Real-Time Risk Evaluation Method of Power System Equipment and N-m Fault Contingency Plan Generation Under Typhoon Meteorological Environment

Abstract

In order to improve the power grid operation risk early warning and pre-control capabilities in typhoon and other disastrous climates, the historical fault features of power grid are extracted by utilizing the machine learning technology based on the meteorological environment data, geographic environment, and the status of power transmission and transformation equipment. The meteorological risk probability of digital transmission channel is quantified to realize the advanced quantitative warning of high-risk faults in the power grid under typhoon weather environment. The proposed method can genarate the current and future N-m fault handling plans based on the current power grid status and significantly enhance the timeliness and pertinence of fault prediction. This method overcomes the difficulty that the meteorological risk model needs to be continuously revised according to the actual situation, and takes into account the response of various safety automatic devices in the power grid after a high-risk equipment failure and the control measures that need to be taken. The practical application shows that this method can provide significant decision supports for risk pre-control in the power grid dispatching operation.

Keywords

• meteorological risk
• machine learning
• quantitative warning
• fault contingency plan generation
• online dynamic security analysis