CN110018389B - Online fault monitoring method and system for power transmission line - Google Patents
Online fault monitoring method and system for power transmission line Download PDFInfo
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Abstract
The invention provides a method and a system for monitoring on-line faults of a power transmission line, which comprises the steps of integrating operating parameters and environmental parameters of the power transmission line under different faults to form a fault history database, and constructing a neural network identification model of the power transmission line under different faults according to the fault history database; collecting operation parameters and environment parameters of the power transmission line, inputting the operation parameters and the environment parameters into a neural network identification model, and analyzing the faults of the power transmission line; judging the cause of the fault according to the fault analysis result, and carrying out picture acquisition on possible fault points of the power transmission line; and carrying out feature extraction and analysis on the acquired picture, and obtaining a fault occurrence point according to an analysis result. The method and the device can judge the fault point at the first time, shorten the fault finding time, eliminate the fault as early as possible, arrange to recover power transmission and improve the power supply reliability.
Description
Technical Field
The disclosure relates to the field of power transmission lines in cooling and heating technologies, in particular to a power transmission line online fault monitoring method and system.
Background
The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
An overhead transmission line is a main component of an electric power system and a transmission network, and bears most industrial and domestic transmission tasks, but is also the most prone to failure in the electric power system due to long lines, multiple branches, complex network structure, susceptibility to external force and natural environment and the like. Common transmission line faults are mainly divided into transient faults and permanent faults according to properties, the transient faults mainly include flashover caused by lightning overvoltage and short circuit caused by birds, the permanent faults are mostly caused by weather or equipment, and the like, such as transient overvoltage caused by ice and snow weather or line aging breaks down a transmission line insulation device, and the problems of permanent short circuit of the transmission line caused by equipment installation, storms, earthquakes and the like. The faults can be classified into transverse faults and longitudinal faults according to specific classification, wherein the transverse faults are mainly single-phase, two-phase and three-phase short circuits, the longitudinal faults mainly have the problem of one-phase and two-phase line breakage, and the faults are easy to cause accidents such as tripping and the like of a power transmission line, so that the fault reasons need to be found out at the first time when the faults occur, the problems are solved in a targeted manner, or preventive measures are made for a certain fault in advance.
As known by the inventor, the distributed fault positioning and monitoring system is mostly adopted for the power transmission line at present to solve the problems of inaccurate fault point positioning, difficulty in accurately identifying fault reasons and the like in the traditional monitoring mode. However, practical experience shows that, due to the fact that the distance measurement error is large, the distance of a power transmission line is long, a part of lines have more T-connection lines, fault points are difficult to search, the required time of the existing device is often long, generally hours or even longer time, so that the time spent on searching the fault points greatly exceeds the time consumed for repairing the faults, the power supply recovery and the accident first-aid repair speed are seriously influenced, the power consumption loss is enlarged, and the accident enlargement is easily caused.
Disclosure of Invention
In order to solve the defects of the prior art, the invention provides a power transmission line online fault monitoring method and a power transmission line online fault monitoring system. When a line breaks down, a worker can analyze the data transmitted back by the terminal, judge a fault point at the first time, shorten the fault finding time, eliminate the fault as soon as possible, arrange to recover power transmission, improve the power supply reliability, and reduce anxiety and complaints of customers.
In order to achieve the purpose, the technical scheme of the disclosure is as follows:
a method for monitoring online faults of a power transmission line comprises the following steps:
integrating the operating parameters and the environmental parameters of the power transmission line under different faults to form a fault history database, and constructing a neural network identification model of the power transmission line under different faults according to the fault history database;
collecting operation parameters and environment parameters of the power transmission line, inputting the operation parameters and the environment parameters into a neural network identification model, and analyzing faults of the power transmission line;
judging the cause of the fault according to the fault analysis result, and carrying out picture acquisition on possible fault points of the power transmission line;
and carrying out feature extraction and analysis on the acquired picture, and obtaining a fault occurrence point according to an analysis result.
Further, the operating parameters of the power transmission line include current, voltage, temperature and magnetic field intensity of the power transmission line, and the environmental parameters are real-time weather parameters.
Further, the constructing of the neural network identification model specifically includes:
taking a fault history database as a sample database, and extracting an operation data change difference value before and after the fault of the power transmission line from the sample database as a judgment threshold value;
extracting fault reasons and environmental parameters corresponding to the judgment threshold values and fault occurrence points of the power transmission line;
and (3) taking the judgment threshold value and the environmental parameter as input variables, taking the fault reason and the fault occurrence point as output variables to carry out sample training, and adopting a circulating neural network to learn and construct a neural network identification model.
