Power distribution network accurate fault positioning method and system of electrical quantity sensing network PMU

Abstract

The invention discloses a power distribution network accurate fault positioning method and system of an electrical quantity sensing network PMU, and relates to the technical field of power system fault positioning. Electrical information of the power distribution network is acquired through the PMU device; analyzing and processing the electrical information, and identifying a fault area; and determining a fault position according to the identified fault area in combination with configuration information of the PMU device. By integrating the phasor measurement unit (PMU) and the advanced data analysis technology, the positioning precision and speed of the power distribution network fault are remarkably improved, so that the reliability of the power grid is enhanced, and the maintenance and operation cost is optimized. In addition, the method supports integration of intelligent power grid technologies, improves emergency response and recovery strategies, provides data-driven decision support, and promotes modernization and efficiency improvement of a power system.

Description

Technical Field

[0001] The present invention relates to the technical field of power system fault location, and specifically to a method and system for accurately locating faults in a distribution network of an electrical quantity sensor network PMU. Background Art

[0002] In the operation and management of modern power systems, the stability and reliability of distribution networks are crucial, especially when faced with fault diagnosis and handling. The introduction of Phasor Measurement Units (PMUs) has greatly revolutionized the way power grids are monitored. PMUs provide high-precision time-synchronized electrical quantity measurements, such as voltage, current, and frequency phase angles, enabling system operators to monitor the state of the grid in real time and respond quickly to dynamic changes in the grid. Nevertheless, the complexity of distribution networks and their wide geographical distribution pose significant challenges in locating the source of faults. Existing technologies rely on traditional fault indicators and SCADA systems, which often have limitations in fault location accuracy and response speed. For example, although fault indicators can show the presence of a fault, they often have difficulty providing the exact location of the fault, especially in modern power grid environments where distributed sources and renewable energy sources are widely connected.

[0003] In addition, the existing technologies often lack sufficient flexibility and accuracy when dealing with fault location under complex network configurations and rapidly changing load conditions. This not only increases the recovery time of the system, but may also lead to incorrect maintenance decisions, which in turn affects the reliability and economic operation of the entire power grid. For example, existing PMU applications are mainly concentrated at the transmission network level, while their applications in distribution networks are relatively few, because the fault diagnosis requirements of distribution networks are significantly different from those of transmission networks. The fault diagnosis and location requirements of distribution networks are more frequent and more dependent on the fine integration of geographic and topological information. The method proposed by our invention deploys PMU devices in the distribution network and combines them with the geographic information system (GIS). On the basis of collecting electrical information, it can accurately analyze and process through algorithms, and can identify and locate the fault area in real time. In addition, by integrating the configuration information of the PMU device, the present invention can more accurately analyze the cause and specific location of the fault, thereby significantly improving the speed and accuracy of fault response, reducing unnecessary operation and maintenance costs, and improving the overall stability and safety of the power grid.

[0004] By integrating precise time synchronization technology with advanced data processing technology, the present invention not only fills the gaps in existing technologies, but also provides an unprecedented solution for efficient management of distribution networks. This method is expected to play a key role in fault management, preventive maintenance and emergency response strategy formulation of power systems, especially in the context of transition to smart grids. Summary of the invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the technical problem solved by the present invention is: how to improve the speed and accuracy of fault location in the distribution network, and realize rapid identification and accurate location of the fault area by combining the configuration information and real-time monitoring data of the PMU device.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for accurately locating faults in a distribution network of an electrical quantity sensor network PMU, which comprises the following steps:

[0008] Deploy PMU devices; collect electrical information of the distribution network through the PMU devices; analyze and process the electrical information to identify the fault area; determine the fault location based on the identified fault area and the configuration information of the PMU devices.

[0009] As a preferred solution of the method for accurately locating faults in a distribution network of an electrical quantity sensing network PMU described in the present invention, the electrical information of the distribution network includes voltage, current, frequency and harmonic content.

[0010] The analysis and processing include filtering out noise, filling in missing values ​​and data standardization, real-time monitoring of time series data of voltage and current, real-time calculation of the phase difference of the voltage at each PMU point, application of Fourier transform to analyze the spectrum data of the voltage, and real-time recording of harmonic changes.

[0011] As a preferred solution of the method for accurately locating faults in a distribution network of an electrical quantity sensing network PMU described in the present invention, the identification of the fault area includes checking abnormal patterns in time series data, comparing phase angles between different PMUs, analyzing abnormal changes in spectrum data, and determining whether there is a fault area.

