Defect simulation system and test method for medium-and-low-voltage real-type power distribution network

Abstract

The invention discloses a medium and low voltage real type power distribution network defect simulation system and a test method thereof, and relates to the technical field of power system fault diagnosis and recovery, and the method comprises the steps: deploying a sensor to collect power distribution network operation data, carrying out the preprocessing, integrating a power distribution network data set, extracting a power distribution network fault feature vector from the power distribution network data set through FFT conversion, and carrying out the fault diagnosis and recovery; the method comprises the following steps: constructing a fault diagnosis model by using a support vector machine (SVM) based on a power distribution network fault feature vector, establishing a simulation model of a medium and low voltage power distribution network by using power system simulation software PSCAD, simulating a power distribution network fault in the simulation model, diagnosing the fault in the simulation model by using the fault diagnosis model, and formulating and implementing a recovery strategy according to a diagnosis result. Evaluating the effect of the recovery strategy, adjusting the recovery strategy according to the evaluation result, repeatedly testing in the simulation environment until the optimal recovery strategy is found, applying the optimal recovery strategy to the actual power distribution network, and continuously monitoring and improving the performance of the actual power distribution network.

Description

Technical Field

[0001] The invention relates to the technical field of power system fault diagnosis and restoration, in particular to a medium and low voltage true type distribution network defect simulation system and a test method thereof. Background Art

[0002] With the development of smart distribution network technology, the complexity of medium and low voltage distribution networks continues to increase. The detection and diagnosis of distribution network faults have become a key link to ensure the safe and stable operation of distribution networks. Traditional fault detection methods mainly rely on manual inspections and simple automation equipment. These methods are powerless in the face of increasingly complex distribution network structures. In recent years, with the advancement of sensing technology and data processing technology, it has become possible to monitor the operating status of distribution networks in real time by deploying sensor networks. However, how to effectively extract valuable fault features from a large amount of collected data and use these features to accurately diagnose the type and location of faults is still a challenge. In addition, traditional fault recovery strategies are often based on static models and cannot adapt to the dynamic changes in the distribution network operating environment.

[0003] At present, some studies have attempted to improve the accuracy of fault diagnosis through data-driven methods. Fourier transform FFT is used to perform frequency domain analysis on distribution network data, extract fault features from it, and then combine it with machine learning algorithms such as support vector machine SVM to build a fault diagnosis model. This method can improve the accuracy of fault diagnosis to a certain extent, but there are still some limitations in practical applications. First, traditional FFT analysis usually only considers a single type of signal feature, which makes it difficult to fully capture the complex characteristics of distribution network faults. Second, existing fault diagnosis models perform poorly when processing multivariate data, especially when faced with multiple different types of signals. In addition, most of the existing fault recovery strategies are based on static optimization methods, which fail to fully consider the real-time changes in the operating status of the distribution network, resulting in limited effectiveness of the recovery strategy. Therefore, a fault diagnosis and recovery strategy is needed that can effectively integrate multiple signal features and adapt to the dynamic changes of the distribution network in real time. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a medium and low voltage real distribution network defect simulation system and a test method thereof to solve the problem that traditional FFT analysis usually only considers a single type of signal characteristics and is difficult to fully capture the complex characteristics of distribution network faults.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a method for simulating defects in a medium and low voltage real distribution network, which comprises:

[0008] Deploy sensors to collect distribution network operation data and pre-process and integrate distribution network data sets;

[0009] The distribution network fault feature vector is extracted from the distribution network data set using FFT transform, and the fault diagnosis model is constructed based on the distribution network fault feature vector using support vector machine (SVM).

[0010] Use the power system simulation software PSCAD to build a simulation model of medium and low voltage distribution networks and simulate distribution network faults in the simulation model;

[0011] Use the fault diagnosis model to diagnose faults in the simulation model, formulate and implement recovery strategies based on the diagnosis results, and evaluate the effectiveness of the recovery strategies;

[0012] Adjust the restoration strategy based on the evaluation results and test it repeatedly in the simulation environment until the optimal restoration strategy is found. Apply the optimal restoration strategy to the actual distribution network and continuously monitor and improve its performance.

