Analysis system for comprehensive judgment of grounding fault line selection of 10KV line
By designing an analysis system for comprehensive judgment of 10KV line grounding fault lines, using real-time monitoring and intelligent algorithms to make comprehensive judgments, the problem of difficulty in positioning of grounding faults is solved, and efficient and accurate fault positioning and fault type identification are achieved.
Patent Information
- Application Number
- CN202411791288.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-06
- Publication Date
- 2025-03-28
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
The 10KV line grounding fault is difficult to accurately locate in complex environments, resulting in low emergency repair efficiency, and different types of grounding faults perform differently, increasing the difficulty of judgment.
An analysis system for comprehensive judgment of 10KV line grounding fault line selection is designed. By monitoring three-phase voltage, current and zero-sequence voltage in real time, combining intelligent algorithms such as fuzzy reasoning and decision trees, comprehensive judgment is made, fault lines are accurately positioned, and fault types are identified.
It significantly improves the accuracy and efficiency of fault positioning, can accurately locate the fault lines in a short time, and generates detailed fault analysis reports to provide precise guidance to emergency repair personnel.
Smart Images
Figure CN119716634A_ABST
Abstract
Description
Technical Field
[0001] The invention relates to the field of information technology, and in particular to an analysis system for comprehensive judgment of line selection for a 10KV line grounding fault. Background Art
[0002] 10KV line grounding fault line selection is a key challenge in power system operation and maintenance. Traditional methods often have difficulty in accurately locating faulty lines in complex fault situations, resulting in low repair efficiency. Especially in the case of multiple lines in parallel, unbalanced loads, or harmonic interference, a single zero-sequence current or voltage judgment method is prone to misjudgment. At the same time, different types of grounding faults (such as metallic grounding, high-resistance grounding, etc.) have different manifestations, which increases the difficulty of judgment. In addition, the instantaneous characteristics of faults are often fleeting, and how to capture and analyze key information in a short time is also a major problem. Accurately identifying faulty lines is not only related to the rapid restoration of power supply, but also directly affects the safe and stable operation of the power grid. Summary of the invention
[0003] The present invention provides an analysis system for comprehensive judgment of 10KV line grounding fault line selection, which mainly includes:
[0004] It includes a data layer, a functional layer and a presentation layer. The data layer includes a line selection request start module, a line selection data operation and exchange module and a line selection request receiving module. The functional layer includes a line selection processing program module and a data access program module. The presentation layer includes a small current line selection data input module, each station end information input module and a data output module. The line selection processing program module includes a line selection start preprocessing unit, a zero-sequence current amplitude, phase and direction comparison unit of each feeder and a line selection processing result unit. The data access program module includes a data processing start unit, a data access request unit and a data processing end unit.
[0005] Furthermore, the fault feature extraction process includes: when the zero-sequence voltage of the entire system is generated, the line selection is automatically started, the zero-sequence current signal of each feeder, the ABC three-phase voltage and the ABC three-phase current are obtained, the changes in each phase voltage and the zero-sequence current of each feeder are automatically determined, and the zero-sequence current waveform sent from the zero-sequence current input terminal of each feeder is compared with the system ABC three-phase current, and the line selection data is formed through IVD signal decomposition.
[0006] Furthermore, the process of determining the system grounding fault includes: determining the grounding fault generated by the system according to the zero-sequence voltage of the system obtained by the system zero-sequence voltage transformer and the ADC three-phase-to-ground voltage calculation, and determining the fault phase according to the difference in line voltage.
[0007] Furthermore, the process of determining the ground fault branch includes: using the zero-sequence current waveform obtained by the zero-sequence current transformer of each loop, selecting three preferred lines with the most obvious zero-sequence current changes of each feeder, and the lines with the largest waveform amplitude are predetermined as three fault lines as alternative lines.
[0008] Furthermore, the determination of the zero-sequence current characteristics of each feeder when a ground fault occurs includes: determining the waveform, phase, phase and voltage characteristics of the zero-sequence current of each feeder, and the signal sent by the zero-sequence voltage transformer to form a VI characteristic curve, and three circuits whose waveforms of each zero-sequence current are the same as the phase of the VI characteristic curve are selected as fault lines.