[0012] The abnormal pattern in the inspection time series data is expressed as,

[0013]

[0014] in, Respectively represent the functions based on the comparison results of the change in voltage and current with the threshold value, which are used to output three states. 0, 1, and 2 are the detection values ​​generated by the function, ΔV i (t) represents the voltage change of the i-th PMU at time t, ΔI i (t) represents the current change of the i-th PMU at time t, θ V1 ,θ V2 Respectively represent the primary and secondary thresholds of voltage change, θ I1 ,θ I2 They respectively represent the primary threshold and secondary threshold of current change.

[0015] The phase angle between the different PMUs is expressed as,

[0016]

[0017] in, represents the phase angle of the ith PMU at time t, represents the phase angle of the jth PMU at time t, θ φ1 ,θ φ2 Represent the first and second thresholds of phase difference, D φ,ij (t) is a function representing the phase difference between the i-th and j-th PMUs.

[0018] The real-time recorded harmonic changes are expressed as:

[0019]

[0020] in, A function representing the comparison between the spectrum analysis result and the threshold value, represents the Fourier transform result of the i-th PMU voltage, They represent the primary threshold and secondary threshold of the voltage spectrum respectively.

[0021] As a preferred solution of the method for accurately locating faults in a distribution network of an electrical quantity sensor network PMU according to the present invention, wherein: the fault area is determined as follows: D φ,ij (t) If any of the detection values ​​is 2, it is judged that there is a fault area; when the detection value is 1 and 0, if D φ,ij (t) and If the output detection value is 1, it is judged that there is a fault area. φ,ij (t) and If the detection values ​​output at different times are 1, a time window is defined for the electrical information with the detection value of 1. When the number of times the detection value is 1 exceeds the detection number threshold within the time window, it is determined that a fault area exists. After determining that a fault area exists, the fault area is marked.

[0022] As a preferred solution of the method for accurately locating faults in a distribution network of an electrical quantity sensing network PMU described in the present invention, the marking of the fault area includes, in combination with a geographic information system, marking the fault area on a digital map of the power grid, and the marking includes the fault time and the detection value.

[0023] The geographic information system is integrated with the PMU real-time monitoring system. When the PMU system detects electrical information anomalies, the PMU real-time monitoring system automatically triggers a fault marking program.

[0024] As a preferred solution of the method for accurately locating faults in a distribution network of the electrical quantity sensing network PMU described in the present invention, the configuration information of the PMU device includes the starting point and end point of the power grid branch line, the line inlet and line outlet of the transformer, each branch node and connection point, and the power supply line. The configuration information of the PMU device is integrated into the fault diagnosis module. When performing fault diagnosis, the real-time monitoring data and PMU configuration information are combined for analysis, and the geographical and topological data of the configuration information are used to locate the location where the fault occurs.

[0025] As a preferred solution of the method for accurately locating faults in a distribution network of an electrical quantity sensor network PMU of the present invention, wherein: the location of the fault location includes when ΔI i (t) is positive and greater than θ I1 , and ΔV i (t) is negative and |ΔV i (t)|>θ V1 When a fault occurs, the faulty line is immediately disconnected, and the data recording and accident playback tools are used to analyze the grid operation data before and after the fault occurs, determine the cause of the fault, and reconfigure the grid routing as needed.

[0026] When ΔV i (t) is negative, |ΔV i (t)|>θ V1 or ΔI i (t) is positive, ΔI i (t)>θ I1 When , it means that the PMU detects an abnormality. The adjacent PMU that detects the abnormality is verified. If both adjacent PMUs have abnormalities, it is determined that the transformer equipment is faulty. When the transformer equipment fails, the faulty transformer equipment is immediately shut down, and the faulty transformer equipment is checked using diagnostic tools to determine the cause of the fault.

[0027] when And |ΔV i (t)|>θ V1 ,|ΔI i (t)|>θ I1 If the node fails, the connection and interface of the failed node should be repaired, loose connections should be reinforced, and corroded nodes should be replaced. After the repair is completed, electrical tests should be performed to verify whether all connections have been restored to normal.

[0028] Another object of the present invention is to provide accurate fault location of the distribution network of the electrical quantity sensor network PMU, which can solve the problems of the prior art in the rapid identification and location accuracy of distribution network faults through real-time data analysis and a highly integrated geographic information system.

[0029] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for accurately locating faults in a distribution network of an electrical quantity sensor network PMU, comprising:

[0030] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the method for accurately locating faults in a distribution network of an electrical quantity sensing network PMU as described above are implemented.

[0031] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the distribution network accurate fault location method of the electrical quantity sensor network PMU as described above.

