System and method for positioning grounding fault of power distribution network

Abstract

The invention relates to the technical field of power systems, and discloses a power distribution network grounding fault positioning system and method, and the system comprises a fault detection module, a signal processing module, a fault positioning module, a data communication module, a central control system, a human-computer interface module, and a power management module. The fault detection module is used for monitoring current and voltage of the power distribution network in real time and detecting abnormal changes so as to judge a grounding fault; the signal processing module is responsible for real-time processing of collected signals, hardware acceleration is carried out by adopting efficient algorithms of fast Fourier transform and wavelet transform and utilizing a field programmable gate array of a digital signal processor, and fault detection is carried out through a current sensor and a voltage sensor of the fault detection module. Current and voltage changes of the power distribution network can be monitored in real time, abnormal conditions can be rapidly detected, it is ensured that grounding faults can be found in the first time, measures can be taken in time, and the influence of the faults on power grid operation is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a system and method for locating ground faults in a distribution network. Background Art

[0002] A ground fault refers to an electrical connection between a phase conductor in a power system and the ground, which causes current to flow to the ground through the grounding point. This fault is a common type of fault in distribution networks and has a serious impact on the stability and safety of the system. In modern distribution networks, ground faults are one of the common problems that affect the safe operation of power grids. Timely detection and location of ground faults are of great significance for maintaining the stable operation of power grids. However, the existing distribution network ground fault detection and location technologies still have many deficiencies and challenges in practical applications: Traditional distribution network monitoring systems usually rely on a small number of monitoring points, and the sensitivity and real-time performance of the monitoring equipment are low, resulting in slow response speed of fault detection, and abnormal current and voltage changes cannot be discovered in the first place, which easily misses the best time to handle the fault, and then increases the impact of the fault on the operation of the power grid; Existing signal processing technologies have low processing speed and efficiency when facing complex and large-scale distribution network data. In addition, many systems still rely on traditional Fourier transform algorithms, which have high computational complexity and slow processing speed and cannot meet the needs of real-time fault analysis. In addition, the performance limitations of hardware devices further reduce the overall efficiency of the system. In existing fault location systems, a single location algorithm is usually used, such as the impedance method or the traveling wave method. The algorithm has poor robustness and adaptability in complex environments and is easily affected by noise and other interference, resulting in low fault location accuracy. In addition, the existing system lacks effective data optimization and learning mechanisms and cannot continuously optimize the fault location model based on historical data. The existing system lacks redundant communication links in its design, and the continuity and reliability of data transmission are difficult to guarantee. At the same time, there is a lack of effective compression and optimization mechanisms during data transmission, which leads to increased communication burden and low transmission efficiency.

[0003] Therefore, those skilled in the art provide a system and method for locating ground faults in a distribution network to solve the problems raised in the above background technology. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a system and method for locating ground faults in a distribution network to solve the problems in the background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a ground fault location system for distribution network, including a fault detection module, a signal processing module, a fault location module, a data communication module, a central control system, a human-machine interface module and a power management module; the fault detection module is used to monitor the current and voltage of the distribution network in real time, detect abnormal changes, and judge the ground fault; the signal processing module is responsible for real-time processing of the collected signals, using efficient algorithms such as fast Fourier transform and wavelet transform, and using digital signal processors or field programmable gate arrays for hardware acceleration; the fault location module uses a variety of fault location algorithms for comprehensive analysis based on the processed signals, and introduces The machine learning algorithm is introduced to optimize the fault location model through historical data training to provide / form multi-point collaborative monitoring; the data communication module designs redundant communication links to perform data compression and optimization; the central control system is used to aggregate data from each fault detection module, analyze the fault situation of the entire network, and command each distributed module to work together, and adopts a distributed computing architecture and load balancing technology to delegate some data processing and decision-making functions to local nodes; the human-machine interface module provides an intuitive fault information display interface, including fault location, fault type and fault time; the power management module adopts redundant power supply design and high-reliability power management strategy to ensure that the system can obtain power supply under various circumstances.

