Fault detection method and system of transformer substation and storage medium

Abstract

The invention discloses a fault detection method and system for a transformer substation and a storage medium, and the method comprises the steps: S1, obtaining the equipment fault data of the transformer substation, and carrying out the preprocessing of the equipment fault data; s2, selecting a kernel function, and constructing a kernel matrix based on the preprocessed equipment fault data; s3, centralizing the kernel matrix to obtain a centralized kernel matrix; s4, calculating eigenvalues of the centralized kernel matrix and eigenvectors corresponding to the eigenvalues; s5, sorting the feature values and the feature vectors, and intercepting part of the feature vectors according to a preset threshold value to form a vector matrix; s6, calculating the contribution rate of each component in the vector matrix based on the vector matrix; and S7, performing PCA and KPCA analysis on the transformer substation according to a preset contribution rate threshold, determining equipment fault features of the transformer substation, screening out key information, and reducing misjudgment and missed judgment risks.

Description

Technical Field

[0001] The present application relates to the technical field of power system protection, and in particular to a fault detection method, system and storage medium for a substation. Background Art

[0002] In recent years, high voltage direct current (HVDC) technology has achieved breakthrough development and has been widely used in the operation of power systems. As a very novel transmission technology, HVDC technology occupies an extremely important position in the new power system because of its unique advantages. Compared with traditional high voltage AC transmission technology, high voltage direct current transmission technology has many advantages such as low line cost, large transmission capacity, low loss, ability to complete asynchronous networking and limit short-circuit current of power system. Therefore, it is widely used in long-distance, high-power, asynchronous networking transmission occasions. The HVDC system is a very large and complex system. Various faults are inevitable during operation. When the system fails, it is necessary to identify the fault in time and organize personnel to repair it to avoid the bad situation of the entire system being shut down.

[0003] Among the equipment in the substation, the data of some equipment (such as transformers, circuit breakers, etc.) has highly nonlinear characteristics. When faced with data with certain nonlinear characteristics, existing fault diagnosis methods may not be able to effectively capture key information through dimensionality reduction methods, resulting in misjudgment or missed judgment. Summary of the invention

[0004] In view of the defects existing in the prior art, the present application provides a fault detection method, system and storage medium for a substation to solve the problem that the existing fault detection methods are difficult to capture key information, misjudgment or missed judgment.

[0005] To achieve the above objectives, the present application provides a fault detection method for a substation, comprising the following steps:

[0006] S1. Obtain equipment failure data of the substation and pre-process the equipment failure data;

[0007] S2. Select a kernel function and construct a kernel matrix based on the preprocessed device failure data;

[0008] S3, performing centralization processing on the kernel matrix to obtain a centralized kernel matrix;

[0009] S4, calculating the eigenvalues ​​of the centralized kernel matrix and the eigenvectors corresponding to the eigenvalues;

[0010] S5, sorting the eigenvalues ​​and the eigenvectors, and intercepting part of the eigenvectors according to a preset threshold value to form a vector matrix;

[0011] S6. Based on the vector matrix, calculate the contribution rate of each component in the vector matrix;

[0012] S7. Perform PCA and KPCA analysis on the substation according to a preset contribution rate threshold to determine equipment failure characteristics of the substation.

[0013] Furthermore, the kernel function is a Gaussian kernel function and satisfies the following relationship:

[0014]

[0015] Among them, k(x i ,x j ) is the kernel function, x i is the faulty equipment of the substation, x j is the fault feature of the equipment fault in the substation, i is the number of faulty equipment in the substation, j is the number of fault features of the faulty equipment, and σ is the standard deviation.

[0016] Furthermore, the core matrix and the centralized core matrix satisfy the following relationship:

[0017] K'=K-1 m K-K1 m +1 m K1 m ;

[0018] Wherein, K' is the centralized kernel matrix, K is the kernel matrix determined based on the kernel function and the equipment failure data of the substation, 1 m is a vector of all 1s, and m is the number of fault samples of the substation.

