Fault analysis method and system based on relay protection device

Abstract

The invention discloses a fault analysis method and system based on a relay protection device, and relates to the technical field of relay protection devices, and the key points of the technical scheme comprise the following steps: obtaining fault recording data and line parameter characteristics of the relay protection device, acquiring real-time impedance change characteristics between the power line where the relay protection device is located and the adjacent power line; processing and analyzing the historical diagnosis data, the real-time impedance change characteristics, the reference diagnosis characteristics and the sampling standard-reaching rate proportion of the relay protection device to obtain a preprocessing diagnosis difficulty value; processing and analyzing the fault recording data and the line parameter characteristics to obtain an actual fault diagnosis difficulty value; and outputting a first fault diagnosis scheme and a second fault diagnosis scheme for the actual fault diagnosis difficulty value according to the actual fault diagnosis difficulty value, the device operation and maintenance qualified rate proportion, the diagnosis error frequency and the preprocessing diagnosis difficulty value pair. The method has the effect of improving the reliability and applicability of fault analysis of the relay protection device.

Description

Technical Field

[0001] This invention relates to the field of relay protection device technology, and more specifically, to a fault analysis method and system based on relay protection devices. Background Technology

[0002] With the continuous expansion of the power grid and the increasing complexity of line structures in current power system operations, the reliability and efficiency of fault diagnosis for relay protection devices, as core equipment for fault detection and isolation, are becoming increasingly critical. Traditional relay protection fault analysis methods rely solely on fault recording data from a single line for diagnosis, failing to fully integrate the line's own parameter characteristics and ignoring the electrical correlation between the target line and adjacent lines. This makes it difficult to capture the cross-line fault impact and correlation changes during fault diagnosis, easily leading to diagnostic biases. Furthermore, existing methods lack a quantitative assessment of fault diagnosis difficulty, often employing fixed diagnostic procedures to handle fault scenarios of varying complexity, failing to allocate sufficient diagnostic resources for high-difficulty faults. Traditional solutions fail to combine historical experience with real-time fault characteristics, resulting in a lack of effective reference points for the diagnostic process and making it difficult to adapt to the dynamically changing operating states of the power system. Summary of the Invention

[0003] In view of the shortcomings of the existing technology, the purpose of this invention is to provide a fault analysis method and system based on relay protection devices.

[0004] To achieve the above objectives, the present invention provides the following technical solution: A fault analysis method based on relay protection devices, the method comprising the following steps: Obtain fault recording data and line parameter characteristics of relay protection devices, and obtain real-time impedance change characteristics between the power line where the relay protection device is located and adjacent power lines; The preprocessing diagnostic difficulty value is obtained by processing and analyzing historical diagnostic data, real-time impedance change characteristics, reference diagnostic characteristics, and the sampling compliance rate of relay protection devices. The fault recording data and line parameter characteristics are processed and analyzed to obtain the actual fault diagnosis difficulty value; based on the actual fault diagnosis difficulty value, the equipment operation and maintenance qualification rate, the frequency of diagnosis errors, and the pre-processing diagnosis difficulty value, the first fault diagnosis scheme and the second fault diagnosis scheme are output according to the actual fault diagnosis difficulty value.

[0005] Preferably, the preprocessing diagnostic difficulty value is obtained by processing and analyzing historical diagnostic data, real-time impedance change characteristics, reference diagnostic characteristics, and the sampling compliance rate of relay protection devices. This specifically includes the following steps: Reference diagnostic features for historical fault characteristics and the scope of historical fault impact in historical diagnostic data; The fault cross-line propagation prediction area is obtained by matching the real-time impedance change characteristics from the reference diagnostic features. The pre-processing diagnostic difficulty value is obtained based on the fault cross-line propagation prediction area and the sampling compliance rate of the relay protection device.

[0006] Preferably, the fault recording data and line parameter characteristics are processed and analyzed to obtain the actual fault diagnosis difficulty value, specifically including the following steps: The influence correlation coefficient is obtained based on the repetition frequency of fault characteristic quantities and line parameter characteristics in the fault recording data. The actual fault diagnosis difficulty value of power lines is obtained based on the influence correlation coefficient.

[0007] Preferably, based on the actual fault diagnosis difficulty value, the equipment operation and maintenance qualification rate, the frequency of diagnosis errors, and the pre-processing diagnosis difficulty value, a first fault diagnosis scheme and a second fault diagnosis scheme are output according to the actual fault diagnosis difficulty value, specifically including the following steps: If the actual fault diagnosis difficulty value is greater than or equal to the preset diagnosis warning threshold, the first fault diagnosis scheme is output based on the proportion of equipment operation and maintenance qualification rate of other power lines and the frequency of diagnosis errors. If the actual fault diagnosis difficulty value is less than the preset diagnosis warning threshold, a second fault diagnosis scheme is output based on the preprocessed diagnosis difficulty value and the actual fault diagnosis difficulty value.

[0008] Preferably, the reference diagnostic features of historical fault characteristics and historical fault impact range in the historical diagnostic data are statistically analyzed, specifically including the following steps: Obtain historical fault diagnosis data between the power line where the relay protection device is located and adjacent power lines; Extract historical fault feature quantities from historical fault diagnosis data that exhibit the same frequency of repetition of fault feature quantities. Extract the historical fault impact range of adjacent power lines affected by the target power line from historical fault diagnosis data under the characteristic conditions of the historical fault feature quantity; The historical fault characteristic quantity and the historical fault impact range are combined to form the reference diagnostic characteristics.

[0009] Preferably, the fault cross-line propagation prediction region is obtained by matching the real-time impedance change characteristics from the reference diagnostic features, specifically including the following steps: The real-time impedance variation characteristics between the power line where the relay protection device is located and adjacent power lines are statistically analyzed. The fault propagation prediction area of ​​adjacent power lines is obtained by matching real-time impedance change characteristics with reference diagnostic characteristics.