[0006] Preferably, the fault detection module includes a current sensor, a voltage sensor and a preprocessing unit; the current sensor monitors the current change of the distribution network in real time and collects current data; the voltage sensor monitors the voltage change of the distribution network in real time and collects voltage data; the preprocessing unit performs preliminary filtering and denoising on the collected current and voltage signals; The signal processing module includes a digital signal processor, a field programmable gate array and a data compression unit; the digital signal processor executes a signal processing algorithm; the field programmable gate array hardware accelerates signal processing; and the data compression unit compresses data to reduce the amount of data that needs to be processed and transmitted.

[0007] Preferably, the fault location module includes a multi-algorithm fusion unit, a machine learning unit and a multi-point collaborative monitoring unit; the multi-algorithm fusion unit comprehensively uses the impedance method, the traveling wave method and the transient analysis method; the machine learning unit uses historical data to train and optimize the fault location model; the multi-point collaborative monitoring unit summarizes multi-point monitoring data; The data communication module includes a communication protocol unit, a redundant communication link unit, a data compression and optimization unit and a network monitoring and management unit; the communication protocol unit adopts a high-reliability and low-latency communication protocol; the redundant communication link unit designs and implements redundant communication links; the data compression and optimization unit compresses and optimizes data before communication; the network monitoring and management unit monitors and manages the network status in real time.

[0008] Preferably, the central control system includes a data aggregation unit, a distributed computing unit and a local decision unit; the data aggregation unit aggregates the data of each fault detection module to perform fault condition analysis on the entire network; the distributed computing unit adopts a distributed computing architecture; the local decision unit delegates part of the data processing and decision-making functions to the local node; The human-machine interface module includes a fault information display unit, an operation control unit and a user training unit; the fault information display unit provides an intuitive fault information display interface; the operation control unit simplifies the operation process; the user training unit provides training and guidance to help operators use the system proficiently.

[0009] Preferably, the power management module includes a battery unit, a power converter unit and a redundant power design unit; the battery unit provides emergency power; the power converter unit converts and adjusts the power supply voltage; and the redundant power design unit designs a redundant power system.

[0010] Preferably, the signal processing module is based on fast Fourier transform, and its basic formula is as follows: and , in, is the frequency domain signal, represents the base of the natural logarithm, j represents the imaginary unit, N is the number of signal samples, is the circumference of a circle, k is the frequency index, and n is the sample point index of the signal; Continuous wavelet transform formula , Discrete Wavelet Transform Formula , in, are the wavelet coefficients, is the mother wavelet, is the translation parameter, is the conjugate of the mother wavelet, is the conjugate form of the mother wavelet, is the scale factor, is the original signal, is the coefficient after discrete wavelet transform, is the index of the data, represents the index of the mother wavelet, represents the wavelet basis function at scale j and position k, is the summation symbol.

[0011] Preferably, the multi-algorithm fusion unit includes an impedance method, a transient analysis method and a traveling wave method; The impedance method includes the single-ended impedance method and the double-ended impedance method, and its basic formula is as follows: Single-ended impedance method: , Two-terminal impedance method: , in, is the line characteristic impedance, is the total length of the line, The distance measurement point to the fault point distance; The transient analysis method includes transient voltage and current analysis and fault location calculation, and its basic formula is as follows: Transient voltage and current analysis: , Fault location calculation: ; The traveling wave method includes the traveling wave propagation time formula and traveling wave calculation, and its basic formula is as follows: Traveling wave propagation time formula: , Traveling wave calculation: , in, is the propagation speed of the traveling wave on the line, is the time difference of the traveling wave from the fault point to the measurement point, and is the time it takes for the traveling wave to reach the measuring points A and B.

[0012] Preferably, the machine learning unit uses historical data to train and optimize the fault location model based on the machine learning model, and its basic formula is as follows: , in, is the Lagrange multiplier, is a classification label, is the kernel function, is bias; The multi-point collaborative monitoring unit aggregates multi-point monitoring data based on a data fusion algorithm and performs comprehensive analysis. The basic formula is as follows: , in, is the fault impedance of the ith monitoring point, is the weight of the i-th monitoring point; , , , in, is the state estimate, is the Kalman gain, is the measured value, is the measurement matrix, is the estimated error covariance, is the measurement noise covariance.

