CN117351663A - A risk early warning method and system for abnormal operation status of relay protection function - Google Patents

A risk early warning method and system for abnormal operation status of relay protection function Download PDF

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CN117351663A
CN117351663A CN202311114483.3A CN202311114483A CN117351663A CN 117351663 A CN117351663 A CN 117351663A CN 202311114483 A CN202311114483 A CN 202311114483A CN 117351663 A CN117351663 A CN 117351663A
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change data
risk
matrix
abnormal
risk level
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周海成
石恒初
张明祥
陈秋涛
尹昭舜
魏乔所
王飞
杨鹏辉
李学妨
李理
李秀兰
宋哲
张西
宋银松
丁世琼
钱秋明
郑托
王怀策
熊晓川
尹丕祥
黄博
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Puer Supply Power Bureau of Yunnan Power Grid Co Ltd
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Puer Supply Power Bureau of Yunnan Power Grid Co Ltd
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    • H02J—ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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    • G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
    • G08B21/18—Status alarms
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING SYSTEMS, e.g. PERSONAL CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
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    • G08B21/18—Status alarms
    • G08B21/182—Level alarms, e.g. alarms responsive to variables exceeding a threshold
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    • G08B21/185—Electrical failure alarms
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    • G08B31/00—Predictive alarm systems characterised by extrapolation or other computation using updated historic data
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J13/00—Circuit arrangements for providing remote monitoring or remote control of equipment in a power distribution network
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Abstract

本申请提供一种继电保护功能运行状态异常风险预警方法及系统，包括：获取设备的状态信息变化数据和定值变化数据；基于所述状态信息变化数据和定值变化数据，获取所述状态信息变化数据和定值变化数据中的异常数据；基于所述状态信息变化数据和定值变化数据的种类和数量，生成风险分析模型；基于所述异常数据和风险分析模型，获取风险等级阈值；基于所述风险等级阈值，获取设备的风险等级，以解决目前的继电保护设备无法正确判断供电设备的故障类型和等级，导致需要将整个供电设备停机进行维修排查，影响用户正常用电而且维修效率较低的问题。

This application provides a method and system for abnormal risk warning of relay protection function operation status, including: obtaining status information change data and fixed value change data of the equipment; based on the status information change data and fixed value change data, obtaining the status Abnormal data in the information change data and fixed value change data; generate a risk analysis model based on the type and quantity of the state information change data and fixed value change data; obtain a risk level threshold based on the abnormal data and risk analysis model; Based on the risk level threshold, the risk level of the equipment is obtained to solve the problem that the current relay protection equipment cannot correctly determine the fault type and level of the power supply equipment, which leads to the need to shut down the entire power supply equipment for maintenance and troubleshooting, affecting the normal use of electricity and maintenance of the user. The problem of lower efficiency.