[0019] Furthermore, the feature vector and the preset threshold satisfy the following relationship:

[0020]

[0021] Among them, λ i is the eigenvalue of the covariance matrix, i is the number of eigenvalues ​​of the covariance matrix, k is the number of eigenvectors of the intercepted part, n is the number of fault features, and t is a preset threshold.

[0022] Furthermore, the contribution rate and eigenvalue of each component in the dimension reduction matrix satisfy the following relationship:

[0023]

[0024] Among them, λ iis the eigenvalue of the covariance matrix, i is the number of eigenvalues ​​of the covariance matrix, n is the number of fault features, y i is the contribution rate of the i-th fault feature.

[0025] Furthermore, the equipment fault data includes switch status, protection action signal, timing signal, inspection auxiliary equipment data, online detection signal and main equipment information.

[0026] Furthermore, S1, the step of obtaining equipment fault data of the substation and preprocessing the equipment fault data specifically includes:

[0027] Set the data collection time window;

[0028] When a fault in the substation satisfies a fault start condition, the equipment fault data is acquired based on the data acquisition time window, and the equipment fault data is grouped;

[0029] The device fault data after being grouped is corrected.

[0030] The present application also provides a fault detection system for a substation, the fault detection system comprising:

[0031] An acquisition module, used for acquiring equipment failure data of the substation and preprocessing the equipment failure data;

[0032] A kernel function selection module, used for constructing a kernel matrix based on the preprocessed equipment fault data;

[0033] A centralized processing module, used for performing centralized processing on the core matrix to obtain a centralized core matrix;

[0034] A feature calculation module, used to calculate the eigenvalues ​​of the centralized kernel matrix and the eigenvectors corresponding to the eigenvalues;

[0035] An interception module, used for sorting the eigenvalues ​​and the eigenvectors, and intercepting part of the eigenvectors according to a preset threshold value to form a vector matrix;

[0036] The analysis and determination module is used to calculate the contribution rate of each component in the vector matrix based on the vector matrix, perform PCA and KPCA analysis on the substation according to a preset contribution rate threshold, and determine the equipment fault characteristics of the substation.

[0037] The present application also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the fault detection method for a substation as described in any one of claims 1 to 7 is implemented.

[0038] Compared with the prior art, the advantages of this application are:

[0039] The present application selects a kernel function, constructs a kernel matrix based on the preprocessed equipment failure data, centralizes the kernel matrix to obtain a centralized kernel matrix, calculates the eigenvalues ​​of the centralized kernel matrix and the eigenvectors corresponding to the eigenvalues, sorts the eigenvalues ​​and the eigenvectors, intercepts part of the eigenvectors according to a preset threshold to form a vector matrix, calculates the contribution rate of each component in the vector matrix, performs PCA and KPCA analysis on the substation according to a preset contribution rate threshold, determines the equipment failure characteristics of the substation, thereby screening out key information and reducing the risk of misjudgment and missed judgment. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0041] Figure 1 A schematic diagram of a flow chart of a fault detection method for a substation according to an embodiment of the present application;

[0042] Figure 2 A schematic diagram of a fault detection method for a substation according to an embodiment of the present application;

[0043] Figure 3 A schematic diagram of a scalable and adjustable time window according to an embodiment of the present application. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0045] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0046] In the description of the present application, the terms "first / second" involved are merely used to distinguish similar objects and do not represent a specific order for the objects. It can be understood that "first / second" can be interchanged with a specific order or sequence where permitted, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0047] It should be understood that the orientation or position relationship is based on the orientation or position relationship shown in the drawings. These orientation terms are only for the convenience of describing the present application 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 cannot be understood as limiting the present application.

[0048] Power system fault diagnosis refers to the use of knowledge of the power system and its protection devices and information such as relay protection to accurately identify the location, type and malfunction of faulty components, thereby laying the foundation for taking measures to repair and improve existing deficiencies and improving the operation level of the power system.

[0049] In order to help the operation and maintenance personnel in the substation quickly judge and locate the cause of the fault, the automatic identification technology of the key information of the equipment fault is studied. The key to the identification of key fault information is feature extraction. The most relevant features are extracted from the fault phenomenon, and quantitative and qualitative analysis is performed on them to establish a feature library, which involves various disciplines such as electronic technology, statistical mathematics, modern control theory, signal processing and pattern recognition, computer science, artificial intelligence, etc.