[0032] Beneficial effects of the invention: The invention significantly improves the accuracy and speed of locating distribution network faults by integrating phasor measurement units (PMUs) with advanced data analysis techniques, thereby enhancing the reliability of the power grid and optimizing maintenance and operating costs. In addition, the invention supports the integration of smart grid technologies, improves emergency response and recovery strategies, and provides data-driven decision support to promote the modernization and efficiency of power systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0034] Figure 1 This is an overall flow chart of a method for accurately locating faults in a distribution network of an electrical quantity sensing network PMU provided in a first embodiment of the present invention.

[0035] Figure 2 This is an overall framework diagram of a distribution network precise fault location system of an electrical quantity sensing network PMU provided in a second embodiment of the present invention. DETAILED DESCRIPTION

[0036] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0037] Example 1, reference Figure 1, which is an embodiment of the present invention, provides a method for accurately locating faults in a distribution network of an electrical quantity sensor network PMU, characterized in that:

[0038] S1: Deploy PMU devices.

[0039] S2: Collect electrical information of the distribution network through the PMU device.

[0040] The electrical information of the distribution network includes voltage, current, frequency and harmonic content.

[0041] S3: Analyze and process the electrical information to identify the fault area.

[0042] The analysis and processing include filtering out noise, filling in missing values ​​and data standardization, real-time monitoring of voltage and current time series data, real-time calculation of the phase difference of the voltage at each PMU point, application of Fourier transform to analyze the voltage spectrum data, and real-time recording of harmonic changes.

[0043] Identifying the fault area includes checking for abnormal patterns in the time series data, comparing the phase angles between different PMUs, and analyzing abnormal changes in the spectrum data to determine whether a fault area exists.

[0044] Checking for unusual patterns in time series data is represented as,

[0045]

[0046] in, Respectively represent the functions based on the comparison results of the change in voltage and current with the threshold value, which are used to output three states. 0, 1, and 2 are the detection values ​​generated by the function, ΔV i (t) represents the voltage change of the i-th PMU at time t, ΔI i (t) represents the current change of the i-th PMU at time t, θ V1 ,θ V2 Respectively represent the primary and secondary thresholds of voltage change, θ I1 ,θ I2 They represent the primary threshold and secondary threshold of current change respectively.

[0047] The phase angle between different PMUs is expressed as,

[0048]

[0049] in, represents the phase angle of the ith PMU at time t, represents the phase angle of the jth PMU at time t, θ φ1 ,θ φ2 Represent the first and second thresholds of phase difference, Dφ,ij (t) is a function representing the phase difference between the i-th and j-th PMUs.

[0050] The real-time recording of harmonic changes is expressed as,

[0051]

[0052] in, A function representing the comparison between the spectrum analysis result and the threshold value, represents the Fourier transform result of the i-th PMU voltage, They represent the primary threshold and secondary threshold of the voltage spectrum respectively.

[0053] The voltage phase angle is easier to measure and calculate accurately than the current phase angle because the voltage is relatively stable and less affected by external factors. However, the change of current may be caused by many factors, such as load fluctuations, line losses, etc., which increases the complexity and uncertainty of current phase angle analysis.

[0054] Compared with current, voltage is more stable under no-load or variable-load conditions, and its measurement results are less affected by load changes, making the spectrum analysis results more reliable.

[0055] The fault area includes: D φ,ij (t) If any of the detection values ​​is 2, it is judged that there is a fault area; when the detection value is 1 and 0, if D φ,ij (t) and If the output detection value is 1, it is judged that there is a fault area. φ,ij (t) and If the detection values ​​output at different times are 1, a time window is defined for the electrical information with the detection value of 1. When the number of times the detection value is 1 exceeds the detection number threshold within the time window, it is determined that a fault area exists. After determining that a fault area exists, the fault area is marked.

[0056] Marking the fault area includes, in combination with a geographic information system, marking the fault area on a digital map of the power grid, and the marking includes the fault time and the detection value.

[0057] The geographic information system is integrated with the PMU real-time monitoring system. When the PMU system detects abnormal electrical information, the PMU real-time monitoring system automatically triggers the fault marking program.

[0058] S4: Determine the fault location based on the identified fault area and the configuration information of the PMU device.

[0059] The configuration information of the PMU device includes the starting point and end point of the power grid branch line, the input and output ports of the transformer, each branch node and connection point, and the power supply line. The configuration information of the PMU device is integrated into the fault diagnosis module. When performing fault diagnosis, the real-time monitoring data and PMU configuration information are combined for analysis, and the geographical and topological data of the configuration information are used to locate the location of the fault.

