce time, average opening speed, average closing speed, and opening rebound amplitude.

4. An extended system for circuit breaker preventive test data samples, characterized in that, include: The acquisition module is used to acquire preventive test data of the circuit breaker to be evaluated since it was put into operation. A screening module is used to screen out several state parameters characterizing the state of the power system from the preventive test data; The first extension module is used to extend the data of each state parameter in each trial by means of a random perturbation method to obtain an extended sample dataset; The second extension module is used to calculate the uncertainty estimate and gain of the extended sample data in the extended sample dataset, and generate extended data of the extended sample data based on the uncertainty estimate and the gain. The first extension module is specifically used for: Based on the random perturbation formula, a random perturbation is added to the data of each state parameter in each trial to obtain an extended sample dataset; The random perturbation formula is as follows: ； In the formula, To obtain the first by the random perturbation method The characteristic parameter is the first The estimated value of the second time. , ; To expand the number of sample data, ; They are independent and identically distributed random variables, where the variance is... The selection is determined based on the actual application.

5. The extended system for circuit breaker preventive test data samples according to claim 4, characterized in that, The second extension module is specifically used for: The initial uncertainty of the extended sample data is set, and the uncertainty estimate of the extended sample data is obtained by calculating it based on the uncertainty estimate calculation formula. The gain is calculated based on the uncertainty estimate using the gain calculation formula, and the uncertainty estimate is updated based on the gain. The extended data is calculated based on the extended data calculation formula, according to the gain and the updated uncertainty estimate. The formula for calculating the uncertainty estimate is as follows: ； In the formula, The uncertainty estimate is... The initial uncertainty is... , Assuming an environmental noise parameter matrix, This is the system parameter matrix; The gain calculation formula is as follows: ； In the formula, For the gain, Assume measurement noise; The extended data calculation formula is as follows: ； In the formula, For the extended data, For characteristic parameter estimates, These are measured values.

6. The extended system for circuit breaker preventive test data samples according to claim 4, characterized in that, When the circuit breaker to be evaluated is a high-voltage vacuum circuit breaker, several state parameters specifically include: Insulation resistance to ground, insulation resistance at the break point, leakage current, and circuit resistance; mechanical characteristic parameters include asynchrony of opening and closing, opening time, closing time, closing bounce time, average opening speed, average closing speed, and opening rebound amplitude.

7. An extended device for a sample of preventive test data for circuit breakers, characterized in that, The device includes a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is configured to execute the method for expanding the circuit breaker preventive test data sample according to any one of claims 1-3, based on instructions in the program code.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store program code for executing the method for expanding the circuit breaker preventive test data sample according to any one of claims 1-3.

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