Further, the power transmission line fault analysis specifically includes:
when the transmission line has a fault, converting the collected operating parameters and environmental parameters of the transmission line into corresponding signals, and inputting the signals into a neural network identification model;
the neural network identification model carries out big data fault diagnosis based on the fault history database according to the similarity matching of the time sequences;
and judging the reason of the fault and the fault point of the power transmission line which is possible to have the fault according to the big data fault diagnosis result, and acquiring pictures of the fault point which is possible to have the fault.
Further, the image extraction feature analysis specifically includes:
analyzing the obtained picture by adopting a picture feature extraction algorithm to form corresponding analysis data;
analyzing the prestored electric transmission line picture by adopting a picture feature extraction algorithm to form corresponding discrimination data;
and comparing the analysis data with the discrimination data, and outputting the power transmission line point corresponding to the picture as a fault point when the comparison difference is greater than a set threshold value.
An online fault monitoring system for a power transmission line, comprising:
the data acquisition module is used for acquiring the operation data of the power transmission line when the power transmission line fails and sending the operation data to the big data analysis platform together with the current environmental parameters;
the big data analysis platform is used for integrating the operating parameters and the environmental parameters of the power transmission line under different faults to form a fault history database, and establishing a neural network identification model of the power transmission line under different faults according to the fault history database; and
when the transmission line has a fault, inputting the collected operating parameters and environmental parameters of the transmission line into a neural network identification model to analyze the fault of the transmission line;
the image acquisition module is used for acquiring images of possible fault points of the power transmission line according to the fault analysis result;
and the picture processing module is used for extracting and analyzing the characteristics of the acquired picture and obtaining a fault occurrence point according to an analysis result.
Further, the operating parameters of the power transmission line include current, voltage, temperature and magnetic field intensity of the power transmission line, and the environmental parameters are real-time weather parameters.
Further, the constructing of the neural network identification model specifically includes:
taking a fault history database as a sample database, and extracting an operation data change difference value before and after the fault of the power transmission line from the sample database as a judgment threshold value;
extracting fault reasons and environmental parameters corresponding to the judgment threshold values and fault occurrence points of the power transmission line;
and (3) taking the judgment threshold value and the environmental parameter as input variables, taking the fault reason and the fault occurrence point as output variables to carry out sample training, and adopting a circulating neural network to learn and construct a neural network identification model.
Further, the power transmission line fault analysis specifically includes:
when the transmission line has a fault, converting the collected operating parameters and environmental parameters of the transmission line into corresponding signals, and inputting the signals into a neural network identification model;
the neural network identification model carries out big data fault diagnosis based on the fault history database according to the similarity matching of the time sequences;
and judging the reason of the fault and the fault point of the power transmission line which is possible to have the fault according to the big data fault diagnosis result, and acquiring pictures of the fault point which is possible to have the fault.
Further, the image extraction feature analysis specifically includes:
analyzing the obtained picture by adopting a picture feature extraction algorithm to form corresponding analysis data;
analyzing the prestored electric transmission line picture by adopting a picture feature extraction algorithm to form corresponding discrimination data;
and comparing the analysis data with the discrimination data, and outputting the power transmission line point corresponding to the picture as a fault point when the comparison difference is greater than a set threshold value.
Compared with the prior art, the beneficial effect of this disclosure is:
according to the intelligent power grid fault monitoring system and method, the transmission line data are collected through the sensor, fault points and fault reasons are analyzed according to historical operation data, and intelligent power grid fault monitoring, action signal remote transmission, fault positioning and distribution automation functions can be achieved. When a line breaks down, a worker can analyze the data transmitted back by the terminal, judge a fault point at the first time, shorten the fault finding time, eliminate the fault as soon as possible, arrange to recover power transmission, improve the power supply reliability, and reduce anxiety and complaints of customers.
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The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application.
Fig. 1 is a flowchart of a power transmission line monitoring method according to an embodiment of the present disclosure;
fig. 2 is a schematic structural diagram of a power transmission line monitoring device according to an embodiment of the present disclosure.
Detailed Description
The present disclosure is further described with reference to the following detailed description of illustrative embodiments and accompanying drawings.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
In the present disclosure, terms such as "upper", "lower", "left", "right", "front", "rear", "vertical", "horizontal", "side", "bottom", and the like indicate orientations or positional relationships based on those shown in the drawings, and are only relational terms determined for convenience in describing structural relationships of the parts or elements of the present disclosure, and do not refer to any parts or elements of the present disclosure, and are not to be construed as limiting the present disclosure.
In the present disclosure, terms such as "fixedly connected", "connected", and the like are to be understood in a broad sense, and mean either a fixed connection or an integrally connected or detachable connection; may be directly connected or indirectly connected through an intermediate. The specific meanings of the above terms in the present disclosure can be determined on a case-by-case basis by persons skilled in the relevant art or technicians, and are not to be construed as limitations of the present disclosure.
