A fault detection method and system for high voltage test equipment

Abstract

The present invention relates to the field of electrical equipment fault detection, and specifically to a fault detection method and system for high-voltage test equipment. The method preliminarily identifies time periods where transient faults may exist through frequency domain analysis, extracts voltage transient features in these time periods in combination with time domain analysis, and confirms faults through current features. The feature vector at the moment of fault is constructed for dimensionality reduction processing and used for classification model training. Finally, based on the trained classification model, real-time fault detection of DC high-voltage generators is achieved. The present invention realizes real-time transient fault detection of DC high-voltage generators by combining frequency domain and time domain analysis, extracting voltage and current features and SVM classification.

Description

Technical Field

[0001] The invention relates to the field of electrical equipment fault detection, and in particular to a fault detection method and system for high-voltage test equipment. Background Art

[0002] The main functions of high-voltage test equipment include testing the insulation strength, withstand voltage and anti-electric shock performance of the equipment to ensure that it can withstand various voltage stresses in actual operation. Among them, the DC high voltage generator is a crucial device, and its core components usually include transformers, rectifier circuits, filter circuits and control systems. Compared with AC high voltage generators, DC high voltage generators can more effectively identify minor defects and aging problems in insulating materials because the DC electric field can more realistically simulate the voltage stress state of the equipment in actual operation.

[0003] However, various problems may occur in the actual operation of DC high-voltage generators, such as unstable output voltage, overheating of equipment, abnormal discharge and other faults. These faults not only affect the accuracy of the test results, but may also damage the equipment itself and even cause safety accidents. Among them, unstable output voltage is a common fault of DC high-voltage generators. Frequency domain analysis is an important means for detecting voltage fluctuation faults in the prior art. It usually converts the voltage signal in the time domain into the frequency domain signal through fast Fourier transform FFT to identify the frequency components and abnormal patterns in the voltage fluctuation.

[0004] For example, a Chinese patent application document with publication number CN118534268A discloses an arc fault identification method, device, equipment and storage medium, which extracts frequency domain feature information of the arc signal to be tested as input feature information; inputs the input feature information into arc fault identification models of at least two algorithm types to obtain classification results, and the classification results constitute a classification result set; and determines the fault detection result of the arc data to be tested according to the distribution of the classification results in the classification result set. However, this method still has some technical defects, such as the common frequency domain transformation can usually only perform spectrum conversion on all data in the entire signal window, which makes it unable to effectively capture and identify sudden transient changes. Summary of the invention

[0005] In response to the above-mentioned problem of being unable to effectively capture transient changes, in the first aspect, the present invention proposes a fault detection method for high-voltage test equipment, including: obtaining a voltage sequence and a current sequence of a DC high-voltage generator; dividing the voltage sequence using windows; obtaining the high-frequency component of each window based on frequency domain analysis, and recording the window with a high-frequency component greater than a set high-frequency threshold as a suspected fault window; obtaining the time domain transient characteristics of the voltage at each sampling moment in each suspected fault window, the time domain transient characteristics are positively correlated with the voltage mean in the corresponding suspected fault window and the difference between the voltage at the corresponding sampling moment, are positively correlated with the second-order derivative of the voltage at the corresponding sampling moment, and are positively correlated with the highest voltage in the corresponding suspected fault window. The time difference between the highest voltage peak and the lowest voltage trough is negatively correlated; the sampling moment corresponding to the time-domain transient feature greater than the set threshold is recorded as the suspected fault sampling moment; the normal current interval is obtained based on the peak mean and the trough mean of the current sequence; in response to the current at the suspected fault sampling moment being in the normal current interval, the corresponding suspected fault sampling moment is determined to be the transient fault sampling moment; the time-domain transient features of the voltage at the normal sampling moment and the transient fault sampling moment are obtained to obtain a feature matrix and add labels; the real-time voltage is obtained, the time-domain transient features of the real-time voltage are calculated and input into the trained classification model, and in response to the output result being a fault label, the staff is notified to perform maintenance.

