Power system risk assessment method based on big data and human caused factors

Abstract

The present invention discloses a power system risk assessment method based on big data and human caused factors. The method includes the following four steps: 1, the probability of the occurrence of human caused errors of an operator in the operation process of a system is assessed through using an AHP-SLIM method; 2, the failure probabilities of power transmission and transformation equipment, secondary system equipment and software are obtained through big data mining technology; 3, the failure risks of various kinds of functions of the secondary equipment or software are assessed based on the functional decomposition of an IEC 61850; and 4, the fault probability of traditional primary equipment is corrected based on the failure probabilities, so that the risk assessment of the system can be completed. According to the method of the invention, based on existing research, the failure probabilities of the operator and the failure probabilities of the secondary system are considered in the risk assessment of the primary system, and therefore, the risk assessment result of the power system is more accurate. The method can characterize risks faced by a current power grid more objectively compared with a traditional assessment method.

Description

technical field [0001] The invention belongs to the field of power system risk assessment, and specifically relates to a power system risk assessment method based on big data and human factors. Background technique [0002] At present, there are many researches on power system risk assessment, but they mainly focus on the research on the failure probability and failure consequences of primary equipment, and rarely consider system failures caused by human reliability and secondary equipment outage into the system Therefore, the risk results obtained by the assessment are often inaccurate, and sometimes there are large discrepancies. [0003] With the continuous development of power information technology, the data of the power system is growing explosively, with the characteristics of mass and heterogeneity. It is time-consuming and labor-intensive to use conventional software and tools to analyze, and big data mining technology has the advantage of rapidly processing such da...

AI Technical Summary

Problems solved by technology

[0002] At present, there are many studies on the risk assessment of power systems, but they mainly focus on the failure probability and consequences of primary equipment, and seldom consider the system failure caused by human reliability and secondary equipment outage. Therefore, the risk results obtained from the assessment are often inaccurate, and sometimes there are even large discrepancies

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