Discriminative Signal Recognition for Transient Stability Assessment via Discrete Mutual Information Approximation and Eigen Decomposition of Laplacian Matrix
Jiacheng Liu, Jun Liu, Xiaoming Liu, Xinglei Liu, Yu Zhao
Abstract
Transient stability assessment (TSA) is of great significance for the security of power systems. The widely studied postfault TSA based on machine learning methods relies on real-time transient response captured by phasor measurement units (PMUs), which faces difficulties when directly applied to large-scale power systems with a tremendous number of signals as inputs. In this article, we propose a complete scheme for recognizing the most discriminative PMU signals for TSA. First, the original PMU measurement trajectories are projected into uniformly distributed low-dimensional space while maintaining the inherent local structure. Then, a probabilistic dueling clustering method enhanced by a corrected Calinski-Harabaz index is proposed. It is able to divide the projected signals into discrete segments, and then the mutual information between signals and transient stability can be computed as the correlation indicator. Afterward, a signal recognition method based on Eigen decomposition of Laplacian matrix in the information domain is proposed to select the most discriminative signals, which aims to search for the global optimum of maximized relevance and minimized redundancy, and a parallel framework is adopted to improve the recognition efficiency. Key steps of the whole signal recognition scheme are strictly demonstrated in a theoretical way, and case studies on an actual power system provided by China Electric Power Research Institute also verify the effectiveness of the selected signals.