Relay protection loop fault detection method, device and system

Abstract

The invention relates to the technical field of electrical variable measurement, in particular to a relay protection loop fault detection method, device and system, and the method comprises the steps: obtaining multi-dimensional data of a plurality of current transformers in a target relay protection loop; analyzing the change trend of the multi-dimensional data to obtain initial fault possibilities corresponding to a plurality of preset fault types; analyzing the time performance of the feature distance corresponding to the multi-dimensional data, adjusting the feature distance corresponding to the multi-dimensional data according to the analysis result of the time performance, and adjusting the initial fault possibility according to the adjusted feature distance corresponding to the multi-dimensional data to obtain target fault possibilities corresponding to a plurality of preset fault types; and determining a target fault category corresponding to each current transformer according to the target fault possibility. According to the method, the electrical variables of the current transformer are measured to obtain the multi-dimensional data, and the change trend and the time performance of the multi-dimensional data are analyzed, so that the accuracy of relay protection loop fault detection is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of measuring electrical variables, and in particular to a method, device and system for detecting faults in a relay protection circuit. Background Art

[0002] In the context of increasing electricity demand, the safety of the power system also needs to be improved. The relay protection circuit composed of a relay protection device and a secondary circuit is an important protection device of the power system. The relay protection device is used to detect faults in the power system and take protective measures. The secondary circuit is a circuit system that realizes this function. The secondary circuit is an electrical circuit formed by connecting the secondary equipment of the power system, which is responsible for monitoring and protecting the primary equipment. The secondary circuit consists of four parts: AC secondary current and voltage circuit, power supply, signal circuit, and control circuit. At the same time, the substation has changed from the traditional mode to the smart substation. The secondary circuit can realize the comprehensive detection of the operating status of the automation equipment of the smart substation.

[0003] In the prior art, there is a relay protection circuit fault detection method based on a multidimensional Hausdorff distance (Hausdorff distance, a distance between two subsets) algorithm, which determines whether the circuit is faulty and the type and location of the fault by calculating whether the Hausdorff distance of current, zero-sequence current, and resistance is normal. However, this method only calculates the fault condition at a single moment and the Hausdorff distance of a single feature, which will lead to deviations in the calculation results, and the deviation value will lead to insufficient accuracy of fault detection.

[0004] Therefore, how to improve the accuracy of relay protection circuit fault detection is an urgent problem that needs to be solved. Summary of the invention

[0005] In order to solve the technical problem of how to improve the accuracy of relay protection circuit fault detection, the purpose of the present invention is to provide a relay protection circuit fault detection method, device and system, and the technical solution adopted is as follows: The present application provides a method for detecting a fault in a relay protection circuit, the method comprising: Acquire multi-dimensional data of multiple current transformers in a target relay protection circuit, wherein the multi-dimensional data includes current time series data, zero-sequence current time series data, and resistance time series data; For any of the current transformers, analyzing the change trend of the multi-dimensional data to obtain initial fault probabilities corresponding to a plurality of preset fault types; Analyzing the time performance of the characteristic distance corresponding to the multidimensional data, adjusting the characteristic distance corresponding to the multidimensional data according to the analysis result of the time performance, and adjusting the initial fault probability according to the adjusted characteristic distance corresponding to the multidimensional data to obtain the target fault probability corresponding to the multiple preset fault types; Determine a target fault category corresponding to each of the current transformers according to the target fault possibility, and perform fault repair on each of the current transformers according to the target fault category; The analyzing the time performance of the characteristic distance corresponding to the multi-dimensional data includes: According to the multidimensional data of any two current transformers, a current distance, a zero-sequence current distance and a resistance distance are obtained, wherein the current distance refers to the distance between the current values ​​of the two current transformers in the same time window, the zero-sequence current distance refers to the distance between the zero-sequence current values ​​of the two current transformers in the same time window, and the resistance distance refers to the distance between the resistance values ​​of the two current transformers in the same time window; Taking the current distance, zero-sequence current distance and resistance distance as characteristic distances; According to the numerical value and duration of the characteristic distance, a time representation of the characteristic distance of each current transformer is obtained; The step of adjusting the characteristic distance corresponding to the multidimensional data according to the analysis result of the time performance includes: adjusting the current distance according to the time performance of the current distance to obtain an adjusted current distance; According to the time performance of the zero-sequence current distance, the zero-sequence current distance is adjusted to obtain an adjusted zero-sequence current distance; The resistance distance is adjusted according to the time performance of the resistance distance to obtain an adjusted resistance distance.

[0006] In some embodiments, after acquiring multi-dimensional data of multiple current transformers in the target relay protection circuit, the method further includes: According to a preset wavelet basis function and a preset number of decomposition layers, a discrete wavelet transform is performed on the multidimensional data to obtain an initial approximation coefficient and an initial detail coefficient; According to a preset soft threshold function, the initial approximation coefficient and the initial detail coefficient are processed to obtain a target approximation function and a target detail function; The target approximation function and the target detail function are subjected to inverse discrete wavelet transform to obtain the denoised multidimensional data, and the denoised multidimensional data is used for change trend analysis.

[0007] In some embodiments, the multiple preset fault types include loop short circuit, loop ground short circuit, loop open circuit and specific number loop short circuit, and the analysis of the change trend of the multi-dimensional data to obtain the initial fault probability corresponding to the multiple preset fault types includes: According to the preset neighborhood monitoring time, a time window is obtained; Analyze the change trend of the current time series data, and obtain the initial fault possibility corresponding to the loop short circuit according to the average current in the time window; Analyze the change trends of the current time series data and the zero-sequence current time series data, and obtain the initial fault possibility of the loop ground short circuit according to the average current and the zero-sequence current in the time window; Analyze the change trend of the resistance time series data, and obtain the initial fault possibility corresponding to the circuit break according to the average resistance in the time window; The change trend of the resistance time series data and the phase change of the current time series data are analyzed, and the initial fault possibility corresponding to the short circuit of the specific numbered loop is obtained according to the average resistance and phase change in the time window.

[0008] In some embodiments, adjusting the initial fault probability according to the adjusted characteristic distance corresponding to the multi-dimensional data to obtain the target fault probability corresponding to the multiple preset fault types includes: Obtaining an adjusted multi-dimensional Hausdorff distance according to the adjusted current distance, the adjusted zero-sequence current distance, and the adjusted resistance distance; Determining an adjustment index of the feature distance according to the adjusted multi-dimensional Hausdorff distance; The initial fault probability is adjusted according to the adjustment index of the characteristic distance to obtain the target fault probability corresponding to the multiple preset fault types.

[0009] In some embodiments, determining the adjustment index of the characteristic distance according to the adjusted multi-dimensional Hausdorff distance includes: Obtaining the period of the adjusted multi-dimensional Hausdorff distance; Obtaining a periodic law according to the variance of the characteristic distances corresponding to the multiple time windows within the period; An adjustment index of the characteristic distance is determined according to the periodic law, the number of the time windows, and the range of the characteristic distance in each time window.