[0013] In a second aspect, the present invention provides a medium and low voltage real distribution network defect simulation system, comprising:

[0014] Data acquisition module: deploy sensors to collect distribution network operation data sets and provide the original data source for subsequent analysis;

[0015] Data preprocessing module: cleans and formats the collected data to ensure that the data quality meets the analysis requirements;

[0016] Feature extraction module: Apply FFT transformation to extract feature vectors related to distribution network faults from processed data to provide a basis for modeling;

[0017] Fault diagnosis model building module: Based on the extracted distribution network fault feature vector, a fault diagnosis model is built using support vector machine;

[0018] Simulation environment building module: Use power system simulation software such as PSCAD to build simulation models of medium and low voltage distribution networks to simulate fault conditions in real distribution network environments;

[0019] Fault diagnosis module: Use the fault diagnosis model in the simulation model to identify faults and formulate preliminary recovery strategies accordingly;

[0020] Recovery strategy optimization module: evaluates the diagnostic results and the effectiveness of the recovery strategy, and continuously adjusts and optimizes the recovery strategy based on the evaluation feedback until the best effect is achieved;

[0021] Continuous Improvement Module: Apply the finalized restoration strategy to the actual distribution network and further improve its performance through continuous monitoring.

[0022] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the method for simulating defects in a medium and low voltage real distribution network as described in the first aspect of the present invention is implemented.

[0023] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the method for simulating defects in a medium and low voltage real distribution network as described in the first aspect of the present invention is implemented.

[0024] The beneficial effects of the present invention are as follows: by deploying a sensor network to comprehensively collect distribution network operation data, and using FFT transformation to extract fault feature vectors from multiple types of signals, a fault diagnosis model is constructed based on these feature vectors using a support vector machine, thereby improving the accuracy and robustness of fault diagnosis; by establishing a simulation model in the power system simulation software PSCAD and simulating distribution network faults, the fault recovery strategy is further optimized, so that it can better adapt to the dynamic changes of the distribution network, thereby improving the overall operation efficiency and reliability of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0026] Figure 1 This is a flow chart of the method for simulating defects in a medium and low voltage real distribution network of Example 1.

[0027] Figure 2 This is a system diagram of the medium and low voltage real distribution network defect simulation system of Example 1. DETAILED DESCRIPTION

[0028] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0029] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, and provides a method for simulating defects in a medium and low voltage real distribution network, comprising the following steps:

[0030] S1 deploys sensors to collect distribution network operation data and pre-processes and integrates distribution network data sets;

[0031] Deploy current transformers, voltage transformers, temperature sensors and power meters at substations, feeder exit points, load concentration areas and distributed energy access points to collect distribution network operation data sets, and use wavelet transform to denoise the collected distribution network operation data sets;

[0032] Deploy current transformers, voltage transformers, temperature sensors and power meters at substations, feeder exit points, load concentration areas and distributed energy access points to collect current, voltage, temperature and power data, and use wavelet transform to denoise the collected distribution network operation data sets;

[0033] The data after denoising are current x1, voltage x2, temperature x3, and power x4;

[0034] Assume that the collected distribution network data set is x, where x = (x1, x2, ..., x4).

[0035] S2 uses FFT transformation to extract distribution network fault feature vectors from the preprocessed distribution network operation data set, and uses support vector machine (SVM) to build a fault diagnosis model based on the distribution network fault feature vectors;

[0036] The distribution network fault feature vector is extracted from the preprocessed distribution network operation data set using FFT transformation. The expression is:

[0037]

[0038] Among them, X i represents the representation of the i-th type of data in the frequency domain; f represents the frequency, t represents the time index, N represents the number of sampling points of the signal, and x i represents the operating data of the ith distribution network, e represents the base of the natural logarithm, j represents the imaginary unit, and π represents the circumference of a circle;

[0039] The FFT transform is used to convert the time domain signal into the frequency domain signal, and the fault feature vector is extracted from it, which enhances the characterization capability of the fault feature.

