Multi-lateral cooperative diagnosis and positioning method for single-phase ground fault of power distribution network

Abstract

The present application relates to the technical field of power system diagnosis, in particular to a power distribution network single-phase grounding fault multi-edge collaborative diagnosis and positioning method, comprising the following steps: S1: real-time acquisition of current and voltage signals in the power distribution network through a sensor network; S2: preprocessing of the current and voltage signals; S3: analysis of the current unbalance degree and voltage mutation point to identify whether a single-phase grounding fault has occurred; S4: calculation of the propagation time difference of the fault signal using the time difference of arrival method to preliminarily estimate the location of the fault; S5: correction of the fault location to locate the specific fault point; S6: generation of a fault location report; S7: triggering of a processing measure to send a maintenance instruction to relevant personnel according to the fault location result. The present application provides accurate fault location and automated processing procedures by combining multi-data source analysis and power grid topology, effectively improving fault detection accuracy and processing efficiency, and ensuring stable operation of the power grid.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system diagnosis, in particular to a multi-edge cooperative diagnosis and positioning method for single-phase grounding fault of distribution network. BACKGROUND

[0002] With the continuous expansion and increasing complexity of power grid, single-phase grounding fault in distribution network has become a common fault type affecting the safe and stable operation of power system. Such fault is usually caused by aging of power grid line, external environmental influence or equipment damage, which will lead to current imbalance, line short circuit and even large-area power outage when the fault occurs. Therefore, how to quickly and accurately detect and locate single-phase grounding fault is a key link to ensure the safety of power grid and reduce power outage time. Traditional fault detection methods mostly rely on manual inspection or single sensor data collection, which is not only inefficient but also inaccurate, and it is difficult to find and repair faults in time.

[0003] In the prior art, although some automatic devices and systems are introduced for fault detection, these methods are often limited by insufficient device coverage and limited data processing capacity, resulting in inaccurate fault positioning. In addition, the existing methods usually ignore the effective use of power grid topology structure and historical fault data, and cannot provide intelligent fault analysis and processing mechanism. Therefore, it is particularly necessary to develop a fault diagnosis and positioning method based on power grid topology structure combined with multiple data sources. SUMMARY

[0004] Based on the above purpose, the present application provides a multi-edge cooperative diagnosis and positioning method for single-phase grounding fault of distribution network.

[0005] The multi-edge cooperative diagnosis and positioning method for single-phase grounding fault of distribution network comprises the following steps:

[0006] S1: Real-time acquisition of current and voltage signals in the distribution network through a sensor network, and transmission of the acquired data to a data processing unit;

[0007] S2: Preprocessing of the current and voltage signals transmitted to the data processing unit, including denoising processing and outlier rejection to generate standard data;

[0008] S3: Based on the standard data, analyzing the unbalance degree of current and the mutation point of voltage, and using a fault detection algorithm to identify whether a single-phase grounding fault occurs;

[0009] S4: If a single-phase grounding fault is detected, the data of different sampling points are analyzed in time sequence, and the time difference of fault signal propagation is calculated by using the time difference of arrival method to preliminarily estimate the location of the fault;

[0010] S5: On the basis of the preliminary position estimation, combined with historical fault data and power grid topology, a regression analysis algorithm is used to correct the fault position to locate the specific fault point;

[0011] S6: A fault location report is generated, and the report content includes the specific line, position and occurrence time of the fault;

[0012] S7: According to the fault location result, trigger processing measures to send maintenance instructions to relevant personnel.

[0013] Optionally, the S1 specifically comprises:

[0014] S11: A current and voltage sensor is arranged at a predetermined node of the power distribution network for real-time acquisition of three-phase current and voltage signals;

[0015] S12: The acquired current and voltage signals are preliminarily packaged, and the packaging includes adding a timestamp and a sampling point position label to each sampling signal;

[0016] S13: The packaged current and voltage data are transmitted to the data processing unit through a communication network, and the communication network uses optical fiber or 5G communication technology;

[0017] S14: During data transmission, TCP / IP protocol is used for data packaging and transmission to ensure data integrity.

[0018] Optionally, the S2 specifically comprises:

[0019] S21: After receiving the current and voltage signals transmitted to the data processing unit, the data is first filtered, and a frequency domain filtering algorithm based on fast Fourier transform is used to remove high-frequency noise and interference signals in the signal;

[0020] S22: The filtered current and voltage signals are subjected to outlier detection, and an outlier rejection algorithm based on statistics is used to identify abnormal data points outside the normal range by calculating the mean and standard deviation of the signal, and the abnormal data points are rejected;

[0021] S23: The signal after removing noise and outliers is subjected to data smoothing, and a sliding average algorithm is used to smooth the signal to reduce short-term fluctuations in the signal;

[0022] S24: The current and voltage signals processed by S23 are subjected to normalization processing, and a linear normalization algorithm is used to convert the numerical range of the signal to a standardized interval, ensuring that data in different measurement units can be uniformly processed, and finally generating standard data.