[0050] According to the differences in the sources and manifestations of fault feature extraction, the existing key fault information identification technologies can be divided into the following three categories: mathematical model-based methods, empirical knowledge-based methods, and data-driven methods. The mathematical model-based method is to understand the change law of equipment parameters under different working conditions and the range that should be achieved in theory by establishing an accurate mathematical model of the equipment. By understanding the relationship between various abnormal conditions in the process and model parameters and states, it is used to judge the real-time operating status of the equipment.

[0051] The method based on empirical knowledge mainly refers to the expert system method, which refers to a program system with expert-level problem-solving capabilities in a specific field. It uses a large number of rules to express the experience and professional knowledge accumulated by experts over the years, and gradually uses rules for reasoning to determine the operating status of equipment. It is an intelligent computer program.

[0052] Data-driven methods are more capable of fully extracting the fault signs, causal logic and other relationships implicit in the data. They do not need to establish precise mathematical models and require less prior knowledge. They can be divided into three categories: methods based on statistical analysis, signal analysis and artificial intelligence.

[0053] Statistical analysis mainly includes PCA principal component analysis, Fisher analysis discriminant method (FDA) and PLS partial least squares method. PCA principal component analysis is a commonly used feature extraction method that can eliminate data correlation and reduce data dimension. Fisher analysis discriminant method (FDA) and PCA have some similarities. They are both linear dimensionality reduction methods that can project high-dimensional training data into a few vector directions to characterize the system operation characteristics in the data. However, FDA takes into account the category information of the sample, so it can be used to identify the category to which the individual belongs. In addition, partial least squares (PLS) is a multivariate correction method based on factor analysis, and its mathematical basis is similar to PCA. However, PLS goes a step further than PCA. It introduces a new matrix Y, which contains the fault category information corresponding to the sample matrix X. Unlike PCA, the PLS method simultaneously performs principal component decomposition on the fault category matrix Y and the sample matrix X, and maximizes their covariance.

[0054] Signal analysis is often more suitable for the identification of shaft rubbing faults, and mainly includes wavelet transform, Hilbert-Huang transform (HHT) and spectral analysis. Wavelet transform belongs to time-frequency analysis. Compared with Fourier transform, it is more suitable for analyzing non-stationary signals because it can provide time and frequency information at the same time without losing time or frequency resolution. HHT transform is mainly used to analyze nonlinear and non-stationary signals, especially when the signal frequency changes with time. Spectral analysis is used to analyze the frequency domain characteristics of signals. Its basic idea is to convert the signal from the time domain (time domain) to the frequency domain (frequency domain) in order to better understand the frequency components and spectral structure of the signal. Spectral analysis can help identify the periodicity or frequency components in the signal and reveal the implicit laws and characteristics in the signal.

[0055] The AI-based methods mainly utilize the research results in the field of AI, including fuzzy logic, neural networks and other methods. Some commonly used methods for feature extraction include autoencoders and convolutional neural networks. Autoencoders are an artificial neural network that is often used for feature extraction and data reconstruction. The basic idea is to train the network to learn how to encode and decode the input data, thereby achieving feature extraction. Convolutional neural networks are a type of neural network specifically designed for processing grid data (such as images). It extracts the spatial features of the input data through convolutional layers and pooling layers, and then performs classification or regression tasks through fully connected layers and activation functions. During the training process, CNN will automatically learn the features in the data, thereby achieving the purpose of feature extraction.