[0009] Furthermore, the process of determining the direction of the grounding zero-sequence current waveform includes: three preferred lines are selected in which the direction of the grounding current is the same as the direction of the current generated by the fault.
[0010] Furthermore, the process of determining the ground fault loop includes: having the same waveform, the same VI curve, the same phase, and the same current direction generated by the current waveform. If the above conditions are met at the same time, it is a ground fault line.
[0011] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:
[0012] The present invention discloses a comprehensive judgment method for line selection of 10KV line grounding fault. The method quickly identifies grounding faults by real-time monitoring of three-phase voltage, current and zero-sequence voltage. Once a fault is detected, the present invention starts the line selection program, analyzes the changes in zero-sequence current of each feeder, and screens out suspected fault lines. Subsequently, the present invention deeply analyzes the zero-sequence current waveform, phase and zero-sequence voltage relationship of these lines, and draws VI characteristic curves to evaluate their correlation with the fault point. The innovation lies in that the present invention integrates multi-source information, uses fuzzy reasoning, decision trees and other intelligent algorithms for comprehensive judgment, and accurately locates the fault line. In addition, the present invention can also identify the type of fault and generate an intuitive report containing information such as the name and distance of the fault line, providing precise guidance for repair personnel. This multi-dimensional and intelligent fault handling method significantly improves the accuracy and efficiency of fault location, and provides a strong guarantee for the reliable operation of the power system. BRIEF DESCRIPTION OF THE DRAWINGS
[0013] Figure 1 This is an analysis system diagram for comprehensive judgment of line selection for a 10KV line grounding fault according to the present invention. DETAILED DESCRIPTION
[0014] The following will describe the technical solutions in the embodiments of the present invention in detail in conjunction with the accompanying drawings in the embodiments of the present invention. The described embodiments are only part of the embodiments of the present invention.
[0015] like Figure 1 In this embodiment, a 10KV line grounding fault line selection comprehensive judgment analysis system may specifically include:
[0016] S101, obtaining the ABC three-phase voltage, ABC three-phase current and zero-sequence voltage signal of the 10KV line, judging whether a ground fault occurs in the system according to the change of the zero-sequence voltage, and if a ground fault occurs, starting the ground fault line selection program.
[0017] The ABC three-phase voltage, ABC three-phase current and zero-sequence voltage signals of the 10KV line are obtained. According to the change of the zero-sequence voltage, it is determined whether the system has a ground fault. If the zero-sequence voltage exceeds the set threshold, it is determined that the system has a ground fault and the ground fault line selection program is started. After the ground fault line selection program is started, the zero-sequence current signal of each feeder is obtained, and the zero-sequence current amplitude, phase and direction of each feeder are analyzed and compared. The three feeders with the most obvious zero-sequence current changes are selected as suspected fault lines. The zero-sequence current waveforms of the three suspected fault lines are deeply analyzed, and the VI characteristic curve is constructed by zero-sequence current and zero-sequence voltage. The phase characteristics of the VI curves of each line are compared, and the lines with consistent phases are selected as alternative fault lines. The direction of the zero-sequence current waveform of the alternative line is further analyzed to determine whether the current direction is consistent with the fault direction. If it is consistent, the line is determined as the fault line, and if it is inconsistent, the line is excluded. Comprehensively considering the zero-sequence current amplitude, VI curve phase characteristics and current direction of the suspected fault line, after comparative analysis, the line that meets the maximum amplitude, consistent phase and correct direction is selected as the final ground fault line. According to the determined ground fault line, an intelligent algorithm is used to calculate and analyze the fault type, fault distance and other information to obtain detailed fault information. The results of ground fault line selection and fault analysis information are summarized and organized, and intuitively presented to the on-duty personnel through the human-computer interaction interface to assist the on-duty personnel in making fault handling decisions. The line selection results and fault information are synchronously transmitted to the distribution automation master station system to trigger automated operations such as fault isolation and power restoration to improve power supply reliability. Various data in the line selection and analysis process are recorded and archived to form a ground fault case library. Through data mining and analysis, the line selection algorithm is continuously improved and optimized to improve the speed and accuracy of line selection.