[0010] Preferably, the pre-processing diagnostic difficulty value is obtained based on the predicted fault cross-line propagation area and the sampling compliance rate of the relay protection device, specifically including the following steps: The percentage of sampling compliance rate of relay protection devices within the predicted area of ​​cross-line fault propagation is statistically analyzed. The number of protection devices deployed on the power lines where the relay protection devices are located is statistically analyzed, and the influence correlation coefficient is calculated by comparing the number of protection devices deployed to obtain the unit correlation coefficient. The comprehensive adjacent correlation value is obtained by multiplying the unit correlation coefficient and the sampling compliance rate ratio. The adjacent interference of adjacent power line sampling on fault diagnosis is obtained by multiplying the sampling compliance rate ratio and harmonic distortion rate. The preprocessing diagnostic difficulty value of adjacent power lines is obtained by summing the comprehensive adjacent correlation value and adjacent interference value.

[0011] Preferably, based on the percentage of qualified operation and maintenance of equipment on other power lines and the frequency of diagnostic errors, a first fault diagnosis scheme is output according to the actual fault diagnosis difficulty value, specifically including the following steps: The remaining power lines are those that are excluded from the adjacent power lines. Statistical analysis of the equipment maintenance qualification rate of the remaining lines, and obtaining the frequency of diagnostic errors for each of the remaining lines in historical periods; The percentage of qualified equipment operation and maintenance and the frequency of diagnostic errors are integrated into auxiliary diagnostic factors. The diagnostic weight ratio is obtained by calculating the proportion of each auxiliary diagnostic factor in the overall auxiliary diagnostic factors. Obtain the fault diagnosis carrying capacity of the remaining lines, and multiply the fault diagnosis carrying capacity and the diagnosis weight ratio to obtain the actual diagnosis capacity of the remaining lines. The difference between the actual fault diagnosis difficulty value and the diagnosis warning threshold is used to obtain the additional diagnosis requirement; Based on the actual diagnostic capabilities, the additional diagnostic requirements are processed for fault diagnosis, and then the first fault diagnosis solution is output.

[0012] Preferably, a second fault diagnosis scheme is output based on the preprocessed diagnostic difficulty value and the actual fault diagnosis difficulty value, specifically including the following steps: Extract the comprehensive neighbor correlation value and neighbor interference amount contained in the preprocessing diagnostic difficulty value; The correlation strength level of adjacent power lines to the fault diagnosis of the target line is determined based on the comprehensive adjacent correlation value; Based on the correlation strength level, key adjacent lines that have an impact on the fault diagnosis of the target line are selected. A fault diagnosis dataset is formed by integrating sampled data from key adjacent lines with fault recording data and line parameter characteristics of the target line. Adjust the detection accuracy threshold for fault diagnosis based on the pre-processing diagnostic difficulty value; The fault diagnosis dataset is used to diagnose the fault type, fault location, and fault severity of the target line according to the detection accuracy threshold, thus obtaining the second fault diagnosis scheme.

[0013] A fault analysis system based on relay protection devices includes: Acquisition module: Acquires fault recording data and line parameter characteristics of relay protection devices, and acquires real-time impedance change characteristics between the power line where the relay protection device is located and adjacent power lines; Processing module: Processes and analyzes historical diagnostic data, real-time impedance change characteristics, reference diagnostic characteristics, and the sampling compliance rate of relay protection devices to obtain a pre-processed diagnostic difficulty value; Output module: Processes and analyzes fault recording data and line parameter characteristics to obtain the actual fault diagnosis difficulty value; Based on the actual fault diagnosis difficulty value, the equipment operation and maintenance qualification rate, the frequency of diagnosis errors, and the pre-processed diagnosis difficulty value, outputs the first fault diagnosis scheme and the second fault diagnosis scheme.

[0014] Compared with the prior art, the present invention has the following beneficial effects: This invention acquires fault recording data and line parameter characteristics of the relay protection device itself, and obtains real-time impedance change characteristics of the target line and adjacent lines. This provides a more complete basis for fault analysis, enabling more accurate capture of fault correlation characteristics and avoiding diagnostic biases caused by incomplete data. By quantifying both the pre-processed diagnostic difficulty value and the actual fault diagnosis difficulty value, a clear standard for measuring the complexity of fault diagnosis is established. This helps the diagnostic system to more rationally adapt diagnostic resources, avoiding excessive waste or insufficiency, and improving the scientific rigor and efficiency of the diagnostic process. The invention adaptively outputs diagnostic solutions for fault scenarios of varying difficulty. When the fault diagnosis difficulty is high, it utilizes maintenance and diagnostic data from other lines to assist in the analysis; when the difficulty is low, it optimizes the diagnostic process by combining correlation information from adjacent lines. This method ensures accuracy in diagnosing high-difficulty faults while improving efficiency in diagnosing low-difficulty faults, balancing diagnostic accuracy and timeliness. By fully utilizing the combination of historical diagnostic data and real-time data, historical experience can effectively guide real-time fault analysis, while real-time data can reflect the dynamic changes of the current fault. This allows fault diagnosis to be supported by experience and adaptable to the differences in actual scenarios, thereby improving the reliability and applicability of fault analysis of relay protection devices and meeting the fault diagnosis needs of complex power networks. Attached Figure Description

[0015] Figure 1 This invention provides a schematic diagram illustrating the steps of a fault analysis method based on a relay protection device. Figure 2 This invention presents a schematic diagram of a fault analysis system based on a relay protection device. Detailed Implementation

[0016] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0017] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0018] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0019] Reference Figures 1-2 As shown.

[0020] The embodiments further illustrate the fault analysis method and system based on relay protection devices proposed in this invention.