[0023] Optionally, the S3 specifically comprises:

[0024] S31: Based on the standardized current data, the unbalance degree of three-phase current is calculated, the differences between three-phase currents are analyzed, and whether there is a significant current imbalance phenomenon is determined by comparing the amplitude and phase of each phase current;

[0025] S32: The mutation point of the voltage signal is detected, the rapid decline or sudden rise of the voltage value is identified by monitoring the change trend of each phase voltage in real time, and the time and amplitude of these changes are marked for subsequent fault judgment;

[0026] S33: The change of current unbalance degree and the time and amplitude of voltage mutation point are combined, and a fault detection algorithm is used to comprehensively analyze the characteristics to determine whether it is a single-phase grounding fault. The fault detection algorithm is based on a pre-set characteristic threshold, and by comparing the current and voltage characteristics with the characteristic data during normal operation, it is identified whether it conforms to the typical characteristics of single-phase grounding fault;

[0027] S34: If the characteristic analysis result in S33 conforms to the characteristics of single-phase grounding fault, the time point, position and related data are recorded and marked as fault point.

[0028] Optionally, S33 specifically includes:

[0029] S331: The change of current unbalance degree and the change rate of voltage are synchronously collected, and by assigning different weights to current and voltage characteristics, a comprehensive fault characteristic parameter is calculated by combining the changes of the two;

[0030] S332: Set the threshold value of fault identification, when the calculated fault characteristic parameter exceeds the threshold value, determine that the current state is a single-phase grounding fault;

[0031] S333: Analyze the changes of current and voltage characteristics in combination with time dimension, observe the abnormal duration of characteristics, and when the characteristic values of current and voltage remain abnormal for a period of pre-set time, it is confirmed that a single-phase grounding fault occurs;

[0032] S334: Based on the fault characteristic parameter and the time analysis result, the time, position and related characteristic information of the fault occurrence are recorded.

[0033] Optionally, S4 specifically includes:

[0034] S41: After detecting a single-phase grounding fault, data of different position sampling points is collected, and the time point when each position receives a fault signal is obtained;

[0035] S42: The data of different sampling points is analyzed in time sequence to determine the exact time t i of each sampling point receiving a fault signal, and by comparing the time points t iRecord the time difference between the received signal and other sampling points;

[0036] S43: The fault location is initially estimated using the time difference of arrival method. Let the propagation speed of the fault signal in the conductor be v, then the distance difference Δd between the fault point and the sampling point... ij Δd is calculated using the following formula: ij =v×Δt ij , where Δd ij Let Δt be the difference in propagation distance of the fault signal between different sampling points, v be the propagation speed of the fault signal, and Δt be the distance between the sampling points. ij The time difference between different sampling points is used to preliminarily estimate the relative distance between the fault location and each sampling point by calculating the distance difference;

[0037] S44: By combining the distance differences between different sampling points, the approximate location of the fault can be calculated using the triangulation method;

[0038] S45: Compare the fault location calculated in S44 with the geographical location information of the distribution network to preliminarily determine the specific location range of the fault.

[0039] Optionally, S44 specifically includes:

[0040] S441: First, determine the known location coordinates of multiple sampling points in the distribution network, and then determine the relative distance difference between each pair of sampling points to the fault point based on the distance difference of fault signal propagation calculated in S43.

[0041] S442: Based on the known location and distance difference of the sampling points, establish multiple sets of location equations to represent the distance relationship between each sampling point and the fault point;

[0042] S443: Use data from multiple sampling points to form a set of equations, and use the least squares method to solve for the approximate location of the fault point by analyzing these equations.

[0043] Optionally, S5 specifically includes:

[0044] S51: Collect historical fault data, which includes the location, time and related electrical characteristics of past single-phase ground faults;

[0045] S52: Analyze the length and impedance characteristics of each line by combining the power grid topology data;

[0046] S53: Apply the multivariate regression analysis algorithm to correct the fault location. Based on the impedance analysis results and historical fault data in step S52, establish a regression model.

[0047] S54: Based on the calculation results of the regression model, the preliminary estimated fault location is corrected to ensure that the final positioning result conforms to the actual power grid topology.

