Community power distribution network dynamic risk assessment method and device based on Bayesian network

Abstract

The embodiment of the invention provides a community power distribution network dynamic risk assessment method and device based on a Bayesian network. The community power distribution network dynamicrisk assessment method comprises the following steps: constructing a community power distribution network dynamic risk assessment index system and the Bayesian network; inputting the acquired currentand voltage preset data into a power distribution feeder short-circuit fault diagnosis Bayesian network model to obtain power distribution feeder short-circuit state information; and inputting the weather information, the community power distribution system component operation age limit information and the power distribution feeder short circuit state information into a community power distribution network power failure risk assessment Bayesian network model to obtain a community power failure risk assessment result. The community power distribution network dynamic risk assessment method and device based on a Bayesian network establish a Bayesian network model by using the proposed index system suitable for the dynamic risk assessment of the community power distribution network, and perform the dynamic risk assessment through the real-time data, so that the real-time dynamic assessment of the power failure risk of the community power distribution network is realized, and the reliability and accuracy of the dynamic assessment are improved.

Description

technical field [0001] The invention relates to the technical field of electric power risk monitoring, in particular to a method and device for dynamic risk assessment of community distribution networks based on Bayesian networks. Background technique [0002] The community distribution network is the part of the low-voltage side of the secondary step-down substation in the power system that supplies power to users, and is the core connecting the main network and users. Most of the community distribution network goes deep into the city center and densely populated areas, and has the characteristics of complex structure, frequent load changes, and large network loss. According to incomplete statistics, more than 80% of the faults in the community power system come from the community distribution network. Accurate risk assessment of community distribution network blackout risk and timely discovery of potential safety hazards can not only provide an important basis for the pla...

AI Technical Summary

Problems solved by technology

[0003] At present, the risk assessment index system for large power grids is mature, but this index system focuses on the statistics of historical power outages, and focuses on evaluating the operational risk level of the power grid from the perspective of system reliability, and does not go deep into the level of component operation status for comprehensive analysis risk assessment

However, there are many factors that affect the blackout risk of the community distribution network, and the various influencing factors are interrelated. The traditional power system risk assessment index system cannot essentially conduct a comprehensive, real-time and dynamic risk assessment of the community distribution network.

In addition, the current risk assessment method focuses on static assessment, which is difficult to reflect the real-time dynamic changes of risk and the influence relationship between risk factors, and it is difficult to provide effective early warning for possible risk accidents

Images