[0021] A fault analysis method based on relay protection devices, the method comprising the following steps: Obtain fault recording data and line parameter characteristics of relay protection devices, and obtain real-time impedance change characteristics between the power line where the relay protection device is located and adjacent power lines; The preprocessing diagnostic difficulty value is obtained by processing and analyzing historical diagnostic data, real-time impedance change characteristics, reference diagnostic characteristics, and the sampling compliance rate of relay protection devices. The fault recording data and line parameter characteristics are processed and analyzed to obtain the actual fault diagnosis difficulty value; based on the actual fault diagnosis difficulty value, the equipment operation and maintenance qualification rate, the frequency of diagnosis errors, and the pre-processing diagnosis difficulty value, the first fault diagnosis scheme and the second fault diagnosis scheme are output according to the actual fault diagnosis difficulty value.

[0022] The preprocessing diagnostic difficulty value is obtained by processing and analyzing historical diagnostic data, real-time impedance change characteristics, reference diagnostic characteristics, and the sampling compliance rate of relay protection devices. This process includes the following steps: Reference diagnostic features for historical fault characteristics and the scope of historical fault impact in historical diagnostic data; The fault cross-line propagation prediction area is obtained by matching the real-time impedance change characteristics from the reference diagnostic features. The pre-processing diagnostic difficulty value is obtained based on the fault cross-line propagation prediction area and the sampling compliance rate of the relay protection device.

[0023] Historical fault diagnosis data is collected from the power lines where the relay protection device is located and adjacent power lines. This data covers various information about past fault occurrences. From this historical data, historical fault characteristic quantities are extracted based on the frequency of repetition of the same fault characteristic quantity. The frequency of repetition refers to the number of times the same type of fault characteristic (such as overcurrent or overvoltage) occurs per unit time, while the historical fault characteristic quantity is the specific electrical parameter performance corresponding to the fault at that frequency, such as the peak current and duration of an overcurrent fault at a certain frequency. The historical fault spillover range affected by the target power line is extracted from the historical data under the condition of the historical fault characteristic quantity. This is the area of ​​adjacent lines affected by the fault when the target line experiences this type of fault, such as the length of the line segment in the adjacent line where the voltage fluctuation exceeds a threshold after a short-circuit fault occurs on the target line. The extracted historical fault characteristic quantity is combined with the corresponding historical fault spillover range to form reference diagnostic features.

[0024] The fault cross-line propagation prediction area is based on real-time data matching. The real-time impedance change characteristics between the power line where the relay protection device is located and adjacent power lines are statistically analyzed. These real-time impedance change characteristics reflect the dynamic changes in the electrical coupling relationship between the target line and adjacent lines at the current moment. For example, a sudden drop in impedance between lines may indicate a short-circuit risk. This real-time impedance change characteristic is matched with previously constructed reference diagnostic characteristics. The entry that most closely matches the current real-time impedance change characteristic is found. The corresponding historical fault impact range is the fault cross-line propagation prediction area in the current scenario. For example, if the real-time impedance change characteristic matches the impedance characteristics of a historical short-circuit fault, then the impact range of the adjacent lines corresponding to that short-circuit fault is the current fault cross-line propagation prediction area.

[0025] The preprocessing diagnostic difficulty value is calculated by combining the sampling compliance rate percentage. First, the sampling compliance rate percentage of relay protection devices within the fault cross-line propagation prediction area is statistically analyzed. This percentage refers to the ratio of the number of relay protection devices whose sampling data meets the diagnostic accuracy requirements within the prediction area to the total number of relay protection devices in that area. The number of protection devices deployed on the power lines where the relay protection devices are located is also statistically analyzed, which is the total number of relay protection devices installed on the target line. The unit correlation coefficient = influence correlation coefficient ÷ number of protection devices deployed. The influence correlation coefficient is obtained based on the repetition frequency of fault characteristics and line parameter characteristics in the fault recording data, reflecting the correlation strength between fault characteristics and line parameters. The comprehensive adjacent correlation value = unit correlation coefficient × sampling compliance rate percentage. This value reflects the comprehensive impact of the correlation between adjacent lines and the target line and the data reliability. The adjacent interference amount = sampling compliance rate percentage × harmonic distortion rate. The harmonic distortion rate is the degree of deviation of harmonic components in the line from the fundamental frequency. This value reflects the degree of interference of adjacent line sampling data on fault diagnosis. The preprocessing diagnostic difficulty value is obtained by adding the comprehensive adjacent correlation value and the adjacent interference amount. The preprocessing diagnostic difficulty value = comprehensive adjacent correlation value + adjacent interference amount. The higher this value, the higher the complexity of the pre-diagnosis in the fault scenario.

[0026] Assuming the target line has 10 protection devices deployed, and the impact correlation coefficient is 20; there are 5 relay protection devices in the fault cross-line propagation prediction area, of which 4 have passed the sampling standard, the sampling pass rate is 4÷5=0.8; the harmonic distortion rate is 0.2. Therefore, the unit correlation coefficient is 20÷10=2, the comprehensive adjacent correlation value is 2×0.8=1.6, the adjacent interference is 0.8×0.2=0.16, and the final preprocessing diagnostic difficulty value is 1.6+0.16=1.76. This value represents the preprocessing diagnostic difficulty in the current scenario.

[0027] The actual fault diagnosis difficulty value is obtained by processing and analyzing fault recording data and line parameter characteristics, specifically including the following steps: The influence correlation coefficient is obtained based on the repetition frequency of fault characteristic quantities and line parameter characteristics in the fault recording data. The actual fault diagnosis difficulty value of power lines is obtained based on the influence correlation coefficient.