[0013] Preferably, the network monitoring and management unit monitors the flow change based on network flow monitoring through a sliding window algorithm, and its basic formula is as follows: , in, is the average flow rate at time t, is the window size, is the flow rate at time i; , in, is the network health score, It's network delay. is the maximum acceptable delay, is the packet loss rate, and is the weight coefficient.

[0014] A method for locating ground faults in a distribution network comprises the following steps: Step S1: Use current sensors and voltage sensors to monitor the current and voltage changes of the distribution network in real time, collect current and voltage data, and perform preliminary filtering and denoising on the collected current and voltage signals to obtain purified signals; Step S2: Input the purified signal into a digital signal processor, perform fast Fourier transform and wavelet transform, extract fault features, and use field programmable gate array for hardware acceleration. At the same time, compress the processed signal data through a data compression unit; Step S3: Input the processed signal to the multi-algorithm fusion unit, and use the impedance method, traveling wave method and transient analysis method to locate the fault. During the location process, the machine learning unit is used to optimize the fault location model through historical data training to improve the location accuracy. Step S4: performing data transmission, and during the data transmission process, designing and implementing redundant communication links through redundant communication link units to ensure the continuity and reliability of data transmission, and at the same time, compressing and optimizing data through data compression and optimization units before communication; Step S5: Summarize the data of each fault detection module, analyze the fault situation of the entire network, and delegate some data processing and decision-making functions to the local node; Step S6: Display the fault information on the interface.

[0015] The present invention provides a system and method for locating ground faults in a distribution network, which have the following beneficial effects: 1. The present invention can monitor the current and voltage changes of the distribution network in real time through the current sensor and voltage sensor of the fault detection module, quickly detect abnormal conditions, ensure that the grounding fault can be discovered in the first time, and help to take measures in time to reduce the impact of the fault on the operation of the power grid.

[0016] 2. The present invention adopts efficient algorithms such as fast Fourier transform and wavelet transform, and uses digital signal processor or field programmable gate array for hardware acceleration to improve the speed and efficiency of signal processing and ensure that fault signals can be processed and analyzed quickly and accurately.

[0017] 3. The present invention uses a variety of fault location algorithms for comprehensive analysis through the fault location module, and introduces a machine learning algorithm to optimize the fault location model through historical data training to improve the accuracy of fault location and enhance the robustness and adaptability of the system in complex environments.

[0018] 4. The present invention adopts a high-reliability and low-latency communication protocol through a data communication module and designs redundant communication links to ensure the continuity and reliability of data transmission. In addition, the data is compressed and optimized through a data compression and optimization unit to improve transmission efficiency and reduce communication burden. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is a system diagram of the present invention; Figure 2 is a schematic diagram of a fault detection module of the present invention; Figure 3 is a schematic diagram of a signal processing module of the present invention; Figure 4 It is a schematic diagram of a fault location module of the present invention; Figure 5 It is a schematic diagram of a data communication module of the present invention; Figure 6 It is a schematic diagram of the central control system of the present invention; Figure 7 It is a schematic diagram of a human-machine interface module of the present invention; Figure 8 is a schematic diagram of a power management module of the present invention; Fig. 9 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0020] In order to make the technical personnel in the technical field understand the scheme of the present invention, the technical scheme in the embodiment of the present invention will be clearly and completely described below in combination with the drawings in the embodiment of the present invention. Obviously, the described embodiment is a partial embodiment of the present invention, not a complete embodiment. Based on the embodiment of the present invention, other embodiments obtained by ordinary technicians in the field without creative work should fall within the scope of protection of the present invention.

[0021] The present invention is described in detail below in conjunction with the accompanying drawings: Example

[0022] Please refer to the attached Figure 1 - Attachment Fig. 9 The embodiment of the present invention provides a ground fault location system for a distribution network, including a fault detection module, a signal processing module, a fault location module, a data communication module, a central control system, a human-machine interface module and a power management module; the fault detection module is used to monitor the current and voltage of the distribution network in real time, detect abnormal changes, and determine the ground fault; the signal processing module is responsible for real-time processing of the collected signals, using efficient algorithms such as fast Fourier transform and wavelet transform, and using digital signal processors or field programmable gate arrays for hardware acceleration; the fault location module uses a variety of fault location algorithms for comprehensive analysis based on the processed signals, and introduces machine learning algorithms The method optimizes the fault location model through historical data training to provide / form multi-point collaborative monitoring; the data communication module designs redundant communication links to compress and optimize data; the central control system is used to summarize the data from each fault detection module, analyze the fault situation of the entire network, and command the distributed modules to work together. It also adopts distributed computing architecture and load balancing technology to delegate some data processing and decision-making functions to local nodes; the human-machine interface module provides an intuitive fault information display interface, including fault location, fault type and fault time; the power management module adopts redundant power supply design and high-reliability power management strategy to ensure that the system can obtain power supply under various circumstances.

