Electrical fault detection and control system

Abstract

The present invention relates to the field of fault detection technology, specifically to an electrical fault detection control system, the system comprising: a phase monitoring module, an abnormal positioning module, a current control module, and a result verification module. In the present invention, the phase difference is calculated by collecting and combining dynamic time regularization, and an abnormal mark is generated by comparing the phase tolerance threshold to improve the phase delay detection accuracy. Based on the phase difference value sequence of adjacent nodes, linear regression is used to analyze the slope jump, and the accuracy of candidate section determination is enhanced. A constant current source collects multi-node voltage differences, and a Kalman filter smoothes the voltage drop differences and compares the threshold to improve the anti-interference ability of complex working conditions. The path coordinate overlap comparison and the review instruction form a closed-loop verification mechanism to ensure the reliability of the positioning result. Multi-stage collaborative optimization shortens the fault response time and reduces false operations. Dynamic modeling and multi-source signal fusion improve the robustness of discrimination, reduce dependence on experience or thresholds, and enhance adaptive capabilities.

Description

Technical Field

[0001] The present invention relates to the technical field of fault detection, and in particular to an electrical fault detection control system. Background Art

[0002] The field of fault detection technology encompasses the identification, judgment, and processing of various abnormal conditions that may occur in electrical equipment or systems during operation. Its core is the continuous monitoring and identification of problems such as short circuits, open circuits, overloads, voltage fluctuations, and temperature anomalies that may occur in circuits, electrical equipment, or the entire system during operation. Fault detection technology also involves determining whether a fault exists by setting parameters, comparing thresholds, and analyzing current and voltage fluctuations. This technical field systematically encompasses the research and implementation of fault signal acquisition, fault type identification, fault location, and related response mechanisms. It is commonly used in power systems, industrial control, automation equipment, and smart terminals, and is an essential component in ensuring the safe and stable operation of electrical systems.

[0003] The electrical fault detection and control system refers to a system that collects electrical parameter data such as voltage, current, and frequency, identifies the fault type using pre-set detection rules, and executes relevant control operations based on the fault determination results. Its specific technical content includes electrical status monitoring based on real-time current and voltage sampling, differential calculation to identify short-term overload conditions, determination of open circuits or poor contact by setting the rate of change of electrical parameters, identification of abnormal power supply conditions using frequency offsets, and control of circuit disconnection or alarm activation using abnormal trigger signals, thus establishing a fault detection and control linkage mechanism. The system's overall technical process is primarily composed of an analog electrical parameter acquisition unit, digital signal recognition logic, fault type logical judgment rules, and a control response instruction set.

[0004] Fixed thresholds or preset rules struggle to adapt to dynamic load fluctuations, leading to misjudgments or missed detections. Short-term overload detection relies on differential calculations but lacks contextual feature associations, making it prone to false alarms due to transient fluctuations. Single electrical parameter analysis cannot distinguish between similar faults such as poor contact and power supply anomalies, reducing maintenance efficiency. Independent monitoring units lack cross-node timing correlations, resulting in insufficient early identification of progressive faults. The one-way trigger control mechanism lacks closed-loop verification, and deviations from detection rules can directly lead to false disconnections or delayed alarms. Voltage fluctuation analysis does not coordinate frequency offset and phase delay changes, making it prone to misjudging the root cause of the fault. Existing technologies have limited detection accuracy and reliability in scenarios with multi-node coupled faults or transient anomalies, increasing operational costs and safety risks. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an electrical fault detection and control system.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: the electrical fault detection and control system includes:

[0007] The phase monitoring module is used to obtain the voltage waveform sampling time point through the synchronous voltage acquisition device, call the dynamic time warping algorithm to calculate the phase difference between the sampling start time and the zero crossing time point and the standard node reference value, compare the phase delay parameter item with the phase tolerance value threshold, generate a phase anomaly mark, and pass the phase anomaly mark to the anomaly positioning module;

[0008] An anomaly location module is used to obtain a phase difference sequence of adjacent nodes based on the phase anomaly mark, perform a sudden improvement judgment on the phase difference change slope of three consecutive nodes through a linear regression model, generate candidate segment coordinates, and transmit the candidate segment coordinates to the current control module;

[0009] The current control module is used to start the constant current source device according to the candidate segment coordinates, collect multi-node voltage difference data after constant current is injected, use the Kalman filter algorithm to smooth the voltage drop difference sequence between adjacent nodes, compare the voltage drop difference value with the set threshold parameter, generate a resistance sudden rise segment mark, and pass the resistance sudden rise segment mark to the result verification module.

[0010] As a further solution of the present invention, the phase anomaly mark is specifically a phase delay level, a waveform offset feature, and a time series label. The candidate segment coordinates include a node index interval, a change rate identifier, and a time label. The resistance surge segment mark specifically refers to the voltage drop change rate, the target node index, and the abnormal segment length.

[0011] As a further solution of the present invention, the dynamic time warping algorithm uses a Gaussian window function to align the starting time to the reference time axis of the standard node reference value, with a window width of 1 / 10 to 1 / 5 of the sampling period and a weight factor of 0.6 to 0.8;

[0012] The state transition matrix of the Kalman filter algorithm is [1, Δt; 0, 1], the observation matrix is [1, 0], the process noise covariance is 0.01 to 0.05, and the observation noise covariance is 0.1 to 0.3.

[0013] As a further solution of the present invention, the phase monitoring module includes:

[0014] The waveform sampling submodule monitors the voltage waveform through a synchronous voltage acquisition device, records the starting time coordinates within the sampling period, detects the timing position of the first zero crossing point, extracts the time interval data from the starting time to the zero crossing point, and generates a time point sequence;

[0015] The phase difference calculation submodule calls the dynamic time warping algorithm to align the starting time of the time point sequence to the reference time axis of the standard node reference value, calculates the timing offset between the two in the zero crossing point interval, obtains the mean of the offset as the waveform alignment deviation, and generates the phase difference parameter;

[0016] The phase anomaly determination submodule compares the phase difference parameter with a preset phase tolerance threshold range to determine whether the offset mean exceeds the upper or lower threshold. If so, the binary flag variable is assigned a value of 1, otherwise 0, and a phase anomaly flag is generated;

[0017] The phase tolerance threshold range is determined by optimizing historical fault data using a gradient descent method, with an upper limit of 5% to 8% of a sampling period and a lower limit of 2% to 4% of a sampling period.

