Intelligent power distribution network fault positioning system and method

Abstract

The invention discloses an intelligent power distribution network fault positioning system and method, and the system comprises a sensing module which monitors the current, voltage, load and temperature of a power grid in real time through the deployment of a line sensor, an intelligent electric meter and distributed feeder automation equipment; the communication module is used for constructing a high-speed communication network covering the whole power distribution network, so that fault information can be quickly transmitted to a control center from terminal equipment; the data analysis module is used for processing and analyzing the collected mass data through big data analysis, machine learning and an artificial intelligence algorithm, identifying fault features and establishing a fault diagnosis model; the fault isolation and positioning module is used for accurately positioning a fault point based on the change of the electrical signal when the fault occurs; and the geographic information system integration module combines the fault positioning information with a geographic information system and visually displays the fault position in the form of a map. According to the invention, the fault processing efficiency is obviously improved, the power failure time and range are reduced, and the toughness of the power grid is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network fault location, and in particular to a smart distribution network fault location system and method. Background Art

[0002] In the distribution network, fast and accurate fault location is a very important link to ensure the normal operation of the distribution network. When a fault occurs, such as a short circuit fault, a quick response must be made to reduce the scope of the short circuit fault.

[0003] The distribution network fault location system disclosed in the existing publication number CN105974273B includes a monitoring data acquisition module, a fault detection and location module and a monitoring center; the monitoring data acquisition module is used to collect the status monitoring data of the operation of each major equipment in the distribution network, and communicates with the fault detection and location module through a communication network; the fault detection and location module is used to process and analyze the status monitoring data to realize remote real-time monitoring, health assessment and fault location of the health status of each major equipment in the distribution network; the monitoring center is used to realize the advanced application of the monitoring data acquisition module and the intelligent and energy-based monitoring of the fault detection and location module; and realizes real-time, objective and intelligent monitoring, assessment and fault location of the health status of the main equipment in the distribution network, with high automation and efficiency.

[0004] The existing technology has realized real-time, objective and intelligent monitoring of the health status of the main equipment in the distribution network; however, the existing technology cannot accurately determine the location of the fault, and cannot intuitively display the geographical location, which is not convenient for subsequent maintenance. For this reason, we propose a smart distribution network fault location system and method. Summary of the invention

[0005] The purpose of the present invention is to provide a smart distribution network fault location system and method to solve the problems in the prior art.

[0006] To achieve the above object, the present invention provides the following technical solution: a smart distribution network fault location system, comprising:

[0007] The perception module monitors the current, voltage, load, and temperature of the power grid in real time by deploying line sensors, smart meters, and distributed feeder automation equipment;

[0008] Communication module, which builds a high-speed communication network covering the entire distribution network, so that fault information can be quickly transmitted from terminal equipment to the control center;

[0009] The data analysis module processes and analyzes the massive amounts of data collected, identifies fault characteristics, and establishes a fault diagnosis model through big data analysis, machine learning, and artificial intelligence algorithms;

[0010] Fault isolation and location module, which accurately locates the fault point based on the changes in electrical signals when a fault occurs;

[0011] The GIS integration module combines the fault location information with the GIS to visually display the fault location in the form of a map;

[0012] Mobile interactive module to receive fault alerts, view fault locations, and obtain maintenance guidance information;

[0013] The learning optimization module continuously optimizes the fault location algorithm based on historical fault cases and processing experience.

[0014] Preferably, a data fusion module is also included to clean the collected data, unify the format, process outliers, eliminate noise, and then fuse information from different data sources.

[0015] Preferably, it also includes a fault classification module, which determines whether it is a fault based on the abnormal detection result and uses preset rules or algorithm models to identify the fault type.

[0016] Preferably, the fault isolation and positioning module includes waveform analysis, fault traveling wave method, and transient feature recognition.

[0017] A method for locating a fault in a smart power distribution network comprises the following steps:

[0018] S1. Data collection: Real-time monitoring of the current, voltage, load and temperature of the power grid through line sensors, smart meters and distributed feeder automation equipment configured in the distribution network;

[0019] S2. Data preprocessing and fusion: clean the collected data, unify the format, process outliers, eliminate noise, ensure data quality, and integrate information from different data sources;

[0020] S3. Real-time monitoring and anomaly detection: Use big data analysis and machine learning algorithms to monitor real-time data streams and automatically detect anomalies in power grid operation through pattern recognition, threshold comparison, and time series analysis;

[0021] S4, fault judgment and classification: Based on the abnormal detection results, use the preset rules or algorithm model to determine whether it is a fault and further identify the fault type;

[0022] S5. Fault location: Apply traveling wave analysis, time difference method, transient characteristic analysis, Kalman filtering, combined with power grid topology and electrical model, to calculate and determine the precise location of the fault point;

[0023] S6. Fault location display: Combine the fault location information with the geographic information system to visually display the fault location in the form of a map.

[0024] Preferably, it also includes S7, information notification and decision support: reporting the fault information to the dispatch center and the emergency repair team in real time, including the fault type, location, and impact range, while providing decision support and recommending isolation plans and load transfer strategies.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] 1. A system that integrates advanced information technology, communication technology and intelligent algorithms, designed to quickly and accurately locate faults in the distribution network and shorten fault repair time; significantly improves fault handling efficiency, reduces power outage time and scope, and enhances the resilience of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0028] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0029] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention.