Description

Method and system for early warning abnormal risk of operation state of relay protection function
Technical Field
The application relates to the technical field of relay protection, in particular to a relay protection function operation state abnormal risk early warning method and system.
Background
With the development of economy and various industries, the power supply demands of people are continuously increasing. As an important guarantee for economic development, the stability, reliability and safety problems of the operation of the power system are increasingly important. However, the process from power generation to power utilization of the power system has complexity and coupling, which makes the stability, reliability and safety of the operation of the power system face a plurality of problems and challenges.
The relay protection device is one of important equipment of the intelligent substation, can effectively reduce the influence on equipment and other aspects caused by the fault of the substation, and is an important guarantee for ensuring the safe operation of the power system. When the power system is in operation, once the power system fails, the intelligent substation can send out early warning under the protection of the relay protection device, so that relevant technicians are reminded of overhauling, and the failure loss is reduced.
Although the traditional relay protection equipment early warning technology can monitor and early warn the running state and faults of equipment, the traditional relay protection equipment early warning technology has several problems: firstly, when a system alarms, the equipment is proved to have faults, but the type and the grade of the equipment faults are not clear, and the shutdown maintenance of the whole transformer substation can only be checked; secondly, a large number of physical parameters are generated in the normal operation process of the relay protection device, and the processing of the data depends on expert experience; an expert engaged in operation and maintenance is required to analyze the faults according to experience so as to make judgment and diagnosis. However, modern early warning systems require accurate early warning and diagnosis before faults occur, and because of the development of network technology and signal processing technology, relay protection equipment generates massive information every moment, if only experts are relied on, all information generated by the relay protection equipment cannot be processed, so that the type and grade of the faults cannot be judged correctly, and the maintenance efficiency is reduced.
Disclosure of Invention
The embodiment of the application provides a relay protection function running state abnormal risk early warning method and system, which are used for solving the technical problems that the conventional relay protection equipment cannot accurately judge the fault type and grade of power supply equipment, so that the whole power supply equipment is required to be stopped for maintenance and investigation, the normal electricity consumption of a user is influenced, and the maintenance efficiency is lower.
The first aspect of the application provides a relay protection function running state abnormal risk early warning method, which comprises the following steps:
acquiring state information change data and fixed value change data of equipment;
acquiring abnormal data in the state information change data and the fixed value change data based on the state information change data and the fixed value change data;
generating a risk analysis model based on the types and the amounts of the state information change data and the constant value change data;
acquiring a risk level threshold based on the abnormal data and a risk analysis model;
and acquiring the risk level of the equipment based on the risk level threshold.
In some embodiments, the method further comprises:
and acquiring the abnormal type of the equipment based on the risk level threshold.
In some embodiments, the method further comprises:
generating early warning information based on the risk level of the equipment and the abnormality type of the equipment;
based on the early warning information, sending the early warning information to a designated device; the designated device is an electronic device or a Bluetooth device capable of receiving information.
In some embodiments, the generating the risk analysis model based on the type and the number of the state information change data and the constant value change data includes:
constructing a data matrix based on the types and the amounts of the state information change data and the constant value change data;
acquiring a regression projection matrix of the data matrix based on the data matrix;
acquiring a kernel norm of the regression projection matrix based on the regression projection matrix;
acquiring a first diagonal matrix and a second diagonal matrix based on the regression projection matrix;
calculating the numerical values of the first diagonal matrix and the second diagonal matrix, and generating a weight matrix based on the numerical values of the first diagonal matrix and the second diagonal matrix and the kernel norm;
generating a matrix model based on the weight matrix and the data matrix;
and based on the matrix model, performing sparsification processing by using a PCA model to generate a risk analysis model.
In some embodiments, the acquiring the risk level of the device based on the risk level threshold comprises:
if the risk level threshold is smaller than 3, the risk level of the equipment is normal;
if the risk level threshold is greater than 3 and less than 7, or the risk level threshold is 3 or 7, the risk level of the equipment is abnormal;
if the risk level threshold is greater than 7, the risk level of the device is severe.
In some embodiments, the matrix model is:
X＝R’x；
wherein R' is a weight matrix, and x is a data matrix;
the risk analysis model is as follows:
wherein R is a regression projection matrix, R is a kernel norm of the regression projection matrix, T is a transpose of the matrix, beta is a non-negative regularization coefficient, gamma is a regularization parameter of the kernel norm, and tr () is a trace of the matrix.
In some embodiments, the status information change data includes: control word change data, soft clamp plate state change data, functional clamp plate state change data, outlet clamp plate state change data and primary operation mode change data.
