Qun Li, Ningyu Zhang*, Jianhua Zhou, Xinyao Zhu, Peng Li
Energy Engineering, Vol.120, No.6, pp. 1485-1502, 2023, DOI:10.32604/ee.2023.022856
Abstract The access of unified power flow controllers (UPFC) has changed the structure and operation mode of power grids all across the world, and it has brought severe challenges to the traditional real-time calculation of security correction based on traditional models. Considering the limitation of computational efficiency regarding complex, physical models, a data-driven power system security correction method with UPFC is, in this paper, proposed. Based on the complex mapping relationship between the operation state data and the security correction strategy, a two-stage deep neural network (DNN) learning framework is proposed, which divides the offline training task of security correction into… More >
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