[0056] Figure 1 A schematic diagram of a flow chart of a fault detection method for a substation according to an embodiment of the present application. Figure 2is a schematic diagram of a fault detection method for a substation according to an embodiment of the present application, Figure 3 FIG. 1 is a schematic diagram of a scalable and adjustable time window according to an embodiment of the present application. Figure 1-Figure 3 As shown, the embodiment of the present application provides a fault detection method for a substation, comprising the following steps:

[0057] S1. Obtain equipment failure data of the substation and pre-process the equipment failure data;

[0058] S2. Select a kernel function and construct a kernel matrix based on the preprocessed device failure data;

[0059] S3, performing centralization processing on the kernel matrix to obtain a centralized kernel matrix;

[0060] S4, calculating the eigenvalues ​​of the centralized kernel matrix and the eigenvectors corresponding to the eigenvalues;

[0061] S5, sorting the eigenvalues ​​and the eigenvectors, and intercepting part of the eigenvectors according to a preset threshold value to form a vector matrix;

[0062] S6. Based on the vector matrix, calculate the contribution rate of each component in the vector matrix;

[0063] S7. Perform PCA and KPCA analysis on the substation according to a preset contribution rate threshold to determine equipment failure characteristics of the substation.

[0064] Specifically, preprocessing may include filtering, denoising, normalizing and other operations on the data to improve data quality, obtain equipment fault data of the substation, and preprocess the equipment fault data. The kernel function can select radial basis kernel function, polynomial kernel function, linear kernel function, etc. Centralization processing can eliminate redundant information in the kernel matrix and improve the accuracy of fault diagnosis. The eigenvalues ​​and eigenvectors are obtained by solving the characteristic equation, sorting according to the size of the eigenvalues, selecting the eigenvectors corresponding to the first few largest eigenvalues, and calculating the contribution rate of each component in the vector matrix based on the vector matrix. The contribution rate can indicate the contribution degree of each eigenvector component to fault diagnosis. PCA and KPCA analysis can further extract fault features and reduce the dimension of the data. Among them, according to the preset contribution rate threshold, the equipment fault features of the substation are determined. The contribution rate threshold can be set according to the actual application requirements. The larger the threshold, the more obvious the fault features are identified. It should be noted that through PCA and KPCA analysis, the fault characteristics of the equipment can be effectively represented, the complexity of fault diagnosis can be reduced, the diagnostic accuracy can be improved, the contribution rate threshold can be set according to actual needs, and the fault diagnosis results can be flexibly adjusted, thereby filtering out redundant information in the key fault information and screening out effective information, making the fault diagnosis results more accurate and reducing the risk of misjudgment.

[0065] In some embodiments, the kernel function is a Gaussian kernel function and satisfies the following relationship:

[0066]

[0067] Among them, k(x i ,x j ) is the kernel function, x i is the faulty equipment of the substation, x j is the fault feature of the equipment fault in the substation, i is the number of faulty equipment in the substation, j is the number of fault features of the faulty equipment, and σ is the standard deviation.

[0068] Specifically, a Gaussian kernel function is selected as the kernel function, and a kernel matrix is ​​constructed based on the preprocessed equipment fault data. The Gaussian kernel function has good locality and adaptability to nonlinear relationships, and can capture local features and complex structures in fault data, thereby improving the accuracy of fault diagnosis, adapting to nonlinear relationships, and improving the reliability of fault diagnosis.

[0069] In some embodiments, the core matrix and the centralized core matrix satisfy the following relationship:

[0070] K'=K-1 m K-K1 m +1 m K1 m ;

[0071] Wherein, K' is the centralized kernel matrix, K is the kernel matrix determined based on the kernel function and the equipment failure data of the substation, 1 m is a vector of all 1s, and m is the number of fault samples of the substation.

[0072] Specifically, adjustments and optimizations are made according to different application scenarios and kernel functions. For example, for the Gaussian kernel function, a matrix with all 1s can be used for calculation and normalization. The centralization matrix can be further adjusted and optimized according to the needs of actual problems to improve the performance and effect of the algorithm.

[0073] In some embodiments, the feature vector and the preset threshold satisfy the following relationship:

[0074]

[0075] Among them, λ i is the eigenvalue of the covariance matrix, i is the number of eigenvalues ​​of the covariance matrix, k is the number of eigenvectors of the intercepted part, n is the number of fault features, and t is a preset threshold.