[0023] The fault detection module can quickly detect the occurrence of grounding faults, reduce the impact of faults on power grid operation, and improve the stability and safety of the power grid; the signal processing module improves the speed and efficiency of signal processing to ensure the real-time performance of the system; the fault location module improves the accuracy of fault location, enhances the robustness and adaptability of the system in complex environments, and improves the accuracy and reliability of fault location; the data communication module ensures the stability and timeliness of data transmission, guarantees the continuity and reliability of data transmission, reduces the risk of single point failures, compresses and optimizes data, improves transmission efficiency, and reduces communication burden; the central control system improves the overall efficiency of fault detection and location, improves the scalability and response speed of the system, and ensures the efficient operation and reliability of the system; the human-machine interface module facilitates operators to manage and maintain faults, improves the work efficiency of operators, reduces the possibility of misoperation, and improves overall operating efficiency; the power management module ensures that the system can obtain a stable power supply under various circumstances, and improves the reliability and stability of the system.

[0024] The fault detection module includes a current sensor, a voltage sensor and a preprocessing unit; the current sensor monitors the current change of the distribution network in real time and collects current data; the voltage sensor monitors the voltage change of the distribution network in real time and collects voltage data; the preprocessing unit performs preliminary filtering and denoising on the collected current and voltage signals; The signal processing module includes a digital signal processor, a field programmable gate array and a data compression unit; the digital signal processor executes the signal processing algorithm; the field programmable gate array hardware accelerates signal processing; and the data compression unit compresses the data to reduce the amount of data that needs to be processed and transmitted.

[0025] The current sensor provides high-precision current measurement to ensure the accuracy of fault detection; the voltage sensor provides high-precision voltage measurement to ensure the accuracy of fault detection; the preprocessing unit improves the signal quality, reduces the interference components in the signal, and improves the accuracy and reliability of subsequent signal processing; the digital signal processor ensures that the signal can be processed and analyzed quickly and accurately, and ensures that the system can respond to and process fault signals quickly; the field programmable gate array improves the signal processing speed and efficiency, meets high real-time requirements, and improves the overall processing capability of the system; the data compression unit reduces the amount of data that needs to be processed and transmitted, improves transmission efficiency, reduces storage costs, and improves the speed and efficiency of data transmission.

[0026] The fault location module includes a multi-algorithm fusion unit, a machine learning unit and a multi-point collaborative monitoring unit; the multi-algorithm fusion unit comprehensively uses the impedance method, the traveling wave method and the transient analysis method; the machine learning unit uses historical data to train and optimize the fault location model; the multi-point collaborative monitoring unit aggregates multi-point monitoring data; The data communication module includes a communication protocol unit, a redundant communication link unit, a data compression and optimization unit, and a network monitoring and management unit; the communication protocol unit adopts a high-reliability and low-latency communication protocol; the redundant communication link unit designs and implements redundant communication links; the data compression and optimization unit compresses and optimizes data before communication; the network monitoring and management unit monitors and manages the network status in real time.

[0027] The multi-algorithm fusion unit improves the accuracy and reliability of fault location, reduces the errors caused by a single algorithm, and improves the accuracy of fault location; the machine learning unit gradually improves the accuracy and efficiency of the positioning algorithm, improves the adaptive ability, and further improves the accuracy of fault location; the multi-point collaborative monitoring unit improves the accuracy of fault location, reduces the impact of single-point failures, enhances the reliability and stability of the system, and improves the system's comprehensive monitoring capabilities for faults; the communication protocol unit reduces data loss, improves the real-time nature of data transmission, and ensures that fault information can be transmitted in a timely manner; the redundant communication link unit ensures the continuity of data transmission and reduces the risk of single-point failures; the data compression and optimization unit reduces the amount of data, improves transmission speed and efficiency, reduces transmission costs, and optimizes the use of storage resources; the network monitoring and management unit optimizes network performance to ensure efficient and stable data transmission.