[0048] Optionally, the S6 specifically comprises:

[0049] S61: Collect and integrate fault positioning data, including the specific location of the fault point, the corresponding line number, the fault detection time, and the related electrical parameters;

[0050] S62: Compare the fault location with the power grid geographic information system to determine which specific line the fault location is on, and based on the known distribution network topology, the fault positioning report will list the specific line number where the fault occurred;

[0051] S63: Record the fault detection time of the fault detection marker, and if there are multiple time records, take the earliest time point as the fault occurrence time;

[0052] S64: Generate a fault positioning report.

[0053] Optionally, the S7 specifically comprises:

[0054] S71: According to the fault positioning result, automatically trigger the maintenance instruction generation program, generate the maintenance task instruction according to the fault location, line number and occurrence time recorded in the fault report, and the maintenance task includes the detailed description of the fault location and the repair operation to be performed;

[0055] S72: Send the generated maintenance instruction to the relevant maintenance personnel, and the maintenance personnel information is automatically distributed through the pre-configured task management platform to ensure that the maintenance team closest to the fault location receives the maintenance task.

[0056] The beneficial effects of the present application are:

[0057] The present application, by collecting current and voltage signals in real time, analyzing current imbalance and voltage mutation points, and using regression analysis and triangular positioning method to accurately calculate the fault location, effectively improves the accuracy of fault detection. Compared with the traditional fault detection method, it can quickly locate the fault point, reduce the fault response time, and avoid long-time power failure and unnecessary manual intervention.

[0058] The present application, by integrating historical fault data and power grid topology, automatically generates maintenance instructions and adjusts power distribution after the fault occurs, ensuring that the fault impact is minimized, not only improving the fault handling efficiency, but also enhancing the stability and safety of the power grid. BRIEF DESCRIPTION OF DRAWINGS

[0059] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description only belong to the present application, and other drawings can also be obtained by those skilled in the art without creative effort.

[0060] Fig. 1 The schematic diagram of the fault multi-lateral collaborative diagnosis and positioning method of the embodiment of the present application is shown in the figure.

[0061] Fig. 2 The schematic diagram of the judgment flow of the fault detection algorithm used in the embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0062] The present application will be described in detail below with reference to the drawings and specific embodiments. It should be noted that in order to make the embodiments more detailed, the following embodiments are the best, preferred embodiments, and other alternative ways can also be used by those skilled in the art to implement them; and the drawings are only used to more specifically describe the embodiments, and are not intended to specifically limit the present application.

[0063] It should be noted that in the specification, "one embodiment", "embodiment", "exemplary embodiment", "some embodiments" and the like indicate that the described embodiments can include specific features, structures or characteristics, but not necessarily every embodiment includes the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in combination with an embodiment, it should be within the knowledge of those skilled in the related art to realize this feature, structure or characteristic in combination with other embodiments (whether or not explicitly described).

[0064] Generally, the terms can be understood at least in part from the use in the context. For example, depending at least in part on the context, the term "one or more" as used herein can be used to describe any feature, structure or characteristic in the singular or can be used to describe combinations of features, structures or characteristics in the plural. In addition, the term "based on" can be understood as not necessarily intended to convey a set of exclusive factors, but can instead, depending at least in part on the context, allow the presence of other factors not necessarily explicitly described.

[0065] As shown in the figure, the multi-lateral collaborative diagnosis and positioning method for single-phase grounding fault of the power distribution network comprises the following steps: Figs. 1-2

[0066] S1: Real-time acquisition of current and voltage signals in the power distribution network through a sensor network, and transmission of the acquired data to a data processing unit;

[0067] ​S2: Preprocess the current and voltage signals transmitted to the data processing unit, including denoising and outlier removal, to generate standard data;

[0068] S3: Based on the standard data, analyze the imbalance of the current and the mutation point of the voltage, and use the fault detection algorithm to identify whether a single-phase ground fault occurs;

[0069] S4: If a single-phase ground fault is detected, the time series analysis of the data at different sampling points is performed to calculate the propagation time difference of the fault signal using the time difference method, and the location of the fault is preliminarily estimated;

[0070] S5: Based on the preliminary location estimation, combined with historical fault data and power grid topology, the regression analysis algorithm is used to correct the fault location to locate the specific fault point;

[0071] S6: Generate a fault location report, including the specific line, location and time of the fault;

[0072] S7: According to the fault location result, trigger the processing measures to send maintenance instructions to relevant personnel, and reduce the impact of the fault on the operation of the power grid.