[0006] The present invention uses frequency domain analysis to preliminarily identify the time period when the fault may exist, then locates the time point when the fault may occur by combining the analysis of time domain transient characteristics, and then performs secondary verification in combination with current characteristics to ensure that the fault signal is distinguished from the fluctuations in the normal operation of the equipment, and obtains the precise time when the transient fault occurs. Finally, the classification model established by historical data is used to achieve real-time detection. Compared with the prior art, this method can more accurately identify the time point of transient faults during detection, has higher real-time performance and reliability, and greatly improves the stability of system operation, especially in complex signal environments.

[0007] Further, obtaining the high-frequency components of each window based on frequency domain analysis also includes: using discrete wavelet transform DWT to decompose the voltage sequence of each window into multiple layers; obtaining the first In the window The high frequency energy ratio of the layer is recorded as , the specific method of obtaining is:

[0008]

[0009]

[0010] in Indicates After the window wavelet transform Layer detail factor; Indicates In the window sampling points; Indicates The total number of decomposition layers of wavelet transform within a window; Indicates After the window wavelet transform The high-frequency energy proportion of the layer; the high-frequency energy proportion is the high-frequency component of the window.

[0011] The voltage sequence is decomposed in multiple layers through discrete wavelet transform (DWT) to extract the proportion of high-frequency energy. This method can effectively separate the high-frequency components in the signal, especially has a good ability to capture sudden high-frequency fault signals. Compared with the traditional frequency domain transformation algorithm, discrete wavelet transform can more accurately reflect the high-frequency anomalies in the local voltage signal and improve the screening accuracy of suspected fault windows.

[0012] Furthermore, the window whose high-frequency component is greater than the set high-frequency threshold is recorded as a suspected fault window, and the method further includes: obtaining the high-frequency energy proportion of the highest layer in all time windows of the historical normal voltage sequence; recording the mean and standard deviation of the high-frequency energy proportion of the highest layer as and ; The high frequency threshold is recorded as , the specific method of obtaining is:

[0013]

[0014] in Represents a parameter adjustment coefficient; in response to the presence of a highest layer where the proportion of high-frequency energy is greater than the high-frequency threshold, the corresponding window is recorded as a suspected fault window.

[0015] Furthermore, the calculation method of the time domain transient characteristics is specifically as follows:

[0016]

[0017] in Indicates any suspected fault window Neidi Time domain transient characteristics of voltage at moment ; Indicates any suspected fault window Neidi The second derivative of the voltage at time t; Indicates any suspected fault window The first Voltage at the moment; It means the The suspected failure window at the time The mean voltage within Indicates any suspected fault window The time difference between the highest voltage peak and the lowest voltage trough.

[0018] By calculating the time-domain transient characteristics of the voltage, the changes in the fault signal are further amplified. Through comprehensive judgment of multiple factors such as the voltage mean, second-order derivative, and the time difference between the peak and the trough, the time-domain analysis effectively extracts the significant characteristics of the fault point, and the combination of frequency-domain information further improves the accuracy of fault location.

[0019] Furthermore, the sampling moment corresponding to the time-domain transient feature greater than the set threshold is recorded as the suspected fault sampling moment, and it also includes: using the maximum between-class variance method to determine the adaptive threshold for all the acquired time-domain transient features; in response to the existence of a time-domain transient feature greater than the adaptive threshold, the corresponding sampling moment is recorded as the suspected fault sampling moment.

[0020] Furthermore, the time-domain transient characteristics of the voltage at all sampling moments are obtained to obtain a characteristic matrix and add labels, which also includes: obtaining the time-domain transient characteristics of the voltage at all transient fault sampling moments to construct a first matrix, and adding a fault label after each characteristic vector in the first matrix; obtaining a historical normal voltage sequence of a set length, and obtaining the time-domain transient characteristics of the voltage at all sampling moments in the historical normal voltage sequence to construct a second matrix, and adding a normal label after each characteristic vector in the second matrix; merging the first matrix and the second matrix to obtain a characteristic matrix.

[0021] Furthermore, the feature matrix is ​​input into a classification model for training to obtain a trained classification model, which also includes: using PCA principal component analysis to reduce the dimension of the first matrix and the second matrix; inputting the reduced labeled first matrix and second matrix into an SVM support vector machine for training to obtain a trained SVM classification model.

[0022] By reducing the dimension of the feature matrix through PCA principal component analysis, data redundancy is effectively reduced, overfitting is avoided, and the training speed of the classifier is improved. Combined with SVM support vector machine for model training, the model has stronger generalization ability in complex high-dimensional data environment.