[0040] The energy E of each signal within the target frequency range i for:

[0041]

[0042] Among them, f h Indicates the upper limit of the target frequency range, f l Indicates the lower limit of the target frequency range;

[0043] The expression of distribution network fault characteristic vector E is:

[0044] E=[E1,E2,...,E m ] T;

[0045] Among them, E1 represents the energy of the first type of signal within the target frequency range, E2 represents the energy of the second type of signal within the target frequency range, and E m Represents the energy of the mth type signal within the target frequency range;

[0046] The traditional fault feature extraction method only considers a single type of signal feature, which is difficult to fully capture the complex characteristics of distribution network faults. The present invention extracts fault features of multiple types of signals through FFT transformation, thereby improving the accuracy and robustness of fault diagnosis.

[0047] First, determine the kernel function K(E i ,E), select RBF as the kernel function:

[0048] K(E i ,E j )=exp(-γ∥E i -E j ∥ 2 );

[0049] Among them, exp represents the exponential function, γ represents a positive constant, and E i and E j represents two different fault feature vectors;

[0050] The fault diagnosis model is constructed by using SVM. By selecting the appropriate kernel function, the model can handle nonlinear problems and improve the diagnosis accuracy.

[0051] A fault diagnosis model is constructed based on the distribution network fault feature vector using support vector machine SVM, which is expressed as:

[0052]

[0053] Where D represents the fault diagnosis result, n represents the number of fault feature vectors, and α i represents the Lagrange multiplier, y i represents the label of the training sample, K(E i ,E j ) represents the kernel function, b represents the bias term;

[0054] Existing fault diagnosis models perform poorly when processing multivariate data, especially when faced with multiple different types of signals. The present invention improves the accuracy of fault diagnosis by combining multiple signal features through the SVM model.

[0055] S3 uses the power system simulation software PSCAD to build a simulation model of the medium and low voltage distribution network and simulates distribution network faults in the model;

[0056] A basic model including substations, feeders, load nodes, and distributed energy access points was constructed in PSCAD, and these elements were connected according to the topological structure of the actual distribution network;

[0057] According to the data of the actual distribution network, the line resistance, transformer ratio, load type and size, and distributed power capacity in the basic model are set;

[0058] Set initial operating conditions, including voltage level, frequency, and load distribution of the distribution network;

[0059] Define the various types of failures that may occur and select the geographical location and time when the failures may occur;

[0060] Using PSCAD to build a simulation model of medium and low voltage distribution networks can simulate various fault scenarios in actual distribution networks. By simulating the occurrence of faults and their impacts in the simulation model, the behavior changes of the distribution network before and after the fault can be more intuitively observed, and the distribution network operation data during the fault period can be recorded;

[0061] Start the PSCAD simulation, observe the behavior changes of the distribution network before and after the fault occurs, and record the distribution network operation data during the fault period.

[0062] S4 uses the fault diagnosis model to diagnose faults in the simulation model, formulates and implements recovery strategies based on the diagnosis results, and evaluates the effectiveness of the recovery strategies;

[0063] During the simulation process, we collect the current I, voltage V, temperature T and power P data in the simulation model in real time and convert them into the distribution network fault feature vector E, which is then input into the SVM model to obtain the fault diagnosis result D and record the fault recovery time T. rec , trouble-free operation time R sys , compare the voltage fluctuation of the system before and after the fault to obtain the fault resistance S sys ;

[0064] Based on these data, the fault recovery strategy can be continuously optimized to achieve the goal of minimizing fault recovery time and maximizing system reliability and stability by disconnecting non-critical loads, reclosing attempts, switching backup power sources, etc.

[0065] According to the diagnosis results, a recovery strategy R is developed and implemented. The goal of this strategy is to minimize the fault recovery time T. rec , while maximizing the system's trouble-free operating time R sys and fault resistance S sysThe recovery strategy R will be implemented by disconnecting non-critical loads, reclosing attempts, and switching to backup power sources. The effect of the recovery strategy is evaluated by a comprehensive evaluation function F, which takes into account three factors: recovery time, system reliability, and stability. The expression of the comprehensive evaluation function F is:

[0066]

[0067] This process not only improves the efficiency of fault recovery, but also improves the overall operating efficiency and reliability of the distribution network through continuous optimization and testing;

[0068] Among them, w i represents the importance weight of the i-th type of fault, w 1,i represents the weight of the recovery time for the i-th type of failure, w 2,i represents the weight of the system's trouble-free operation time for the i-th type of fault, w 3,i It represents the weight of the system's fault resistance capability for the i-th type of fault.