As one or more embodiments, as shown in fig. 1, a method for monitoring an online fault of a power transmission line includes:
integrating the operating parameters and the environmental parameters of the power transmission line under different faults to form a fault history database, and constructing a neural network identification model of the power transmission line under different faults according to the fault history database;
collecting operation parameters and environment parameters of the power transmission line, inputting the operation parameters and the environment parameters into a neural network identification model, and analyzing the faults of the power transmission line;
judging the cause of the fault according to the fault analysis result, and carrying out picture acquisition on possible fault points of the power transmission line;
and carrying out feature extraction and analysis on the acquired picture, and obtaining a fault occurrence point according to an analysis result.
The power transmission line operation parameters comprise power transmission line current, voltage, temperature and magnetic field intensity, and the environment parameters are real-time weather parameters.
The construction of the neural network identification model specifically comprises the following steps:
taking a fault history database as a sample database, and extracting an operation data change difference value before and after the fault of the power transmission line from the sample database as a judgment threshold value;
extracting fault reasons and environmental parameters corresponding to the judgment threshold values and fault occurrence points of the power transmission line;
and (3) taking the judgment threshold value and the environmental parameter as input variables, taking the fault reason and the fault occurrence point as output variables to carry out sample training, and adopting a circulating neural network to learn and construct a neural network identification model.
The power transmission line fault analysis specifically comprises the following steps:
when the transmission line has a fault, converting the collected operating parameters and environmental parameters of the transmission line into corresponding signals, and inputting the signals into a neural network identification model;
the neural network identification model carries out big data fault diagnosis based on the fault history database according to the similarity matching of the time sequences;
and judging the reason of the fault and the fault point of the power transmission line which is possible to have the fault according to the big data fault diagnosis result, and acquiring pictures of the fault point which is possible to have the fault.
The image extraction feature analysis specifically comprises:
analyzing the obtained picture by adopting a picture feature extraction algorithm to form corresponding analysis data;
analyzing the prestored electric transmission line picture by adopting a picture feature extraction algorithm to form corresponding discrimination data;
and comparing the analysis data with the discrimination data, and outputting the power transmission line point corresponding to the picture as a fault point when the comparison difference is greater than a set threshold value.
As one or more embodiments, as shown in fig. 2, an online fault monitoring system for a power transmission line includes:
the data acquisition module is used for acquiring the operation data of the power transmission line when the power transmission line fails and sending the operation data to the big data analysis platform together with the current environmental parameters;
the big data analysis platform is used for integrating the operating parameters and the environmental parameters of the power transmission line under different faults to form a fault history database, and establishing a neural network identification model of the power transmission line under different faults according to the fault history database; and
when the transmission line has a fault, inputting the collected operating parameters and environmental parameters of the transmission line into a neural network identification model to analyze the fault of the transmission line;
the image acquisition module is used for acquiring images of possible fault points of the power transmission line according to the fault analysis result;
and the picture processing module is used for extracting and analyzing the characteristics of the acquired picture and obtaining a fault occurrence point according to an analysis result.
The power transmission line operation parameters comprise power transmission line current, voltage, temperature and magnetic field intensity, and the environment parameters are real-time weather parameters.
The construction of the neural network identification model specifically comprises the following steps:
taking a fault history database as a sample database, and extracting an operation data change difference value before and after the fault of the power transmission line from the sample database as a judgment threshold value;
extracting fault reasons and environmental parameters corresponding to the judgment threshold values and fault occurrence points of the power transmission line;
and (3) taking the judgment threshold value and the environmental parameter as input variables, taking the fault reason and the fault occurrence point as output variables to carry out sample training, and adopting a circulating neural network to learn and construct a neural network identification model.
The power transmission line fault analysis specifically comprises the following steps:
when the transmission line has a fault, converting the collected operating parameters and environmental parameters of the transmission line into corresponding signals, and inputting the signals into a neural network identification model;
the neural network identification model carries out big data fault diagnosis based on the fault history database according to the similarity matching of the time sequences;
and judging the reason of the fault and the fault point of the power transmission line which is possible to have the fault according to the big data fault diagnosis result, and acquiring pictures of the fault point which is possible to have the fault.
The image extraction feature analysis specifically comprises:
analyzing the obtained picture by adopting a picture feature extraction algorithm to form corresponding analysis data;
analyzing the prestored electric transmission line picture by adopting a picture feature extraction algorithm to form corresponding discrimination data;
and comparing the analysis data with the discrimination data, and outputting the power transmission line point corresponding to the picture as a fault point when the comparison difference is greater than a set threshold value.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Although the present disclosure has been described with reference to specific embodiments, it should be understood that the scope of the present disclosure is not limited thereto, and those skilled in the art will appreciate that various modifications and changes can be made without departing from the spirit and scope of the present disclosure.