[0073] S1 specifically includes:

[0074] S11: Arrange current and voltage sensors at predetermined nodes of the distribution network to collect real-time three-phase current and voltage signals, with a fixed high frequency set to ensure the capture of dynamic changes in the power grid;

[0075] S12: Preliminary data packaging of the collected current and voltage signals, including adding time stamp and sampling point position label to each sampling signal to ensure that each data packet contains accurate space-time information;

[0076] S13: Through a low-delay and high-bandwidth communication network, the packaged current and voltage data are transmitted to the data processing unit, the communication network uses optical fiber or 5G communication technology to ensure the stability and real-time of data transmission, and the transmission delay is not more than the set millisecond level;

[0077] S14: In the data transmission process, use data transmission protocol to ensure lossless data transmission, the protocol specifically uses TCP / IP protocol for data packaging and transmission to ensure data integrity; Through the above steps, efficient and real-time collection and transmission of current and voltage signals in the distribution network can be realized, ensuring the accuracy and timeliness of the data, providing reliable basic data support for subsequent fault analysis and positioning.

[0078] S2 includes:

[0079] S21: After receiving the current and voltage signals transmitted to the data processing unit, the data is first filtered, using a frequency domain filtering algorithm based on fast Fourier transform (FFT), to remove high-frequency noise and interference signals in the signal and retain the main frequency band information related to fault diagnosis; the transformation formula of FFT is:

[0080] Wherein, X(f) is the result of the frequency domain signal, x(n) is the value of the time domain signal, N is the total number of sampling points, f is the frequency, n is the sampling point number of the signal, by setting the threshold in the frequency domain, the signal exceeding the set noise frequency range can be removed;

[0081] S22: Abnormal value detection is performed on the filtered current and voltage signals, using an abnormal value elimination algorithm based on statistics, by calculating the mean and standard deviation of the signal, identifying abnormal data points outside the normal range, and eliminating them; the abnormal value elimination algorithm formula is: |x i -μ x |>3σ x , wherein, x i is a single data point in the signal, μ x is the mean of the data, σ x is the standard deviation of the data, the signal data exceeding this range will be considered abnormal and eliminated;

[0082] S23: Data smoothing is performed on the signal after removing noise and eliminating abnormal values, using a sliding average algorithm to smooth the signal and reduce short-term fluctuations in the signal, using the formula of the sliding average algorithm: Wherein, y i is the signal value after smoothing, x i+k is the average value of the current signal point x i and the subsequent M data points, and M is the length of the sliding window;

[0083] S24: The current and voltage signals processed in S23 are normalized, the signal value range is converted to a standardized interval through a linear normalization algorithm, ensuring that data in different measurement units can be uniformly processed, and finally generating standard data as the basis for subsequent fault analysis.

[0084] S3 specifically includes:

[0085] S31: Based on the current data after standardization, the unbalance degree of three-phase current is calculated, the differences between three-phase currents are analyzed, and whether there is a significant current imbalance phenomenon is determined by comparing the amplitude and phase of each phase current; the unbalance degree is determined by the following formula: Wherein, ΔI is the unbalance degree of three-phase current, I max is the maximum value of three-phase current, I minImin is the minimum value of the three-phase current avg Iavg is the average value of the three-phase current; when AI exceeds a set threshold value, it indicates that there is a significant current imbalance phenomenon, indicating that the amplitude difference between the three-phase currents is large;

[0086] S32: Mutation point detection is performed on the voltage signal, the change trend of each phase voltage is monitored in real time, the rapid drop or sudden rise of the voltage value is identified, and the time and amplitude of these changes are marked for subsequent fault judgment; the rapid change of the voltage is identified through the following formula: Wherein, AV is the voltage change rate, V(t1) and V(t2) are the voltage values at time points t1 and t2 respectively, and t2-t1 is the time difference between two samplings; when AV exceeds the preset mutation threshold value, it indicates that the voltage has a rapid drop or rise, and these change points are marked for subsequent fault analysis;

[0087] S33: The change of the current imbalance degree and the time and amplitude of the voltage mutation point are combined, the fault detection algorithm is used to comprehensively analyze the characteristics, and it is judged whether it is a single-phase ground fault; the fault detection algorithm is based on the preset characteristic threshold value, and by comparing the current and voltage characteristics with the characteristic data in normal operation, it is identified whether it conforms to the typical characteristics of single-phase ground fault;

[0088] S34: If the characteristic analysis result in S33 conforms to the characteristics of single-phase ground fault, the time point, position and related data are recorded and marked as fault point for subsequent fault positioning.

