An Intelligent Risk Forewarning Method for Operation of Power System Considering Multi-Region Extreme Weather Correlation

Abstract

Extreme weather events pose significant risks to power systems, necessitating effective risk forewarning and management strategies. A few existing researches have concerned the correlation of the extreme weather in different regions of power system, and traditional operation risk assessment methods gradually cannot satisfy real-time requirements. This motivates us to present an intelligent risk forewarning method for the operation of power systems considering multi-region extreme weather correlation. Firstly, a novel multi-region extreme weather correlation model based on vine copula is developed. Then, a risk level classification method for power system operations is introduced. Further, an intelligent risk forewarning model for power system operations is proposed. This model effectively integrates the multi-region extreme weather correlation and the risk level classification of the system. By employing the summation wavelet extreme learning machine, real-time monitoring and risk forewarning of the system’s operational status are achieved. Simulation results show that the proposed method can rapidly identify potential risks and provides timely risk forewarning information, helping enhance the resilience of power system operations.

Multi-Indicator Fused Resilience Assessment of Power Grids Considering Wind-Photovoltaic Output Uncertainty during Typhoon Disasters

“…Then, the multi-stage resilience indicators were calculated based on the optimization model results [29,30]. However, most of the current research mainly focused on evaluating specific disaster scenarios, ignoring the diversity of disasters [31] and the uncertainty of renewable energy in the power grid, and less considered multiple contingencies beyond N-3 [18,32]. In addition, the size of the missing area in the system performance curve was often used as a resilience indicator and subsequently improved [25,29,33,34].…”