Claims (6)
1. A method for monitoring online faults of a power transmission line is characterized by comprising the following steps:
integrating the operating parameters and the environmental parameters of the power transmission line under different faults to form a fault history database, and constructing a neural network identification model of the power transmission line under different faults according to the fault history database;
collecting operation parameters and environment parameters of the power transmission line, inputting the operation parameters and the environment parameters into a neural network identification model, and analyzing the faults of the power transmission line;
judging the cause of the fault according to the fault analysis result, and carrying out picture acquisition on possible fault points of the power transmission line;
carrying out feature extraction analysis on the collected pictures, and obtaining fault occurrence points according to analysis results;
the construction of the neural network identification model specifically comprises the following steps:
taking a fault history database as a sample database, and extracting an operation data change difference value before and after the fault of the power transmission line from the sample database as a judgment threshold value; extracting fault reasons and environmental parameters corresponding to the judgment threshold values and fault occurrence points of the power transmission line; taking the judgment threshold value and the environmental parameter as input variables, taking the fault reason and the fault occurrence point as output variables to carry out sample training, learning by adopting a circulating neural network, and constructing a neural network identification model;
the power transmission line fault analysis specifically comprises the following steps:
when the transmission line has a fault, converting the collected operating parameters and environmental parameters of the transmission line into corresponding signals, and inputting the signals into a neural network identification model; the neural network identification model carries out big data fault diagnosis based on the fault history database according to the similarity matching of the time sequences; and judging the reason of the fault and the fault point of the power transmission line which is possible to have the fault according to the big data fault diagnosis result, and acquiring pictures of the fault point which is possible to have the fault.
2. The method for monitoring the online fault of the power transmission line according to claim 1, wherein the operation parameters of the power transmission line comprise current, voltage, temperature and magnetic field intensity of the power transmission line, and the environmental parameters are real-time weather parameters.
3. The method for monitoring the online faults of the power transmission line according to claim 1, wherein the image extraction feature analysis specifically comprises:
analyzing the obtained picture by adopting a picture feature extraction algorithm to form corresponding analysis data;
analyzing the prestored electric transmission line picture by adopting a picture feature extraction algorithm to form corresponding discrimination data;
and comparing the analysis data with the discrimination data, and outputting the power transmission line point corresponding to the picture as a fault point when the comparison difference is greater than a set threshold value.
4. The utility model provides a transmission line online fault monitoring system which characterized in that includes:
the data acquisition module is used for acquiring the operation data of the power transmission line when the power transmission line fails and sending the operation data to the big data analysis platform together with the current environmental parameters;
the big data analysis platform is used for integrating the operating parameters and the environmental parameters of the power transmission line under different faults to form a fault history database, and establishing a neural network identification model of the power transmission line under different faults according to the fault history database; and
when the transmission line has a fault, inputting the collected operating parameters and environmental parameters of the transmission line into a neural network identification model to analyze the fault of the transmission line; the image acquisition module is used for acquiring images of possible fault points of the power transmission line according to the fault analysis result;
the image processing module is used for extracting and analyzing the characteristics of the collected images and obtaining fault occurrence points according to the analysis results;
the construction of the neural network identification model specifically comprises the following steps:
taking a fault history database as a sample database, and extracting an operation data change difference value before and after the fault of the power transmission line from the sample database as a judgment threshold value;
extracting fault reasons and environmental parameters corresponding to the judgment threshold values and fault occurrence points of the power transmission line;
taking the judgment threshold value and the environmental parameter as input variables, taking the fault reason and the fault occurrence point as output variables to carry out sample training, learning by adopting a circulating neural network, and constructing a neural network identification model;
the power transmission line fault analysis specifically comprises the following steps:
when the transmission line has a fault, converting the collected operating parameters and environmental parameters of the transmission line into corresponding signals, and inputting the signals into a neural network identification model;
the neural network identification model carries out big data fault diagnosis based on the fault history database according to the similarity matching of the time sequences;
and judging the reason of the fault and the fault point of the power transmission line which is possible to have the fault according to the big data fault diagnosis result, and acquiring pictures of the fault point which is possible to have the fault.
5. The transmission line online fault monitoring system of claim 4, wherein the transmission line operating parameters include transmission line current, voltage, temperature and magnetic field strength, and the environmental parameters are real-time weather parameters.
6. The online fault monitoring system of the transmission line according to claim 4, wherein the image extraction feature analysis specifically includes:
analyzing the obtained picture by adopting a picture feature extraction algorithm to form corresponding analysis data;
analyzing the prestored electric transmission line picture by adopting a picture feature extraction algorithm to form corresponding discrimination data;
and comparing the analysis data with the discrimination data, and outputting the power transmission line point corresponding to the picture as a fault point when the comparison difference is greater than a set threshold value.
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