[0028] The central control system includes a data aggregation unit, a distributed computing unit and a local decision unit; the data aggregation unit aggregates the data of each fault detection module and performs fault analysis on the entire network; the distributed computing unit adopts a distributed computing architecture; the local decision unit delegates some data processing and decision-making functions to local nodes; The human-machine interface module includes a fault information display unit, an operation control unit and a user training unit; the fault information display unit provides an intuitive fault information display interface; the operation control unit simplifies the operation process; the user training unit provides training and guidance to help operators use the system proficiently.

[0029] The data aggregation unit provides fault condition analysis for the entire network and improves the accuracy of fault detection and location; the distributed computing unit improves the system's data processing capabilities and efficiency, and can flexibly adjust computing resources according to the expansion of the distribution network; the local decision-making unit reduces data transmission delays, improves fault response speed, improves system reliability, and improves the pertinence and effectiveness of decision-making; the fault information display unit improves the clarity and readability of information display; the operation control unit improves work efficiency, reduces the risk of operator misoperation, improves system safety, and improves the accuracy and efficiency of fault handling; the user training unit improves the overall operation level and helps operators continuously improve their skills.

[0030] The power management module includes a battery unit, a power converter unit and a redundant power design unit; the battery unit provides emergency power; the power converter unit converts and adjusts the power supply voltage; and the redundant power design unit designs a redundant power system.

[0031] The battery unit avoids data loss and system downtime, improves system reliability and stability, and ensures system continuity; the power converter unit optimizes power conversion efficiency, reduces energy loss, improves the overall energy efficiency of the system, and extends the service life of the equipment; the redundant power supply design unit ensures the continuity of power supply, significantly improves system reliability, reduces the risk of single point failures, and avoids downtime caused by power problems.

[0032] The signal processing module is based on fast Fourier transform, and its basic formula is as follows: and , in, is the frequency domain signal, N is the number of signal samples, k is the frequency index, and n is the sample point index of the signal. represents the base of natural logarithm, j represents the imaginary unit, is pi; Sampling: Sample the input signal x(t) to obtain a discrete signal x(n); Compute the discrete Fourier transform: Substitute the sampled signal x(n) into the DFT formula: and , Optimize calculation: The Fast Fourier Transform algorithm is used to optimize the DFT calculation. The FFT algorithm reduces the computational complexity to O(N 2 ) is reduced to O(NlogN); The original signal is decomposed into odd and even sequences, and the DFT of each part is recursively calculated and then combined to obtain the final result.

[0033] Continuous wavelet transform formula , Select mother wavelet: select a suitable mother wavelet function ψ(t); Scale and translation parameters: set the range of scale parameter a and translation parameter b; Compute the wavelet coefficients: Calculate the wavelet coefficients for each scale a and translation b: , In actual calculations, integration is usually implemented through numerical methods, such as convolution operations.

[0034] Discrete wavelet transform formula: , Select discrete mother wavelet: Select a suitable discrete mother wavelet function; Discretization of scale and translation parameters: Discretize scale j and translation k parameters; Compute discrete wavelet coefficients: Calculate the wavelet coefficients for each discrete scale j and discrete translation k: , Use decomposition filters to perform multi-scale decomposition of the signal and extract wavelet coefficients; in, are the wavelet coefficients, is the mother wavelet, is the translation parameter, is the conjugate of the mother wavelet, is the conjugate form of the mother wavelet, is the scale factor, is the original signal, is the coefficient after discrete wavelet transform, is the index of the data, represents the index of the mother wavelet, represents the wavelet basis function at scale j and position k, is the summation symbol.