[0018] As a further solution of the present invention, the abnormality locating module includes:

[0019] The phase sequence sampling submodule screens the phase difference value set of adjacent nodes based on the phase anomaly mark, extracts the difference data of three consecutive nodes in time sequence, and generates an adjacent phase difference sequence;

[0020] The slope jump determination submodule calls a linear regression model to perform least squares fitting on the phase difference value of each node in the adjacent phase difference sequence, calculates the absolute value of the slope difference of the fitted straight line between adjacent segments, and compares it with the upper limit of the preset jump determination threshold. If the absolute value of the slope difference exceeds the threshold, it is marked as a sudden change segment and a slope jump coefficient is generated.

[0021] The absolute value of the slope difference is dimensionlessly processed into a percentage, and the sudden jump determination threshold is 15% to 25%;

[0022] The candidate segment calibration submodule extracts the start and end node numbers corresponding to the mutation segment according to the slope jump coefficient, calculates the node spacing between adjacent mutation points, and merges multiple mutation points into a single interval if the spacing is less than the preset segment merging length to generate candidate segment coordinates;

[0023] The combined length of the preset sections is 1.5 to 2.5 times the electrical node spacing, and the node spacing is pre-calibrated to 0.5 to 1.2 meters based on the cable model.

[0024] As a further solution of the present invention, the current control module includes:

[0025] The constant current injection control submodule collects the coordinates of the candidate segment, analyzes the physical node distribution corresponding to the coordinates, sets the constant current source output parameters according to the node spacing, activates the constant current source device to inject a preset current value into the target segment, synchronously measures the voltage of multiple nodes to ground, calculates the voltage difference between adjacent nodes, and generates multi-node voltage difference data;

[0026] The voltage difference abnormality analysis submodule calls the multi-node voltage difference data, extracts the voltage drop difference values of adjacent nodes in node order to form a sequence, and uses the formula:

[0027] ;

[0028] Iteratively calculate the sequence, eliminate noise interference by correcting the difference value item by item, and generate a smoothed pressure drop difference sequence;

[0029] in, Representative The corrected pressure drop difference in the iteration, Represents the current node in The original value of the pressure drop difference collected in the iteration, Representative The difference in voltage drop between adjacent nodes in the iteration, Represents the suppression factor for the environmental noise amplitude, which is used to reduce the impact of noise on the difference value. Representative The amplitude of the ambient noise collected by the sensor in the iteration, Represents the dynamic sensitivity coefficient, which is used to adjust the response speed of the correction value. Represents the total number of data nodes participating in the calculation in the current segment, Represents the baseline voltage drop offset obtained through long-term monitoring data statistics, which is used to eliminate environmental steady-state interference;

[0030] The threshold discrimination submodule calls the smoothed voltage drop difference sequence, traverses the corrected difference value of each node in the sequence, compares the value with the preset resistance sudden rise threshold parameter, marks the node interval where the difference value exceeds the threshold, and generates a resistance sudden rise segment mark according to the node interval coordinates;

[0031] The preset resistance sudden rise threshold parameter is 3 to 5 times the voltage drop difference value under normal working conditions, and is determined by statistically analyzing historical fault data.

[0032] As a further embodiment of the present invention, the system further comprises:

[0033] The result verification module is used to receive the candidate segment coordinates and the resistance sudden rise segment mark, perform path coordinate comparison operation, output the positioning result when the overlap exceeds the ratio value threshold, trigger the review instruction and return it to the phase monitoring module and current control module when it does not meet the requirements, and output the positioning result to the external alarm unit.

[0034] As a further solution of the present invention, the positioning result includes the fault coordinate value, matching overlap, and positioning confidence level.

[0035] As a further solution of the present invention, the result verification module includes:

[0036] The segment receiving submodule calls the starting node number and the ending node number of the candidate segment coordinates, extracts the coordinate range of the resistance sudden rise segment mark, merges the node number intervals and mark states of the two into a unified data format, and generates a segment set;

[0037] The overlap calculation submodule compares the coordinate ranges of the starting point of the candidate segment and the ending point of the resistance sudden rise segment in the segment set, calculates the length of the intersection interval between the two on the node number axis, counts the percentage of the number of intersection nodes to the total number of nodes in the candidate segment, and generates an overlap ratio value;

[0038] The verification decision submodule compares the overlap ratio value with a preset ratio threshold value. If the ratio value exceeds the threshold value, the positioning result is output to the external alarm unit. If it does not exceed the threshold value, a review instruction is generated and sent back to the phase monitoring module and the current control module.

[0039] The ratio value threshold is 75% to 90%, which is determined by cross-validation optimization and is set to 80% when the positioning accuracy reaches 95% confidence level.

