Electric power industry control system network management method based on multi-dimensional virtual link

Abstract

The invention aims at providing an electric power industry control system network management method based on a multi-dimensional virtual link, wherein the electric power industry control system network management method based on the multi-dimensional virtual link can effectively promote the network management level of an electric power industry control system. According to the network management characteristics of the electric power industry control system, a network management model based on the multi-dimensional virtual link is established by means of the abstract electric power industry control system service logical relation, and accordingly the network management level of the electric power industry control system is promoted comprehensively and systematically. The electric power industry control system network management method based on the multi-dimensional virtual link can be applied to the network management field.

Description

technical field [0001] The invention relates to a network management method, in particular to a multi-dimensional virtual link-based network management method for an electric power industry control system. Background technique [0002] In recent years, with the rapid development of power communication network and computer information technology, the application of new smart grid technology, the standardization of grid equipment management and the promotion of integration have greatly improved the operation level of grid equipment. At present, power enterprises at all levels have established a relatively complete power secondary system, but in the power production and operation, factors such as equipment quality, human misoperation, natural disasters, etc., have inevitably caused the reliability of the power industry control system equipment to run. decline, which directly threatens the safe and stable operation of primary power equipment. At present, the problems faced by t...

AI Technical Summary

Problems solved by technology

At present, power enterprises at all levels have established a relatively complete power secondary system, but in the power production and operation, factors such as equipment quality, human misoperation, natural disasters, etc., have inevitably caused the reliability of the power industry control system equipment to run. decline, which directly threatens the safe and stable operation of primary power equipment

At present, the problems faced by the network management of the power industry control system are more complicated and diverse. One is that the collection of information is too simple. It is difficult to reflect the complete operating status of the system and equipment in detail, which brings great difficulties to the location and cause analysis of system abnormalities

The second is the lack of correlation of events. The event information of different business systems in the power industry control system lacks a unified correlation analysis, making it difficult to achieve overall risk management

The third is that the failure site cannot be accurately analyzed. Once a device failure occurs in the power industry control system, due to the many links involved in data collection and the lack of a logical model for data services, it is difficult to analyze and locate the cause of the failure

like figure 1 As shown, the existing network management method is to collect relevant status, alarm, event, log 2 and other information for the network management object 1 according to the requirements of different systems or devices. The above-mentioned information has a large amount of data and is superimposed on the same plane. It is often difficult to reflect the complete operating status of the system and equipment in detail. Therefore, what is displayed on the network management human-machine interface 3 is the fault phenomenon, and cannot provide auxiliary decision-making information for technicians, so that it is helpful for the positioning and cause analysis of system abnormalities. great difficulty

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