[0023] Furthermore, obtaining the voltage sequence and current sequence of the DC high-voltage generator also includes: installing a high-voltage mutual inductance sensor and a Hall current sensor at the output end of the DC high-voltage generator respectively; using an ADC module to connect the two sensors, collecting the output voltage and current of the DC high-voltage generator and storing them in a database; obtaining the voltage sequence and current sequence of the DC high-voltage generator from the database.

[0024] Furthermore, it also includes performing data cleaning operations on the data stored in the database, and the data cleaning includes mean filtering noise reduction and Z-Score algorithm standardization.

[0025] Through data cleaning such as mean filtering noise reduction and Z-Score standardization, noise and abnormal data can be effectively removed, ensuring the reliability and consistency of the data, and providing a higher quality data foundation for subsequent analysis.

[0026] In a second aspect, the present invention provides a fault detection system for high voltage test equipment, comprising a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the fault detection method for high voltage test equipment of the present invention is implemented.

[0027] The technical effects of the present invention are:

[0028] The present invention realizes accurate detection of transient faults by combining frequency domain and time domain analysis and combining voltage and current signal characteristics. The method first identifies the suspected fault window through frequency domain analysis, then extracts the suspected fault time point using time domain transient characteristics, and finally confirms the fault through current characteristics. Real-time monitoring and alarm are achieved through a classification model trained with historical data. Compared with the prior art, the present invention has higher detection accuracy and real-time performance in complex signal environments, and improves the safety and stability of equipment operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0030] Figure 1 is a flow chart schematically showing a fault detection method of a high voltage test equipment in an embodiment of the present invention;

[0031] Figure 2 is a grayscale schematic diagram schematically showing a DC high voltage generator in an embodiment of the present invention;

[0032] Figure 3 It is a block diagram schematically showing the structure of a fault detection system of a high-voltage test equipment in an embodiment of the present invention. DETAILED DESCRIPTION

[0033] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0034] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0035] Fault detection method embodiment of high voltage test equipment:

[0036] like Figure 1 As shown, a fault detection method for high-voltage test equipment of the present invention comprises:

[0037] S1. Collect the historical output voltage and current of the DC high voltage generator.

[0038] like Figure 2 As shown, the DC high voltage generator is mainly used to detect the insulation of some power equipment. If the output voltage is unstable when it is working, it may affect the detection result of the power equipment. Especially when the instantaneous voltage is too large, it may damage the power equipment being tested and cause unnecessary losses.

[0039] In this embodiment, a high-voltage mutual inductance sensor and a Hall current sensor are first installed at the output end of the DC high-voltage generator; then an ADC module with a sampling rate of 10kHz is used to connect the two sensors to collect the real-time output voltage and current of the DC high-voltage generator; finally, the voltage and current at each sampling moment are stored in the database for subsequent analysis and model training. In this embodiment, in addition to the sampling rate of 10kHz, the implementer can also select 8kHz, 12kHz or 14kHz according to actual conditions to ensure that transient fault signals can be captured. It should be noted that the data stored in the database need to undergo data cleaning operations, including mean filtering noise reduction and Z-Score algorithm standardization. The above two preprocessing algorithms belong to well-known technologies and will not be repeated here.

[0040] Through data cleaning such as mean filtering noise reduction and Z-Score standardization, noise and abnormal data can be effectively removed, ensuring the reliability and consistency of the data, and providing a higher quality data foundation for subsequent analysis.

[0041] S2. Perform frequency domain analysis on the voltage to obtain a suspected fault window.

[0042] Since the sampling frequency in this embodiment is relatively high and the working time of the DC high voltage generator is usually not less than 30 minutes each time, the voltage and current data containing transient faults for 30 consecutive minutes are selected from the database to obtain the voltage sequence containing transient faults. And the current sequence ; Then obtain the normal operation data of the DC high voltage generator without failure for 30 consecutive minutes from the database and extract the normal voltage sequence And the normal current sequence .