[0035] The multi-algorithm fusion unit includes impedance method, transient analysis method and traveling wave method; The impedance method includes the single-ended impedance method and the double-ended impedance method. The basic formula is as follows: Single-ended impedance method: , Two-terminal impedance method: , in, is the line characteristic impedance, is the total length of the line, The distance measurement point to the fault point distance; The transient analysis method includes transient voltage and current analysis and fault location calculation. Its basic formula is as follows: Transient voltage and current analysis: , Fault location calculation: ; The traveling wave method includes the traveling wave propagation time formula and traveling wave calculation. Its basic formula is as follows: Traveling wave propagation time formula: , Traveling wave calculation: , in, is the propagation speed of the traveling wave on the line, is the time difference of the traveling wave from the fault point to the measurement point, and is the time it takes for the traveling wave to reach the measuring points A and B.

[0036] The impedance method is relatively simple to calculate, easy to implement and apply, and is used in various power systems. The single-ended impedance method can quickly calculate the fault location, and the double-ended impedance method improves the positioning accuracy. It is used for real-time fault detection and positioning, and can quickly respond to and handle faults. The transient analysis method uses the transient characteristics of the fault to improve the accuracy of fault location. It is used for rapid fault location and can analyze and locate the fault at the moment it occurs. The traveling wave method uses the traveling wave propagation time for positioning, quickly detects and locates the fault point after the fault occurs, reduces the power outage time, and is used for fault positioning of long-distance transmission lines.

[0037] The machine learning unit uses historical data to train and optimize the fault location model based on the machine learning model. The basic formula is as follows: , in, is the Lagrange multiplier, is a classification label, is the kernel function, is bias; The multi-point collaborative monitoring unit aggregates multi-point monitoring data based on the data fusion algorithm and conducts comprehensive analysis. The basic formula is as follows: , in, is the fault impedance of the ith monitoring point, is the weight of the i-th monitoring point; , , , in, is the state estimate, is the Kalman gain, is the measured value, is the measurement matrix, is the estimated error covariance, is the measurement noise covariance.

[0038] The machine learning unit uses historical data to train the machine learning model, continuously optimizes the fault location algorithm, and improves the location accuracy and efficiency. The machine learning model can make adaptive adjustments based on new data to maintain high-precision fault location capabilities. The multi-point collaborative monitoring unit aggregates multi-point monitoring data based on data fusion and conducts comprehensive analysis to improve the accuracy of fault location. Multi-point collaborative monitoring reduces the impact of single-point failures and improves the overall reliability and stability of the system. The Kalman filter can update the fault status estimation in real time, improve the real-time and accuracy of positioning, provide accurate fault location information by filtering out measurement noise, and reduce the possibility of misjudgment.

[0039] The network monitoring and management unit monitors network traffic based on the sliding window algorithm to monitor traffic changes. The basic formula is as follows: , in, is the average flow rate at time t, is the window size, is the flow rate at time i; , in, is the network health score, It's network latency. is the maximum acceptable delay, is the packet loss rate, and is the weight coefficient.

[0040] Initialization parameters: Set the maximum acceptable delay ; Set weight coefficient and ; Initialize network delay D and packet loss rate P; Data collection: Collect network delay data D and packet loss rate P in real time; Calculate the network health score: Calculating the Network Health Score NHS: , Monitor network health: Based on the calculation results NHS, judge the network health status; If the NHS is below the preset threshold, it indicates that the network health is poor and appropriate measures need to be taken; Adjust network parameters: Dynamically adjust network parameters and optimize network performance based on network health status scores; Continuous Monitoring and Feedback: Continuously collect and monitor network data and update network health status scores in real time; Continuously adjust and optimize network management strategies based on feedback results to ensure stable network operation.

[0041] A method for locating ground faults in a distribution network comprises the following steps: Step S1: Use current sensors and voltage sensors to monitor the current and voltage changes of the distribution network in real time, collect current and voltage data, and perform preliminary filtering and denoising on the collected current and voltage signals to obtain purified signals; Step S2: Input the purified signal into a digital signal processor, perform fast Fourier transform and wavelet transform, extract fault features, and use field programmable gate array for hardware acceleration. At the same time, compress the processed signal data through a data compression unit; Step S3: Input the processed signal to the multi-algorithm fusion unit, and use the impedance method, traveling wave method and transient analysis method to locate the fault. During the location process, the machine learning unit is used to optimize the fault location model through historical data training to improve the location accuracy. Step S4: performing data transmission, and during the data transmission process, designing and implementing redundant communication links through redundant communication link units to ensure the continuity and reliability of data transmission, and at the same time, compressing and optimizing data through data compression and optimization units before communication; Step S5: Summarize the data of each fault detection module, analyze the fault situation of the entire network, and delegate some data processing and decision-making functions to the local node; Step S6: Display the fault information on the interface.