[0010] In some embodiments, the performing fault repair on each of the current transformers according to the target fault category includes: Determining a fault circuit according to the target fault category; Check the status of the lines and devices connected to the current transformer in the fault loop, and perform fault repair on the lines and devices with abnormal status.

[0011] The present application also provides a relay protection circuit fault detection device, the device comprising: A data acquisition module, used to acquire multi-dimensional data of multiple current transformers in a target relay protection circuit, wherein the multi-dimensional data includes current time series data, zero-sequence current time series data and resistance time series data; An initial fault analysis module, for analyzing the change trend of the multi-dimensional data for any current transformer, and obtaining initial fault possibilities corresponding to a plurality of preset fault types; a target fault analysis module, configured to analyze the time performance of the characteristic distance corresponding to the multidimensional data, adjust the characteristic distance corresponding to the multidimensional data according to the analysis result of the time performance, and adjust the initial fault possibility according to the adjusted characteristic distance corresponding to the multidimensional data, so as to obtain the target fault possibility corresponding to the multiple preset fault types; The fault category determination module is used to determine the target fault category corresponding to each of the current transformers according to the target fault possibility, and perform fault repair on each of the current transformers according to the target fault category.

[0012] An embodiment of the present application also provides a relay protection circuit fault detection system, the system comprising a computer-readable storage medium, the computer-readable storage medium storing a plurality of instructions, the instructions being suitable for loading by a processor to execute the steps in the relay protection circuit fault detection method as described above.

[0013] The present invention has the following beneficial effects: First, multidimensional data of multiple current transformers in a target relay protection circuit are obtained, wherein the multidimensional data include current time series data, zero-sequence current time series data, and resistance time series data; then, for any of the current transformers, the change trend of the multidimensional data is analyzed to obtain initial fault possibilities corresponding to multiple preset fault types; then, the time expression of the characteristic distance corresponding to the multidimensional data is analyzed, and the characteristic distance corresponding to the multidimensional data is adjusted according to the analysis result of the time expression, and the initial fault possibility is adjusted according to the adjusted characteristic distance corresponding to the multidimensional data to obtain the target fault possibility corresponding to the multiple preset fault types; finally, according to the target fault possibility, the target fault category corresponding to each of the current transformers is determined, and fault repair is performed on each of the current transformers according to the target fault category. In the present application, current time series data, zero-sequence current time series data and resistance time series data are obtained, which provides multi-dimensional information and can more comprehensively reflect the status of the relay protection circuit. Different types of time series data can complement each other, reduce the limitations of a single data source, and improve the reliability of fault detection. By analyzing the changing trends of multi-dimensional data, the trends and patterns of the data can be identified, which is helpful for early detection of potential fault signs. Dynamically adjusting the characteristic distance according to the time performance of the characteristic distance can more accurately reflect the actual fault situation. The static characteristic distance cannot adapt to different fault scenarios, and dynamic adjustment can improve adaptability and accuracy. Combined with the adjusted characteristic distance, the initial fault possibility is re-evaluated to obtain a more accurate target fault possibility, which can eliminate misjudgment and improve the accuracy of fault detection. According to the target fault possibility, the specific fault category can be accurately located to avoid blind maintenance and improve maintenance efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. 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 paying creative work.

[0015] Figure 1 A schematic diagram of an implementation environment of a relay protection circuit fault detection method provided by an embodiment of the present invention; Figure 2 A schematic flow chart of a relay protection circuit fault detection method provided by one embodiment of the present invention; Figure 3 A schematic structural diagram of a relay protection circuit fault detection device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0016] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of a relay protection circuit fault detection method, device and system proposed by the present invention, its specific implementation method, structure, features and effects in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0017] It should be noted that the terms "first", "second", etc. in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0018] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0019] The specific scheme of a relay protection circuit fault detection method, device and system provided by the present invention is described in detail below with reference to the accompanying drawings.

[0020] See also Figure 1 , Figure 1 The following is a schematic diagram of an implementation environment of a relay protection circuit fault detection method provided by an embodiment of the present invention. Figure 1As shown, the implementation environment includes a fault detection terminal 101 and a relay protection circuit 102. The fault detection terminal 101 may be a terminal device equipped with a relay protection circuit fault detection system, including but not limited to a laptop computer, tablet computer, handheld computer, PAD, desktop computer, etc. with local computing capability; the relay protection circuit fault detection system may be implemented in the form of a target client, and the target client may be a client supporting relay protection circuit fault detection, such as a video client, an instant messaging client, a browser client, etc.; the fault detection terminal 101 may, but is not limited to, communicate with the relay protection circuit 102 through a network, and the network may include but is not limited to: a wired network, a wireless network, wherein the wired network includes: a local area network, a metropolitan area network, and a wide area network, and the wireless network includes: Bluetooth, WIFI, and other networks that implement wireless communication. The fault detection terminal 101 may, but is not limited to, include a human-computer interaction screen, a processor, and a memory. The human-computer interaction screen may, but is not limited to, be used to display the target fault category. The processor may, but is not limited to, be used to respond to human-computer interaction operations, perform corresponding operations, or generate corresponding instructions.

[0021] As an optional manner, the multi-dimensional data of multiple current transformers in the relay protection circuit may be collected through the relay protection circuit 102, and the relay protection circuit 102 may include multiple current transformers.

[0022] As an optional manner, the fault detection terminal 101 may also be a server, which may be a single server, a server cluster composed of multiple servers, or a cloud server. The above is only an example, and this embodiment does not make any limitation to this.

[0023] As an optional method, the following steps of the relay protection circuit fault detection method may be performed on the fault detection terminal 101: Acquire multi-dimensional data of multiple current transformers in a target relay protection circuit, wherein the multi-dimensional data includes current time series data, zero-sequence current time series data, and resistance time series data; For any of the current transformers, analyzing the change trend of the multi-dimensional data to obtain initial fault probabilities corresponding to a plurality of preset fault types; Analyzing the time performance of the characteristic distance corresponding to the multidimensional data, adjusting the characteristic distance corresponding to the multidimensional data according to the analysis result of the time performance, and adjusting the initial fault probability according to the adjusted characteristic distance corresponding to the multidimensional data to obtain the target fault probability corresponding to the multiple preset fault types; According to the target fault possibility, a target fault category corresponding to each of the current transformers is determined, and fault repair is performed on each of the current transformers according to the target fault category.

[0024] Based on the above method, current time series data, zero-sequence current time series data and resistance time series data are obtained, which provides multi-dimensional information and can more comprehensively reflect the status of the relay protection circuit. Different types of time series data can complement each other, reduce the limitations of a single data source, and improve the reliability of fault detection. By analyzing the changing trends of multi-dimensional data, the trends and patterns of the data can be identified, which is helpful for early detection of potential fault signs. Dynamically adjusting the feature distance according to the time performance of the feature distance can more accurately reflect the actual fault situation. The static feature distance cannot adapt to different fault scenarios, and dynamic adjustment can improve adaptability and accuracy. Combined with the adjusted feature distance, the initial fault possibility is re-evaluated to obtain a more accurate target fault possibility, which can eliminate misjudgment and improve the accuracy of fault detection. According to the target fault possibility, the specific fault category can be accurately located to avoid blind maintenance and improve maintenance efficiency.