[0060] The location where the fault occurs includes: i (t) is positive and greater than θ I1 , and ΔV i (t) is negative and ΔV i (t)|>θ V1 When a fault occurs, the faulty line is immediately disconnected, and the data recording and accident playback tools are used to analyze the grid operation data before and after the fault occurs, determine the cause of the fault, and reconfigure the grid routing as needed.

[0061] When ΔV i (t) is negative, |ΔV i (t)|>θ V1 or ΔI i (t) is positive, ΔI i (t)>θ I1 When , it means that the PMU detects an abnormality. The adjacent PMU that detects the abnormality is verified. If both adjacent PMUs have abnormalities, it is determined that the transformer equipment is faulty. When the transformer equipment fails, the faulty transformer equipment is immediately shut down, and the faulty transformer equipment is checked using diagnostic tools to determine the cause of the fault.

[0062] when And |ΔV i (t)|>θ V1 ,|ΔI i (t)|>θ I1 If the node fails, the connection and interface of the failed node should be repaired, loose connections should be reinforced, and corroded nodes should be replaced. After the repair is completed, electrical tests should be performed to verify whether all connections have been restored to normal.

[0063] Example 2, reference Figure 2 , which is an embodiment of the present invention, provides a system for a method for accurately locating faults in a distribution network of an electrical quantity sensor network PMU, characterized in that it includes a data acquisition module, a data processing and analysis module, a fault diagnosis module, and a decision support and execution module.

[0064] The data acquisition module collects electrical information of the distribution network through the PMU device.

[0065] The electrical information of the distribution network includes voltage, current, frequency and harmonic content.

[0066] The data processing and analysis module is responsible for filtering out noise, filling in missing values ​​and data standardization, real-time monitoring of voltage and current time series data, real-time calculation of the voltage phase difference of each PMU point, application of Fourier transform to analyze the voltage spectrum data, and real-time recording of harmonic changes.

[0067] The fault diagnosis module checks for abnormal patterns in the time series data, compares the phase angles between different PMUs, and analyzes abnormal changes in the spectrum data to determine whether there is a fault area.

[0068] Decision support and execution module: When a line fails, immediately disconnect the faulty line, determine the cause of the fault, and reconfigure the grid route as needed. When an equipment fails, immediately deactivate the faulty transformer equipment, use diagnostic tools to check the faulty transformer equipment, and determine the cause of the fault. When a node fails, repair the connections and interfaces of the faulty node, reinforce loose connections, and replace corroded nodes. After the repair is completed, perform electrical testing to verify that all connections have returned to normal.

[0069] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program code.

[0070] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0071] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0072] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0073] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for accurately locating faults in a distribution network of an electrical quantity sensor network PMU, characterized in that: include: Deployment of PMU devices; Collect electrical information of the distribution network through PMU devices; Analyze and process electrical information to identify fault areas; The fault location is determined based on the identified fault area and the configuration information of the PMU device.

2. The method for accurately locating faults in a distribution network of an electrical quantity sensor network PMU according to claim 1, characterized in that: The electrical information of the distribution network includes voltage, current, frequency and harmonic content; The analysis and processing include filtering out noise, filling in missing values ​​and data standardization, real-time monitoring of time series data of voltage and current, real-time calculation of the phase difference of the voltage at each PMU point, application of Fourier transform to analyze the spectrum data of the voltage, and real-time recording of harmonic changes.

3. The method for accurately locating faults in a distribution network of an electrical quantity sensor network PMU according to claim 2, characterized in that: The identifying of the fault area includes checking the abnormal patterns in the time series data, comparing the phase angles between different PMUs, analyzing the abnormal changes in the spectrum data, and determining whether there is a fault area; The abnormal pattern in the inspection time series data is expressed as, Among them, A Vi (t), A Ii (t) represents the function of comparing the change of voltage and current with the threshold value, which is used to output three states. 0, 1, and 2 are the detection values ​​generated by the function. ΔV i (t) represents the voltage change of the i-th PMU at time t, ΔI i (t) represents the current change of the i-th PMU at time t, θ V1 ,θ V2 Respectively represent the primary and secondary thresholds of voltage change, θ I1 ,θ I2 They represent the primary threshold and secondary threshold of current change respectively; The phase angle between the different PMUs is expressed as, in, represents the phase angle of the ith PMU at time t, represents the phase angle of the jth PMU at time t, θ φ1 ,θ φ2 Represent the first and second thresholds of phase difference, D φ,ij (t) is a function representing the phase difference between the i-th and j-th PMUs; The real-time recorded harmonic changes are expressed as: in, A function representing the comparison between the spectrum analysis result and the threshold value, represents the Fourier transform result of the i-th PMU voltage, They represent the primary threshold and secondary threshold of the voltage spectrum respectively.

