Abstract
High penetration of renewable generations has imposed a significant challenge on power system small signal stability, as the stochastic nature of renewable energy makes the uncertainty scenarios increase dramatically. This paper proposes a data-driven model-free online small signal stability assessment (SSSA) method based on deep learning. The proposed method can provide real-time small signal stability results based on various historical data including wind speed, solar irradiation and power system operation data. The first step of the approach is to carry out the small signal analysis using modal analysis (MA) to generate the training database. Then from labeled database and other historical data, a deep learning based method is employed to learn how to make the right prediction of SSSA in real-time, without the knowledge of future wind or solar data. The effectiveness of the proposed method is evaluated and demonstrated in the paper by a typical 16-machine 68-bus test system.
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