[0028] Obtaining the correlation coefficient is crucial for connecting fault characteristics with line attributes. The repetition frequency of fault characteristics refers to the number of times the same type of fault characteristic (such as overcurrent or voltage surge) occurs per unit time in fault waveform data. It reflects the activity level of the fault; a higher frequency indicates more frequent manifestation of the fault characteristics and stronger persistence or recurrence of the fault. Line parameter characteristics are the inherent electrical properties of power lines, including parameters such as resistance, reactance, and capacitance. These parameters determine the electrical characteristics of the line, and different parameters affect the propagation and manifestation of faults within the line.

[0029] When calculating the influence correlation coefficient, it is necessary to consider the degree of correlation between these two features. First, the correlation between the repetition frequency of the fault feature and the line parameter features should be determined. For example, by statistically analyzing the matching degree of their changing trends, the response pattern of the line parameters when the fault feature appears can be determined. The influence correlation coefficient = repetition frequency of the fault feature × weight coefficient of the line parameter features. The weight coefficient of the line parameter features is determined according to the degree of influence of the line parameters on this type of fault. For example, if the reactance parameter of a certain line has a greater impact on overcurrent faults, then the weight coefficient of the corresponding reactance will be higher. If the repetition frequency of a fault feature is 8 (i.e., the fault feature appears 8 times per unit time), and the weight coefficient of the corresponding line parameter feature (such as reactance) is 2.5, then the influence correlation coefficient is 8 × 2.5 = 20. This value reflects the strength of the correlation between the fault feature and the current line parameters. The higher the value, the closer the binding relationship between the fault and the line.

[0030] The actual fault diagnosis difficulty value is obtained based on the influence correlation coefficient. The actual fault diagnosis difficulty value is a quantitative reflection of the complexity of fault diagnosis. It is directly mapped from the influence correlation coefficient, because the influence correlation coefficient already reflects the degree of correlation between the activity of fault characteristics and line attributes. The higher the degree of correlation, the more deeply the fault performance will be bound to the line characteristics. During diagnosis, the mutual influence of fault characteristics and line parameters needs to be considered at the same time, and the higher the complexity of diagnosis.

[0031] The actual fault diagnosis difficulty value = influence correlation coefficient × diagnosis complexity coefficient, where the diagnosis complexity coefficient is a fixed coefficient (e.g., set to 0.8) based on industry experience or historical diagnosis data, used to convert the correlation coefficient into a value that meets the diagnosis difficulty assessment standard. If the influence correlation coefficient is 20 and the diagnosis complexity coefficient is 0.8, then the actual fault diagnosis difficulty value is 20 × 0.8 = 16. The higher this value, the greater the difficulty in diagnosing the fault in the power line, requiring a more complex diagnosis solution; conversely, the lower the value, the easier the diagnosis, and a relatively simplified diagnosis process can be used.

[0032] Based on the actual fault diagnosis difficulty value, the equipment operation and maintenance qualification rate, the frequency of diagnosis errors, and the pre-processing diagnosis difficulty value, a first fault diagnosis plan and a second fault diagnosis plan are output according to the actual fault diagnosis difficulty value, specifically including the following steps: If the actual fault diagnosis difficulty value is greater than or equal to the preset diagnosis warning threshold, the first fault diagnosis scheme is output based on the proportion of equipment operation and maintenance qualification rate of other power lines and the frequency of diagnosis errors. If the actual fault diagnosis difficulty value is less than the preset diagnosis warning threshold, a second fault diagnosis scheme is output based on the preprocessed diagnosis difficulty value and the actual fault diagnosis difficulty value.

[0033] The preset diagnostic warning thresholds are determined using historical data.

[0034] The statistical analysis of historical fault characteristics and the reference diagnostic features of historical fault impact range in historical diagnostic data includes the following steps: Obtain historical fault diagnosis data between the power line where the relay protection device is located and adjacent power lines; Extract historical fault feature quantities from historical fault diagnosis data that exhibit the same frequency of repetition of fault feature quantities. Extract the historical fault impact range of adjacent power lines affected by the target power line from historical fault diagnosis data under the characteristic conditions of the historical fault feature quantity; Among them, the historical fault characteristic quantity and the historical fault impact range are combined to form the reference diagnostic characteristics.

[0035] Historical fault diagnosis data is a collection of diagnostic records for all faults that occurred during the past operation of the target power line and adjacent power lines where the relay protection device is located. It covers information such as the time of fault occurrence, fault type, changes in electrical parameters corresponding to the fault, and the response of adjacent lines. For example, if the target line is a 10kV distribution line and the adjacent lines are two branches connected to it, then it is necessary to collect diagnostic data for all short-circuit and overcurrent faults of these three lines over the past three years.

[0036] It is necessary to extract historical fault characteristic quantities with the same repetition frequency. The repetition frequency of a fault characteristic quantity refers to the number of times a certain type of fault characteristic (such as overcurrent) occurs per unit time. For example, if the overcurrent characteristic appears 6 times in 1 minute in a short-circuit fault, this is the repetition frequency of the fault characteristic quantity. Fault cases with the same repetition frequency of fault characteristic quantities are selected from historical fault diagnosis data. The corresponding historical fault characteristic quantities are extracted from these cases. These historical fault characteristic quantities are the specific electrical performance parameters corresponding to the fault at that frequency, such as the peak current, duration, and voltage fluctuation amplitude of the overcurrent fault. For example, all cases with a fault characteristic quantity repetition frequency of 6 times / minute are selected from historical data. The peak current of the overcurrent fault in these cases is mostly around 1200A, and the duration is about 5 seconds. These parameters constitute the historical fault characteristic quantities at that frequency.

[0037] Extract the historical fault impact range under the corresponding conditions. The historical fault impact range is represented by indicators such as the length of the affected line segment in adjacent lines and the number of affected relay protection devices. For example, for an overcurrent fault with a repetition frequency of 6 times / minute and a peak current of 1200A, historical data shows that at this time, the voltage fluctuation of approximately 2 kilometers of adjacent branch lines exceeded the normal threshold, and 3 relay protection devices on this line segment detected abnormalities. Therefore, the 2-kilometer line segment and 3 devices on the adjacent line constitute the historical fault impact range under this condition.