[0018] Specifically, when the zero-sequence voltage of the 10KV line exceeds the set threshold of 5V, the system determines that a ground fault has occurred and automatically starts the line selection program. The program obtains the zero-sequence current data of each feeder, extracts the fundamental component of the current through Fourier transform, compares the amplitude, phase and direction of the current of each line, and selects three lines with an amplitude exceeding 100A, a phase difference less than 10°, and a direction pointing to the fault point as suspected fault lines. The VI curves of the current and voltage data of these three lines are drawn, and the curves are fitted by the least squares method and the correlation coefficient is calculated. The lines with a correlation coefficient greater than 0.95 are determined as alternative fault lines. The current direction of the alternative lines is then analyzed by current vector synthesis, and the lines with an angle less than 5° with the fault direction are confirmed as fault lines. Finally, the current amplitude, VI curve fitting degree and current direction of the fault line are comprehensively compared, and the lines that meet the conditions for all three indicators are locked as the final ground fault line. According to the current and voltage waveform characteristics of the fault line, wavelet transform and support vector machine algorithm are used to calculate detailed information such as the fault distance of 1.5km and the fault type of single-phase grounding. The line selection results and fault information are presented through a graphical interface and transmitted to the main distribution station at the same time, triggering fault isolation and automatic reclosing operations, and restoring power supply within an average of 5 minutes. The data of the entire line selection process is recorded in the case library, and the line selection model is optimized through big data analysis, so that the line selection accuracy rate reaches more than 99%.
[0019] S102: Obtain zero-sequence current signals of each feeder, compare and analyze amplitude changes of zero-sequence currents of each feeder, and select three feeders with the most obvious amplitude changes as suspected fault lines.
[0020] The zero-sequence current signal of each feeder is obtained through the zero-sequence current transformer installed on each feeder, and the zero-sequence current signal is converted into a digital signal and transmitted to the data processing unit. The data processing unit receives the zero-sequence current digital signal of each feeder, extracts the amplitude information of the zero-sequence current of each feeder, and stores it in the database. The data processing unit reads the historical amplitude data of the zero-sequence current of each feeder in the database, and calculates the change rate of the zero-sequence current amplitude of each feeder. According to the calculated change rate of the zero-sequence current amplitude of each feeder, the feeders are sorted, and the larger the amplitude change rate, the higher the ranking. The top three feeders are selected as suspected fault lines, and the number information of the suspected fault lines is transmitted to the fault location unit. After the fault location unit receives the number information of the suspected fault line, the zero-sequence current, voltage and other related information of the three suspected fault lines are read from the database. The fault location unit calculates the ground fault probability of each suspected fault line based on the information of the suspected fault line read. The calculated ground fault probabilities of each suspected fault line are compared, and the line with the highest probability is the fault line. Output the fault line number and related information to provide a basis for subsequent fault isolation and maintenance.
[0021] Specifically, by installing a zero-sequence current transformer on each feeder, the zero-sequence current signal of each feeder can be obtained in real time, and the analog signal is converted into a digital signal using an A / D converter, and the sampling is performed at a sampling frequency of 4000Hz. Each sampling point is represented by a 16-bit binary number and transmitted to the data processing unit through the RS485 bus. After the data processing unit receives the zero-sequence current digital signal of each feeder, the amplitude information of the zero-sequence current of each feeder is extracted using the fast Fourier transform (FFT) algorithm, and the extracted amplitude data is stored in the MySQL database. Then, the data processing unit reads the zero-sequence current amplitude historical data of each feeder in the last 10 minutes from the database, fits the change curve of the zero-sequence current amplitude of each feeder using the least squares method, and calculates the slope of the curve, which is the change rate of the zero-sequence current amplitude of each feeder. The feeders are sorted according to the change rate, and the three feeders with the largest change rate are selected as suspected fault lines, and their number information is transmitted to the fault location unit via the TCP / IP protocol. After receiving the number of the suspected fault line, the fault location unit reads the zero-sequence current, zero-sequence voltage and other related information of the three lines in the last minute from the database, and calculates the ground fault probability of each line using the phase comparison method, where the fault probability P = |cos(φ_u-φ_i)|, φ_u is the zero-sequence voltage phase angle, and φ_i is the zero-sequence current phase angle. The calculated fault probabilities are compared, and the line with the highest probability is determined to be the fault line, and its number and related information are output through the Web service interface for the dispatcher to isolate and repair the fault.