[0076] Specifically, the KPCA method is applied to the substation, and the eigenvectors corresponding to the k largest eigenvalues ​​are selected as ω1, ω2, ..., ω k , according to the corresponding eigenvalue size, the matrix W is formed from top to bottom in rows kn The eigenvalue selection method refers to the above relationship. The larger t is, the more eigenvalues ​​are retained. The selection can be made according to the complexity of the substation fault information.

[0077] In some embodiments, the contribution rate and eigenvalue of each component in the dimension reduction matrix satisfy the following relationship:

[0078]

[0079] Among them, λ i is the eigenvalue of the covariance matrix, i is the number of eigenvalues ​​of the covariance matrix, n is the number of fault features, y i is the contribution rate of the i-th fault feature.

[0080] Specifically, the above formula is used to calculate the contribution rates of all principal component vectors in turn:

[0081] All principal component vectors are sorted in descending order, and then the optimal fault characteristics of the smart substation are selected based on the principle that the cumulative contribution rate reaches 90%.

[0082] In some embodiments, the device fault data includes switch status, protection action signal, timing signal, inspection auxiliary equipment data, online detection signal and main equipment information.

[0083] Specifically, the fault data of various equipment in the substation, such as current, voltage, power, temperature, etc., are collected through sensors, protection devices and other monitoring equipment. Then the collected fault data is preprocessed, including filtering, denoising, normalization and other operations, to eliminate interference and outliers in the data and improve the accuracy of fault diagnosis.

[0084] In some embodiments, S1, the step of obtaining equipment fault data of a substation and preprocessing the equipment fault data specifically includes:

[0085] Set the data collection time window;

[0086] When a fault in the substation satisfies a fault start condition, the equipment fault data is acquired based on the data acquisition time window, and the equipment fault data is grouped;

[0087] The device fault data after being grouped is corrected.

[0088] Specifically, a data collection time window is set. For example, the data collection time window can be set to within 3 minutes after the fault occurs. When the equipment in the substation fails and meets the fault start condition, the equipment fault data is obtained based on the data collection time window. The size of the collection window involves the grouping of fault alarms. t1 represents the moment when the fault start condition is met after the fault, and then a fixed time window is collected until t2, and then it is a floating collection time. The floating collection time is calculated in the following way: if Δalarm / Δt (the number of alarms in the t time interval) is greater than a certain threshold, the floating time window is extended, otherwise, the floating time window stops.

[0089] The embodiment of the present application also provides a fault detection system for a substation, the fault detection system comprising:

[0090] An acquisition module, used for acquiring equipment failure data of the substation and preprocessing the equipment failure data;

[0091] A kernel function selection module, used for constructing a kernel matrix based on the preprocessed equipment fault data;

[0092] A centralized processing module, used for performing centralized processing on the core matrix to obtain a centralized core matrix;

[0093] A feature calculation module, used to calculate the eigenvalues ​​of the centralized kernel matrix and the eigenvectors corresponding to the eigenvalues;

[0094] An interception module, used for sorting the eigenvalues ​​and the eigenvectors, and intercepting part of the eigenvectors according to a preset threshold value to form a vector matrix;

[0095] The analysis and determination module is used to calculate the contribution rate of each component in the vector matrix based on the vector matrix, perform PCA and KPCA analysis on the substation according to a preset contribution rate threshold, and determine the equipment fault characteristics of the substation.

[0096] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned substation fault detection method is implemented.

[0097] It should be noted that the storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a ROM (Read-Only Memory), a RAM (Random Access Memory), a magnetic disk or an optical disk.

[0098] It will be appreciated by those skilled in the art that all or some of the steps, systems, and functional modules / units in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. In a hardware implementation, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed by several physical components in cooperation. Some 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 storage medium, which may include a computer-readable storage medium (or a non-transitory medium) and a communication medium (or a temporary medium).

[0099] As is known to those of ordinary skill in the art, the term computer-readable storage media includes volatile and nonvolatile, 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-readable storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, it is known to those of ordinary skill in the art that communication media typically contain computer-readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transmission mechanism, and may include any information delivery medium.