[0030] In an embodiment of the present invention, a smart distribution network fault location system includes:

[0031] The perception module monitors the current, voltage, load, and temperature of the power grid in real time by deploying line sensors, smart meters, and distributed feeder automation equipment;

[0032] Communication module, which builds a high-speed communication network covering the entire distribution network, so that fault information can be quickly transmitted from terminal equipment to the control center;

[0033] The data analysis module processes and analyzes the massive amounts of data collected, identifies fault characteristics, and establishes a fault diagnosis model through big data analysis, machine learning, and artificial intelligence algorithms;

[0034] Fault isolation and location module, which accurately locates the fault point based on the changes in electrical signals when a fault occurs;

[0035] Distributed Feeder Automation (DFA), the DFA system can automatically execute the preset control strategy after detecting a fault, isolate the fault area, restore power supply to the non-fault area, and assist in fault location;

[0036] The GIS integration module combines the fault location information with the GIS to visually display the fault location in the form of a map;

[0037] Mobile interactive module to receive fault alerts, view fault locations, and obtain maintenance guidance information;

[0038] The learning optimization module continuously optimizes the fault location algorithm based on historical fault cases and processing experience.

[0039] Preferably, a data fusion module is also included to clean the collected data, unify the format, process outliers, eliminate noise, and then fuse information from different data sources.

[0040] Preferably, it also includes a fault classification module, which determines whether it is a fault based on the abnormal detection result and uses preset rules or algorithm models to identify the fault type.

[0041] Preferably, the fault isolation and positioning module includes waveform analysis, fault traveling wave method, and transient feature recognition.

[0042] See also Figure 1 , a method for locating a fault in a smart distribution network, comprising the following steps:

[0043] S1. Data collection: The line sensors, smart meters and distributed feeder automation equipment configured in the distribution network monitor the current, voltage, load and temperature of the power grid in real time to achieve a comprehensive perception of the power grid status;

[0044] S2. Data preprocessing and fusion: clean the collected data, unify the format, process outliers, eliminate noise, ensure data quality, and integrate information from different data sources;

[0045] S3. Real-time monitoring and anomaly detection: Use big data analysis and machine learning algorithms to monitor real-time data streams and automatically detect anomalies in power grid operation through pattern recognition, threshold comparison, and time series analysis;

[0046] S4, fault judgment and classification: Based on the abnormal detection results, use the preset rules or algorithm model to determine whether it is a fault and further identify the fault type;

[0047] S5. Fault location: Apply traveling wave analysis, time difference method, transient characteristic analysis, Kalman filtering, combined with power grid topology and electrical model, to calculate and determine the precise location of the fault point;

[0048] S6. Fault location display: Combine the fault location information with the geographic information system to visually display the fault location in the form of a map.

[0049] Preferably, it also includes S7, information notification and decision support: reporting the fault information to the dispatch center and the emergency repair team in real time, including the fault type, location, and impact range, while providing decision support and recommending isolation plans and load transfer strategies.

[0050] The system integrates advanced information technology, communication technology and intelligent algorithms, and is designed to quickly and accurately locate faults in the distribution network and shorten fault repair time; it significantly improves fault handling efficiency, reduces the duration and scope of power outages, and enhances the resilience of the power grid.

[0051] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A smart distribution network fault location system, characterized in that: include: The perception module monitors the current, voltage, load, and temperature of the power grid in real time by deploying line sensors, smart meters, and distributed feeder automation equipment; Communication module, which builds a high-speed communication network covering the entire distribution network, so that fault information can be quickly transmitted from terminal equipment to the control center; The data analysis module processes and analyzes the massive amounts of data collected, identifies fault characteristics, and establishes a fault diagnosis model through big data analysis, machine learning, and artificial intelligence algorithms; Fault isolation and location module, which accurately locates the fault point based on the changes in electrical signals when a fault occurs; The GIS integration module combines the fault location information with the GIS to visually display the fault location in the form of a map; Mobile interactive module to receive fault alerts, view fault locations, and obtain maintenance guidance information; The learning optimization module continuously optimizes the fault location algorithm based on historical fault cases and processing experience.

2. A smart distribution network fault location system according to claim 1, characterized in that: It also includes a data fusion module, which cleans the collected data, unifies the format, processes outliers, eliminates noise, and then fuses information from different data sources.

3. The intelligent distribution network fault location system according to claim 1, characterized in that: It also includes a fault classification module, which uses preset rules or algorithm models to determine whether it is a fault and identify the fault type based on the abnormal detection results.

4. The intelligent distribution network fault location system according to claim 1, characterized in that: The fault isolation and positioning module includes waveform analysis, fault traveling wave method, and transient feature recognition.

5. A method for locating faults in a smart distribution network, characterized in that: The following steps are involved: S1. Data collection: Real-time monitoring of the current, voltage, load and temperature of the power grid through line sensors, smart meters and distributed feeder automation equipment configured in the distribution network; S2. Data preprocessing and fusion: clean the collected data, unify the format, process outliers, eliminate noise, ensure data quality, and integrate information from different data sources; S3. Real-time monitoring and anomaly detection: Use big data analysis and machine learning algorithms to monitor real-time data streams and automatically detect anomalies in power grid operation through pattern recognition, threshold comparison, and time series analysis; S4, fault judgment and classification: Based on the abnormal detection results, use the preset rules or algorithm model to determine whether it is a fault and further identify the fault type; S5. Fault location: Apply traveling wave analysis, time difference method, transient characteristic analysis, Kalman filtering, combined with power grid topology and electrical model, to calculate and determine the precise location of the fault point; S6. Fault location display: Combine the fault location information with the geographic information system to visually display the fault location in the form of a map.

6. A smart distribution network fault location method according to claim 5, characterized in that: It also includes S7, information notification and decision support: reporting fault information to the dispatch center and emergency repair team in real time, including fault type, location, and impact range, while providing decision support and recommending isolation plans and load transfer strategies.

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