[0069] S5 adjusts the restoration strategy based on the evaluation results and repeatedly tests it in the simulation environment until the optimal restoration strategy is found. The optimal restoration strategy is applied to the actual distribution network, and its performance is continuously monitored and improved.

[0070] Repeatedly test different strategy combinations in the simulation environment, adjust the strategy parameters according to the results of the previous round, set a threshold T, and when F(R(D))≤T, continue the next round of strategy parameter adjustment until F(R(D))>T;

[0071] The recovery strategy R when F(R(D))>T is applied to the actual distribution network. The current, voltage, temperature and power data in the actual distribution network are continuously collected and converted into distribution network fault feature vectors. The fault diagnosis results are input into the SVM model. The recovery strategy R is continuously improved in combination with the comprehensive evaluation function. The expression of the updated recovery strategy R is:

[0072]

[0073] Among them, R * represents the updated recovery strategy, t represents the number of iterations, Δw i represents the increment of weight;

[0074] By repeatedly testing and adjusting the recovery strategy in a simulation environment, we can dynamically adapt to changes in the operating status of the distribution network and find the optimal recovery strategy. This method overcomes the shortcomings of traditional methods in dynamic adaptability and improves the real-time and effectiveness of the recovery strategy.

[0075] This embodiment also provides a medium and low voltage real distribution network defect simulation system, including:

[0076] Data acquisition module: deploy sensors to collect distribution network operation data sets and provide the original data source for subsequent analysis;

[0077] Data preprocessing module: cleans and formats the collected data to ensure that the data quality meets the analysis requirements;

[0078] Feature extraction module: Apply FFT transformation to extract feature vectors related to distribution network faults from processed data to provide a basis for modeling;

[0079] Fault diagnosis model building module: Based on the extracted distribution network fault feature vector, a fault diagnosis model is built using support vector machine;

[0080] Simulation environment building module: Use power system simulation software such as PSCAD to build simulation models of medium and low voltage distribution networks to simulate fault conditions in real distribution network environments;

[0081] Fault diagnosis module: Use the fault diagnosis model in the simulation model to identify faults and formulate preliminary recovery strategies accordingly;

[0082] Recovery strategy optimization module: evaluates the diagnostic results and the effectiveness of the recovery strategy, and continuously adjusts and optimizes the recovery strategy based on the evaluation feedback until the best effect is achieved;

[0083] Continuous Improvement Module: Apply the finalized restoration strategy to the actual distribution network and further improve its performance through continuous monitoring.

[0084] This embodiment also provides a computer device, which is suitable for the case of a medium and low voltage real distribution network defect simulation method, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the medium and low voltage real distribution network defect simulation method proposed in the above embodiment.

[0085] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0086] The present embodiment also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for simulating defects in a medium and low voltage real distribution network as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic memory, a flash memory, a disk or an optical disk.

[0087] In summary, the present invention improves the accuracy and robustness of fault diagnosis by: deploying a sensor network to comprehensively collect distribution network operation data, and using FFT transformation to extract fault feature vectors from multiple types of signals, and using support vector machine to build a fault diagnosis model based on these feature vectors, and further optimizes the fault recovery strategy by establishing a simulation model in the power system simulation software PSCAD and simulating distribution network faults, so that it can better adapt to the dynamic changes of the distribution network, thereby improving the overall operation efficiency and reliability of the distribution network.