[0100] Exemplarily, the computer-readable storage medium may be an internal storage unit of the electronic device of the aforementioned embodiment, such as a hard disk or memory of the electronic device. The computer-readable storage medium may also be an external storage device of the electronic device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device.

[0101] The above is only a specific implementation of the present application, so that those skilled in the art can understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest range consistent with the principles and novel features applied for herein.

Claims

1. A fault detection method for a substation, characterized in that: The steps include: S1. Obtain equipment failure data of the substation and pre-process the equipment failure data; S2. Select a kernel function and construct a kernel matrix based on the preprocessed device failure data; S3, performing centralization processing on the kernel matrix to obtain a centralized kernel matrix; S4, calculating the eigenvalues ​​of the centralized kernel matrix and the eigenvectors corresponding to the eigenvalues; S5, sorting the eigenvalues ​​and the eigenvectors, and intercepting part of the eigenvectors according to a preset threshold value to form a vector matrix; S6. Based on the vector matrix, calculate the contribution rate of each component in the vector matrix; S7. Perform PCA and KPCA analysis on the substation according to a preset contribution rate threshold to determine equipment failure characteristics of the substation.

2. The fault detection method for a substation according to claim 1, characterized in that: The kernel function is a Gaussian kernel function and satisfies the following relationship: Among them, k(x i ,x j ) is the kernel function, x i is the faulty equipment of the substation, x j is the fault feature of the equipment fault in the substation, i is the number of faulty equipment in the substation, j is the number of fault features of the faulty equipment, and σ is the standard deviation.

3. The fault detection method for a substation according to claim 2, characterized in that: The core matrix and the centralized core matrix satisfy the following relationship: K'=K-1 m K-K1 m +1 m K1 m ; Wherein, K' is the centralized kernel matrix, K is the kernel matrix determined based on the kernel function and the equipment failure data of the substation, 1 m is a vector of all 1s, and m is the number of fault samples of the substation.

4. The fault detection method for a substation according to claim 1, characterized in that: The feature vector and the preset threshold satisfy the following relationship: Among them, λ i is the eigenvalue of the covariance matrix, i is the number of eigenvalues ​​of the covariance matrix, k is the number of eigenvectors of the intercepted part, n is the number of fault features, and t is a preset threshold.

5. The fault detection method for a substation according to claim 4, characterized in that: The contribution rate and eigenvalue of each component in the dimension reduction matrix satisfy the following relationship: Among them, λ i is the eigenvalue of the covariance matrix, i is the number of eigenvalues ​​of the covariance matrix, n is the number of fault features, y i is the contribution rate of the i-th fault feature.

6. The fault detection method for a substation according to claim 1, characterized in that: The equipment fault data includes switch status, protection action signal, timing signal, inspection auxiliary equipment data, online detection signal and main equipment information.

7. The fault detection method for a substation according to claim 1, characterized in that: S1, the step of obtaining equipment failure data of the substation and preprocessing the equipment failure data specifically includes: Set the data collection time window; When a fault in the substation satisfies a fault start condition, the equipment fault data is acquired based on the data acquisition time window, and the equipment fault data is grouped; The device fault data after being grouped is corrected.

8. A fault detection system for a substation, characterized in that: The fault detection system comprises: An acquisition module, used for acquiring equipment fault data of the substation and preprocessing the equipment fault data; A kernel function selection module, used for constructing a kernel matrix based on the preprocessed equipment fault data; A centralized processing module, used for performing centralized processing on the core matrix to obtain a centralized core matrix; A feature calculation module, used to calculate the eigenvalues ​​of the centralized kernel matrix and the eigenvectors corresponding to the eigenvalues; An interception module, used for sorting the eigenvalues ​​and the eigenvectors, and intercepting part of the eigenvectors according to a preset threshold value to form a vector matrix; The analysis and determination module is used to calculate the contribution rate of each component in the vector matrix based on the vector matrix, perform PCA and KPCA analysis on the substation according to a preset contribution rate threshold, and determine the equipment fault characteristics of the substation.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for detecting a fault in a substation according to any one of claims 1 to 7 is implemented.

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