[0025] As an optional example, this embodiment does not limit the execution subject of the above-mentioned relay protection circuit fault detection method. The above-mentioned relay protection circuit fault detection method can be executed on the fault detection terminal 101. For example, when the fault detection terminal 101 is a desktop computer, some or all steps of the above-mentioned relay protection circuit fault detection method can be executed on the desktop computer.

[0026] The above section introduces the contents of an exemplary implementation environment for applying the technical solution of the present application. Next, the relay protection circuit fault detection method of the present application will be introduced.

[0027] In order to solve the problem of how to improve the accuracy of relay protection circuit fault detection in the prior art, the embodiments of the present application respectively propose a relay protection circuit fault detection method, a relay protection circuit fault detection device, an electronic device, and a relay protection circuit fault detection system. These embodiments will be described in detail below.

[0028] See also Figure 2 , Figure 2 A schematic diagram of a flow chart of a relay protection circuit fault detection method provided by an embodiment of the present invention, which can be applied to Figure 1 It should be understood that the method can also be applied to other exemplary implementation environments and specifically executed by devices in other implementation environments, and this embodiment does not limit the implementation environment to which the method is applicable.

[0029] like Figure 2 As shown, in an exemplary embodiment, the relay protection circuit fault detection method includes at least steps S210 to S240, which are described in detail as follows: In step S210, multi-dimensional data of a plurality of current transformers in a target relay protection circuit is acquired, wherein the multi-dimensional data includes current time series data, zero-sequence current time series data and resistance time series data.

[0030] Among them, the current transformer is a device used to measure alternating current. By converting large current into small current, the current can be safely measured and monitored. Current time series data refers to the sequence of current values ​​continuously collected by the current transformer over a period of time. By analyzing the current time series data, we can understand the changing trend of the current in the loop, identify current anomalies, and determine whether there is a fault. Zero-sequence current time series data refers to the sequence of zero-sequence current values ​​collected by the zero-sequence current transformer in a three-phase system. The zero-sequence current is the vector sum of the three-phase currents and is mainly used to detect ground faults. Resistance time series data refers to the sequence of loop resistance values ​​collected by a resistance sensor or other measuring equipment over a period of time. It is obtained by dividing the loop voltage sampled by the current transformer by the phase current data. By analyzing the resistance time series data, it is possible to detect whether there are faults such as poor contact and resistance changes in the loop.

[0031] For example, there is a relay protection circuit, which includes three current transformers, one zero-sequence current transformer, and one resistance sensor. The current transformers are installed at different positions of the circuit to collect three-phase current data; the zero-sequence current transformer is installed on the neutral line of the three-phase system to collect zero-sequence current data; the resistance sensor is installed at the key node of the circuit to collect circuit resistance data.

[0032] In step S220, for any of the current transformers, the change trend of the multi-dimensional data is analyzed to obtain initial fault probabilities corresponding to a plurality of preset fault types.

[0033] The change trend of multidimensional data refers to the change trend and pattern of multidimensional data (such as current time series data, zero-sequence current time series data, and resistance time series data) over time within a period of time. By analyzing the change trend of multidimensional data, abnormal changes in data can be identified, and then possible fault types can be inferred. The change trend can include characteristics such as data fluctuations, mutations, and trends.

[0034] In step S230, the time expression of the characteristic distance corresponding to the multidimensional data is analyzed, the characteristic distance corresponding to the multidimensional data is adjusted according to the analysis result of the time expression, and the initial fault possibility is adjusted according to the adjusted characteristic distance corresponding to the multidimensional data to obtain the target fault possibility corresponding to the multiple preset fault types.

[0035] Among them, feature distance is a measurement method used to quantify the similarity between multidimensional data. In relay protection circuit fault detection, feature distance can be used to compare multidimensional data in normal state and current state to evaluate the similarity and difference of data. By calculating the feature distance, abnormal changes in data can be identified, and then the possible fault type can be inferred. Common feature distance methods include multidimensional Hausdorff distance, Euclidean distance, etc. Time performance refers to the trend and pattern of feature distance changes over time. By analyzing the time performance of feature distance, the development process and dynamic changes of faults can be understood. Time performance analysis can help dynamically adjust feature distance and improve the accuracy and real-time performance of fault detection. Time performance can include characteristics such as fluctuations, trends, and mutations of feature distance.

[0036] In step S240, a target fault category corresponding to each of the current transformers is determined according to the target fault possibility, and fault repair is performed on each of the current transformers according to the target fault category.

[0037] Exemplarily, there is a relay protection circuit, which includes three current transformers, a zero-sequence current transformer and a resistance sensor. Through the previous steps, the multi-dimensional data of each current transformer is obtained, and the target fault probability is calculated. Now it is necessary to determine the target fault category of each current transformer according to the target fault probability, and perform fault repair. When the target fault category is a circuit breaker fault, check and replace the connecting wire, and test and verify after reconnection. When the target fault category is a single-phase grounding fault, check and repair the grounding, and test and verify after reconnection. When the target fault category is poor contact, clean and tighten the contact point, and test and verify after reconnection.

[0038] It can be seen from the above steps S210 to S240 that the solution proposed in this embodiment obtains current time series data, zero-sequence current time series data and resistance time series data, provides multi-dimensional information, can more comprehensively reflect the state of the relay protection circuit, and different types of time series data can complement each other, reduce the limitations of a single data source, and improve the reliability of fault detection; by analyzing the changing trends of multi-dimensional data, the trends and patterns of the data can be identified, which is helpful for early detection of potential fault signs; dynamically adjusting the feature distance according to the time performance of the feature distance can more accurately reflect the actual fault situation. The static feature distance cannot adapt to different fault scenarios, and dynamic adjustment can improve adaptability and accuracy; combined with the adjusted feature distance, the initial fault possibility is re-evaluated to obtain a more accurate target fault possibility, which can eliminate misjudgment and improve the accuracy of fault detection; according to the target fault possibility, the specific fault category can be accurately located to avoid blind maintenance and improve maintenance efficiency.

[0039] In one embodiment of the present application, after acquiring multidimensional data of multiple current transformers in the target relay protection circuit, the method further includes: According to a preset wavelet basis function and a preset number of decomposition layers, a discrete wavelet transform is performed on the multidimensional data to obtain an initial approximation coefficient and an initial detail coefficient; According to a preset soft threshold function, the initial approximation coefficient and the initial detail coefficient are processed to obtain a target approximation function and a target detail function; The target approximation function and the target detail function are subjected to inverse discrete wavelet transform to obtain the denoised multidimensional data, and the denoised multidimensional data is used for change trend analysis.

[0040] Among them, the wavelet basis function is a set of mathematical functions used to perform multi-scale analysis of signals in discrete wavelet transform. Selecting appropriate wavelet basis functions can better extract the characteristics of the signal and improve the denoising effect.