In some embodiments, the anomaly type of the device comprises: the protection functions of the control word fixed value, the soft pressing plate, the functional hard pressing plate and the outlet hard pressing plate are invalid; constant value inspection abnormality, constant value call failure, soft press plate call failure, function hard press plate call failure, and exit hard press plate call failure.
The application provides an abnormal risk early warning system for an operation state of a relay protection function, which is applied to the abnormal risk early warning method for the operation state of the relay protection function in any one of the first aspect, and comprises the following steps:
the first acquisition module is used for acquiring state information change data and constant value change data of the equipment;
a second acquisition module configured to acquire abnormal data in the state information change data and the constant value change data based on the state information change data and the constant value change data;
a generation module configured to generate a risk analysis model based on the kind and the number of the state information change data and the constant value change data;
a third acquisition module configured to acquire a risk level threshold based on the abnormal data and a risk analysis model;
and a fourth acquisition module configured to acquire a risk level of the device based on the risk level threshold.
In some embodiments, the system further comprises:
and a fifth acquisition module configured to acquire an abnormality type of the device based on the risk level threshold.
The embodiment of the application provides a relay protection function running state abnormal risk early warning method and system, comprising the following steps: acquiring state information change data and fixed value change data of equipment; acquiring abnormal data in the state information change data and the fixed value change data based on the state information change data and the fixed value change data; generating a risk analysis model based on the types and the amounts of the state information change data and the constant value change data; acquiring a risk level threshold based on the abnormal data and a risk analysis model; based on the risk level threshold value, the risk level of the equipment is obtained, so that the relay protection equipment can accurately judge the fault type and the level of the power supply equipment, the fault point can be maintained in a targeted manner, the whole power supply equipment is not required to be stopped for maintenance and investigation, and the normal electricity consumption of a user is not influenced.
Drawings
In order to more clearly illustrate the technical solutions of the present application, the drawings that are needed in the embodiments will be briefly described below, and it will be obvious to those skilled in the art that other drawings can be obtained from these drawings without inventive effort.
FIG. 1 is a flowchart of an abnormal risk early warning method for the operation state of a relay protection function in the present application;
fig. 2 is a schematic structural diagram of an abnormal risk early warning system for the operation state of the relay protection function in the present application.
Reference numerals illustrate:
1-a first acquisition module; 2-a second acquisition module; 3-generating a module; 4-a third acquisition module; 5-a fourth acquisition module; 6-a fifth acquisition module.
Detailed Description
In order to better understand the technical solutions in the present application, the following description will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, shall fall within the scope of the present application.
Because in some technologies, relay protection equipment cannot correctly judge the fault type and grade of the power supply equipment, the whole power supply equipment is required to be stopped for maintenance and investigation, the normal power consumption of a user is affected, and the maintenance efficiency is lower.
As can be seen from fig. 1, the present application provides a method for early warning abnormal risk of operation state of relay protection function, comprising: acquiring state information change data and fixed value change data of equipment; the state information change data includes: control word change data, soft press plate state change data, functional press plate state change data, outlet press plate state change data and primary operation mode change data; acquiring state information change data and constant value change data of power supply equipment; acquiring abnormal data in the state information change data and the fixed value change data based on the state information change data and the fixed value change data; the abnormal data is data information which is automatically screened out when the state information change data is inconsistent with the set normal operation data or the fixed value of the customized change data is inconsistent; the inconsistent data information automatically triggers preset early warning conditions, and a risk analysis model is generated based on the types and the quantities of the state information change data and the constant value change data; the generating a risk analysis model based on the types and the amounts of the state information change data and the constant value change data comprises: constructing a data matrix x based on the types and the amounts of the state information change data and the constant value change data; the data matrix x is:
wherein m is the number of the collected data, and n is the kind of the collected data;
acquiring a regression projection matrix R of the data matrix based on the data matrix x;
based on the regression projection matrix R, obtaining a nuclear norm I R I of the regression projection matrix; the nuclear norms the R is as follows:
wherein R is ij The ith row and the jth column of the regression projection matrix;
acquiring a first diagonal matrix A and a second diagonal matrix B based on the regression projection matrix R; the first diagonal matrix a is:
wherein R 'is' i Is the ith row, x of the weight matrix i The ith row of the data matrix, T is the transpose of the matrix;
the second diagonal matrix B is:
calculating the numerical values of the first diagonal matrix and the second diagonal matrix, and calculating the numerical value of an inverse matrix by using the numerical values of the first diagonal matrix and the second diagonal matrix, wherein the numerical values of the inverse matrix are as follows:
(αB+xAx T ) -1 ＝(αA) -1 -(αA) -1 (αB)(I+x T (aA) -1 xA) -1 x T (αA) -1 ；
wherein, alpha is a parameter of a nuclear norm I R I of the regression projection matrix R;
generating a weight matrix R' based on the numerical value of the inverse matrix and the kernel norm R; the weight matrix R' is:
R’＝(αA) -1 -(αB) -1 xA·[I+x T (αB) -1 xA]x T (αB) -1 ；
generating a matrix model X based on the weight matrix and the data matrix; the matrix model X is:
X＝R’x；
and based on the matrix model X, performing sparsification processing by using a PCA model to generate a risk analysis model. The risk analysis model is as follows:
in the formula, β is a non-negative regularization coefficient, γ is a regularization parameter of a kernel norm, and tr () is a trace of the matrix.
Acquiring a risk level threshold based on the abnormal data and a risk analysis model; acquiring the risk level of the equipment based on the risk level threshold; inputting the abnormal data into the risk analysis model to obtain a risk level threshold; the acquiring the risk level of the device based on the risk level threshold value comprises: if the risk level threshold is smaller than 3, the risk level of the equipment is normal; i.e. f (X) < 3; if the risk level threshold is greater than 3 and less than 7, or the risk level threshold is 3 or 7, the risk level of the equipment is abnormal; namely, f (X) is more than or equal to 3 and less than or equal to 7; if the risk level threshold is greater than 7, the risk level of the equipment is serious; i.e. f (X) > 7. According to the risk level, a maintainer can judge whether the equipment has faults, the type of the faults, namely abnormal and serious, is obtained through the risk level, and proper maintenance tools are selected to be processed in a targeted mode through the fault type.
In this embodiment, the method further comprises: and acquiring the abnormal type of the equipment based on the risk level threshold. The exception types of the device include: the protection functions of the control word fixed value, the soft pressing plate, the functional hard pressing plate and the outlet hard pressing plate are invalid; constant value inspection abnormality, constant value call failure, soft press plate call failure, function hard press plate call failure, and exit hard press plate call failure. As shown in the following graph, if the risk level threshold is 4.0, the risk level is abnormal, the abnormal type is obtained according to the risk level and the risk level threshold and is the calling failure of the hard pressing plate, the risk is that the information of the hard pressing plate is unknown, the hidden danger exists in the protection function, and the processing method is to check the reason of the calling failure of the hard pressing plate at the protection outlet and timely perform the defect elimination treatment of the calling function defect.
In this embodiment, the method further comprises: generating early warning information based on the risk level of the equipment and the abnormality type of the equipment; based on the early warning information, sending the early warning information to a designated device; the designated device is an electronic device or a Bluetooth device capable of receiving information. Through obtaining the early warning information, maintenance personnel can obtain whether equipment breaks down and fault type and grade in the first time to the first time is maintained equipment and is selected suitable maintenance tool, improves maintenance efficiency.
The application provides a relay protection function running state abnormal risk early warning method and system, firstly, abnormal data are screened out from acquired control word change data, soft pressing plate state change data, function pressing plate state change data, outlet pressing plate state change data, primary running mode change data and constant value change data of equipment, then the abnormal data are input into a risk analysis model, a risk level threshold is acquired, and the risk level of the equipment is acquired through the risk level threshold; acquiring the abnormal type of the equipment through the risk level threshold value; the processing method of the maintenance equipment is obtained at the first time through the risk level and the abnormal type of the equipment, so that maintenance personnel are informed of carrying a proper maintenance tool at the first time to repair the fault point of the power supply equipment, the whole power supply equipment is not required to be powered off, the position of the fault point is inquired, the maintenance time is saved, and the maintenance efficiency is improved.
As can be seen from fig. 2, a second aspect of the present application provides an abnormal risk early warning system for an operation state of a relay protection function, which is applied to the abnormal risk early warning method for an operation state of a relay protection function in any one of the foregoing embodiments, and includes: a first obtaining module 1, configured to obtain state information change data and constant value change data of the device; a second acquisition module 2 configured to acquire abnormal data in the state information change data and the constant value change data based on the state information change data and the constant value change data; a generation module 3 configured to generate a risk analysis model based on the kinds and the number of the state information change data and the constant value change data; a third obtaining module 4 configured to obtain a risk level threshold based on the abnormal data and a risk analysis model; a fourth obtaining module 5 is configured to obtain a risk level of the device based on the risk level threshold. The functional effects of the above-mentioned system in executing the above-mentioned method can be seen in the above-mentioned method embodiments, and are not described herein.
As can be seen from fig. 2, the system further comprises: a fifth obtaining module 6 is configured to obtain an anomaly type of the device based on the risk level threshold. The functional effects of the above-mentioned system in executing the above-mentioned method can be seen in the above-mentioned method embodiments, and are not described herein.
The foregoing detailed description of the embodiments of the present application has further described the objects, technical solutions and advantageous effects thereof, and it should be understood that the foregoing is merely a specific implementation of the embodiments of the present application, and is not intended to limit the scope of the embodiments of the present application, and any modifications, equivalent substitutions, improvements, etc. made on the basis of the technical solutions of the embodiments of the present application should be included in the scope of the embodiments of the present application.