[0040] Compared with the prior art, the advantages and positive effects of the present invention are:

[0041] In the present invention, the phase difference is calculated by collecting and combining the dynamic time warping algorithm, and an abnormal mark is generated by comparing the phase tolerance threshold to improve the phase delay detection accuracy. Based on the phase difference value sequence of adjacent nodes, a linear regression model is used to analyze the sudden jump of the slope of continuous nodes to enhance the accuracy of candidate segment determination. The constant current source device collects multi-node voltage difference data, combines the Kalman filter to smooth the voltage drop difference sequence and compare the threshold value to improve the anti-interference ability under complex working conditions. The path coordinate overlap comparison and the review instruction form a closed-loop verification mechanism to ensure the reliability of the positioning result. Multi-stage collaborative optimization shortens the fault response time and reduces false operations. Dynamic data modeling and multi-source signal fusion improve the robustness of fault discrimination, avoid missed detection in transient abnormal scenarios, reduce dependence on manual experience or preset thresholds, and enhance adaptive capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 is a system flow chart of the present invention;

[0043] Figure 2 This is a flow chart of the phase monitoring module of the present invention;

[0044] Figure 3 This is a flow chart of the abnormality positioning module of the present invention;

[0045] Figure 4 This is a flow chart of the current control module of the present invention;

[0046] Figure 5 This is a flow chart of the result verification module of the present invention. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0048] In the description of the present invention, it should be understood that the terms "length," "width," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," and the like, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined. Example 1

[0049] See also Figure 1 , electrical fault detection and control system includes:

[0050] The phase monitoring module is used to obtain the voltage waveform sampling time point through the synchronous voltage acquisition device, call the dynamic time warping algorithm to calculate the phase difference between the sampling start time and the zero crossing time point and the standard node reference value, compare the phase delay parameter item with the phase tolerance value threshold, generate a phase anomaly mark, and pass the phase anomaly mark to the anomaly location module;

[0051] The anomaly location module is used to obtain the phase difference value sequence of adjacent nodes based on the phase anomaly mark, determine the sudden improvement of the phase difference value change slope of three consecutive nodes through the linear regression model, generate candidate segment coordinates, and pass the candidate segment coordinates to the current control module;

[0052] The current control module is used to start the constant current source device according to the candidate segment coordinates, collect multi-node voltage difference data after constant current is injected, use the Kalman filter algorithm to smooth the voltage drop difference sequence between adjacent nodes, compare the voltage drop difference value with the set threshold parameter, generate the resistance sudden rise segment mark, and pass the resistance sudden rise segment mark to the result verification module;

[0053] The result verification module is used to receive the candidate segment coordinates and the resistance sudden rise segment mark, perform the path coordinate comparison operation, and output the positioning result when the overlap exceeds the ratio value threshold. If it does not meet the requirements, the review instruction is triggered and sent back to the phase monitoring module and the current control module, and the positioning result is output to the external alarm unit.

[0054] The phase anomaly mark specifically includes the phase delay level, waveform offset characteristics, and time series label. The candidate segment coordinates include the node index interval, change rate identifier, and time label. The resistance sudden rise segment mark specifically refers to the voltage drop change rate, target node index, and abnormal segment length. The positioning result includes the fault coordinate value, matching overlap, and positioning confidence level.

[0055] The dynamic time warping algorithm uses a Gaussian window function to align the starting time to the reference time axis of the standard node reference value. The window width is 1 / 10 to 1 / 5 of the sampling period, and the weight factor is 0.6 to 0.8.

[0056] The state transition matrix of the Kalman filter algorithm is [1, Δt; 0, 1], the observation matrix is [1, 0], the process noise covariance is 0.01 to 0.05, and the observation noise covariance is 0.1 to 0.3.

[0057] See also Figure 2 , the phase monitoring module includes:

[0058] The waveform sampling submodule monitors the voltage waveform through a synchronous voltage acquisition device, records the starting time coordinates within the sampling period, detects the timing position of the first zero crossing point, extracts the time interval data from the starting time to the zero crossing point, and generates a time point sequence;

[0059] The waveform sampling submodule monitors the voltage waveform of the line through the synchronous voltage acquisition device. In a specific implementation scenario, for an AC power system with an operating frequency of 50Hz, the sampling frequency of the voltage acquisition device is set to 10kHz, which means that every The instantaneous voltage value is recorded every millisecond (ms). A complete power frequency cycle T = 1 / 50 = 0.02s = 20ms. During this cycle, 200 points of instantaneous voltage values are collected, 20ms ÷ 0.1ms / point = 200 points.

[0060] After the acquisition is started, the system records the starting time coordinates within the sampling period. The specific values are ms, and the corresponding voltage value at this time . Subsequently, the device continuously collects voltage values and corresponding timestamps to form raw voltage sequence data. By continuously comparing the voltage value signs of adjacent sampling points, the system detects when the voltage waveform crosses zero value. The voltage at ms is V, and at the following sampling point ms, the voltage becomes V, the system determines and A zero crossing occurs between the time points. Use linear interpolation to estimate a more accurate zero crossing time point. , the calculation process is: .

[0061] The system then extracts the time from the start of the record = 10.000ms to the first calculated zero crossing point =All sampled data points within the 15.047ms time interval, including their timestamps and voltage values.

[0062] Table 1 Sampling time interval voltage data table

[0063]

[0064] As shown in Table 1, the table lists some sampling time points and their corresponding voltage values. Based on these extracted data points, the system generates an ordered sequence of time points. ,This sequence forms the basis for subsequent phase difference calculations.

[0065] The phase difference calculation submodule calls the dynamic time warping algorithm to align the starting time of the time point sequence to the reference time axis of the standard node reference value, calculates the timing offset between the two in the zero crossing point interval, and obtains the mean of the offset as the waveform alignment deviation to generate the phase difference parameter;

[0066] The phase difference calculation submodule receives the time point sequence generated by the previous submodule and calls the dynamic time warping (DTW) algorithm. First, the system obtains the voltage waveform time point sequence generated by a specified standard node (in this embodiment, the system stable reference power point) within the same sampling period as the reference sequence. The starting sampling moment of the reference sequence is defined as the zero point of the reference time axis.

[0067] Then, the time point sequence of the node to be tested Perform DTW alignment with the standard reference sequence. The specific execution of DTW is to build a cost matrix, the elements in the matrix The value represents the difference between the test sequence point and the reference sequence point. The Euclidean distance between points Through dynamic programming, we find an optimal path from the starting point to the end point of the matrix, which minimizes the sum of the cumulative distances of all elements along the path. This path clarifies the nonlinear time correspondence between the two time series.