[0043] When a transient fault occurs in a DC high-voltage generator, it usually causes rapid changes and spikes in the voltage signal, thereby introducing some high-frequency components in the output voltage signal and increasing the high-frequency energy. Therefore, in this embodiment, the discrete wavelet transform DWT can be used to decompose the voltage sequence. The input of the algorithm is the voltage sequence, the wavelet function type and the number of decomposition layers. The wavelet function can be selected as db4, which belongs to one of the Dobesi wavelet series and has good time-frequency localization characteristics, and is more suitable for transient signal analysis; and the number of decomposition layers in this embodiment can select the empirical value 4, and the implementer can also select other values ​​according to actual conditions. The output of the algorithm is the approximation coefficient and detail coefficient of each layer after decomposition, where the approximation coefficient captures the low-frequency trend part of the signal, and the detail coefficient corresponds to the highest frequency signal component, which can best reflect the high-frequency changes of transient faults.

[0044] The voltage sequence is decomposed in multiple layers through discrete wavelet transform (DWT) to extract the proportion of high-frequency energy. This method can effectively separate the high-frequency components in the signal, especially has a good ability to capture sudden high-frequency fault signals. Compared with the traditional frequency domain transformation algorithm, discrete wavelet transform can more accurately reflect the high-frequency anomalies in the local voltage signal and improve the screening accuracy of suspected fault windows.

[0045] For normal voltage sequence In this embodiment, a time window with a length of 100 milliseconds as an empirical value can be selected for division, so each window contains The above discrete wavelet transform DWT algorithm is used to decompose the voltage time series in each time window to obtain the normal voltage sequence The detail coefficients of each time window after wavelet transformation are calculated, and the proportion of high-frequency energy in the highest layer of the decomposition result is calculated. The following are some examples: Middle After wavelet transformation of the time window The detail factor of the layer is , then After wavelet transformation of the time window The high-frequency energy ratio of the layer is:

[0046]

[0047]

[0048] in Indicates After wavelet transformation of the time window The high-frequency energy of the layer; Indicates In the time window sampling points; Indicates The total number of decomposition layers of wavelet transform in a time window is 4, which is an empirical value in this implementation; Indicates After wavelet transformation of the time window The high-frequency energy ratio of the layer.

[0049] Since the highest frequency component in the signal is mainly analyzed in this embodiment, the high-frequency energy proportion of the highest layer, i.e., the 4th layer after wavelet transformation, is obtained here Here we first calculate the normal voltage sequence The mean and standard deviation of the high-frequency energy proportion of the 4th layer after wavelet transformation in all time windows are recorded as and ; Then set the energy percentage threshold , the specific calculation method is:

[0050]

[0051] in is the parameter adjustment coefficient, and in this embodiment, the empirical value 3 can be taken. That is, when the voltage sequence containing transient faults After the time window is divided, if the high-frequency energy proportion of the fourth layer after wavelet transformation in a certain time window is greater than the energy proportion threshold , it means that there may be a transient fault in the time window. Example explanation: For a voltage sequence containing a transient fault , there is a time window , the high-frequency energy proportion of the 4th layer after wavelet transform is , and there is , then the time window There may be a transient fault within this time window, and this time window is recorded as a suspected fault window. It should be noted that the above voltage sequence The time window division method and the calculation method of the high-frequency energy ratio after the time window wavelet transform are the same as those of the normal voltage sequence. same.

[0052] In summary, the voltage series can be calculated The high-frequency energy proportion of the 4th layer after wavelet transformation in all time windows is compared with the energy proportion threshold By comparison, the voltage sequence containing transient faults is obtained. All suspected fault windows in. In this embodiment, the high-frequency energy ratio is used as a comparison indicator of the suspected fault window, which has better adaptability than using high-frequency energy alone, mainly reflected in: when the load of the test object of the DC voltage generator changes, the output voltage will also change accordingly, and the voltage change at this time is normal, but if only the high-frequency energy of the current voltage change and the high-frequency energy of the normal voltage are evaluated, the voltage change time window caused by the load change may be misjudged as a suspected fault window; and by comparing the high-frequency energy ratio of each time window, even if the voltage changes due to load changes, its high-frequency energy ratio will hardly change significantly, so it is possible to more accurately obtain the voltage change window caused by transient faults, that is, the suspected fault window.

[0053] S3. Perform voltage time domain analysis based on the suspected fault window to obtain the suspected fault sampling time.