4. The method for accurately locating faults in a distribution network of an electrical quantity sensor network PMU according to claim 3, characterized in that: Determining the fault area includes: D φ,ij (t) If any of the detection values ​​is 2, it is judged that there is a fault area; when the detection value is 1 and 0, if D φ,ij (t) and If the output detection value is 1, it is judged that there is a fault area. φ,ij (t) and If the detection values ​​output at different times are 1, a time window is defined for the electrical information with the detection value of 1. When the number of times the detection value is 1 exceeds the detection number threshold within the time window, it is determined that a fault area exists. After determining that a fault area exists, the fault area is marked.

5. The method for accurately locating faults in a distribution network of an electrical quantity sensor network PMU according to claim 4, characterized in that: The marking of the fault area includes, in combination with a geographic information system, marking the fault area on a digital map of the power grid, the marking including the fault time and the detection value; The geographic information system is integrated with the PMU real-time monitoring system. When the PMU system detects electrical information anomalies, the PMU real-time monitoring system automatically triggers a fault marking program.

6. The method for accurately locating faults in a distribution network of an electrical quantity sensor network PMU according to claim 5, characterized in that: The configuration information of the PMU device includes the starting point and end point of the power grid branch line, the line inlet and outlet of the transformer, each branch node and connection point, and the power supply line. The configuration information of the PMU device is integrated into the fault diagnosis module. When performing fault diagnosis, the real-time monitoring data and PMU configuration information are combined for analysis, and the geographical and topological data of the configuration information are used to locate the location of the fault.

7. The method for accurately locating faults in a distribution network of an electrical quantity sensor network PMU according to claim 6, characterized in that: The location where the fault occurs includes when ΔI i (t) is positive and greater than θ I1 , and ΔV i (t) is negative and ΔV i (t)|>θ V1 If a fault occurs, the faulty line is immediately disconnected, and the power grid operation data before and after the fault is analyzed using data recording and accident playback tools to determine the cause of the fault and reconfigure the power grid routing as needed. When ΔV i (t) is negative, |ΔV i (t)|>θ V1 or ΔI i (t) is positive, ΔI i (t)>θ I1 When the PMU detects an abnormality, adjacent verification is performed on the PMU that detects the abnormality. If both adjacent PMUs are abnormal, it is determined that the transformer equipment is faulty. When the transformer equipment fails, the faulty transformer equipment is immediately deactivated, and the faulty transformer equipment is checked using diagnostic tools to determine the cause of the fault. when And |ΔV i (t)|>θ V1 ,|ΔI i (t)|>θ I1 If the node fails, the connection and interface of the failed node should be repaired, loose connections should be reinforced, and corroded nodes should be replaced. After the repair is completed, electrical tests should be performed to verify whether all connections have been restored to normal.

8. A system for accurately locating faults in a distribution network using the electrical quantity sensor network PMU as claimed in any one of claims 1 to 7, characterized in that: It includes data acquisition module, data processing and analysis module, fault diagnosis module and decision support and execution module; The data acquisition module collects electrical information of the distribution network through the PMU device; The electrical information of the distribution network includes voltage, current, frequency and harmonic content; The data processing and analysis module is responsible for filtering out noise, filling missing values ​​and data standardization, real-time monitoring of voltage and current time series data, real-time calculation of the phase difference of the voltage at each PMU point, application of Fourier transform to analyze the spectrum data of the voltage, and real-time recording of harmonic changes; The fault diagnosis module checks for abnormal patterns in the time series data, compares the phase angles between different PMUs, analyzes abnormal changes in the spectrum data, and determines whether there is a fault area; When a line fails, the decision support and execution module immediately disconnects the faulty line, determines the cause of the fault, and reconfigures the grid route as needed. When an equipment fails, the faulty transformer equipment is immediately deactivated, and the faulty transformer equipment is checked using diagnostic tools to determine the cause of the fault. When a node fails, the connection and interface of the faulty node are repaired, loose connections are reinforced, and corroded nodes are replaced. After the repair is completed, electrical testing is performed to verify whether all connections have been restored to normal.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for accurately locating faults in a distribution network of an electrical quantity sensing network PMU according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for accurately locating faults in a distribution network of an electrical quantity sensing network PMU as claimed in any one of claims 1 to 7 are implemented.

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