[0042] A method for locating ground faults in a distribution network, wherein high-quality signal input is achieved through step 1, a reliable data basis is provided for subsequent processing and analysis, and the accuracy of fault detection is improved; step 2 improves the efficiency and accuracy of signal processing, and provides efficient and reliable characteristic data for fault location; step 3 improves the accuracy and reliability of fault location, and can quickly and accurately determine the location of the fault point; step 4 improves the reliability and efficiency of data transmission, and ensures that fault information can be transmitted to a control center in a timely and accurate manner; step 5 improves the efficiency of fault analysis and processing, and enhances the scalability and real-time response capability of the system; step 6 improves the efficiency and accuracy of fault processing, and facilitates operators to quickly respond to and process faults, and ensures the stable operation of the power grid.

[0043] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A ground fault location system for a distribution network, characterized in that: It includes fault detection module, signal processing module, fault location module, data communication module, central control system, human-machine interface module and power management module; The fault detection module is used to monitor the current and voltage of the distribution network in real time, detect abnormal changes, and determine ground faults; the signal processing module is responsible for real-time processing of the collected signals, using efficient algorithms such as fast Fourier transform and wavelet transform, and using digital signal processors or field programmable gate arrays for hardware acceleration; the fault location module uses a variety of fault location algorithms for comprehensive analysis based on the processed signals, and introduces machine learning algorithms to optimize the fault location model through historical data training to provide / form multi-point collaborative monitoring; the data communication module designs redundant communication links for data compression and optimization; the central control system is used to aggregate data from each fault detection module, analyze the fault situation of the entire network, command each distributed module to work together, and use distributed computing architecture and load balancing technology to delegate some data processing and decision-making functions to local nodes; the human-machine interface module provides an intuitive fault information display interface, including fault location, fault type and fault time; The power management module adopts redundant power design and high-reliability power management strategy to ensure that the system can obtain power supply under various circumstances.

2. A distribution network ground fault location system according to claim 1, characterized in that: The fault detection module includes a current sensor, a voltage sensor and a preprocessing unit; the current sensor monitors the current change of the distribution network in real time and collects current data; the voltage sensor monitors the voltage change of the distribution network in real time and collects voltage data; the preprocessing unit performs preliminary filtering and denoising on the collected current and voltage signals; The signal processing module includes a digital signal processor, a field programmable gate array and a data compression unit; The digital signal processor executes a signal processing algorithm; the field programmable gate array hardware accelerates signal processing; and the data compression unit compresses data to reduce the amount of data that needs to be processed and transmitted.

3. A distribution network ground fault location system according to claim 1, characterized in that: The fault location module includes a multi-algorithm fusion unit, a machine learning unit and a multi-point collaborative monitoring unit; the multi-algorithm fusion unit comprehensively uses the impedance method, the traveling wave method and the transient analysis method; the machine learning unit uses historical data to train and optimize the fault location model; the multi-point collaborative monitoring unit summarizes multi-point monitoring data; The data communication module includes a communication protocol unit, a redundant communication link unit, a data compression and optimization unit, and a network monitoring and management unit; The communication protocol unit adopts a high-reliability and low-latency communication protocol; the redundant communication link unit designs and implements redundant communication links; the data compression and optimization unit compresses and optimizes data before communication; and the network monitoring and management unit monitors and manages the network status in real time.

4. A system for locating ground faults in a distribution network according to claim 1, characterized in that: The central control system includes a data aggregation unit, a distributed computing unit and a local decision unit; the data aggregation unit aggregates the data of each fault detection module to analyze the fault situation of the entire network; the distributed computing unit adopts a distributed computing architecture; the local decision unit delegates part of the data processing and decision-making functions to the local node; The human-machine interface module includes a fault information display unit, an operation control unit and a user training unit; The fault information display unit provides an intuitive fault information display interface; The operation control unit simplifies the operation process; The user training unit provides training and guidance to help operators use the system proficiently.