[0022] S103, for the suspected faulty line, further analyze its zero-sequence current waveform, the relationship between the phase and the zero-sequence voltage, draw a VI characteristic curve, and determine the correlation between each suspected line and the system fault point.
[0023] Obtain the zero-sequence current waveform of each feeder, and select the three lines with the largest amplitude as suspected fault lines according to the amplitude of the waveform. Analyze the zero-sequence current waveform of the suspected fault line, extract its phase information, and compare it with the phase of the system zero-sequence voltage. If the phase is consistent, the line has a high correlation with the system fault point. Draw the VI characteristic curve of the suspected fault line, that is, the relationship curve between the zero-sequence voltage and the zero-sequence current, and judge the degree of correlation between the line and the fault point by the shape of the curve. According to the analysis results of the zero-sequence current amplitude, phase and VI characteristic curve, sort the suspected fault lines, and the line with the highest correlation is the most likely fault line. Further analyze the zero-sequence current waveform of the fault line, and estimate the approximate location of the fault point by the mutation position and mutation amplitude of the waveform. According to the topological structure and line parameters of the fault line, the fault location algorithm is used to calculate the precise location of the fault point. Compare the fault location results with the line inspection information to eliminate misjudgment caused by defects in the line itself. Generate a fault analysis report, which includes the fault line, fault type, fault location, fault cause analysis, etc., to provide a basis for subsequent emergency repair and prevention. Upload the fault analysis results to the main station system, update the line operation status database, and provide data support for intelligent operation and maintenance decisions.
[0024] Specifically, by analyzing the current waveforms collected by the zero-sequence current transformers of each feeder, it is found that the zero-sequence current amplitudes of lines 1, 3, and 5 are the largest, which are 120A, 115A, and 105A, respectively. Therefore, these three lines are regarded as suspected fault lines. Further comparison of the zero-sequence current phases of the three lines with the system zero-sequence voltage phases shows that the phase difference between line 1 and the system zero-sequence voltage is 5°, the phase difference between line 3 is 30°, and the phase difference between line 5 is 90°. It can be seen that line 1 has the greatest correlation with the fault point. The VI characteristic curves of lines 1, 3, and 5 are drawn. The results show that the curve shape of line 1 is closest to the system zero-sequence voltage, which further verifies its high correlation with the fault point. Based on the above analysis, line 1 is determined to be the fault line. By performing wavelet analysis on the zero-sequence current waveform of line 1, it is found that there is an obvious mutation at 2.5km away from the substation, and it is preliminarily estimated that the fault point is located near this. According to the segmented line parameters of Line 1, the impedance location method was used to calculate the fault point location as 2.7 km away from the substation. The location result was compared with the most recent line inspection record to eliminate the possibility of misjudgment due to defects in the line itself. Finally, a fault analysis report was generated, which recorded in detail the fault line, fault type (single-phase grounding), fault location (2.7 km away from Line 1) and possible fault causes (such as insulator damage, wire breakage, etc.), providing guidance for emergency repairs. At the same time, the fault information was uploaded to the main station system, and the line operation status database was updated to provide data support for subsequent intelligent operation and maintenance decisions.
[0025] S104. Combining multiple source information such as the zero-sequence current waveform, phase, VI characteristic curve, and fault current direction of the suspected line, an intelligent algorithm is used to make a comprehensive judgment to determine the final fault line.