[0089] The fault detection algorithm in S33 specifically includes:

[0090] S331: The change of the current imbalance degree and the change rate of the voltage are synchronously collected, different weights are assigned to the current and voltage characteristics, the changes of the two are combined, and a comprehensive fault characteristic parameter is calculated for judging the possibility of fault;

[0091] S332: Set the threshold value for fault recognition, when the calculated fault characteristic parameter exceeds the threshold value, judge that the current state is a single-phase ground fault, the threshold value is set based on historical fault data and actual operation environment, to ensure that it accurately reflects the characteristics of the fault;

[0092] S333: Analyze the changes of the current and voltage characteristics in combination with the time dimension, observe the abnormal duration of the characteristics, and when the characteristic values of the current and voltage remain abnormal for a period of time, it is confirmed that a single-phase ground fault occurs, and the time analysis ensures the stability and reliability of the detection process;

[0093] S334: based on the fault characteristic parameters and the time analysis result, record the time, location and related characteristic information of the fault occurrence, for subsequent fault positioning and repair operation; through the above steps, the fault detection algorithm can accurately identify the single-phase ground fault in the distribution network by comprehensively analyzing the current unbalance degree and the voltage change characteristics.

[0094] The specific operation steps of the fault detection algorithm are as follows:

[0095] The change of current unbalance degree ΔI and the voltage change rate ΔV are synchronously collected, and these characteristic data are compared with the set threshold value. The fault characteristic parameter F is calculated by the following formula: F = αΔI + βΔV, wherein F is the fault characteristic parameter, ΔI is the change of current unbalance degree, ΔV is the voltage change rate, and α and β are weight coefficients, representing the influence weight of current and voltage characteristics on fault. The weight value is set according to the actual operation situation;

[0096] Set the threshold value F of fault identification threshold When the fault characteristic parameter F meets the following condition, it is judged as a single-phase ground fault, and the condition formula is: F ≥ F threshold , wherein F threshold is the preset fault identification threshold value, which is adjusted through historical fault data and actual operation environment. When F exceeds this threshold value, it means that the current and voltage characteristics meet the characteristics of single-phase ground fault;

[0097] According to the change trend of the fault characteristic parameter F, further combined with the analysis of time dimension, the duration of current and voltage characteristics is observed. If the characteristic value exceeds the set threshold value for a period of time T, it is confirmed that a single-phase ground fault occurs. The set time threshold condition is: t duration ≥ T, wherein t duration is the duration of current unbalance and voltage mutation point remaining abnormal, and T is the set minimum duration threshold value. If the time condition is met, the fault identification result will be confirmed;

[0098] Finally, combined with the above analysis result, the time, location, characteristic parameter F and other information at the time of fault occurrence are recorded and used for subsequent fault positioning and repair.

[0099] The preliminary estimation of the fault occurrence position in S4 specifically includes:

[0100] S41: when the single-phase ground fault is detected, start collecting data of different position sampling points. The sampling points are distributed at multiple nodes of the power grid and can record the change time of current and voltage signals in real time. Through the collection of fault signals by each sampling point, the time point when each position receives the fault signal is obtained;

[0101] S42: Perform time-series analysis on the data from different sampling points to determine the exact time t when each sampling point receives the fault signal. i And by comparing time points t i The time difference between the received signal and other sampling points is recorded as Δt. ij Where i and j represent different sampling points, the formula is: Δt ij =t j -t i ;

[0102] S43: The fault location is initially estimated using the time difference of arrival method. Let the propagation speed of the fault signal in the conductor be v, then the distance difference Δd between the fault point and the sampling point... ij Δd is calculated using the following formula: ij =v×Δt ij , where Δd ij Let Δt be the difference in propagation distance of the fault signal between different sampling points, v be the propagation speed of the fault signal, and Δt be the distance between the sampling points. ij The time difference between different sampling points is used to preliminarily estimate the relative distance between the fault location and each sampling point by calculating the distance difference;

[0103] S44: By combining the distance differences between different sampling points, the approximate location of the fault can be calculated using the triangulation method;

[0104] S45: Compare the fault location calculated in S44 with the geographical location information of the distribution network to preliminarily determine the specific location range of the fault, providing a basis for subsequent precise location; by performing time series analysis on the fault signals at different sampling points and combining the time difference of arrival method, this method can quickly and accurately estimate the location of the fault, significantly improving the efficiency and accuracy of fault location.

[0105] S44 specifically includes:

[0106] S441: First, determine the known location coordinates of multiple sampling points in the distribution network, and then determine the relative distance difference between each pair of sampling points to the fault point based on the distance difference of the fault signal propagation calculated in S43. This step is determined based on the time difference of each sampling point receiving the fault signal.