[0041] Among them, discrete wavelet transform (also known as DWT) is a method to decompose a signal into different frequency components. Through discrete wavelet transform, multidimensional data can be decomposed into approximate coefficients and detail coefficients. Through DWT, the signal can be decomposed into high-frequency and low-frequency parts of different scales, which helps to remove noise and retain the main characteristics of the signal.

[0042] Among them, the initial approximation coefficient represents the low-frequency part of the signal, and the initial detail coefficient represents the high-frequency part of the signal. The approximation coefficient reflects the overall trend of the signal, and the detail coefficient reflects the local change of the signal. By analyzing these coefficients, the noise in the signal can be identified and processed.

[0043] Among them, the soft threshold function is a method for removing the part of the detail coefficients after wavelet transformation that is less than the threshold. The soft threshold function can effectively remove the noise in the signal while retaining the main features of the signal. Using the soft threshold function to process the initial approximation coefficients and the initial detail coefficients can remove noise, retain the main features of the signal, and improve the quality of the signal.

[0044] In fact, the inverse discrete wavelet transform is the inverse process of the discrete wavelet transform, which is used to recombine the processed approximate coefficients and detail coefficients into a denoised signal, and can restore the denoised approximate coefficients and detail coefficients to denoised multidimensional data for further analysis and processing.

[0045] Exemplarily, the preset wavelet basis function is the dbN wavelet function, and the preset number of decomposition layers is 4. The multidimensional data after denoising is recorded as , , .in, represents the multidimensional data at the i-th current transformer, n represents the number of current transformers, They represent the current time series data, zero-sequence current time series data and resistance time series data at the i-th current transformer, respectively. Represents the current data at the Tth moment at the i-th current transformer, where T is the total number of monitoring moments. In this embodiment, the noise in the multidimensional data is effectively removed through wavelet transform and soft threshold processing, which improves the quality of the signal and makes the subsequent analysis more accurate. The denoised multidimensional data can more clearly reflect the changing trend and pattern of the signal, help to more accurately identify and locate faults, and improve the sensitivity and accuracy of fault detection. Denoising can reduce misjudgment caused by noise, ensure the reliability of fault detection results, and reduce false alarm rate and missed alarm rate.

[0046] In one embodiment of the present application, the multiple preset fault types include loop short circuit, loop ground short circuit, loop open circuit and specific number loop short circuit, and the analysis of the change trend of the multi-dimensional data to obtain the initial fault probability corresponding to the multiple preset fault types includes: According to the preset neighborhood monitoring time, a time window is obtained; Analyze the change trend of the current time series data, and obtain the initial fault possibility corresponding to the loop short circuit according to the average current in the time window; Analyze the change trends of the current time series data and the zero-sequence current time series data, and obtain the initial fault possibility of the loop ground short circuit according to the average current and the zero-sequence current in the time window; Analyze the change trend of the resistance time series data, and obtain the initial fault possibility corresponding to the circuit break according to the average resistance in the time window; The change trend of the resistance time series data and the phase change of the current time series data are analyzed, and the initial fault possibility corresponding to the short circuit of the specific numbered loop is obtained according to the average resistance and phase change in the time window.

[0047] Among them, there is a correlation between current, zero-sequence current, and resistance characteristics. By analyzing the change trend of multi-dimensional data, the initial fault probability of any fault can be obtained. In addition, in order to reduce the false detection rate, the time of data anomaly should also be analyzed. When the abnormal performance is small, the abnormal characteristics are amplified, and the abnormal performance of the characteristics is adaptively adjusted. The time of the abnormal performance is counted to comprehensively obtain the target fault probability of any fault.

[0048] Among them, the following types of faults are common: when the circuit is short-circuited, the current decreases; when the circuit is short-circuited to ground, the current value decreases, and zero-sequence current appears at the same time; when the circuit is open, the resistance increases; when a circuit with a specific number is short-circuited, the resistance increases, and the current waveform phase changes. Therefore, the faults can be divided into short circuit and open circuit, and it can be found that under the two faults of short circuit and open circuit, the circuit short circuit and the circuit ground short circuit can be distinguished by whether the zero-sequence current appears, and the circuit open circuit and the circuit short circuit with a specific number can be distinguished by whether the current waveform phase changes.

[0049] The preset neighborhood monitoring moments refer to several key monitoring points that are pre-set within a period of time for collecting and analyzing data. By collecting data at these key moments, the changing trend of the signal can be captured more accurately, and the sensitivity of fault detection can be improved.

[0050] The time window refers to a period of time before and after the preset neighborhood monitoring time, which is used to analyze the change trend of data. By setting the time window, you can focus on analyzing the data within a certain period of time, reduce noise interference, and improve the accuracy of the analysis.

[0051] Among them, by counting and analyzing the change trend of current time series data, the fluctuation, mutation and trend of the data can be identified. Change trend analysis can help identify abnormal changes in current and infer possible fault types.

[0052] The average value of the current time series data is calculated within the time window. The average current can reflect the overall level of the current and is used to determine whether the current is within the normal range.

[0053] Among them, by counting and analyzing the change trend of the resistance time series data, the fluctuation, mutation and trend of the data can be identified. The change trend analysis can help identify the abnormal change of the resistance and infer the possible fault type.

[0054] The average value of the resistance time series data is calculated within the time window. The average resistance can reflect the overall level of resistance and is used to determine whether the resistance is within the normal range.

[0055] The phase change refers to the phase change of the current time series data within the time window. The phase change can help identify the phase relationship of the current and is used to determine the short circuit fault of a specific numbered loop.

[0056] Exemplarily, any moment and 5 monitoring moments in the left neighborhood and 5 monitoring moments in the right neighborhood are used as the time window of the moment; the fault manifestation of loop short circuit is the reduction of current, so the trend of current change can be analyzed. As the time window extends, the average current decreases instead, which means that the trend of current change is decreasing, so the loop short circuit fault can be tested, and the more obvious the current reduction, the more serious the loop short circuit fault manifestation. The representation method of the initial fault possibility corresponding to the loop short circuit can be:

[0057] in, Indicates the initial fault probability corresponding to the short circuit of any current transformer at any time; Indicates the time window size of the current transformer; , Respectively represent the average current r and s moments before the time window; is the maximum and minimum normalization function.

[0058] in, It means adding the difference between the average current at relatively few moments and the average current at relatively many moments within the time window, which indicates the trend of current change. The larger the difference, the greater the possibility of initial fault corresponding to the short circuit.

[0059] For example, if there is a zero-sequence current on the basis of a loop short circuit fault, and the larger the zero-sequence current is, the greater the possibility of a loop ground short circuit fault. The initial fault possibility of a loop ground short circuit can be expressed as:

[0060] in, Indicates the initial fault probability of ground short circuit of any current transformer at any time; Indicates the initial fault probability corresponding to the short circuit of any current transformer at any time; is the zero-sequence current at time r before the time window; is the maximum and minimum normalization function.

[0061] in, It represents the sum of zero-sequence currents within the time window. The larger the value, the more likely it is that zero-sequence current will appear, and the greater the possibility of initial fault of loop ground short circuit.