[0026] Obtain the zero-sequence current waveform data of each feeder, monitor and record the zero-sequence current waveform of each feeder in real time through the data acquisition module to ensure the integrity and accuracy of the data. Extract the characteristic parameters of the zero-sequence current waveform, including amplitude, phase and frequency, and use the signal processing algorithm to extract the features of the collected zero-sequence current waveform to form a characteristic data set. Calculate the VI characteristic curve of each feeder, and generate the VI characteristic curve of each feeder through the VI characteristic curve calculation module according to the zero-sequence voltage signal provided by the zero-sequence voltage transformer and the zero-sequence current signal of each feeder. Analyze the phase consistency of the zero-sequence current waveform of each feeder, compare the phase difference of the zero-sequence current waveform of each feeder, and screen out the feeders with consistent phases as suspected fault lines. Determine the directionality of the zero-sequence current waveform of each feeder, use the current direction determination algorithm to analyze the direction of the zero-sequence current waveform of each feeder, and determine the feeder consistent with the fault current direction. Comprehensively integrate the zero-sequence current waveform, phase, VI characteristic curve and current direction information of each feeder to construct a multi-source information data set to provide comprehensive data support for the intelligent algorithm. Use intelligent algorithms for comprehensive judgment, input multi-source information data sets, use machine learning or deep learning algorithms for comprehensive analysis, and output preliminary judgment results of the faulty line. Perform secondary verification on the preliminary judgment results, and further confirm the accuracy of the faulty line by comparing the historical fault data and real-time monitoring data of each feeder. Determine the final faulty line, combine the judgment results of the intelligent algorithm and the results of the secondary verification, output the final faulty line judgment results, and record the relevant data for subsequent analysis.
[0027] Specifically, the zero-sequence current waveform data of each feeder is obtained, and the zero-sequence current waveform of each feeder is monitored and recorded in real time through the data acquisition module to ensure the integrity and accuracy of the data. For example, the system collects 1000 data points per second to record the zero-sequence current waveform of feeders A, B, and C, and the data accuracy reaches the milliampere level. The characteristic parameters of the zero-sequence current waveform are extracted, and the frequency domain analysis of the collected zero-sequence current waveform is performed using the fast Fourier transform (FFT) algorithm to extract the characteristic parameters of the fundamental amplitude of 5A and the third harmonic amplitude of 0.5A. The VI characteristic curve of each feeder is calculated, and the VI characteristic curve of feeder A is generated through the VI characteristic curve calculation module according to the zero-sequence voltage signal (such as peak value 100V) provided by the zero-sequence voltage transformer and the zero-sequence current signal of each feeder. It is found that the current is 3A when the voltage is 50V. The phase consistency of the zero-sequence current waveform of each feeder is analyzed, and the phase of the zero-sequence current waveform of feeders A, B, and C is compared. It is found that the phase difference between feeders A and C is 0 degrees, and it is determined to be consistent in phase. Determine the directionality of the zero-sequence current waveform of each feeder. Use the current direction determination algorithm to analyze that the zero-sequence current waveform direction of feeder A is from the busbar to the feeder, which is consistent with the fault current direction. Combine the zero-sequence current waveform, phase, VI characteristic curve and current direction information of each feeder to construct a multi-source information data set to provide comprehensive data support for the intelligent algorithm. Use the support vector machine (SVM) algorithm for comprehensive judgment, input the multi-source information data set, and after training and testing, output feeder A as the preliminary judgment result. Perform a secondary verification on the preliminary judgment result, and further confirm the accuracy of feeder A by comparing the historical fault data of feeder A (such as 3 similar faults occurred in the past month) and real-time monitoring data. Determine the final fault line, combine the judgment results of the SVM algorithm and the results of the secondary verification, output feeder A as the final fault line, and record the relevant data for subsequent analysis.
[0028] S105. According to the fault line judgment result, the fault location information is output to guide the repair personnel to find the fault point quickly and accurately, so as to achieve efficient isolation and recovery of the fault.
[0029] S106. In the comprehensive judgment method for selecting the grounding fault line of the 10KV line, the intelligent algorithm includes classification algorithms such as fuzzy reasoning, decision tree, support vector machine, etc., and a comprehensive evaluation result is obtained by fusion calculation of multi-source judgment criteria.