[0054] Frequency domain analysis performs well in identifying high-frequency energy anomalies, but it can only obtain the time period when energy anomalies occur, and the time location of transient faults is not accurate enough. Time domain analysis can further amplify and refine these transient features, ensure a low missed detection rate, and improve the sensitivity and accuracy of overall detection.

[0055] In step S2, the voltage sequence containing the transient fault is obtained. For each suspected fault window, the mean value of the voltage in the window can be calculated first, which is used to detect the deviation degree of the voltage in the window later; then the time domain transient characteristics of the output voltage at each sampling time are obtained. The specific acquisition process is as follows:

[0056] For any suspected fault window The first At this moment, first obtain its transient amplitude ,have:

[0057]

[0058] in Indicates a suspected failure window The first Voltage at the moment; It means the The suspected failure window at the time Then for the suspected fault window , obtain the maximum peak and minimum trough of the voltage in the window, which are recorded as and , calculate the peak-trough time difference ,have:

[0059]

[0060] in Indicates The suspected fault window at the time Maximum voltage peak The corresponding moment; It means the The suspected fault window at the time Minimum voltage valley The corresponding moment; is the normalization function. Then for the suspected fault window The first Voltage at the moment , calculate its voltage change acceleration ,have:

[0061]

[0062] Finally, get any suspected fault window Neidi Time domain transient characteristics of voltage at time , specifically:

[0063]

[0064] in Indicates a suspected failure window Neidi The voltage change acceleration of the voltage at the moment is greater, indicating that the DC high voltage generator is The more likely the output voltage at a moment is to change and the shorter the time of change is, the more likely a transient fault is to occur at that moment. The bigger; Indicates a suspected failure window The time difference between the peak and valley of the voltage within the voltage fluctuation provides the duration of the voltage fluctuation. The shorter the voltage fluctuation time in the suspected fault window at the time, the more likely it is that the DC high-voltage generator has a transient fault. The bigger; Suspected fault window Neidi The instantaneous amplitude of the voltage at the moment, which reflects the voltage signal based on the voltage mean The fluctuation amplitude of the transient state may indicate the occurrence of a fault when the transient amplitude is large. The bigger.

[0065] At this point, the time-domain transient characteristics of the voltage at each sampling time in each suspected fault window are obtained. Here, all the obtained time-domain transient characteristics are used to determine the adaptive time-domain transient feature segmentation threshold using the maximum inter-class variance method, which is recorded as , and get all the values ​​greater than The sampling time corresponding to the time domain transient characteristics is constructed as a suspected transient fault time set ,Right now The maximum inter-class variance method mentioned above belongs to the well-known technology and will not be described in detail here.

[0066] By calculating the time-domain transient characteristics of the voltage, the changes in the fault signal are further amplified. Through comprehensive judgment of multiple factors such as the voltage mean, second-order derivative, and the time difference between the peak and the trough, the time-domain analysis effectively extracts the significant characteristics of the fault point, and the combination of frequency-domain information further improves the accuracy of fault location.

[0067] S4. Calculate the current characteristics of each suspected transient fault sampling point and determine the transient fault sampling time.

[0068] In step S3, the sampling moments of all suspected transient faults are obtained, and relying solely on the voltage signal may lead to false alarms, because voltage fluctuations may be caused by non-fault factors, such as external environmental interference causing errors in the data acquisition equipment. Therefore, this embodiment subsequently combines the transient characteristics of the current signal to eliminate false faults where only the voltage signal is abnormal but the current change is small, thereby improving the robustness and reliability of the overall detection system.

[0069] For a DC high voltage generator, the equipment it tests usually includes high resistance products such as zinc oxide lightning arresters, so the current change of the DC high voltage generator is relatively small during normal operation. However, if a DC high voltage generator has a transient fault, the current change is more obvious than during normal operation. In this embodiment, the derivative method is used to obtain the normal current sequence. All the peaks and troughs in the wave, and calculate the mean of all the peaks and record it as ; Calculate the mean of all troughs and record it as , get the floating range of normal current The above derivative method for obtaining the peaks and troughs is a well-known technique and will not be described in detail here.