[0038] Historical fault characteristics are combined with their corresponding historical fault spread ranges to form reference diagnostic features. Essentially, reference diagnostic features are a set of correspondences between the frequency of fault characteristic repetition, historical fault characteristics, and historical fault spread ranges. By matching the characteristics of a real-time fault with the corresponding reference diagnostic features, the cross-line propagation status of the current fault can be determined. For example, a historical fault characteristic with a repetition frequency of 6 times / minute and a peak current of 1200A+ lasting for 5 seconds, combined with the historical fault spread range of a 2-kilometer section of adjacent lines and 3 devices, becomes a reference diagnostic feature. When a real-time fault exhibits the same repetition frequency and characteristic, this record can be used to determine the current fault spread range.

[0039] The fault cross-line propagation prediction region is obtained by matching the real-time impedance change characteristics with the reference diagnostic features, specifically including the following steps: The real-time impedance variation characteristics between the power line where the relay protection device is located and adjacent power lines are statistically analyzed. The fault propagation prediction area of ​​adjacent power lines is obtained by matching real-time impedance change characteristics with reference diagnostic characteristics.

[0040] First, we need to statistically analyze the real-time impedance variation characteristics between the power line where the relay protection device is located and adjacent power lines. Real-time impedance variation characteristics refer to the dynamic changes in the electrical coupling relationship between the target line and adjacent lines, specifically the fluctuations in the impedance value between the lines over time. Impedance is a core parameter reflecting the tightness of the electrical connection between lines. Under normal operation, the impedance between lines is relatively stable. However, when a fault occurs in the target line, the voltage and current of the line will change abruptly, leading to abnormal fluctuations in the impedance between the lines. For example, a short-circuit fault will cause a significant drop in the impedance between the lines. It is necessary to collect the voltage and current data of the target line and adjacent lines in real time using sensors on the line. Impedance = Voltage ÷ Current. We need to continuously record the amplitude and rate of change of this value; this information together constitutes the real-time impedance variation characteristics.

[0041] The predicted fault propagation area across lines is obtained by matching real-time impedance change characteristics with reference diagnostic characteristics. Reference diagnostic characteristics are a set of corresponding fault characteristic repetition frequencies, historical fault characteristic quantities, and historical fault impact ranges constructed based on historical data. The matching process involves finding the reference diagnostic characteristics that are most similar to the current real-time impedance change characteristics. Specifically, the similarity between the real-time impedance change characteristics and the corresponding impedance characteristics in each reference diagnostic characteristic is calculated: similarity = 1 - |real-time impedance change amplitude - historical impedance change amplitude| ÷ historical impedance change amplitude. The closer this value is to 1, the higher the degree of matching.

[0042] Once the reference diagnostic feature with the highest matching degree is found, the historical fault spread range corresponding to that feature is the fault cross-line propagation prediction area in the current scenario.

[0043] The pre-processing diagnostic difficulty value is obtained based on the predicted fault propagation area across lines and the sampling compliance rate of relay protection devices. The specific steps include: The percentage of sampling compliance rate of relay protection devices within the predicted area of ​​cross-line fault propagation is statistically analyzed. The number of protection devices deployed on the power lines where the relay protection devices are located is statistically analyzed, and the influence correlation coefficient is calculated by comparing the number of protection devices deployed to obtain the unit correlation coefficient. The comprehensive adjacent correlation value is obtained by multiplying the unit correlation coefficient and the sampling compliance rate ratio. The adjacent interference of adjacent power line sampling on fault diagnosis is obtained by multiplying the sampling compliance rate ratio and harmonic distortion rate. The preprocessing diagnostic difficulty value of adjacent power lines is obtained by summing the comprehensive adjacent correlation value and adjacent interference value.

[0044] The sampling compliance rate of relay protection devices within the fault cross-line propagation prediction area is calculated. This compliance rate is the ratio of the number of relay protection devices whose sampling data accuracy and completeness meet the fault diagnosis requirements within the prediction area to the total number of devices in the area. This ratio directly reflects the reliability of the sampling data within the area; a higher ratio indicates stronger data reference value. For example, if there are 5 relay protection devices within the fault cross-line propagation prediction area, and the sampling data of 4 of them meet the diagnostic criteria, then the compliance rate is 4 ÷ 5 = 0.8.

[0045] The influence correlation coefficient is derived from the repetition frequency of fault characteristics and line parameter characteristics in fault recording data, reflecting the correlation strength between fault characteristics and line parameters. The number of protection devices deployed is the total number of relay protection devices installed on the target power line. The unit correlation coefficient = influence correlation coefficient ÷ number of protection devices deployed; it reflects the correlation strength between the fault corresponding to a single protection device and the line. For example, if the influence correlation coefficient is 20 and the target line has 10 protection devices deployed, then the unit correlation coefficient is 20 ÷ 10 = 2.

[0046] The comprehensive adjacent correlation value is the product of the unit correlation coefficient and the sampling compliance rate percentage. The comprehensive adjacent correlation value = unit correlation coefficient × sampling compliance rate percentage. It integrates the correlation strength of a single device with the reliability of regional sampling data, reflecting the overall correlation between adjacent lines and the target line. If the unit correlation coefficient is 2 and the sampling compliance rate percentage is 0.8, then the comprehensive adjacent correlation value is 2 × 0.8 = 1.6.

[0047] Harmonic distortion rate refers to the degree to which harmonic components in adjacent lines deviate from the fundamental frequency. The higher the harmonic distortion rate, the greater the interference to the sampled data, and the more significant the impact on fault diagnosis. Adjacent interference amount = sampling pass rate percentage × harmonic distortion rate; it quantifies the degree of interference of adjacent line sampled data on the diagnosis of the target line. Assuming the current harmonic distortion rate is 0.2, then the adjacent interference amount is 0.8 × 0.2 = 0.16.