[0107] S442: Based on the known location and distance difference of the sampling points, establish multiple sets of location equations to represent the distance relationship between each sampling point and the fault point. These equations represent the relative distance difference between the fault point and multiple sampling points. The specific location of the fault point can be further determined through these equations.

[0108] S443: Form a set of equations using the data of multiple sampling points, solve the approximate position of the fault point by solving these equations using the least square method, which ensures that even if the number of sampling points is large or there is a certain error in the signal, accurate positioning can still be performed through multiple sets of data; by applying the triangular positioning method combined with the least square method, the specific position of the fault point can be quickly and accurately calculated, greatly improving the accuracy of fault positioning and ensuring the efficiency of subsequent maintenance work.

[0109] The steps of calculating the approximate position of the fault occurrence using the triangular positioning method are as follows:

[0110] First, determine the position coordinates (x i , y i ) of multiple sampling points, where i represents different sampling points, and x i and y i are the known coordinates of the sampling point in the power distribution network; calculate the distance difference Δd ij of the fault signal propagation according to the time difference in step S43, which represents the distance difference between two sampling points and the fault point;

[0111] Then, according to the known positions of different sampling points and the distance difference, a set of triangular positioning equations is established; specifically, for each sampling point i and j, the position (x, y) of the fault point satisfies the following relationship:

[0112] Where (x, y) is the unknown coordinate of the fault point, (x i , y i ) and (x j , y j ) are the known coordinates of sampling points i and j, and Δd ij is the distance difference calculated according to the propagation time difference;

[0113] Next, form multiple equations using the distance differences Δd ij of multiple sampling points, construct a linear equation set, and the expression is: And solve the equation set using the least square method, and finally obtain the approximate position (x, y) of the fault point;

[0114] Finally, compare the solved fault position with the geographical position information of the power grid to further verify the calculation result and ensure the rationality and accuracy of the fault point position.

[0115] S5 specifically includes:

[0116] S51: Collect historical fault data, including the location, time and related electrical characteristics of past single-phase ground faults, which will be used to analyze the similarity of the characteristics of the current fault and historical faults;

[0117] S52: In combination with the power grid topology data, analyze the length and impedance characteristics of each line; set the impedance of the power grid line as Z, the specific formula is: Z = R + jX, where R is the resistance of the line, X is the reactance of the line, and Z is the total impedance of the line; by combining the length L of the line and its impedance Z, the total impedance of each line is calculated to determine the influence of each line on signal propagation; lines with lower impedance and closer to the preliminary position are more likely to fail;

[0118] S53: Apply multiple regression analysis algorithm to correct the fault location, according to the impedance analysis results in step S52 and historical fault data, establish a regression model, set the fault location L and multiple characteristic variables x1, x2, …, x n There is a linear relationship, and the expression of the fault location L is: L = β0+ β1x1+ β2x2+ … + β n x n , where x1, x2, …, x n are the electrical characteristics of the line (such as impedance, load, current, etc.), β0, β1, …, β n are regression coefficients, and the regression model is trained by historical data;

[0119] S54: Based on the calculation results of the regression model, correct the preliminary estimated fault location to ensure that the final positioning result can conform to the actual power grid topology; after the regression model is trained by historical data, it can provide a more accurate prediction value for the current fault location, and the preliminary estimated location will be adjusted according to the fault location output by the model, the specific process is: according to the impedance characteristics of each line and the historical fault mode, the preliminary estimated location is corrected to make it more consistent with the physical characteristics of the actual power grid operation and match the historical fault occurrence mode, so as to obtain a more accurate fault point location; through the above steps, combined with historical fault data, power grid topology and the calculation results of the regression model, the preliminary estimated fault location can be effectively corrected, and the accuracy of fault location can be improved.

[0120] S6 specifically includes:

[0121] S61: Collect and integrate fault location data, including the specific location of the fault point, the corresponding line number, the fault detection time and related electrical parameters (such as current, voltage, etc.), which will be used as basic information for generating fault reports;

[0122] S62: Compare the fault location with the power grid geographic information system (GIS) to confirm which specific line the fault location is on, and based on the known distribution network topology, the fault location report will list the specific line number where the fault occurred;

[0123] S63: record the fault occurrence time of the fault detection mark, if there are multiple time records, take the earliest time point as the fault occurrence time;

[0124] S64: generate a fault location report, the report content includes:

[0125] The specific line name and number of the fault occurrence;

[0126] The geographic location of the fault occurrence, including latitude and longitude or specific node in the power distribution network;

[0127] The time of the fault occurrence, accurate to seconds;

[0128] Related electrical characteristic data, such as current, voltage, etc. at the time of the fault, for subsequent analysis.

