Unbalanced disturbance evaluation in power grid using spiked covariance model and phase transition phenomenon. (March 2021) - Mao, Jun-Yi; Han, Song; Li, Hong-Qian; Zhou, Zhong-Qiang
Research highlights: A random matrix theory-based evaluation method for unbalanced disturbance events. Phase transition phenomenon of eigen characteristics is used to locate disturbance. Experiments show superior performance in noise resistance and computational efficiency. The detection results from current amplitude data are more sensitive and accurate. Abstract: This paper proposes a random matrix theory (RMT)-based evaluation method for unbalanced disturbance events in power grid using Spiked covariance model (SpCM) and phase transition (PT) phenomenon. It firstly constructs the SpCM and sample covariance matrix (SaCM) by three-phase data source with noise. Subsequently, the maximum eigenvalue of SaCM (MESCM) would be taken as a disturbance identification index. Meanwhile, its corresponding dynamic threshold is defined by the PT of eigenvalues. Consequently, according to the network location associated with the changing elements of minimum eigenvectors in both SpCM and SaCM, the unbalanced disturbance location can be rapidly achieved by the PT of eigenvector if the threshold for MESCM is violated. The case studies have been carried on an IEEE 54-machine and 118-bus system with the help of DIgSILENT and Matlab Ⓡ software. The comparisons between the results from the proposed method and traditional RMT-based method indicate that it is valid and efficient. Graphical abstract: Image, graphical abstract