[0068] During this alignment process, the focus is on the zero crossing points of the two sequences. (sequence to be tested) and (reference sequence) nearby. Determine the zero crossing point of the sequence to be tested through the optimal regularization path. =15.047ms is the time point corresponding to the alignment to the reference time axis Assume that the zero crossing point of the standard reference sequence occurs at the reference time axis. =15.000ms. After DTW alignment, the time corresponding to the zero crossing point of the sequence to be measured is calculated as =15.185ms. Calculate the timing offset between these two zero crossing points .

[0069] In order to obtain a stable phase difference evaluation, the system will calculate the timing offset of 10 consecutive power frequency cycles. Calculate the arithmetic mean value in ms and calculate the waveform alignment deviation The calculated mean value is output as the final phase difference parameter, that is, the phase difference parameter is generated. =0.187ms.

[0070] The phase anomaly judgment submodule compares the phase difference parameter with the preset phase tolerance threshold range to determine whether the offset mean exceeds the upper or lower threshold. If so, the binary flag variable is assigned a value of 1, otherwise it is assigned a value of 0, and a phase anomaly flag is generated;

[0071] The phase tolerance threshold range is determined by optimizing historical fault data using the gradient descent method, with an upper limit of 5% to 8% of the sampling period and a lower limit of 2% to 4% of the sampling period.

[0072] The phase anomaly judgment submodule receives the phase difference parameter generated by the previous submodule =0.187ms. The system compares it with the preset phase tolerance threshold range. This threshold range is not fixed, but is obtained by analyzing a large amount of historical fault data (including normal operation and different types of phase faults). Data records) are dynamically determined using a gradient descent optimization algorithm. The optimization goal is to find a set of upper and lower thresholds that can best distinguish between normal and abnormal phase offset states on the historical dataset, thereby achieving the highest classification accuracy.

[0073] Based on the recommendations of the optimization algorithm and the requirements of system stability based on engineering experience, the upper and lower limits of the threshold are set to float within a specific ratio range: the upper limit is 5% to 8% of the sampling period, and the lower limit is 2% to 4% of the sampling period. In this embodiment, the sampling period is 20ms. After performing gradient descent optimization calculations on 500 collected historical events (350 of which are normal and 150 are known phase anomalies), the algorithm converges to determine the optimal upper limit ratio of 7% and the lower limit ratio of 3%. Based on this, the specific threshold is calculated: Phase tolerance threshold upper limit ;Phase tolerance lower threshold .

[0074] Next, the currently calculated phase difference parameter =0.187ms with this threshold range Compare. The judgment conditions are: or In this specific calculation, 0.187ms is between 0.6ms and 1.4ms, satisfying Therefore, the system determines that the phase state of the node is normal and changes the corresponding binary mark variable Assign a value of 0.

[0075] Considering the phase difference parameter of another node, the calculation result is . For comparison: 1.55>1.4, satisfying Condition, determine that the node phase is abnormal. At this time, the system's binary marking variable for the node Assign a value of 1. By executing this judgment process on all nodes in the monitoring network, the phase abnormality mark sequence of each node is finally generated. .

[0076] See also Figure 3 , the anomaly location module includes:

[0077] The phase sequence sampling submodule screens the phase difference value set of adjacent nodes based on the phase anomaly mark, extracts the difference data of three consecutive nodes in time sequence, and generates an adjacent phase difference sequence;

[0078] The phase sequence sampling submodule generates phase anomaly marks for each node based on the previous module. The system traverses the tag set and identifies all nodes with a tag value of 1. These nodes are initially identified as having phase anomalies. Subsequently, the system queries the phase difference between these abnormal nodes and their adjacent nodes. (These values were previously calculated and stored by the phase difference calculation submodule).

[0079] In a specific monitoring line segment, the phase abnormality marks of nodes 5, 6, 7, and 8 They are 0, 1, 1, and 0 respectively. The system selects abnormal nodes 6 and 7. Next, the phase difference parameter values of these two abnormal nodes and their directly adjacent nodes (nodes 5 and 8) are extracted to obtain a set of related phase difference values: .

[0080] The system extracts the phase difference data of three consecutive nodes from the above set according to the physical or logical timing order of the nodes on the line to form the subsequence required for analysis. With node 6 as the center, the sequence is extracted. Then take node 7 as the center and extract the sequence All the extracted three-node subsequences are combined to generate the adjacent phase difference sequence for subsequent slope analysis, whose structure is , the unit is ms.

[0081] The slope jump determination submodule calls a linear regression model to perform least squares fitting on the phase difference value of each node in the adjacent phase difference sequence. It calculates the absolute value of the slope difference of the fitted straight line between adjacent segments and compares it with the upper limit of the preset jump determination threshold. If the absolute value of the slope difference exceeds the threshold, it is marked as a sudden change segment and the slope jump coefficient is generated.

[0082] The absolute value of the slope difference is dimensionlessly converted to a percentage, and the threshold for sudden jump determination is 15% to 25%;

[0083] The slope jump judgment submodule receives the adjacent phase difference sequence generated by the previous submodule The system processes each three-node subsequence in the sequence one by one.

[0084] For the first subsequence , which represents the phase difference between nodes 5, 6, and 7. The system applies the least squares method to perform a piecewise linear fit on these three data points. The node numbers (5, 6, 7) are considered as the independent variable x, and the corresponding phase difference values (0.187, 1.55, 1.62) are considered as the dependent variable y. The slope of the segment from node 5 to node 6 is calculated. Node. Calculate the slope of the segment from node 6 to node 7 node.

[0085] Calculate the absolute value of the difference in the slope of the fitted line between the two adjacent segments (5-6 and 6-7) Node. In order to make a standardized comparison, the absolute value of the slope difference is dimensionless and converted into a percentage. Usually the slope of the segment before the change is As a benchmark (assuming ). Calculate the percentage change in slope: .

