Data-driven power system reliability evaluation based on stacked denoising auto-encoders

Abstract The increasing penetration of renewable energy resources in power systems inadvertently leads to a surge in the number of random states. The calculation of optimal load shedding for all the proliferated states is rather overwhelming. It has been considered a bottleneck in the reliability evaluation process. To address this issue, a deep-learning-based approach is proposed to evaluate system reliability more efficiently considering the fluctuation of generation and loads. In the proposed power flow model, a stacked denoising auto-encoder (SDAE) serves as a multilayer neural network for the deep-learning process. Its stacked structure and encoding-decoding recursion enable it to extract high-order features even from non-linear equations. The features of contingency states are crucial to directly acquire the minimum load curtailment without the time-consuming optimal power flow (OPF). The after-trained model is applied to states simulated with the Monte-Carlo method in the RTS-79 system. The numerical result reveals the great advantage of the SDAE-based method in computation time while ensuring high accuracy. As an alternative way to classic OPF algorithms, it can be integrated with the impact-increment method and other state selection approaches.