[0030] The data acquisition module is started to obtain the zero-sequence current signal, ABC three-phase voltage signal and ABC three-phase current signal of each feeder, and transmit them to the data layer for preliminary processing and storage. The data preprocessing module filters, denoises and normalizes the collected signals, extracts the waveform and phase of the zero-sequence current and the amplitude and phase information of the ABC three-phase voltage, and forms preliminary fault feature data. The fuzzy reasoning module performs fuzzy reasoning calculation based on the preprocessed zero-sequence current waveform and phase information, combined with the preset fuzzy rule base, to obtain the probability score of the grounding fault of each feeder. The decision tree module uses the preprocessed ABC three-phase voltage and current data to construct a decision tree model, and outputs the probability distribution of the grounding fault of each feeder through layer-by-layer judgment of the tree structure. The support vector machine module constructs a support vector machine classification model based on the preprocessed fault feature data, and determines the fault classification results of each feeder through high-dimensional space mapping and optimal hyperplane partitioning. The multi-source criterion fusion module performs weighted fusion on the results output by fuzzy reasoning, decision tree and support vector machine, and uses weighted average or other fusion algorithms to calculate the comprehensive evaluation results and form the final fault line selection decision. The line selection processing program compares the zero-sequence current amplitude, phase and direction of each feeder according to the comprehensive evaluation results, and determines three preferred lines. The line with the largest waveform amplitude and consistent phase is used as the alternative fault line. The VI characteristic curve generation module uses the zero-sequence current waveform of each feeder and the signal sent by the zero-sequence voltage transformer to draw the VI characteristic curve, compare the phase consistency of each zero-sequence current waveform with the VI characteristic curve, and further screen the fault line. The fault line confirmation module combines the results of the line selection processing program and the VI characteristic curve generation module to determine the line with consistent waveform, the same VI curve, the same phase and the same current waveform direction among the three preferred lines, and finally determines the grounding fault line.
[0031] Specifically, the data acquisition module is started to obtain the zero-sequence current signal of each feeder in real time, such as the zero-sequence current of feeder A is 5A, feeder B is 3A, and feeder C is 2A. At the same time, the ABC three-phase voltage signal is collected, such as the voltage of phase A is 10.2kV, phase B is 10.1kV, and phase C is 10.0kV, and the ABC three-phase current signal, such as the current of phase A is 100A, phase B is 98A, and phase C is 95A, and is transmitted to the data layer for preliminary processing and storage. The data preprocessing module filters the collected signals, uses a low-pass filter to remove high-frequency noise, smoothes the zero-sequence current waveform after denoising, and normalizes the amplitude of each signal to the [0, 1] interval, extracts the waveform and phase of the zero-sequence current and the amplitude and phase information of the ABC three-phase voltage, and forms preliminary fault feature data. The fuzzy reasoning module performs fuzzy reasoning calculations based on the preprocessed zero-sequence current waveform and phase information, combined with the preset fuzzy rule base, such as "If the zero-sequence current amplitude is greater than 4A and the phase offset exceeds 30 degrees, the fault probability is high", and obtains the fault probability score of feeder A as 0.8, feeder B as 0.5, and feeder C as 0.3. The decision tree module uses the preprocessed ABC three-phase voltage and current data to construct a decision tree model. Through the layer-by-layer judgment of the tree structure, such as "If the A phase voltage is lower than 10.1kV and the A phase current is greater than 99A, the probability of feeder A fault is high", and outputs the probability distribution of each feeder grounding fault, feeder A is 0.7, feeder B is 0.4, and feeder C is 0.2. The support vector machine module constructs a support vector machine classification model based on the preprocessed fault feature data. Through high-dimensional space mapping and optimal hyperplane partitioning, the fault classification results of each feeder are determined. Feeder A is judged to be faulty, and feeders B and C are judged to be normal. The multi-source criterion fusion module performs weighted fusion on the results of fuzzy reasoning, decision tree and support vector machine outputs, adopts weighted average algorithm, sets weights to 0.4, 0.3 and 0.3 respectively, and calculates the comprehensive evaluation results. The comprehensive scores of feeder A are 0.73, feeder B is 0.43, and feeder C is 0.26, forming the final fault line selection decision. According to the comprehensive evaluation results, the line selection processing program compares the zero-sequence current amplitude, phase and direction of each feeder, and determines feeders A and B with the largest waveform amplitude and consistent phase among feeders A, B and C as alternative fault lines. The VI characteristic curve generation module uses the zero-sequence current waveform of each feeder and the signal sent by the zero-sequence voltage transformer to draw the VI characteristic curve, compares the phase consistency of each zero-sequence current waveform with the VI characteristic curve, and the zero-sequence current waveform of feeder A is completely consistent with the phase of the VI curve, so as to further screen the fault line. The fault line confirmation module combines the results of the line selection processing program and the VI characteristic curve generation module to determine that feeder A has the conditions of consistent waveform, identical VI curve, consistent phase and consistent current waveform direction, and finally determines that feeder A is a ground fault line.