[0070] For the suspected transient fault time set obtained in step S3 , based on a current sequence containing transient faults Get Collection The current corresponding to each suspected fault sampling moment in The current corresponding to the suspected fault sampling time is recorded as . Example: When Belong to the floating range When , it means that the current is normal at the suspected fault sampling time, that is, the abnormality of the time domain transient characteristics of the voltage at the suspected fault sampling time may be caused by non-fault factors; when Not in floating range When , it indicates that the current is abnormal at the suspected fault sampling moment, and the time domain transient characteristics of the voltage at the suspected fault sampling moment are also abnormal, so it can be determined that the suspected fault sampling moment is the actual transient fault sampling moment.

[0071] In summary, based on the floating range of normal current For suspected transient fault time set The suspected fault sampling time in is double-checked to obtain the determined transient fault sampling time.

[0072] S5. Obtain the feature vectors at each historical sampling moment to perform dimensionality reduction and model training to obtain a trained classification model.

[0073] For any transient fault sampling time , get its characteristic vector, including the voltage at the sampling moment ,in Indicates the time window where the sampling moment is located; the proportion of high-frequency energy of the wavelet transform in the time window , the calculation method of the high-frequency energy ratio is shown in step S2; the transient amplitude of the voltage at the sampling moment ; The time window where the sampling moment is located The voltage peak-to-trough time difference ; The voltage change acceleration at this sampling moment ; The current at this sampling moment In summary, any transient fault sampling time There is a corresponding eigenvector , as shown below:

[0074]

[0075] At this point, the characteristic vectors of all transient fault sampling moments are obtained to form the fault feature matrix and recorded as ; Then based on the normal voltage sequence and normal current sequence The same method as above is used to obtain the feature vector at any normal sampling time and form a normal feature matrix, which is recorded as .

[0076] Since high-dimensional feature data may have information redundancy, in this embodiment, PCA principal component analysis can be used to analyze the matrix and Processing is used to reduce feature dimensions and improve the efficiency of subsequent model training while retaining the matrix The input of the algorithm is the feature matrix and hyperparameters , which is used to control the retained principal components. This hyperparameter can be obtained by calculating the explained variance ratio, which will not be described in detail in this embodiment. The output of the algorithm is the principal component matrix, principal component direction and eigenvalue after dimensionality reduction.

[0077] Fault feature matrix The principal component matrix after dimensionality reduction is recorded as , the normal feature matrix The principal component matrix after dimensionality reduction is recorded as ; where the matrix Contains the reduced-dimensional feature vector corresponding to any transient fault sampling moment, and adds a fault label to each reduced-dimensional feature vector in the matrix; and the matrix It contains the reduced-dimensional feature vector corresponding to any normal sampling moment, and adds a normal label to each reduced-dimensional feature vector in the matrix for subsequent model training.

[0078] In this embodiment, a support vector machine (SVM) is used to train the feature vectors after dimension reduction at each sampling moment. The input of the algorithm includes the matrix and , 80% of the data in each matrix is ​​used as the training set, and 20% of the data in each matrix is ​​used as the validation set. It should be noted that: Any feature vector in corresponds to a fault label, Any feature vector in corresponds to a normal label; the input of the algorithm also includes the main hyperparameters, among which the regularization parameter can take the empirical value of 10 in this embodiment, and the kernel function parameter can take the empirical value of 0.1. The output of the algorithm is a trained SVM classification model.

[0079] By reducing the dimension of the feature matrix through PCA principal component analysis, data redundancy is effectively reduced, overfitting is avoided, and the training speed of the classifier is improved. Combined with SVM support vector machine for model training, the model has stronger generalization ability in complex high-dimensional data environment.

[0080] S6. Obtain the real-time output voltage and current of the DC high voltage generator; calculate the feature vector at the current sampling moment, and use the trained classification model to perform classification to obtain the detection result.

[0081] When the DC high-voltage generator is actually working, the voltage and current of 100 milliseconds before the current sampling moment are obtained, the current time window is determined to calculate the feature vector of the current sampling moment, the feature vector of the current sampling moment is obtained using the method of step S5, and the principal component direction obtained by the PCA principal component analysis of step S5 is used to reduce its dimension, and the feature vector after dimension reduction is input into the trained SVM classification model obtained in step S5 to obtain the final fault detection result.