Claims

1. A method for simulating defects in a medium and low voltage real distribution network, characterized in that: include: Collect distribution network operation data through sensors, and pre-process the distribution network operation data to generate distribution network data sets; The distribution network fault feature vector is extracted from the distribution network data set using FFT transform, and the fault diagnosis model is constructed based on the distribution network fault feature vector using support vector machine (SVM). Use the power system simulation software PSCAD to build a simulation model of medium and low voltage distribution networks and simulate distribution network faults in the simulation model; Use the fault diagnosis model to diagnose distribution network faults in the simulation model, formulate and implement recovery strategies based on the diagnosis results, and evaluate the effectiveness of the recovery strategies; Adjust the recovery strategy based on the evaluation results and test it repeatedly in the simulation model until the optimal recovery strategy is found. Apply the optimal recovery strategy to the actual distribution network, continuously monitor the distribution network operation data and improve the optimal recovery strategy.

2. The method for simulating defects in a medium and low voltage real distribution network according to claim 1, characterized in that: The deployment of sensors to collect distribution network operation data and pre-processing integrated distribution network data sets is as follows: Deploy current transformers, voltage transformers, temperature sensors, and power meters at substations, feeder exit points, load concentration areas, and distributed energy access points to collect current, voltage, temperature, and power data, and use wavelet transform to denoise the distribution network data set; The data after denoising are current x1, voltage x2, temperature x3, and power x4; Set the collected distribution network data set to x, x = (x1, x2, ..., x4); The distribution network fault feature vector is extracted from the preprocessed distribution network operation dataset.

3. The method for simulating defects in a medium and low voltage real distribution network according to claim 2, characterized in that: The specific steps of extracting the distribution network fault feature vector from the distribution network data set by using FFT transformation are as follows: The distribution network fault feature vector is extracted from the preprocessed distribution network operation data set using FFT transform.

4. The method for simulating defects in a medium and low voltage real distribution network according to claim 3, characterized in that: The method of extracting the distribution network fault feature vector from the preprocessed distribution network operation data set by using FFT transformation specifically comprises the following steps: Determine the representation of each type of distribution network data in the frequency domain.

5. The method for simulating defects in a medium and low voltage real distribution network according to claim 4, characterized in that: The method of extracting the distribution network fault feature vector from the preprocessed distribution network operation data set by using FFT transformation specifically includes: Determine the energy E of each type of distribution network data within the target frequency range i ; Get the expression of distribution network fault characteristic vector E; A fault diagnosis model is constructed using support vector machine (SVM), which inputs the distribution network fault feature vector and outputs the fault diagnosis result.

6. The method for simulating defects in a medium and low voltage real distribution network according to claim 5, characterized in that: The fault diagnosis model is constructed by using a support vector machine (SVM) based on the distribution network fault feature vector, and the specific steps include: First, determine the kernel function K(E i ,E), select RBF as the kernel function; establish a simulation model of medium and low voltage distribution network in the power system simulation software PSCAD, and simulate the distribution network fault.

7. The method for simulating defects in a medium and low voltage real distribution network according to claim 6, characterized in that: The power system simulation software PSCAD is used to establish a simulation model of a medium and low voltage distribution network, and a distribution network fault is simulated in the simulation model. The specific steps include: Building an element-based basic model in the power system simulation software PSCAD, wherein the elements include substations, feeders, load nodes, and distributed energy access points, and connecting the elements according to the topological structure of the actual distribution network; According to the data of the actual distribution network, the line resistance, transformer ratio, load type and size, and distributed power capacity in the basic model are set.

8. The method for simulating defects in a medium and low voltage real distribution network according to claim 7, characterized in that: The power system simulation software PSCAD is used to establish a simulation model of a medium and low voltage distribution network, and a distribution network fault is simulated in the model. The specific steps also include: Set initial operating conditions; Define the various types of failures that may occur and select the geographical location and time when the failures may occur; Start the power system simulation software PSCAD to perform simulation, observe the behavior changes of the distribution network before and after the fault occurs, and record the distribution network operation data during the fault period; The fault diagnosis model is used to diagnose faults in the simulation model, and recovery strategies are formulated and implemented based on the diagnosis results, and the effectiveness of the recovery strategies is evaluated.