[0032] S107. In the comprehensive judgment method for selecting the grounding fault line of the 10KV line, after the final fault line is determined, it also includes a fault type identification step, which determines the fault type according to the voltage and current characteristics of the fault line to provide a decision-making basis for fault analysis and processing.
[0033] S108. In the comprehensive judgment method for selecting the grounding fault line of the 10KV line, the fault location information includes the name of the fault line, the fault distance, the fault type, etc., which are intuitively presented in the form of text, charts, etc., so that the emergency repair personnel can quickly understand the fault situation.
[0034] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.
Claims
1. An analysis system for comprehensive judgment of 10KV line grounding fault line selection, characterized in that: include: Data layer, functional layer and presentation layer; The data layer includes a line selection request starting module, a line selection data operation and exchange module, and a line selection request receiving module; The functional layer includes a line selection processing program module and a data access program module; The presentation layer includes a small current line selection data input module, each station end information input module and a data output module; The line selection processing program module includes a line selection preprocessing unit, a zero-sequence current amplitude, phase and direction comparison unit of each feeder, and a line selection processing result unit. The data access program module includes a data processing start unit, a data access request unit and a data processing end unit.
2. The analysis system for comprehensive judgment of 10KV line grounding fault line selection as claimed in claim 1, characterized in that: The fault feature extraction process includes: When the zero-sequence voltage of the entire system is generated, the line selection is automatically started to obtain the zero-sequence current signal of each feeder, the ABC three-phase voltage and the ABC three-phase current, and automatically judge the changes in each phase voltage and the zero-sequence current of each feeder. Combined with the system ABC three-phase current, the zero-sequence current waveform sent from the zero-sequence current input terminal of each feeder is compared, and the line selection data is formed through IVD signal decomposition.
3. The analysis system for comprehensive judgment of 10KV line grounding fault line selection as claimed in claim 1, characterized in that: The process of determining a system ground fault includes: The ground fault generated in the system is determined based on the zero-sequence voltage of the system obtained by the system zero-sequence voltage transformer and combined with the ADC three-phase-to-ground voltage calculation, and the fault phase is determined based on the difference in line voltage.
4. The analysis system for comprehensive judgment of 10KV line grounding fault line selection as claimed in claim 1, characterized in that: The process of determining the ground fault branch includes: Using the zero-sequence current waveform obtained by the zero-sequence current transformer of each loop, three preferred lines with the most obvious zero-sequence current changes of each feeder are selected. The lines with the largest waveform amplitude are predetermined as three fault lines as alternative lines.
5. The analysis system for comprehensive judgment of 10KV line grounding fault line selection as claimed in claim 1, characterized in that: The method of determining the zero-sequence current characteristics of each feeder when a ground fault occurs includes: Determine the waveform, phase, phase and voltage characteristics of the zero-sequence current of each feeder, and the signal sent by the zero-sequence voltage transformer to form a VI characteristic curve. The three circuits with the same phase as the waveform of each zero-sequence current and the VI characteristic curve are selected as fault lines.
6. The analysis system for comprehensive judgment of 10KV line grounding fault line selection as claimed in claim 1, characterized in that: The process of determining the direction of the ground zero-sequence current waveform includes: The three preferred lines have the same direction of ground current as the current generated by the fault.
7. The analysis system for comprehensive judgment of 10KV line grounding fault line selection as claimed in claim 1, characterized in that: The process of determining the ground fault loop includes: At the same time, the waveform is consistent, the VI curve is the same, the phase is consistent, and the current direction generated by the current waveform is consistent. If the above conditions are met, it is a ground fault line.