Intelligent Short-Term Voltage Stability Assessment via Spatial Attention Rectified RNN Learning
Lipeng Zhu, David J. Hill, Chao Lü
Abstract
Focusing on fully learning intrinsic spatial and temporal dependencies from smart grids' complicated transients in a computationally efficient way, this article develops an intelligent machine learning approach for online short-term voltage stability (SVS) assessment. Based on static network information and dynamic system responses, spatial correlations are first comprehensively described from both model-based and data-based viewpoints. Such correlations are further formulated as spatial attention factors, which are leveraged to carefully rectify multiple transient trajectories. Taking the rectified trajectories as inputs, the long short-term memory based deep recurrent neural network (RNN) algorithm is employed to learn sequential SVS features. In this way, the RNN learning procedure is comprehensively guided by both spatial and temporal information, thereby deriving a highly reliable and robust classification model for online SVS assessment. Extensive numerical tests on the Nordic test system and the realistic Guangdong Power Grid in South China illustrate the superior reliability, scalability, and applicability of the proposed approach over existing methods.