[0048] The preprocessing diagnostic difficulty value for adjacent power lines is obtained by summing the comprehensive adjacent correlation value and the adjacent interference quantity. The preprocessing diagnostic difficulty value = comprehensive adjacent correlation value + adjacent interference quantity. The higher this value, the more complex the impact of adjacent lines on the fault diagnosis of the target line, and the greater the diagnostic difficulty.

[0049] Based on the percentage of qualified operation and maintenance equipment on other power lines and the frequency of diagnostic errors, the first fault diagnosis plan is output according to the actual fault diagnosis difficulty value, which specifically includes the following steps: The remaining power lines are those that are excluded from the adjacent power lines. Statistical analysis of the equipment maintenance qualification rate of the remaining lines, and obtaining the frequency of diagnostic errors for each of the remaining lines in historical periods; The percentage of qualified equipment operation and maintenance and the frequency of diagnostic errors are integrated into auxiliary diagnostic factors. The diagnostic weight ratio is obtained by calculating the proportion of each auxiliary diagnostic factor in the overall auxiliary diagnostic factors. Obtain the fault diagnosis carrying capacity of the remaining lines, and multiply the fault diagnosis carrying capacity and the diagnosis weight ratio to obtain the actual diagnosis capacity of the remaining lines. The difference between the actual fault diagnosis difficulty value and the diagnosis warning threshold is used to obtain the additional diagnosis requirement; Based on the actual diagnostic capabilities, the additional diagnostic requirements are processed for fault diagnosis, and then the first fault diagnosis solution is output.

[0050] The remaining lines are those other than the target power line, excluding adjacent power lines. These remaining lines serve as the resource carrier for subsequent auxiliary diagnostics. Selecting non-adjacent lines avoids data distortion caused by the potential spread of faults to adjacent lines. For example, if the target line has 3 adjacent lines, and there are 8 other power lines in total, then there are 8-3=5 remaining lines.

[0051] The statistics are as follows: The percentage of qualified maintenance devices and the frequency of diagnostic errors are calculated for the remaining lines. The percentage of qualified maintenance devices is the ratio of the number of relay protection devices on the remaining lines that meet the maintenance standards to the total number of devices on that line; it reflects the reliability of the line's device data. The frequency of diagnostic errors is the number of times an incorrect diagnosis occurred in the historical fault diagnosis of the remaining line; the lower the frequency, the higher the diagnostic reference value of the line. For example, if a remaining line has 10 devices, of which 9 are qualified, the percentage of qualified maintenance devices is 9 ÷ 10 = 0.9; the historical diagnostic error frequency for this line is 1.

[0052] These two indicators are integrated into an auxiliary diagnostic factor, which is calculated as: Auxiliary Diagnostic Factor = Equipment Operation and Maintenance Qualification Rate Percentage ÷ (Diagnostic Error Frequency + 1). This factor comprehensively reflects the reliability of auxiliary diagnosis for the remaining lines.

[0053] First, calculate the sum of the auxiliary diagnostic factors for all remaining lines. Then, calculate the ratio of the auxiliary diagnostic factor for a single remaining line to this sum. The diagnostic weight ratio = auxiliary diagnostic factor for a single remaining line ÷ comprehensive auxiliary diagnostic factor. This represents the resource allocation proportion of that line in auxiliary diagnosis. For example, if the auxiliary diagnostic factor for line A is 0.45, and that for line B is 0.27, and the comprehensive auxiliary diagnostic factor is 0.45 + 0.27 = 0.72, then the diagnostic weight ratio for line A is 0.45 ÷ 0.72 = 0.625, and the diagnostic weight ratio for line B is 0.27 ÷ 0.72 = 0.375.

[0054] Fault diagnosis carrying capacity refers to the additional fault diagnosis workload that a single remaining line can undertake. Actual diagnostic capacity = fault diagnosis carrying capacity × diagnostic weight ratio. It is the auxiliary diagnostic resource actually provided by the line. For example, if the fault diagnosis carrying capacity of line A is 8 and the diagnostic weight ratio is 0.625, then the actual diagnostic capacity is 8 × 0.625 = 5; if the carrying capacity of line B is 6, then the actual diagnostic capacity is 6 × 0.375 = 2.25.

[0055] Additional diagnostic requirements = Actual fault diagnosis difficulty value - Diagnostic warning threshold. This represents the portion of the target line fault diagnosis that exceeds the basic difficulty. For example, assuming the actual fault diagnosis difficulty value is 18 and the diagnostic warning threshold is 15, then the additional diagnostic requirements are 18 - 15 = 3.

[0056] After processing the additional diagnostic requirements based on the actual diagnostic capabilities, a first fault diagnosis solution is output. For example, if the total actual diagnostic capabilities of lines A and B are 5 + 2.25 = 7.25, which is sufficient to cover the additional diagnostic requirement of 3, then by utilizing the resources of these two lines to complete the diagnosis, a first fault diagnosis solution containing information such as fault type and location can be obtained.

[0057] Based on the preprocessing diagnostic difficulty value, a second fault diagnosis scheme is output according to the actual fault diagnosis difficulty value, specifically including the following steps: Extract the comprehensive neighbor correlation value and neighbor interference amount contained in the preprocessing diagnostic difficulty value; The correlation strength level of adjacent power lines to the fault diagnosis of the target line is determined based on the comprehensive adjacent correlation value; Based on the correlation strength level, key adjacent lines that have an impact on the fault diagnosis of the target line are selected. A fault diagnosis dataset is formed by integrating sampled data from key adjacent lines with fault recording data and line parameter characteristics of the target line. Adjust the detection accuracy threshold for fault diagnosis based on the pre-processing diagnostic difficulty value; The fault diagnosis dataset is used to diagnose the fault type, fault location, and fault severity of the target line according to the detection accuracy threshold, thus obtaining the second fault diagnosis scheme.