[0086] The system sets an upper threshold for slope jump judgment. The setting of this threshold is based on a statistical analysis of a large amount of historical phase difference sequence slope change data under normal operation and fault conditions, and determines a limit that can effectively distinguish normal fluctuations from abnormal mutations. It is usually set between 15% and 25%. In this embodiment, by analyzing historical data, As the threshold for sudden jump determination, a slope change percentage below 10% is considered "low" or "stable," between 10% and 20% is considered "moderate," and above 20% is considered "high" or "sudden," indicating a significant change in the phase difference change rate.

[0087] The calculated slope change percentage of 94.86% is compared with the threshold of 20%. Since 94.86% is greater than 20%, the system determines that a sudden slope jump has occurred near node 6 (i.e., segment 6-7 relative to segment 5-6) and marks this location as a potential anomaly.

[0088] For the second subsequence Repeat this process. Involving nodes 6, 7, and 8. Known = 0.07ms / node. Calculate the slope of the section from node 7 to node 8 Node. Calculate the absolute value of the slope difference Node. Calculate the percentage change as a baseline (Note: When the baseline slope is close to zero, the percentage change may be very large or meaningless. In this case, the absolute difference threshold or the average slope can be used. If the average slope is used as the benchmark, the percentage change is .because , the system determines that a slope jump also occurs near node 7 (segment 7-8 relative to segment 6-7).

[0089] Finally, the system generates a slope jump coefficient for each node (in the segment after it), which is the calculated slope change percentage, such as , These coefficients constitute the slope jump coefficient sequence.

[0090] The candidate segment calibration submodule extracts the start and end node numbers corresponding to the mutation segment according to the slope jump coefficient, calculates the node spacing between adjacent mutation points, and merges multiple mutation points into a single interval if the spacing is less than the preset segment merging length to generate candidate segment coordinates;

[0091] The preset segment combined length is 1.5 to 2.5 times the electrical node spacing, and the node spacing is pre-calibrated to 0.5 to 1.2 meters based on the cable model.

[0092] The candidate segment calibration submodule generates a slope jump coefficient sequence based on the previous submodule. And the corresponding mutation segment markers. and , determining that significant slope changes occurred after nodes 6 and 7, and marking the segments associated with these sudden changes as mutation segments. Specifically, the first sudden change occurred after node 6 (involving segments 5-6 and 6-7), and the second sudden change occurred after node 7 (involving segments 6-7 and 7-8). The system extracted the node numbers associated with the sudden changes and identified that the sudden changes occurred near nodes 6 and 7.

[0093] The system merges these consecutive mutation points or adjacent mutation segments to form one or more candidate fault segments. First, the node distance between adjacent mutation points (or core nodes affected by the mutation) is calculated. In this case, the mutation points are located at nodes 6 and 7, which are adjacent and have a distance of one node.

[0094] The system sets a preset segment merging length This length is used to determine whether it is necessary to merge multiple independent mutation points with close distances into a continuous candidate segment. The value is based on the average physical spacing of electrical nodes in the circuit to confirm. The value of is pre-calibrated according to factors such as the cable model used and the laying method, and its value is usually between 0.5 meters and 1.2 meters. In this embodiment, the calibration node spacing corresponding to the cable model used is Meters. Preset segment combined length is set as the electrical node spacing times, of which The value range is 1.5 to 2.5. ,but This means that if the physical distance between two mutation points is less than 2.0 meters (or the difference in node numbers is less than ), they are grouped into the same candidate segment.

[0095] Check the distance between the identified mutation points 6 and 7. They are adjacent nodes with a distance of 1 node, corresponding to a physical distance of m. Due (or node spacing ), meeting the merging conditions. The system merges the two mutation points and defines the range they affect (usually including these mutation points and the immediately preceding and following segments) as a continuous candidate segment. Considering that the mutation occurs after node 6 and node 7, the merged candidate segment covers the starting point of the previous segment that caused the first mutation (node 5) to the end point of the next segment affected by the last mutation (node 8), so the candidate segment is determined to be node 5 to node 8. Finally, the candidate segment coordinates are generated. .

[0096] See also Figure 4 , the current control module includes:

[0097] The constant current injection control submodule collects candidate segment coordinates, analyzes the physical node distribution corresponding to the coordinates, sets the constant current source output parameters based on the node spacing, activates the constant current source device to inject a preset current value into the target segment, synchronously measures the voltage of multiple nodes to ground, calculates the voltage difference between adjacent nodes, and generates multi-node voltage difference data;

[0098] The constant current injection control submodule receives the candidate segment coordinates determined by the previous submodule The system analyzes this coordinate and determines that the physical nodes that need to be detected are nodes 5, 6, 7, and 8. Based on the physical distribution of these nodes and the known node spacing (all 1.0 meters), the system configures the output parameters of the constant current source. Select the two ends of the candidate segment, that is, between nodes 5 and 8, and connect the constant current source device. Set the constant current source to output a stable, preset current value, and select A of DC current.

[0099] The system activates a constant-current source connected between nodes 5 and 8, injecting 5A of current into this line segment. During this stable current injection period, voltage measurement units located in the candidate segment (nodes 5 to 8) and any reference nodes involved are synchronously triggered to collect the voltage value of each node relative to ground.

[0100]

[0101] As shown in Table 2, the table records the ground voltage measurement values of each node in the candidate segment and calculates the voltage difference between adjacent nodes. The system collects these voltage differences V, V, V. Organize these calculated voltage differences in node order to generate a multi-node voltage difference data sequence V, the sequence will be passed to the next submodule for analysis.