[0082] Exemplary description: There is a real-time sampling moment First, obtain the voltage and current data for 100 milliseconds before the sampling time, and determine the time window of the current sampling time. ; Then calculate the sampling time based on the method of step S5 The eigenvector of ,have:

[0083]

[0084] in Indicates the sampling time The voltage, Indicates the time window where the sampling moment is located; Indicates the sampling time Time window The proportion of high-frequency energy in the highest layer of the inner wavelet transform; Indicates the sampling time The transient amplitude of the voltage; Sampling time The time window The voltage peak and trough time difference; Indicates the sampling time The voltage change acceleration; Indicates the sampling time of current.

[0085] Then use the principal component direction obtained in step S5 to eigenvector Perform dimensionality reduction to obtain the feature vector after dimensionality reduction ; Finally, the feature vector Input the trained SVM classification model in step S5. If the output result of the classification model is normal, it means that the DC high-voltage generator has not had a transient fault at the current sampling moment. If the output result of the classification model is a fault, it means that the DC high-voltage generator may have a transient fault at the current sampling moment. The equipment automatically stops and an alarm is sounded to prompt the maintenance personnel to conduct further analysis on the equipment to determine the specific cause of the transient fault.

[0086] The present invention uses frequency domain analysis to preliminarily identify the time period when the fault may exist, then locates the time point when the fault may occur by combining the analysis of time domain transient characteristics, and then performs secondary verification in combination with current characteristics to ensure that the fault signal is distinguished from the fluctuations in the normal operation of the equipment, and obtains the precise time when the transient fault occurs. Finally, the classification model established by historical data is used to achieve real-time detection. Compared with the prior art, this method can more accurately identify the time point of transient faults during detection, has higher real-time performance and reliability, and greatly improves the stability of system operation, especially in complex signal environments.

[0087] Example of a fault detection system for high voltage test equipment:

[0088] On the other hand, the present invention also provides a fault detection system for high voltage test equipment. Figure 3 As shown, a fault detection system for high-voltage test equipment includes a processor and a memory, wherein the memory stores computer program instructions. When the computer program instructions are executed by the processor, a fault detection method for high-voltage test equipment according to the first aspect of the present invention is implemented.

[0089] The fault detection system of the high-voltage test equipment also includes other components well known to those skilled in the art, such as a communication interface, and the configuration and functions of these components are known in the art, so they will not be described in detail here.

[0090] In the present invention, the aforementioned memory may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, apparatus or device. For example, a computer-readable storage medium may be any appropriate magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory RRAM (Resistive Random Access Memory), a dynamic random access memory DRAM (Dynamic Random Access Memory), a static random access memory SRAM (Static Random-Access Memory), an enhanced dynamic random access memory EDRAM (Enhanced Dynamic Random Access Memory), a high-bandwidth memory HBM (High-Bandwidth Memory), a hybrid memory cube HMC (Hybrid Memory Cube), etc., or any other medium that can be used to store the required information and can be accessed by an application, a module, or both. Any such computer storage medium may be part of a device or accessible or connectable to a device. Any application or module described in the present invention may be implemented using computer-readable / executable instructions that may be stored or otherwise maintained by such a computer-readable medium.

[0091] In the description of this specification, "plurality" or "several" means at least two, such as two, three or more, etc., unless otherwise clearly and specifically defined.

[0092] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

Claims

1. A fault detection method for high voltage test equipment, characterized in that: The method comprises: Obtaining the voltage sequence and current sequence of the DC high voltage generator; The voltage sequence is divided into windows; the high-frequency components of each window are obtained based on frequency domain analysis, and the windows with high-frequency components greater than a set high-frequency threshold are recorded as suspected fault windows; the time-domain transient characteristics of the voltage at each sampling moment in each suspected fault window are obtained, and the time-domain transient characteristics are positively correlated with the difference between the voltage mean in the corresponding suspected fault window and the voltage at the corresponding sampling moment, positively correlated with the second-order derivative of the voltage at the corresponding sampling moment, and negatively correlated with the time difference between the highest voltage peak and the lowest voltage trough in the corresponding suspected fault window; the sampling moment corresponding to the time-domain transient characteristics greater than the set threshold is recorded as the suspected fault sampling moment; A normal current interval is obtained based on the peak mean and the trough mean of the current sequence; in response to the current at the suspected fault sampling moment being in the normal current interval, the corresponding suspected fault sampling moment is determined to be a transient fault sampling moment; the time domain transient characteristics of the voltage at the normal sampling moment and the transient fault sampling moment are obtained to obtain a feature matrix and add labels; The real-time voltage is obtained, the time-domain transient characteristics of the real-time voltage are calculated and input into the trained classification model, and in response to the output result being a fault label, the staff is notified to perform maintenance.