[0058] The preprocessing diagnostic difficulty value includes the comprehensive neighbor correlation value and the neighbor interference quantity. The comprehensive neighbor correlation value is calculated using the unit correlation coefficient and the sampling pass rate percentage, reflecting the degree of correlation between adjacent lines and the target line. The neighbor interference quantity is the product of the sampling pass rate percentage and the harmonic distortion rate, reflecting the degree of interference of adjacent line sampling data on the diagnosis. For example, when the preprocessing diagnostic difficulty value is 1.76, the corresponding comprehensive neighbor correlation value is 1.6, and the neighbor interference quantity is 0.16.

[0059] The association strength level of adjacent power lines is determined based on the comprehensive adjacent association value. The association strength level is divided according to the numerical range of the comprehensive adjacent association value: a comprehensive adjacent association value greater than 2 is a high level, between 1 and 2 is a medium level, and less than 1 is a low level. The higher the level, the greater the impact of the adjacent line on the fault diagnosis of the target line. Taking a comprehensive adjacent association value of 1.6 as an example, the corresponding association strength level is medium.

[0060] Critical adjacent lines are selected based on their association strength level. This means choosing adjacent lines with a high or medium association strength level. These lines are core associated objects that significantly influence the fault diagnosis of the target line. For example, if a target line has three adjacent lines, and two of them have a medium association strength level, these two are selected as critical adjacent lines.

[0061] It is necessary to combine the sampled data of the selected key adjacent lines with the fault waveform data and line parameter characteristics of the target line itself. The fault waveform data contains voltage and current waveform information at the time of the fault, while the line parameter characteristics are the inherent properties of the line such as resistance and reactance. The dataset formed by integrating the three can more comprehensively reflect the fault scenario. For example, the sampled data of the key adjacent lines includes their voltage fluctuation records. Combined with the overcurrent fault waveform data and line reactance parameters of the target line, a complete fault diagnosis dataset is formed.

[0062] The detection accuracy threshold is adjusted based on the preprocessing diagnostic difficulty value. The detection accuracy threshold is the critical standard for judging fault characteristics during fault diagnosis. The higher the preprocessing diagnostic difficulty value, the more complex the diagnostic scenario, and the more stringent the detection accuracy threshold needs to be set. For example, if the preprocessing diagnostic difficulty value is 1.76, the detection accuracy threshold can be set to 0.85; if the preprocessing diagnostic difficulty value is 1.2, then the detection accuracy threshold should be set to 0.8.

[0063] The fault diagnosis dataset is judged according to the adjusted detection accuracy threshold to identify the fault type of the target line, such as short circuit or overcurrent, fault location in the middle section of the line, and fault severity as mild or severe, thus finally obtaining the second fault diagnosis scheme.

[0064] A fault analysis system based on relay protection devices includes: Acquisition module: Acquires fault recording data and line parameter characteristics of relay protection devices, and acquires real-time impedance change characteristics between the power line where the relay protection device is located and adjacent power lines; Processing module: Processes and analyzes historical diagnostic data, real-time impedance change characteristics, reference diagnostic characteristics, and the sampling compliance rate of relay protection devices to obtain a pre-processed diagnostic difficulty value; Output module: Processes and analyzes fault recording data and line parameter characteristics to obtain the actual fault diagnosis difficulty value; Based on the actual fault diagnosis difficulty value, the equipment operation and maintenance qualification rate, the frequency of diagnosis errors, and the pre-processed diagnosis difficulty value, outputs the first fault diagnosis scheme and the second fault diagnosis scheme.

[0065] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0066] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0067] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A fault analysis method based on relay protection devices, characterized in that, The method includes the following steps: Obtain fault recording data and line parameter characteristics of relay protection devices, and obtain real-time impedance change characteristics between the power line where the relay protection device is located and adjacent power lines; The preprocessing diagnostic difficulty value is obtained by processing and analyzing historical diagnostic data, real-time impedance change characteristics, reference diagnostic characteristics, and the sampling compliance rate of relay protection devices. The fault recording data and line parameter characteristics are processed and analyzed to obtain the actual fault diagnosis difficulty value; based on the actual fault diagnosis difficulty value, the equipment operation and maintenance qualification rate, the frequency of diagnosis errors, and the pre-processing diagnosis difficulty value, the first fault diagnosis scheme and the second fault diagnosis scheme are output according to the actual fault diagnosis difficulty value.

2. The fault analysis method based on relay protection devices according to claim 1, characterized in that, The preprocessing diagnostic difficulty value is obtained by processing and analyzing historical diagnostic data, real-time impedance change characteristics, reference diagnostic characteristics, and the sampling compliance rate of relay protection devices. This process includes the following steps: Reference diagnostic features for historical fault characteristics and the scope of historical fault impact in historical diagnostic data; The fault cross-line propagation prediction area is obtained by matching the real-time impedance change characteristics from the reference diagnostic features. The pre-processing diagnostic difficulty value is obtained based on the fault cross-line propagation prediction area and the sampling compliance rate of the relay protection device.

3. The fault analysis method based on relay protection devices according to claim 2, characterized in that, The actual fault diagnosis difficulty value is obtained by processing and analyzing fault recording data and line parameter characteristics, specifically including the following steps: The influence correlation coefficient is obtained based on the repetition frequency of fault characteristic quantities and line parameter characteristics in the fault recording data. The actual fault diagnosis difficulty value of power lines is obtained based on the influence correlation coefficient.