[0062] For example, a circuit breaker fault is manifested as an increase in resistance, which can be judged by the trend of resistance change. As the time window extends, the average resistance increases, which means that the trend of resistance change is increasing. Therefore, the circuit breaker fault can be tested, and the more obvious the resistance increase, the more serious the circuit breaker fault. The initial fault possibility corresponding to the circuit breaker can be expressed as:

[0063] in, Indicates the initial fault probability corresponding to the circuit break of any current transformer at any time; Indicates the time window size of the current transformer; , Respectively represent the average resistance r and s moments before the time window; is the maximum and minimum normalization function.

[0064] in, It means adding the difference between the average resistance of all relatively few moments and the average resistance of the relatively majority moments in the time window, which indicates the trend of resistance change. The larger the value, the greater the possibility of initial fault corresponding to circuit break.

[0065] For example, based on the loop open circuit fault, if there is a phase change in the current, then the loop open circuit fault and the specific number loop short circuit fault can be distinguished. The phase change can be distinguished by period. As time changes, the current representative data (extreme values) no longer maintain the original periodic law, which can indicate that the phase has changed. The initial fault probability corresponding to the specific number loop short circuit can be expressed as:

[0066] in, Indicates the initial fault probability corresponding to the short circuit of a specific numbered loop of any current transformer at any time; Indicates the initial fault probability corresponding to the circuit break of any current transformer at any time; Indicates the number of phase maxima in the time window; Indicates , No. The distance between the phase maxima; Indicates , No. The distance between the phase maxima; is the maximum and minimum normalization function.

[0067] in, It means adding the distance differences of all adjacent maximum values, which represents the phase change. The larger the value, the greater the phase change. The greater the phase change, the greater the possibility of initial fault corresponding to the short circuit of the specific numbered circuit.

[0068] For example, the initial fault probabilities corresponding to multiple preset fault types can be integrated into ,in, Indicates the possibility of current fault, Indicates the possibility of resistance failure. It indicates the circuit is short-circuited. When , it indicates that the circuit is short-circuited to ground. When When , it indicates that the specific numbered circuit is short-circuited.

[0069] In this embodiment, by presetting the neighborhood monitoring time and time window, the data in the key time period can be analyzed intensively, noise interference can be reduced, and the accuracy of fault detection can be improved. By analyzing the changing trends of different types of multidimensional data (current time series data, zero-sequence current time series data, resistance time series data), different fault types can be identified more accurately and the ability to identify fault types can be improved. By calculating the average value and change trend within the time window, the misjudgment caused by transient noise can be reduced, ensuring the reliability of the fault detection results. Through efficient analysis methods, fault detection can be completed in a shorter time, the real-time performance of the system can be improved, and faults can be discovered and handled in a timely manner. This method has strong adaptability and can be applied to multidimensional data of different types and complexity, and is suitable for fault detection of various relay protection circuits.

[0070] In one embodiment of the present application, the analyzing the time performance of the characteristic distance corresponding to the multi-dimensional data includes: According to the multi-dimensional data of any two current transformers, a current distance, a zero-sequence current distance and a resistance distance are obtained; Taking the current distance, zero-sequence current distance and resistance distance as characteristic distances; According to the numerical value and duration of the characteristic distance, the time representation of the characteristic distance of each current transformer is obtained.

[0071] Among them, the current distance refers to the distance between the current values ​​of two current transformers in the same time window. Common distance measurement methods include multi-dimensional Hausdorff distance, Euclidean distance, etc. By calculating the current distance, the similarity and difference of the current values ​​of two current transformers in time can be evaluated to identify abnormal changes in current.

[0072] Among them, the zero-sequence current distance refers to the distance between the zero-sequence current values ​​of two current transformers in the same time window. Common distance measurement methods include multi-dimensional Hausdorff distance, Euclidean distance, etc. By calculating the zero-sequence current distance, the similarity and difference of the zero-sequence current values ​​of two current transformers in time can be evaluated, which is used to identify abnormal changes in zero-sequence current.

[0073] Among them, resistance distance refers to the distance between the resistance values ​​of two current transformers in the same time window. Common distance measurement methods include multi-dimensional Hausdorff distance, Euclidean distance, etc. By calculating the resistance distance, the similarity and difference of the resistance values ​​of two current transformers in time can be evaluated, which is used to identify abnormal changes in resistance.

[0074] The temporal performance of the characteristic distance refers to the changing trend and pattern of the characteristic distance over time, including the numerical value and duration of the characteristic distance. By analyzing the temporal performance of the characteristic distance, the fluctuation, trend and duration of the characteristic distance can be identified, thereby inferring the possible fault type and fault development process.

[0075] For example, some faults are relatively minor, such as poor contact, and the fault performance at a single moment may not be obvious. At this time, the fault size can be adjusted, and the possibility of fault occurrence can be further adjusted by statistically analyzing the regularity and continuity in time, making the fault more significant and easier to detect. Calculate the multidimensional Hausdorff distance based on time series of the current, zero-sequence current, and resistance of any two current transformers:

[0076]

[0077] in, Represents the time series-based multidimensional Hausdorff distance between the i-th current transformer and the j-th current transformer at the t-th time; are the current distance, zero-sequence current distance, and resistance distance of the i-th current transformer and the j-th current transformer at the t-th moment respectively; represents the average Euclidean distance function; , They represent the currents of the i-th current transformer and the j-th current transformer at the t-th moment respectively; , They represent the current of the j-th current transformer at the t-1th moment and the t+1th moment respectively. , The calculation method is equivalent to .

[0078] Next, the current distance, zero-sequence current distance, and resistance distance are recorded as characteristic distances, and the time performance of the characteristic distances is analyzed. When the characteristic distance is not very significant, if the characteristic distance persists, it can still be considered that the fault exists, and the longer the continuous existence time and the greater the proportion of the overall existence, the more prominent the time performance. For example, on a primary equipment connection, the line is unstable due to line aging, etc., and the line current distance appears frequently for a period of time due to weather reasons, but it is not completely disconnected, so the value of the current distance is small, then it should still be determined as a circuit breaker fault.

[0079] Time can be expressed as:

[0080] in, Represent the time performance of any characteristic distance of any current transformer; Indicates the number of monitoring moments when the characteristic distance of the current transformer is not 0; Indicates the total number of monitoring moments; Indicates the number of time periods where the feature distance is 0; , Indicates the duration of the time period where the u+1th and uth feature distances are 0; is the maximum and minimum normalization function.

[0081] in, It indicates the length of continuous time. The longer the time length is, the more prominent the time performance is and the greater the time performance is.

[0082] In this embodiment, by calculating the current distance, zero-sequence current distance and resistance distance, the similarities and differences between different current transformers can be more accurately evaluated, which helps to identify abnormal changes in current, zero-sequence current and resistance. By analyzing the time performance of the characteristic distance, the fluctuation, trend and duration of the characteristic distance can be identified, thereby inferring the possible fault type and fault development process. By comprehensively analyzing the numerical value and duration of the characteristic distance, the misjudgment caused by transient noise can be reduced, ensuring the reliability of the fault detection results.

