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Power Grid Transient Stability Assessment Based on Adaptive Differential Evolution Algorithm and ELM

In order to accurately and quickly perform transient stability assessment (TSA), this paper introduces extreme learning machine (ELM) into TSA, and proposes a power system transient evaluation method based on improved adaptive differential evolution algorithm and ELM. First, a set of original features are constructed and set as the input of the ELM model, deep-level relationship between the grid steady state and the key feature are established. Then, an improved adaptive differential evolution algorithm (JADE) is presented to optimize the parameters of extreme learning. By adopting an improved mutation strategy and introducing an optimal particle local optimization mechanism, the global optimization ability is improved. Finally, simulation verification was performed on a provincial power grid. The results show that the proposed method shows better classification performance than the other traditional stability evaluation methods.