[0129] S7 specifically includes:

[0130] S71: according to the fault location result, automatically trigger the maintenance instruction generation program, generate the maintenance task instruction according to the fault location, line number and occurrence time recorded in the fault report, the maintenance task includes detailed description of the fault location and the repair operation to be performed;

[0131] S72: send the generated maintenance instruction to the relevant maintenance personnel, the maintenance personnel information is automatically distributed through the pre-configured task management platform, to ensure that the maintenance team closest to the fault location receives the maintenance task; the specific line of the fault occurrence is listed in detail in the maintenance instruction; the exact geographic location of the fault; the expected fault type (such as single-phase ground fault); the repair tools and equipment to be carried; the recommended repair time window.

[0132] The present application covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present application. In order to make the public have a thorough understanding of the present application, specific details are described in the following preferred embodiments of the present application, and the present application can also be fully understood without the description of these details to those skilled in the art. In addition, in order to avoid unnecessary confusion to the essence of the present application, well-known methods, processes, procedures, elements and circuits, etc. are not described in detail.

[0133] The above is only the preferred embodiment of the present application, it should be pointed out that for ordinary skilled in the art, without departing from the principles of the present application, can also make a number of improvements and refinements, these improvements and refinements should also be considered as the protection scope of the present application.

Claims

1. A method for multi-lateral collaborative diagnosis and location of single-phase ground fault in power distribution network, characterized in that, The method comprises the following steps: S1: Real-time collection of current and voltage signals in the power distribution network through a sensor network, and transmission of the collected data to a data processing unit; S2: Preprocessing of the current and voltage signals transmitted to the data processing unit, the preprocessing comprising denoising processing and outlier rejection, to generate standard data; S3: Based on the standard data, analysis of the unbalance degree of the current and the mutation point of the voltage, and identification of whether a single-phase ground fault occurs using a fault detection algorithm; specifically comprising: S31: Based on the current data after standardization processing, calculation of the unbalance degree of the three-phase current, analysis of the differences between the three-phase currents, and determination of whether there is a significant current imbalance phenomenon by comparing the amplitude and phase of each phase current; S32: Mutation point detection of the voltage signal, identification of rapid decline or sudden rise of the voltage value by real-time monitoring of the change trend of each phase voltage, and marking of the time and amplitude of these changes for subsequent fault judgment; S33: Combination of the change of the current unbalance degree and the time and amplitude of the voltage mutation point, comprehensive analysis of the characteristics using a fault detection algorithm, judgment of whether it is a single-phase ground fault, the fault detection algorithm being based on a pre-set characteristic threshold, comparison of the current current and voltage characteristics with the characteristic data in normal operation to identify whether it conforms to the typical characteristics of a single-phase ground fault; S34: If the characteristic analysis result in S33 conforms to the characteristics of a single-phase ground fault, the time point, location and related data are recorded and marked as the fault point; S33 specifically comprises: S331: Synchronous collection of the change of the current unbalance degree and the change rate of the voltage, and calculation of a comprehensive fault characteristic parameter by assigning different weights to the current and voltage characteristics and combining the changes of the two; S332: Setting of a threshold for fault identification, and judgment of the current state as a single-phase ground fault when the calculated fault characteristic parameter exceeds the threshold; S333: Analysis of the changes of the current and voltage characteristics in the time dimension, observation of the abnormal duration of the characteristics, and confirmation of the occurrence of a single-phase ground fault when the characteristic values of the current and voltage remain abnormal for a pre-set period of time; S334: Recording of the time, location and related characteristic information of the fault occurrence based on the fault characteristic parameter and the time analysis result; S4: When a single-phase ground fault is detected, time series analysis of the data of different sampling points is performed, the propagation time difference of the fault signal is calculated using the time difference of arrival method, and the location of the fault occurrence is preliminarily estimated; specifically comprising: S41: After detecting a single-phase ground fault, data of different position sampling points is collected, and the time point at which each position receives the fault signal is obtained; S42: Time series analysis is performed on the data of different sampling points to determine the exact time when each sampling point receives the fault signal , and by comparing the time points with the time difference of other sampling points, the time difference of each sampling point receiving the signal is recorded; S43: a preliminary estimation of the fault location is made using the time difference of arrival method, assuming that the propagation speed of the fault signal in the conductor is The distance difference between the fault point and the sampling point is The distance difference is calculated by the following formula: Wherein, is the distance difference of the fault signal between different sampling points, is the propagation speed of the fault signal, is the time difference between different sampling points, and the relative distance between the fault location and each sampling point is preliminarily estimated by the calculated distance difference; S44: Using the distance difference of different sampling points, the approximate location of the fault occurrence is calculated using the triangular positioning method; S45: Comparison of the fault location calculated in S44 with the geographical location information of the power distribution network, and preliminary determination of the specific location range of the fault occurrence; S5: Based on the preliminary location estimation, historical fault data and power grid topology are combined, and a regression analysis algorithm is used to correct the fault location to locate the specific fault point. S6: generating a fault location report, the report content including the specific line where the fault occurs, the location and the time of occurrence; S7: according to the fault location result, triggering processing measures to send maintenance instructions to relevant personnel.