[0083] In one embodiment of the present application, adjusting the characteristic distance corresponding to the multidimensional data according to the analysis result of the time performance includes: adjusting the current distance according to the time performance of the current distance to obtain an adjusted current distance; According to the time performance of the zero-sequence current distance, the zero-sequence current distance is adjusted to obtain an adjusted zero-sequence current distance; The resistance distance is adjusted according to the time performance of the resistance distance to obtain an adjusted resistance distance.

[0084] If the characteristic distance itself is larger, the adjustable part is smaller and it can be maintained. If the characteristic distance itself is smaller, the characteristic distance is adjusted by time performance. The stronger the time performance, the greater the characteristic distance adjustment amount, and the easier it is to be identified as a fault. Therefore, the characteristic distance corresponding to the multidimensional data is adjusted according to the analysis results of the time performance. The adjustment method can be:

[0085]

[0086] in, , , Respectively represent the adjusted current distance, the adjusted zero-sequence current distance, and the adjusted resistance distance of the i-th current transformer and the j-th current transformer at the t-th moment; is the current distance between the i-th current transformer and the j-th current transformer at the t-th moment; Represents the time performance of the current distance of any current transformer; represents the time series-based regulated multidimensional Hausdorff distance between the i-th current transformer and the j-th current transformer at the t-th time; represents the exponential function, specifically the exponential function with the natural constant e as the base, , The calculation method is equivalent to .

[0087] In this embodiment, by dynamically adjusting the characteristic distance, the similarity and difference of multidimensional data can be more accurately evaluated according to the changing trend and pattern of the actual data, thereby improving the accuracy of fault detection. Dynamically adjusting the characteristic distance can reduce the misjudgment caused by transient noise and ensure the reliability of the fault detection results. By analyzing the time performance of current distance, zero-sequence current distance and resistance distance, the fault type can be identified from multiple angles and the ability to identify the fault type can be improved. Combining the time performance of multiple characteristic distances, the possibility and severity of the fault can be comprehensively evaluated to provide more comprehensive fault diagnosis information.

[0088] In one embodiment of the present application, adjusting the initial fault probability according to the adjusted characteristic distance corresponding to the multidimensional data to obtain the target fault probability corresponding to the multiple preset fault types includes: Obtaining an adjusted multi-dimensional Hausdorff distance according to the adjusted current distance, the adjusted zero-sequence current distance, and the adjusted resistance distance; Determining an adjustment index of the feature distance according to the adjusted multi-dimensional Hausdorff distance; The initial fault probability is adjusted according to the adjustment index of the characteristic distance to obtain the target fault probability corresponding to the multiple preset fault types.

[0089] Among them, the multidimensional Hausdorff distance is a metric used to measure the maximum distance between two multidimensional data sets. It not only considers the distance between data points, but also the overall structure of the data set. By calculating the multidimensional Hausdorff distance, the similarities and differences between multidimensional data can be more comprehensively evaluated to identify abnormal changes in data.

[0090] The adjustment index refers to a parameter or standard for adjusting the characteristic distance determined according to the adjusted multi-dimensional Hausdorff distance. By determining the adjustment index, the characteristic distance can be adjusted more accurately, thereby improving the accuracy and reliability of fault detection.

[0091] Among them, according to the adjustment index, the initial fault probability is adjusted to obtain the final target fault probability. By adjusting the initial fault probability, the probability of different fault types can be more accurately evaluated, and the accuracy and reliability of fault detection can be improved.

[0092] Exemplarily, when the characteristic distance corresponding to the adjusted multidimensional data belongs to a regular value range and there are periodic changes, the characteristic distance corresponding to any adjusted multidimensional data is relatively regular, and the adjustment index is smaller. The more irregular the numerical performance of the characteristic distance in the cycle, the more it should be adjusted. The regularity of the numerical performance of the characteristic distance can be reflected in the numerical difference between the time windows. The regularity of the numerical performance of the characteristic distance can be obtained by the difference between the windows representing the numerical value, that is, the extreme value. Add the difference in the extreme values ​​between all time windows. The larger the value, the less concentrated the extreme value distribution. Therefore, the more irregular the numerical performance of the characteristic distance in the cycle, the more it should be adjusted. Therefore, according to the change of the adjusted multidimensional Hausdorff distance based on the time series, the adjustment index of the characteristic distance. According to the adjustment index of the distance, adjust the initial fault possibility to obtain the target fault possibility corresponding to multiple preset fault types. The representation of the target fault possibility can be:

[0093] in, Indicates target fault probabilities corresponding to multiple preset fault types; , They represent the target fault probability corresponding to the current fault and the target fault probability corresponding to the resistance fault respectively; , They represent the initial fault probability corresponding to the current fault and the initial fault probability corresponding to the resistance fault respectively; , , They respectively represent the adjustment indicators of current characteristic distance, zero-sequence current characteristic distance, and resistance characteristic distance.

[0094] In this embodiment, by calculating the multidimensional Hausdorff distance, the similarities and differences between multidimensional data can be more comprehensively evaluated, and the accuracy of fault detection can be improved. By determining the adjustment index, the characteristic distance can be adjusted more accurately, misjudgment can be reduced, and the reliability of fault detection can be improved. Through the adjusted multidimensional Hausdorff distance, the fault type can be identified from multiple angles, and the ability to identify the fault type can be improved. Combining the time performance of multiple characteristic distances, the possibility and severity of the fault can be comprehensively evaluated, providing more comprehensive fault diagnosis information.

[0095] In one embodiment of the present application, determining the adjustment index of the feature distance according to the adjusted multi-dimensional Hausdorff distance includes: Obtaining the period of the adjusted multi-dimensional Hausdorff distance; Obtaining a periodic law according to the variance of the characteristic distances corresponding to the multiple time windows within the period; An adjustment index of the characteristic distance is determined according to the periodic law, the number of the time windows, and the range of the characteristic distance in each time window.

[0096] Among them, variance is a statistical concept used to measure the degree of discreteness of a set of data. Periodic law refers to the regularity of the variance of feature distances corresponding to multiple time windows within a certain period. By calculating the variance of feature distances corresponding to multiple time windows within a period, the changing trend and periodic law of feature distances can be identified, so as to better understand the dynamic characteristics of the data.

[0097] The range refers to the difference between the maximum and minimum values ​​in a set of data. The range of the characteristic distance in the time window refers to the difference between the maximum and minimum values ​​of the characteristic distance in a certain time window. By calculating the range of the characteristic distance in the time window, the fluctuation range of the characteristic distance in the time window can be evaluated to judge the stability of the data.

[0098] Exemplarily, the period of the adjusted multidimensional Hausdorff distance is obtained: for the characteristic distance corresponding to the adjusted multidimensional data, 3 is used as the preset time window, and for the adjusted multidimensional Hausdorff distance based on the time series, the variance of the average characteristic distance of any time window of the characteristic distance corresponding to any adjusted multidimensional data is calculated; the time window is increased with 1 as the step size, and 15 is used as the termination time window to calculate the variance of the average characteristic distance of any length time window; the time window corresponding to the minimum value of the variance of the characteristic distance of all length time windows is recorded as the period of the characteristic distance, and the periods of three different characteristic distances constitute the period of the adjusted multidimensional Hausdorff distance; the difference between the maximum and minimum values ​​in the time window within the period of the adjusted multidimensional Hausdorff distance is recorded as the range.

