Multilayer-extreme-learning-machine-based Reliability Assessment Approach for Power Systems with Renewable Energy

With the integration of renewable energy sources, numerous scenarios emerge, escalating computational demands and challenging power system reliability assessment efficiency. In this study, a data-driven-based method is proposed to solve the optimal power flow (OPF) with the least load curtailment. This approach employs the multilayer extreme learning machine (MELM) model to establishes a nonlinear relationship between power system states and minimal load curtailment, thereby providing accurate and expedited solutions for reliability assessment. Case studies have been conducted on the RTS-79 system with 10% penetration renewable energy. The results suggests that the proposed method offers enhancements in the efficiency of power system reliability assessments.