[0102] The voltage difference anomaly analysis submodule calls the multi-node voltage difference data, extracts the voltage drop difference values of adjacent nodes in node order to form a sequence, and uses the formula:

[0103] ;

[0104] Iteratively calculate the sequence, eliminate noise interference by correcting the difference value item by item, and generate a smoothed pressure drop difference sequence;

[0105] in, Representative The corrected pressure drop difference in the iteration, Represents the current node in The original value of the pressure drop difference collected in the iteration, Representative The difference in voltage drop between adjacent nodes in the iteration, Represents the suppression factor for the environmental noise amplitude, which is used to reduce the impact of noise on the difference value. Representative The amplitude of the ambient noise collected by the sensor in the iteration, Represents the dynamic sensitivity coefficient, which is used to adjust the response speed of the correction value. Represents the total number of data nodes participating in the calculation in the current segment, Represents the baseline voltage drop offset obtained through long-term monitoring data statistics, which is used to eliminate environmental steady-state interference;

[0106] The voltage difference abnormality analysis submodule receives the multi-node voltage difference data sequence generated by the previous submodule V, where the subscript These correspond to line sections 5-6, 6-7, and 7-8, respectively. The system processes this sequence using a specific iterative formula designed to correct the measurements, smooth the data, and highlight potential abnormal voltage drops.

[0107] The iteration formula is :

[0108] Parameter explanation and assignment:

[0109] It is The corrected voltage drop difference value (V) of each node interval.

[0110] It is The original measured pressure drop difference value (V) of the interval comes from the sequence .

[0111] is the pressure drop difference (V) between the adjacent previous interval. In the iterative calculation, it represents the spatial proximity effect.

[0112] is the environmental noise suppression factor. By analyzing the correlation between historical noise data and pressure drop measurement error and conducting experimental verification, we can determine It effectively suppresses noise without excessively attenuating true signal changes. This is a unitless value.

[0113] Is the impact of The equivalent ambient noise voltage (V) measured in the interval. Ambient noise may be generated by various sources and can be directly measured by the sensor or indirectly evaluated. Here, it is assumed that the noise amplitude measured by the sensor is , in millivolts (mV). To perform calculations with other voltage values (V) in formulas, unit conversion is required. Set the conversion rule: Divide the measured millivolt value by 1000 to obtain the volt value.

[0114] This rule is based on the physical model of the noise signal directly superimposed on the measured voltage signal. The noise amplitudes corresponding to intervals 1, 2, and 3 are measured to be mV, mV, mV. The converted value used for calculation is V, V, V.

[0115] Is the dynamic sensitivity coefficient, unitless. It adjusts the response speed and smoothness of the correction result to the change of the original data. Through simulation tests, the different The detection effect of the value (from 0.1 to 1.0) on the typical fault signal is found in When the system responds to abnormal pressure drop most quickly and stably, so set .

[0116] is the total number of pressure drop intervals involved in the calculation within the current candidate segment. For the segment from nodes 5 to 8, there are 3 intervals (5-6, 6-7, 7-8), so .

[0117] Is the baseline voltage drop offset (V). It is obtained by statistically averaging the voltage difference data of the line section or the same type of line section running for a long time (such as 100 hours) under the condition of no fault. The calculation results show that the average Systematic deviation of V, set V.

[0118] Iterative calculation process (perform one iteration): First, calculate the summation term in the denominator of the formula:

[0119] ;

[0120] Next, calculate the corrected pressure drop value for each interval .for (range 5-6), need Right now To set the boundary conditions, use The value of The approximate value of V.

[0121] ;

[0122] for (range 6-7), use V.

[0123] ;

[0124] for (range 7-8), use V.

[0125] ;

[0126] After a single iterative calculation, the system obtains the smoothed pressure drop difference sequence This formula is beneficial because it combines the current measurement value, the influence of adjacent intervals, and the equivalent voltage influence of real-time noise. It amplifies abnormal signals through nonlinear operations (absolute value, square root), and uses a normalized denominator and sensitivity coefficient containing all interval information for adaptive smoothing. This effectively suppresses the interference of noise and baseline drift, thereby more clearly revealing potential resistance spikes.

[0127] The threshold discrimination submodule calls the smoothed voltage drop difference sequence, traverses the corrected difference value of each node in the sequence, compares the value with the preset resistance surge threshold parameter, marks the node intervals where the difference value exceeds the threshold, and generates resistance surge segment markers based on the node interval coordinates;

[0128] The preset resistance sudden rise threshold parameter is 3 to 5 times the voltage drop difference value under normal working conditions, which is determined by statistically analyzing historical fault data.

[0129] The threshold discrimination submodule receives the smoothed voltage drop difference sequence generated by the previous submodule V. The system will iterate through each corrected voltage drop difference value in this sequence , and compare them one by one with the preset resistance surge threshold Make a comparison.

[0130] The threshold The setting is based on the analysis of historical normal working condition data. First, collect a large amount of adjacent node voltage difference data when no fault is confirmed, and deduct the known baseline offset. After V, calculate the average value of these normal voltage drop differences In this embodiment, the calculation is Then, according to the sensitivity requirement of fault detection, the threshold is set to the normal average value. times, of which Is a multiplication factor, and its value range is usually set between 3 and 5. This range is selected based on experience and analysis of historical fault cases to ensure that the threshold can effectively distinguish between a significant increase in voltage drop caused by a fault and normal measurement fluctuations. The resistance surge threshold is calculated based on this: V.

[0131] Next, the sequence Each value in V to compare: * compare V: Because , this value does not exceed the threshold. *Compare V: Because , this value exceeds the threshold. The system marks the corresponding node interval 6-7 as having a resistance surge anomaly. *Comparison V: Because , this value also exceeds the threshold. The system marks the corresponding node interval 7-8 as having a resistance surge anomaly.

[0132] Based on the marking results, the system determines that the resistance surge segment is composed of intervals 6-7 and 7-8. The starting node (6) and the ending node (8) of this segment are extracted to generate the resistance surge segment mark. This mark indicates that a voltage drop pattern indicating an abnormal increase in resistance was detected in the line segment between node 6 and node 8.

