Fault Diagnosis for Power Grids under Disaster Weather Based on a Random Self-regulating Algorithm

Abstract

With the change of global climate and environment, different types of disaster weather occur more and more frequently, leading to a large number of power security accidents. The disaster weather not only brings risks to the normal operation of power grids, but also affects their fault diagnosis accuracy. Considering the fault risk of power grids under disaster weather, the self-test information of protection devices and weather data are introduced to propose a novel fault diagnosis method for power grids based on a random self-regulating algorithm. Firstly, a diagnosis analytic model is established based on the alarm, weather and self-test information of power outage areas. Secondly, three types of self-regulating trust factors are introduced into the analytic model to strengthen its fault diagnosis ability in the context of disaster weather. Finally, a random self-regulating algorithm is proposed to solve the analytic model to obtain a fault hypothesis. Case studies verify the feasibility and effectiveness of the proposed method, which also has good fault tolerance to the abnormal action of protection devices and the distortion of alarm information.