[0099] Exemplarily, the adjustment index of the characteristic distance may be expressed as:

[0100] in, , , Respectively represent the adjustment indexes of current characteristic distance, zero-sequence current characteristic distance, and resistance characteristic distance; , , Respectively represent the variance of the average characteristic distance of the time window in the cycle of the adjusted current distance, the adjusted zero-sequence current distance, and the adjusted resistance distance time series, indicating the regularity of the cycle. If the average characteristic distance of each cycle is similar, then the variance is relatively small, the more regular the cycle is, and the smaller the adjustment index is; , , The number of period length time windows representing the adjusted current distance, the adjusted zero-sequence current distance, and the adjusted resistance distance, respectively; , They represent the maximum and minimum values ​​in the time window within the cycle of the adjusted current distance respectively; , respectively represent the maximum and minimum values ​​in the time window within the adjusted zero-sequence current distance; , They represent the maximum and minimum values ​​in the time window within the adjusted resistance distance, respectively; is the maximum and minimum normalization function.

[0101] in, Indicates the range within the time window of the adjusted current distance cycle. The larger the value, the greater the adjustment index of the current distance. Indicates the range within the time window of the adjusted zero-sequence current distance cycle. The larger the value, the greater the adjustment index of the zero-sequence current distance. Indicates the range within the time window of the adjusted resistance distance period. The larger the value, the greater the adjustment index of the resistance distance.

[0102] In this embodiment, by identifying the periodic law, the changing trend of the characteristic distance can be more accurately evaluated to reduce misjudgment. By calculating the range within the time window, the fluctuation range of the characteristic distance can be evaluated to improve the accuracy of fault detection. By analyzing the periodic law and the range, the fault type can be identified from multiple angles to improve the ability to identify the fault type. Combining the variance and range of multiple time windows, the possibility and severity of the fault can be comprehensively evaluated to provide more comprehensive fault diagnosis information.

[0103] In one embodiment of the present application, the performing fault repair on each current transformer according to the target fault category includes: Determining a fault circuit according to the target fault category; Check the status of the lines and devices connected to the current transformer in the fault loop, and perform fault repair on the lines and devices with abnormal status.

[0104] Among them, the , target fault probability corresponding to current fault The current and zero-sequence current indicate a short-circuit fault. It indicates the circuit is short-circuited. When the circuit is short-circuited to ground, , The value range is 0-1. The value range is 0-2), and within the value range, the larger the value, the more serious the fault. Target fault probability corresponding to resistance fault The current and resistance indicate a line break fault. It indicates a circuit disconnection fault. When it is, it indicates a short circuit fault in a specific numbered circuit, and within the value range, the larger the value, the more serious the fault.

[0105] Exemplarily, through the previous multi-dimensional data analysis and feature distance adjustment, the target fault category of each current transformer has been determined. According to the target fault category, determine the circuit involved in the fault. For example, if the target fault category of a current transformer is "loop short circuit", then the circuit in which the current transformer is located is determined to be a fault circuit. Check all connecting lines in the fault circuit, including cables, connectors, connection points, etc. Check all related equipment in the fault circuit, including switches, circuit breakers, relays, etc. If the line is found to be damaged, broken or in poor contact, repair or replace it. If the equipment is found to be faulty or abnormal, repair or replace it.

[0106] In this embodiment, by determining the target fault category, the fault circuit can be accurately identified, unnecessary inspections and repairs can be avoided, and the accuracy of fault detection can be improved. Through detailed line and equipment inspections, ineffective repairs caused by misjudgment can be reduced, ensuring the reliability of fault detection results. By promptly repairing lines and equipment in abnormal conditions, the normal operation of the system can be quickly restored and the reliability of the system can be improved. Through regular inspections and maintenance, potential faults can be prevented and the service life of the equipment can be extended. Through clear fault categories and fault circuits, maintenance personnel can quickly locate problems, shorten maintenance time, and improve maintenance efficiency. Through efficient maintenance, the downtime of the system can be reduced and the continuity of production activities can be ensured. By accurately locating faults, unnecessary repairs can be avoided and maintenance costs can be reduced. Through regular inspections and maintenance, potential faults can be prevented and the cost of overhaul and replacement of equipment can be reduced.

[0107] Figure 3 A schematic diagram of the structure of a relay protection circuit fault detection device provided by an embodiment of the present invention. The device can be applied to Figure 1 The device may also be applicable to other exemplary implementation environments and specifically configured in other devices, and this embodiment does not limit the implementation environment to which the device is applicable.

[0108] like Figure 3 As shown, the exemplary relay protection circuit fault detection device includes: A data acquisition module 301 is used to acquire multi-dimensional data of multiple current transformers in a target relay protection circuit, wherein the multi-dimensional data includes current time series data, zero-sequence current time series data, and resistance time series data; An initial fault analysis module 302 is used to analyze the change trend of the multi-dimensional data for any current transformer to obtain initial fault possibilities corresponding to multiple preset fault types; A target fault analysis module 303 is used to analyze the time performance of the characteristic distance corresponding to the multidimensional data, adjust the characteristic distance corresponding to the multidimensional data according to the analysis result of the time performance, and adjust the initial fault possibility according to the adjusted characteristic distance corresponding to the multidimensional data to obtain the target fault possibility corresponding to the multiple preset fault types; The fault category determination module 304 is used to determine the target fault category corresponding to each of the current transformers according to the target fault possibility, and perform fault repair on each of the current transformers according to the target fault category.

[0109] In this exemplary relay protection circuit fault detection device, current time series data, zero-sequence current time series data and resistance time series data are obtained, providing multi-dimensional information, which can more comprehensively reflect the status of the relay protection circuit. Different types of time series data can complement each other, reduce the limitations of a single data source, and improve the reliability of fault detection. By analyzing the changing trends of multi-dimensional data, the trends and patterns of the data can be identified, which is helpful for early detection of potential fault signs. Dynamically adjusting the characteristic distance according to the time performance of the characteristic distance can more accurately reflect the actual fault situation. The static characteristic distance cannot adapt to different fault scenarios, and dynamic adjustment can improve adaptability and accuracy. Combined with the adjusted characteristic distance, the initial fault possibility is re-evaluated to obtain a more accurate target fault possibility, which can eliminate misjudgment and improve the accuracy of fault detection. According to the target fault possibility, the specific fault category can be accurately located to avoid blind maintenance and improve maintenance efficiency.

[0110] It should be noted that the relay protection circuit fault detection device provided in the above embodiment and the relay protection circuit fault detection method provided in the above embodiment belong to the same concept, wherein the specific manner in which each module and unit performs the operation has been described in detail in the method embodiment, and will not be repeated here. In practical applications, the relay protection circuit fault detection device provided in the above embodiment can distribute the above functions to different functional modules as needed, that is, divide the internal structure of the device into different functional modules to complete all or part of the functions described above, and this is not limited here.