[0133] See also Figure 5 , the result verification module includes:

[0134] The segment receiving submodule calls the starting node number and ending node number of the candidate segment coordinates, extracts the coordinate range of the resistance sudden rise segment mark, merges the node number interval and mark status of the two into a unified data format, and generates a segment set;

[0135] The function of the segment receiving submodule is to integrate the results from different analysis stages. It calls the candidate segment coordinates generated by the anomaly positioning module (specifically the candidate segment calibration submodule) , which indicates the range of the line where the abnormality is initially suspected, starting from node 5 and ending at node 8. At the same time, it calls the resistance sudden rise section mark generated by the current control module (specifically the threshold judgment submodule) , which indicates the specific section where the abnormal resistance characteristics are confirmed by voltage drop analysis, starting from node 6 and ending at node 8.

[0136] The system extracts the coordinate information for these two segments: the candidate segment covers nodes {5, 6, 7, 8}, and the resistance rise segment covers nodes {6, 7, 8}. The two segment types (candidate, resistance rise) and their corresponding node number ranges are then combined into a structured data set for easy comparison. The resulting data structure is the segment set {Type:Candidate,StartNode:5,EndNode:8;Type:ResistanceRise,StartNode:6,EndNode:8}.

[0137] The overlap calculation submodule compares the coordinate ranges of the starting point of the candidate segment in the segment set with the ending point of the resistance surge segment, calculates the length of the intersection interval between the two on the node number axis, and counts the percentage of the number of intersection nodes to the total number of nodes in the candidate segment to generate an overlap ratio value;

[0138] The overlap calculation submodule receives the segment set {Candidate:[5,8],ResistanceRise:[6,8]} generated by the previous submodule. Its core task is to quantify the degree of overlap between the two segments in space (node number axis).

[0139] The system selects candidate segments (including nodes 5, 6, 7, 8) and resistance surge section Compare the coordinate ranges (including nodes 6, 7, and 8). Compute the intersection of these two intervals. The intersection includes all nodes that exist in both segments, that is, nodes {6, 7, and 8}. Calculate the length of the intersection interval, that is, the number of nodes included in the intersection, which is 3 nodes in this case.

[0140] Next, calculate the percentage of the number of intersection nodes to the total number of nodes in the candidate segment. The total number of nodes is Nodes. Calculate the overlap ratio: The final overlap ratio value is generated This value reflects the proportion of abnormal regions verified by the second stage (current injection and voltage drop analysis) among the candidate regions preliminarily located in the first stage (phase analysis).

[0141] The verification decision submodule compares the overlap ratio value with the preset ratio threshold. If the ratio exceeds the threshold, the positioning result is output to the external alarm unit. If it does not exceed the threshold, a review instruction is generated and sent back to the phase monitoring module and the current control module.

[0142] The ratio value threshold is 75% to 90%, which is determined by cross-validation optimization and is set to 80% when the positioning accuracy reaches 95% confidence level.

[0143] The verification decision submodule receives the overlap ratio value calculated by the previous submodule The system compares this value with a preset ratio threshold. Compare to determine whether the confidence of the current positioning result is high enough and whether it can be output as the final result.

[0144] The ratio threshold It is obtained by cross-validation optimization of a large number of historical positioning cases (including cases of successful positioning and positioning deviation). The optimization goal is to set a threshold that can best distinguish high-confidence positioning from low-confidence positioning under the condition of achieving a specific confidence level (such as 95%). The reasonable range of the threshold is usually between 75% and 90%. In this embodiment, based on the goal of achieving 95% confidence, the threshold determined by cross-validation optimization is .

[0145] The calculated overlap ratio value With the set threshold Compare. The judgment condition is In this example, , that is, the overlap ratio value does not exceed the threshold. Based on this, the system determines that the consistency of the analysis results of the current two stages does not meet the preset high confidence standard, and the positioning result needs further confirmation. Therefore, the system does not directly output the fault location result, but generates a review instruction. This instruction contains the current candidate segment and resistance sudden rise section The module sends diagnostic information such as the phase monitoring module and the current control module, and transmits this instruction back to the phase monitoring module and the current control module to start the parameter adjustment, supplementary measurement or re-analysis process that may be needed.

[0146] If in another scenario, the calculated overlap ratio is .because ,satisfy Conditions. The system determines that the positioning result has high credibility. At this time, the system will determine the fault section information (usually the overlapping area) , or the final segment after fine-tuning in combination with the candidate segment information) is output to the external alarm system, operation and maintenance platform or display interface for reference and processing by the operation personnel.

[0147] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. Electrical fault detection and control system, characterized in that, The system comprises: The phase monitoring module is used to obtain the voltage waveform sampling time point through the synchronous voltage acquisition device, call the dynamic time warping algorithm to calculate the phase difference between the sampling start time and the zero crossing time point and the standard node reference value, compare the phase delay parameter item with the phase tolerance value threshold, generate a phase anomaly mark, and pass the phase anomaly mark to the anomaly positioning module; An anomaly location module is used to obtain a phase difference sequence of adjacent nodes based on the phase anomaly mark, perform a sudden improvement judgment on the difference data of three consecutive nodes through a linear regression model, generate candidate segment coordinates, and transmit the candidate segment coordinates to the current control module; The current control module is used to start the constant current source device according to the candidate segment coordinates, collect multi-node voltage difference data after constant current is injected, use the Kalman filter algorithm to smooth the voltage drop difference sequence between adjacent nodes, compare the voltage drop difference value with the set threshold parameter, generate a resistance sudden rise segment mark, and pass the resistance sudden rise segment mark to the result verification module.

2. The electrical fault detection and control system according to claim 1, characterized in that: The phase anomaly mark specifically includes the phase delay level, waveform offset characteristics, and time series label. The candidate segment coordinates include the node index interval, change rate identifier, and time label. The resistance sudden rise segment mark specifically refers to the voltage drop change rate, target node index, and abnormal segment length.

3. The electrical fault detection and control system according to claim 2, characterized in that: The dynamic time warping algorithm uses a Gaussian window function to align the starting time to the reference time axis of the standard node reference value, with a window width of 1 / 10 to 1 / 5 of the sampling period and a weight factor of 0.6 to 0.8; The state transition matrix of the Kalman filter algorithm is [1, Δt; 0, 1], the observation matrix is [1, 0], the process noise covariance is 0.01 to 0.05, and the observation noise covariance is 0.1 to 0.

