Inverse covariance principal component analysis for power system stability studies

The dominant poles (eigenvalues) of system matrices are used extensively in determining the power system stability analysis. The challenge is to find an accurate and efficient way of computing these dominant poles, especially for large power systems. Here we present a novel way for finding the system stability based on inverse covariance principal component analysis (ICPCA) to compute the eigenvalues of large system matrices. The efficacy of the proposed method is shown by numerical calculations over realistic power system data and we also prove the possibility of using ICPCA to determine the eigenvalues closest to any damping ratio and repeated eigenvalues. Our proposed method can also be applied for stability analysis of other engineering applications.