[0111] An embodiment of the present application also provides an electronic device, comprising: one or more processors; a storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the electronic device implements the relay protection circuit fault detection method provided in the above-mentioned embodiments.

[0112] It should be noted that the computer-readable medium shown in the embodiment of the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier, which carries a computer-readable computer program. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. A computer program contained on a computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0113] The flowchart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present application. Wherein, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0114] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. The names of these units do not constitute limitations on the units themselves in some cases.

[0115] Another aspect of the present application further provides a relay protection circuit fault detection system, the system comprising a computer-readable storage medium, the computer-readable storage medium storing a plurality of instructions, the instructions being suitable for a processor to load to execute the steps in any one of the relay protection circuit fault detection methods provided in the embodiments of the present application. The computer-readable storage medium may be included in the electronic device described in the above embodiments, or may exist independently without being assembled into the electronic device.

[0116] It should be noted that the sequence of the above embodiments of the present invention is for description only and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0117] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A method for detecting faults in a relay protection circuit, characterized in that: The method comprises: Acquire multi-dimensional data of multiple current transformers in a target relay protection circuit, wherein the multi-dimensional data includes current time series data, zero-sequence current time series data, and resistance time series data; For any of the current transformers, analyzing the change trend of the multi-dimensional data to obtain initial fault probabilities corresponding to a plurality of preset fault types; Analyzing the time performance of the characteristic distance corresponding to the multidimensional data, adjusting the characteristic distance corresponding to the multidimensional data according to the analysis result of the time performance, and adjusting the initial fault probability according to the adjusted characteristic distance corresponding to the multidimensional data to obtain the target fault probability corresponding to the multiple preset fault types; Determine a target fault category corresponding to each of the current transformers according to the target fault possibility, and perform fault repair on each of the current transformers according to the target fault category; The analyzing the time performance of the characteristic distance corresponding to the multi-dimensional data includes: According to the multidimensional data of any two current transformers, a current distance, a zero-sequence current distance and a resistance distance are obtained, wherein the current distance refers to the distance between the current values ​​of the two current transformers in the same time window, the zero-sequence current distance refers to the distance between the zero-sequence current values ​​of the two current transformers in the same time window, and the resistance distance refers to the distance between the resistance values ​​of the two current transformers in the same time window; Taking the current distance, zero-sequence current distance and resistance distance as characteristic distances; According to the numerical value and duration of the characteristic distance, a time representation of the characteristic distance of each current transformer is obtained; The step of adjusting the characteristic distance corresponding to the multidimensional data according to the analysis result of the time performance includes: adjusting the current distance according to the time performance of the current distance to obtain an adjusted current distance; According to the time performance of the zero-sequence current distance, the zero-sequence current distance is adjusted to obtain an adjusted zero-sequence current distance; The resistance distance is adjusted according to the time performance of the resistance distance to obtain an adjusted resistance distance.

2. The relay protection circuit fault detection method according to claim 1, characterized in that: After the multi-dimensional data of multiple current transformers in the target relay protection circuit are obtained, the method further includes: According to a preset wavelet basis function and a preset number of decomposition layers, a discrete wavelet transform is performed on the multidimensional data to obtain an initial approximation coefficient and an initial detail coefficient; According to a preset soft threshold function, the initial approximation coefficient and the initial detail coefficient are processed to obtain a target approximation function and a target detail function; The target approximation function and the target detail function are subjected to inverse discrete wavelet transform to obtain the denoised multidimensional data, and the denoised multidimensional data is used for change trend analysis.

3. The relay protection circuit fault detection method according to claim 1, characterized in that: The multiple preset fault types include loop short circuit, loop ground short circuit, loop open circuit and specific number loop short circuit, and the analysis of the change trend of the multi-dimensional data to obtain the initial fault probability corresponding to the multiple preset fault types includes: According to the preset neighborhood monitoring time, a time window is obtained; Analyze the change trend of the current time series data, and obtain the initial fault possibility corresponding to the loop short circuit according to the average current in the time window; Analyze the change trends of the current time series data and the zero-sequence current time series data, and obtain the initial fault possibility of the loop ground short circuit according to the average current and the zero-sequence current in the time window; Analyze the change trend of the resistance time series data, and obtain the initial fault possibility corresponding to the circuit break according to the average resistance in the time window; The change trend of the resistance time series data and the phase change of the current time series data are analyzed, and the initial fault possibility corresponding to the short circuit of the specific numbered loop is obtained according to the average resistance and phase change in the time window.

4. The relay protection circuit fault detection method according to claim 1, characterized in that: The adjusting the initial fault probability according to the adjusted characteristic distance corresponding to the multi-dimensional data to obtain the target fault probability corresponding to the multiple preset fault types includes: Obtaining an adjusted multi-dimensional Hausdorff distance according to the adjusted current distance, the adjusted zero-sequence current distance, and the adjusted resistance distance; Determining an adjustment index of the feature distance according to the adjusted multi-dimensional Hausdorff distance; The initial fault probability is adjusted according to the adjustment index of the characteristic distance to obtain the target fault probability corresponding to the multiple preset fault types.

5. The relay protection circuit fault detection method according to claim 4, characterized in that: Determining the adjustment index of the feature distance according to the adjusted multi-dimensional Hausdorff distance includes: Obtaining the period of the adjusted multi-dimensional Hausdorff distance; Obtaining a periodic law according to the variance of characteristic distances corresponding to a plurality of time windows within the period; An adjustment index of the characteristic distance is determined according to the periodic law, the number of the time windows, and the range of the characteristic distance in each time window.

6. The relay protection circuit fault detection method according to claim 1, characterized in that: The performing fault repair on each current transformer according to the target fault category includes: Determining a fault circuit according to the target fault category; Check the status of the lines and devices connected to the current transformer in the fault loop, and perform fault repair on the lines and devices with abnormal status.

7. A relay protection circuit fault detection device, characterized in that: The device comprises: A data acquisition module, used to acquire multi-dimensional data of multiple current transformers in a target relay protection circuit, wherein the multi-dimensional data includes current time series data, zero-sequence current time series data and resistance time series data; An initial fault analysis module, for analyzing the change trend of the multi-dimensional data for any current transformer, and obtaining initial fault possibilities corresponding to a plurality of preset fault types; a target fault analysis module, configured to analyze the time performance of the characteristic distance corresponding to the multidimensional data, adjust the characteristic distance corresponding to the multidimensional data according to the analysis result of the time performance, and adjust the initial fault possibility according to the adjusted characteristic distance corresponding to the multidimensional data, so as to obtain the target fault possibility corresponding to the multiple preset fault types; The fault category determination module is used to determine the target fault category corresponding to each of the current transformers according to the target fault possibility, and perform fault repair on each of the current transformers according to the target fault category.

8. A relay protection circuit fault detection system, characterized in that: The system includes a computer-readable storage medium storing a plurality of instructions, wherein the instructions are suitable for loading by a processor to execute the steps in the relay protection circuit fault detection method according to any one of claims 1 to 6.

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