9. The method for simulating defects in a medium and low voltage real distribution network according to claim 8, characterized in that: The method of using the fault diagnosis model to diagnose the fault in the simulation model, formulating and implementing a recovery strategy according to the diagnosis result, and evaluating the effect of the recovery strategy specifically includes the following steps: During the simulation process, the current I, voltage V, temperature T and power P data in the simulation model are collected in real time and converted into the distribution network fault feature vector E, which is then input into the SVM model to obtain the fault diagnosis result D and record the fault recovery time T. rec , trouble-free operation time R sys , compare the voltage fluctuation of the system before and after the fault to obtain the fault resistance S sys .

10. The method for simulating defects in a medium and low voltage real distribution network according to claim 9, characterized in that: The method of using the fault diagnosis model to diagnose faults in the simulation model, formulating and implementing a recovery strategy based on the diagnosis results, and evaluating the effectiveness of the recovery strategy includes: formulating and implementing a recovery strategy based on the diagnosis results, using a recovery strategy R optimized based on a genetic algorithm, wherein the goal of the recovery strategy is to minimize the fault recovery time T rec , while maximizing the system's trouble-free operating time R sys and fault resistance S sys ,The recovery strategy R is implemented by disconnecting non-critical loads, reclosing attempts, and switching to backup power sources. The ,effectiveness of the recovery strategy is evaluated by the comprehensive evaluation function F, which ,considers three factors including recovery time, system reliability and stability.

11. The method for simulating defects in a medium and low voltage real distribution network according to claim 10, characterized in that: The restoration strategy is adjusted according to the evaluation results, and is repeatedly tested in a simulation environment until the optimal restoration strategy is found. The optimal restoration strategy is applied to the actual distribution network, and its performance is continuously monitored and improved. The specific steps include: In the simulation environment, different strategy combinations are tested repeatedly, and the strategy parameters are adjusted according to the results of the previous round. A threshold T is set. When F(R(D))≤T, the next round of strategy parameter adjustment is continued until F(R(D))>T.

12. The method for simulating defects in a medium and low voltage real distribution network according to claim 11, characterized in that: The restoration strategy is adjusted according to the evaluation results, and is repeatedly tested in a simulation environment until the optimal restoration strategy is found, and the optimal restoration strategy is applied to the actual distribution network to continuously monitor and improve its performance. The specific steps also include: The recovery strategy R when F(R(D))>T is applied to the actual distribution network. The current, voltage, temperature and power data in the actual distribution network are continuously collected, converted into distribution network fault feature vectors, and input into the SVM model to obtain fault diagnosis results. The recovery strategy R is continuously improved in combination with the comprehensive evaluation function.

13. A medium and low voltage real type distribution network defect simulation system, based on the medium and low voltage real type distribution network defect simulation method according to any one of claims 1 to 12, characterized in that: include, Data acquisition module: deploy sensors to collect distribution network operation data sets and provide the original data source for subsequent analysis; Data preprocessing module: cleans and formats the collected data to ensure that the data quality meets the analysis requirements; Feature extraction module: Apply FFT transformation to extract feature vectors related to distribution network faults from processed data to provide a basis for modeling; Fault diagnosis model building module: Based on the extracted distribution network fault feature vector, a fault diagnosis model is built using support vector machine.

14. The medium and low voltage real distribution network defect simulation system according to claim 13, characterized in that: Also includes, Simulation environment building module: Use power system simulation software such as PSCAD to build simulation models of medium and low voltage distribution networks to simulate fault conditions in real distribution network environments; Fault diagnosis module: Use the fault diagnosis model in the simulation model to identify faults and formulate preliminary recovery strategies accordingly; Recovery strategy optimization module: evaluates the diagnostic results and the effectiveness of the recovery strategy, and continuously adjusts and optimizes the recovery strategy based on the evaluation feedback until the best effect is achieved; Continuous Improvement Module: Apply the finalized restoration strategy to the actual distribution network and further improve its performance through continuous monitoring.

15. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for simulating defects in a medium or low voltage real distribution network as described in any one of claims 1 to 12 are implemented.

16. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for simulating defects in a medium or low voltage real distribution network as claimed in any one of claims 1 to 12 are implemented.

Images