2. The power distribution network single-phase-to-ground fault multi-lateral collaborative diagnosis and location method according to claim 1, characterized in that, The S1 specifically comprises: S11: arranging current and voltage sensors at predetermined nodes of the power distribution network for real-time acquisition of three-phase current and voltage signals; S12: preliminarily packaging the acquired current and voltage signals, the packaging including adding a time stamp and a sampling point position label to each sampling signal; S13: transmitting the packaged current and voltage data to a data processing unit through a communication network, the communication network using optical fiber or 5G communication technology; S14: during data transmission, using TCP / IP protocol for data packaging and transmission to ensure data integrity.

3. The power distribution network single-phase-to-ground fault multi-lateral collaborative diagnosis and location method according to claim 1, characterized in that, The S2 specifically comprises: S21: after receiving the current and voltage signals transmitted to the data processing unit, first filtering the data, using a frequency domain filtering algorithm based on fast Fourier transform to remove high-frequency noise and interference signals in the signals; S22: detecting abnormal values of the filtered current and voltage signals, using an abnormal value elimination algorithm based on statistics to identify abnormal data points outside the normal range by calculating the mean and standard deviation of the signals, and eliminating them; S23: performing data smoothing on the signals after removing noise and eliminating abnormal values, using a sliding average algorithm to smooth the signals and reduce short-term fluctuations in the signals; S24: normalizing the current and voltage signals processed in S23, converting the numerical range of the signals to a standardized interval through a linear normalization algorithm to ensure that data in different measurement units can be uniformly processed, and finally generating standard data.

4. The power distribution network single-phase-to-ground fault multi-lateral collaborative diagnosis and location method of claim 1, wherein, The S44 specifically comprises: S441: first determine the known position coordinates of multiple sampling points in the power distribution network, and determine the relative distance difference between each pair of sampling points to the fault point according to the distance difference of the fault signal propagation calculated in S43; S442: based on the known positions of the sampling points and the distance difference, establish a plurality of position equations for representing the distance relationship between each sampling point and the fault point; S443: use the data of multiple sampling points to form a set of equations, solve the approximate position of the fault point by analyzing these equations and using the least squares method.

5. The power distribution network single-phase-to-ground fault multi-lateral collaborative diagnosis and location method according to claim 1, characterized in that, The S5 specifically comprises: S51: collecting historical fault data, including the location, time and related electrical characteristics of past single-phase ground faults; S52: combining power grid topology data to analyze the length and impedance characteristics of each line; S53: applying a multivariate regression analysis algorithm to correct the fault location, establishing a regression model according to the impedance analysis results in step S52 and the historical fault data; S54: based on the calculation results of the regression model, correcting the preliminary estimated fault location to ensure that the final positioning result is consistent with the actual power grid topology.

6. The power distribution network single-phase-to-ground fault multi-lateral coordinated diagnosis and location method according to claim 1, characterized in that, The S6 specifically comprises: S61: collecting and integrating fault location data, including the specific location of the fault point, the corresponding line number, the fault detection time and related electrical parameters; S62: Compare the fault location with the power grid geographic information system to determine which specific line the fault location is on, and based on the known distribution network topology, the fault location report will list the specific line number where the fault occurred; S63: Record the fault detection mark fault occurrence time, if there are multiple time records, take the earliest time point as the fault occurrence time; S64: Generate fault location report.

7. The power distribution network single-phase-to-ground fault multi-lateral coordinated diagnosis and location method according to claim 1, characterized in that, The S7 specifically includes: S71: According to the fault location result, automatically trigger the maintenance instruction generation program, generate the maintenance task instruction according to the fault location, line number and occurrence time recorded in the fault report, the maintenance task includes the detailed description of the fault location and the repair operation to be carried out; S72: Send the generated maintenance instruction to the relevant maintenance personnel, the maintenance personnel information is automatically distributed through the pre-configured task management platform, to ensure that the maintenance team closest to the fault location receives the maintenance task.

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