3.

4. The electrical fault detection and control system according to claim 3, characterized in that: The phase monitoring module includes: The waveform sampling submodule monitors the voltage waveform through a synchronous voltage acquisition device, records the starting time coordinates within the sampling period, detects the timing position of the first zero crossing point, extracts the time interval data from the starting time to the zero crossing point, and generates a time point sequence; the phase difference calculation submodule calls the dynamic time warping algorithm to align the starting time of the time point sequence to the reference time axis of the standard node reference value, calculates the timing offset between the two in the zero crossing point interval, obtains the mean of the offset as the waveform alignment deviation, and generates the phase delay parameter item; The phase anomaly determination submodule compares the phase delay parameter item with the phase tolerance value threshold to determine whether the offset mean exceeds the upper or lower threshold. If so, the binary mark variable is assigned a value of 1, otherwise it is assigned a value of 0, and a phase anomaly mark is generated. The phase tolerance value threshold is determined by optimizing historical fault data using the gradient descent method, with an upper limit of 5% to 8% of the sampling period and a lower limit of 2% to 4% of the sampling period.

5. The electrical fault detection and control system according to claim 4, characterized in that: The abnormality positioning module includes: The phase sequence sampling submodule screens the phase difference value set of adjacent nodes based on the phase anomaly mark, extracts the difference data of three consecutive nodes in time sequence, and generates an adjacent phase difference sequence; The slope jump determination submodule calls a linear regression model to perform least squares fitting on the phase difference value of each node in the adjacent phase difference sequence, calculates the absolute value of the slope difference of the fitted straight line between adjacent segments, and compares it with the upper limit of the preset jump determination threshold. If the absolute value of the slope difference exceeds the threshold, it is marked as a sudden change segment and a slope jump coefficient is generated. The absolute value of the slope difference is dimensionlessly processed into a percentage, and the sudden jump determination threshold is 15% to 25%; The candidate segment calibration submodule extracts the start and end node numbers corresponding to the mutation segment according to the slope jump coefficient, calculates the node spacing between adjacent mutation points, and merges multiple mutation points into a single interval if the spacing is less than the preset segment merging length to generate candidate segment coordinates; The combined length of the preset sections is 1.5 to 2.5 times the electrical node spacing, and the node spacing is pre-calibrated to 0.5 to 1.2 meters based on the cable model.

6. The electrical fault detection and control system according to claim 5, characterized in that: The current control module includes: The constant current injection control submodule collects the coordinates of the candidate segment, analyzes the physical node distribution corresponding to the coordinates, sets the constant current source output parameters according to the node spacing, activates the constant current source device to inject a preset current value into the target segment, synchronously measures the voltage of multiple nodes to ground, calculates the voltage difference between adjacent nodes, and generates multi-node voltage difference data; The voltage difference abnormality analysis submodule calls the multi-node voltage difference data, extracts the voltage drop difference values of adjacent nodes in node order to form a sequence, and uses the formula: ； Iteratively calculate the sequence, eliminate noise interference by correcting the difference value item by item, and generate a smoothed pressure drop difference sequence; in, Representative The corrected pressure drop difference in the iteration, Represents the current node in The original value of the pressure drop difference collected in the iteration, Representative The difference in voltage drop between adjacent nodes in the iteration, Represents the suppression factor for the environmental noise amplitude, which is used to reduce the impact of noise on the difference value. Representative The amplitude of the ambient noise collected by the sensor in the iteration, Represents the dynamic sensitivity coefficient, which is used to adjust the response speed of the correction value. Represents the total number of data nodes participating in the calculation in the current segment, Represents the baseline voltage drop offset obtained through long-term monitoring data statistics, which is used to eliminate environmental steady-state interference; The threshold discrimination submodule calls the smoothed voltage drop difference sequence, traverses the corrected difference value of each node in the sequence, compares the value with the preset resistance sudden rise threshold parameter, marks the node interval where the difference value exceeds the threshold, and generates a resistance sudden rise segment mark according to the node interval coordinates; The preset resistance sudden rise threshold parameter is 3 to 5 times the voltage drop difference value under normal working conditions, and is determined by statistically analyzing historical fault data.

7. The electrical fault detection and control system according to claim 6, characterized in that: The system further comprises: The result verification module is used to receive the candidate segment coordinates and the resistance sudden rise segment mark, perform path coordinate comparison operation, output the positioning result when the overlap exceeds the ratio value threshold, trigger the review instruction and return it to the phase monitoring module and current control module when it does not meet the requirements, and output the positioning result to the external alarm unit.

8. The electrical fault detection and control system according to claim 7, characterized in that: The positioning result includes the fault coordinate value, matching overlap, and positioning confidence level.

9. The electrical fault detection and control system according to claim 8, characterized in that: The result verification module includes: The segment receiving submodule calls the starting node number and the ending node number of the candidate segment coordinates, extracts the coordinate range of the resistance sudden rise segment mark, merges the node number intervals and mark states of the two into a unified data format, and generates a segment set; The overlap calculation submodule compares the coordinate ranges of the starting point of the candidate segment and the ending point of the resistance sudden rise segment in the segment set, calculates the length of the intersection interval between the two on the node number axis, counts the percentage of the number of intersection nodes to the total number of nodes in the candidate segment, and generates an overlap ratio value; The verification decision submodule compares the overlap ratio value with a preset ratio value threshold. If the ratio value exceeds the threshold, the positioning result is output to the external alarm unit. If it does not exceed the threshold, a review instruction is generated and sent back to the phase monitoring module and the current control module. The ratio value threshold is 75% to 90%, which is optimized and determined by the cross-validation method and is set to 80% when the positioning accuracy reaches 95% confidence level.

Images