2. A fault detection method for high voltage test equipment according to claim 1, characterized in that: Obtain the high-frequency components of each window based on frequency domain analysis, including: The voltage sequence of each window is decomposed into multiple layers using discrete wavelet transform DWT; In the window The high frequency energy ratio of the layer is recorded as , the specific method of obtaining is: ； ； in Indicates After the window wavelet transform Layer detail factor; Indicates In the window sampling points; Indicates The total number of decomposition layers of wavelet transform within a window; Indicates After the window wavelet transform The high-frequency energy proportion of the layer; the high-frequency energy proportion is the high-frequency component of the window.

3. A fault detection method for high voltage test equipment according to claim 2, characterized in that: The windows whose high-frequency components are greater than the set high-frequency threshold are recorded as suspected fault windows, including: Obtain the high-frequency energy proportion of the highest layer in all time windows of the historical normal voltage sequence; record the mean and standard deviation of the high-frequency energy proportion of the highest layer as and ; The high frequency threshold is recorded as , the specific method of obtaining is: ； in represents the parameter adjustment coefficient; In response to the presence of a highest layer where the high frequency energy ratio is greater than the high frequency threshold, the corresponding window is recorded as a suspected fault window.

4. A fault detection method for high voltage test equipment according to claim 1, characterized in that: The calculation method of the time domain transient characteristics is specifically as follows: ； in Indicates any suspected fault window Neidi Time domain transient characteristics of voltage at moment ; Indicates any suspected fault window Neidi The second derivative of the voltage at time instant; Indicates any suspected fault window The first Voltage at the moment; It means the The suspected failure window at the time The voltage mean value within Indicates any suspected fault window The time difference between the highest voltage peak and the lowest voltage trough.

5. A fault detection method for high voltage test equipment according to claim 1, characterized in that: The sampling time corresponding to the time domain transient characteristics greater than the set threshold is recorded as the suspected fault sampling time, including: All the acquired time-domain transient features are used to determine the adaptive threshold using the maximum inter-class variance method; In response to the presence of a time-domain transient feature greater than an adaptive threshold, the corresponding sampling moment is recorded as a suspected fault sampling moment.

6. A fault detection method for high voltage test equipment according to claim 1, characterized in that: Obtain the time-domain transient characteristics of the voltage at all sampling moments to obtain the feature matrix and add labels, including: Obtain the time-domain transient characteristics of the voltage at all transient fault sampling moments to construct a first matrix, and add a fault label after each characteristic vector in the first matrix; Obtain a historical normal voltage sequence of a set length, and obtain the time-domain transient characteristics of the voltage at all sampling moments in the historical normal voltage sequence to construct a second matrix, and add a normal label after each feature vector in the second matrix; The first matrix and the second matrix are combined to obtain the characteristic matrix.

7. A fault detection method for high voltage test equipment according to claim 6, characterized in that: Inputting the feature matrix into the classification model for training to obtain a trained classification model includes: Use PCA principal component analysis to reduce the dimension of the first matrix and the second matrix; The first matrix and the second matrix with labels after dimension reduction are input into the SVM support vector machine for training to obtain a trained SVM classification model.

8. A fault detection method for high voltage test equipment according to claim 1, characterized in that: Obtain the voltage sequence and current sequence of the DC high voltage generator, including: A high-voltage mutual inductance sensor and a Hall current sensor are respectively installed at the output end of the DC high-voltage generator; Use the ADC module to connect two sensors, collect the output voltage and current of the DC high voltage generator and store them in the database; The voltage sequence and current sequence of the DC high voltage generator are obtained from the database.

9. A fault detection method for high voltage test equipment according to claim 8, characterized in that: It also includes performing data cleaning operations on the data stored in the database, wherein the data cleaning includes mean filtering noise reduction and Z-Score algorithm standardization.

10. A system for fault detection of high voltage test equipment, characterized in that: It comprises a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the fault detection method for high-voltage test equipment according to any one of claims 1 to 9 is implemented.

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