Power system transient security assessment based on deep learning considering partial observability

Abstract

Due to the ever-increasing complexity of power systems, transient stability analysis has been reshaped into a complicated and critical issue. The real-time identification of the transient stability status would assist the power system operators in decision-making during critical events. This paper proposes a combined transient security assessment (TSA) method based on convolutional neural networks (CNNs) and a novel feature selection technique using sensitivity analysis. CNN benefits from the keen ability to understand raw data features, which decreases the required memory space. The proposed sensitivity analysis technique for feature selection can lead to a trade-off between dependency on measurable data, accuracy, and computational complexity. Furthermore, in the proposed method, an information-theoretic measure, namely mutual information (MI), is used to handle partial observability conditions. The simulation results validate the proposed method's accuracy and robustness using several metrics and comparisons with the state-of-the-art and previously presented methods.