4. The fault analysis method based on a relay protection device according to claim 3, characterized in that, Based on the actual fault diagnosis difficulty value, the equipment operation and maintenance qualification rate, the frequency of diagnosis errors, and the pre-processing diagnosis difficulty value, a first fault diagnosis plan and a second fault diagnosis plan are output according to the actual fault diagnosis difficulty value, specifically including the following steps: If the actual fault diagnosis difficulty value is greater than or equal to the preset diagnosis warning threshold, the first fault diagnosis scheme is output based on the proportion of equipment operation and maintenance qualification rate of other power lines and the frequency of diagnosis errors. If the actual fault diagnosis difficulty value is less than the preset diagnosis warning threshold, a second fault diagnosis scheme is output based on the preprocessed diagnosis difficulty value and the actual fault diagnosis difficulty value.

5. The fault analysis method based on a relay protection device according to claim 4, characterized in that, The statistical analysis of historical fault characteristics and the reference diagnostic features of historical fault impact range in historical diagnostic data includes the following steps: Obtain historical fault diagnosis data between the power line where the relay protection device is located and adjacent power lines; Extract historical fault feature quantities from historical fault diagnosis data that exhibit the same frequency of repetition of fault feature quantities. Extract the historical fault impact range of adjacent power lines affected by the target power line from historical fault diagnosis data under the characteristic conditions of the historical fault feature quantity; The historical fault characteristic quantity and the historical fault impact range are combined to form the reference diagnostic characteristics.

6. The fault analysis method based on a relay protection device according to claim 5, characterized in that, The fault cross-line propagation prediction region is obtained by matching the real-time impedance change characteristics with the reference diagnostic features, specifically including the following steps: The real-time impedance variation characteristics between the power line where the relay protection device is located and adjacent power lines are statistically analyzed. The fault propagation prediction area of ​​adjacent power lines is obtained by matching real-time impedance change characteristics with reference diagnostic characteristics.

7. The fault analysis method based on a relay protection device according to claim 6, characterized in that, The pre-processing diagnostic difficulty value is obtained based on the predicted fault propagation area across lines and the sampling compliance rate of relay protection devices. The specific steps include: The percentage of sampling compliance rate of relay protection devices within the predicted area of ​​cross-line fault propagation is statistically analyzed. The number of protection devices deployed on the power lines where the relay protection devices are located is statistically analyzed, and the influence correlation coefficient is calculated by comparing the number of protection devices deployed to obtain the unit correlation coefficient. The comprehensive adjacent correlation value is obtained by multiplying the unit correlation coefficient and the sampling compliance rate ratio. The adjacent interference of adjacent power line sampling on fault diagnosis is obtained by multiplying the sampling compliance rate ratio and harmonic distortion rate. The preprocessing diagnostic difficulty value of adjacent power lines is obtained by summing the comprehensive adjacent correlation value and adjacent interference value.

8. The fault analysis method based on a relay protection device according to claim 7, characterized in that, Based on the percentage of qualified operation and maintenance equipment on other power lines and the frequency of diagnostic errors, the first fault diagnosis plan is output according to the actual fault diagnosis difficulty value, which specifically includes the following steps: The remaining power lines are those that are excluded from the adjacent power lines. Statistical analysis of the equipment maintenance qualification rate of the remaining lines, and obtaining the frequency of diagnostic errors for each of the remaining lines in historical periods; The percentage of qualified equipment operation and maintenance and the frequency of diagnostic errors are integrated into auxiliary diagnostic factors. The diagnostic weight ratio is obtained by calculating the proportion of each auxiliary diagnostic factor in the overall auxiliary diagnostic factors. Obtain the fault diagnosis carrying capacity of the remaining lines, and multiply the fault diagnosis carrying capacity and the diagnosis weight ratio to obtain the actual diagnosis capacity of the remaining lines. The difference between the actual fault diagnosis difficulty value and the diagnosis warning threshold is used to obtain the additional diagnosis requirement; Based on the actual diagnostic capabilities, the additional diagnostic requirements are processed for fault diagnosis, and then the first fault diagnosis solution is output.

9. The fault analysis method based on a relay protection device according to claim 8, characterized in that, Based on the preprocessing diagnostic difficulty value, a second fault diagnosis scheme is output according to the actual fault diagnosis difficulty value, specifically including the following steps: Extract the comprehensive neighbor correlation value and neighbor interference amount contained in the preprocessing diagnostic difficulty value; The correlation strength level of adjacent power lines to the fault diagnosis of the target line is determined based on the comprehensive adjacent correlation value; Based on the correlation strength level, key adjacent lines that have an impact on the fault diagnosis of the target line are selected. A fault diagnosis dataset is formed by integrating sampled data from key adjacent lines with fault recording data and line parameter characteristics of the target line. Adjust the detection accuracy threshold for fault diagnosis based on the pre-processing diagnostic difficulty value; The fault diagnosis dataset is used to diagnose the fault type, fault location, and fault severity of the target line according to the detection accuracy threshold, thus obtaining the second fault diagnosis scheme.

10. A fault analysis system based on a relay protection device, applied to the fault analysis method based on a relay protection device as described in any one of claims 1 to 9, characterized in that, include: Acquisition module: Acquires fault recording data and line parameter characteristics of relay protection devices, and acquires real-time impedance change characteristics between the power line where the relay protection device is located and adjacent power lines; Processing module: Processes and analyzes historical diagnostic data, real-time impedance change characteristics, reference diagnostic characteristics, and the sampling compliance rate of relay protection devices to obtain a pre-processed diagnostic difficulty value; Output module: Processes and analyzes fault recording data and line parameter characteristics to obtain the actual fault diagnosis difficulty value; Based on the actual fault diagnosis difficulty value, the equipment operation and maintenance qualification rate, the frequency of diagnosis errors, and the pre-processing diagnosis difficulty value, the first fault diagnosis scheme and the second fault diagnosis scheme are output according to the actual fault diagnosis difficulty value.

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