Claims (10)

1. The utility model provides a relay protection function running state abnormal risk early warning method which is characterized in that the method comprises the following steps:
acquiring state information change data and fixed value change data of equipment;
acquiring abnormal data in the state information change data and the fixed value change data based on the state information change data and the fixed value change data;
generating a risk analysis model based on the types and the amounts of the state information change data and the constant value change data;
acquiring a risk level threshold based on the abnormal data and a risk analysis model;
and acquiring the risk level of the equipment based on the risk level threshold.
2. The method for pre-warning abnormal risk of operation state of relay protection function according to claim 1, wherein the method further comprises:
and acquiring the abnormal type of the equipment based on the risk level threshold.
3. The method for pre-warning abnormal risk of operation state of relay protection function according to claim 2, wherein the method further comprises:
generating early warning information based on the risk level of the equipment and the abnormality type of the equipment;
based on the early warning information, sending the early warning information to a designated device; the designated device is an electronic device or a Bluetooth device capable of receiving information.
4. The method for early warning abnormal risk of the operation state of the relay protection function according to claim 1, wherein the generating the risk analysis model based on the type and the number of the state information change data and the constant value change data comprises:
constructing a data matrix based on the types and the amounts of the state information change data and the constant value change data;
acquiring a regression projection matrix of the data matrix based on the data matrix;
acquiring a kernel norm of the regression projection matrix based on the regression projection matrix;
acquiring a first diagonal matrix and a second diagonal matrix based on the regression projection matrix;
calculating the numerical values of the first diagonal matrix and the second diagonal matrix, and generating a weight matrix based on the numerical values of the first diagonal matrix and the second diagonal matrix and the kernel norm;
generating a matrix model based on the weight matrix and the data matrix;
and based on the matrix model, performing sparsification processing by using a PCA model to generate a risk analysis model.
5. The method for pre-warning abnormal risk of operation state of relay protection function according to claim 1, wherein the step of obtaining the risk level of the device based on the risk level threshold comprises:
if the risk level threshold is smaller than 3, the risk level of the equipment is normal;
if the risk level threshold is greater than 3 and less than 7, or the risk level threshold is 3 or 7, the risk level of the equipment is abnormal;
if the risk level threshold is greater than 7, the risk level of the device is severe.
6. The method for pre-warning abnormal risk of operation state of relay protection function according to claim 4, wherein the matrix model is as follows:
X＝R′x；
wherein R' is a weight matrix, and x is a data matrix;
the risk analysis model is as follows:
wherein R is a regression projection matrix, R is a kernel norm of the regression projection matrix, T is a transpose of the matrix, beta is a non-negative regularization coefficient, gamma is a regularization parameter of the kernel norm, and tr () is a trace of the matrix.
7. The method for pre-warning abnormal running state risk of relay protection function according to claim 1, wherein the state information change data comprises: control word change data, soft clamp plate state change data, functional clamp plate state change data, outlet clamp plate state change data and primary operation mode change data.
8. The method for pre-warning abnormal risk of operation state of relay protection function according to claim 2, wherein the abnormal type of the equipment comprises: the protection functions of the control word fixed value, the soft pressing plate, the functional hard pressing plate and the outlet hard pressing plate are invalid; constant value inspection abnormality, constant value call failure, soft press plate call failure, function hard press plate call failure, and exit hard press plate call failure.
9. An abnormal risk early warning system for an operation state of a relay protection function, which is applied to the abnormal risk early warning method for the operation state of the relay protection function according to any one of claims 1 to 8, and is characterized by comprising the following steps:
a first acquisition module (1) for acquiring state information change data and constant value change data of the equipment;
a second acquisition module (2) configured to acquire abnormal data in the state information change data and the constant value change data based on the state information change data and the constant value change data;
a generation module (3) configured to generate a risk analysis model based on the kind and the number of the state information change data and the constant value change data;
a third acquisition module (4) configured to acquire a risk level threshold based on the abnormal data and a risk analysis model;
a fourth acquisition module (5) configured to acquire a risk level of the device based on the risk level threshold.
10. The system for pre-warning abnormal risk of operation state of relay protection function according to claim 9, further comprising:
a fifth acquisition module (6) configured to acquire an anomaly type of the device based on the risk level threshold.
CN202311114483.3A 2023-08-31 2023-08-31 A risk early warning method and system for abnormal operation status of relay protection function Pending CN117351663A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN119087072A (en) * 2024-08-09 2024-12-06 国网青海省电力公司电力科学研究院 A method and device for early warning of risk of relay protection function operation status

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN119087072A (en) * 2024-08-09 2024-12-06 国网青海省电力公司电力科学研究院 A method and device for early warning of risk of relay protection function operation status

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