5. A system for locating ground faults in a distribution network according to claim 1, characterized in that: The power management module includes a battery unit, a power converter unit and a redundant power design unit; the battery unit provides emergency power; the power converter unit converts and adjusts the power supply voltage; and the redundant power design unit designs a redundant power system.

6. A system for locating ground faults in a distribution network according to claim 2, characterized in that: The signal processing module is based on fast Fourier transform, and its basic formula is as follows: and , in, is the frequency domain signal, represents the base of the natural logarithm, j represents the imaginary unit, N is the number of signal samples, is the circumference of a circle, k is the frequency index, and n is the sample point index of the signal; Continuous wavelet transform formula , Discrete Wavelet Transform Formula , in, are the wavelet coefficients, is the mother wavelet, is the translation parameter, is the conjugate of the mother wavelet, is the conjugate form of the mother wavelet, is the scale factor, is the original signal, is the coefficient after discrete wavelet transform, is the index of the data, represents the index of the mother wavelet, represents the wavelet basis function at scale j and position k, is the summation symbol.

7. A system for locating ground faults in a distribution network according to claim 3, characterized in that: The multi-algorithm fusion unit includes an impedance method, a transient analysis method and a traveling wave method; The impedance method includes the single-ended impedance method and the double-ended impedance method, and its basic formula is as follows: Single-ended impedance method: , Two-terminal impedance method: , in, is the line characteristic impedance, is the total length of the line, The distance measurement point to the fault point distance; The transient analysis method includes transient voltage and current analysis and fault location calculation, and its basic formula is as follows: Transient voltage and current analysis: , Fault location calculation: ; The traveling wave method includes the traveling wave propagation time formula and traveling wave calculation, and its basic formula is as follows: Traveling wave propagation time formula: , Traveling wave calculation: , in, is the propagation speed of the traveling wave on the line, is the time difference of the traveling wave from the fault point to the measurement point, and is the time it takes for the traveling wave to reach the measuring points A and B.

8. A system for locating ground faults in a power distribution network according to claim 3, characterized in that: The machine learning unit uses historical data to train and optimize the fault location model based on the machine learning model. The basic formula is as follows: ， in, is the Lagrange multiplier, is a classification label, is the kernel function, is bias; The multi-point collaborative monitoring unit aggregates multi-point monitoring data based on a data fusion algorithm and performs comprehensive analysis. The basic formula is as follows: ， in, is the fault impedance of the ith monitoring point, is the weight of the i-th monitoring point; ， ， ， in, is the state estimate, is the Kalman gain, is the measured value, is the measurement matrix, is the estimated error covariance, is the measurement noise covariance.

9. A system for locating ground faults in a power distribution network according to claim 3, characterized in that: The network monitoring and management unit monitors network traffic changes through a sliding window algorithm based on network traffic monitoring. The basic formula is as follows: ， in, is the average flow rate at time t, is the window size, is the flow rate at time i; ， in, is the network health score, It's network latency. is the maximum acceptable delay, is the packet loss rate, and is the weight coefficient.

10. A method for locating a ground fault in a distribution network, according to a system for locating a ground fault in a distribution network according to any one of claims 1 to 8, characterized in that: The steps include: Step S1: Use current sensors and voltage sensors to monitor the current and voltage changes of the distribution network in real time, collect current and voltage data, and perform preliminary filtering and denoising on the collected current and voltage signals to obtain purified signals; Step S2: Input the purified signal into a digital signal processor, perform fast Fourier transform and wavelet transform, extract fault features, and use field programmable gate array for hardware acceleration. At the same time, compress the processed signal data through a data compression unit; Step S3: Input the processed signal to the multi-algorithm fusion unit, and use the impedance method, traveling wave method and transient analysis method to locate the fault. During the location process, the machine learning unit is used to optimize the fault location model through historical data training to improve the location accuracy. Step S4: performing data transmission, and during the data transmission process, designing and implementing redundant communication links through redundant communication link units to ensure the continuity and reliability of data transmission, and at the same time, compressing and optimizing data through data compression and optimization units before communication; Step S5: Summarize the data of each fault detection module, analyze the fault situation of the entire network, and delegate some data processing and decision-making functions to the local node; Step S6: Display the fault information on the interface.

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