Search

Highlights • Matrix perturbation theory is applied to stability analysis and control of DCPPS. • A perturbation-based eigenvalue tracking method is offered to track targeted modes. • It is a building block for optimally tuning WADCs to damp interarea oscillations. • Delay approximation effect on SOD-based eigenvalue method's accuracy is analyzed. • Identified key factors guide to select suitable parameters to precondition DCPPS. Abstract In this paper, the matrix perturbation theory is introduced to delayed cyber-physical power system (DCPPS) for analyzing the variations of eigenvalues and eigenvectors when small changes are imposed on system parameters and time delays. On one hand, a perturbation-based eigenvalue tracking method is presented to reliably track the targeted interarea oscillation modes so that wide-area damping controllers (WADCs) can be optimally designed to damp them. On the other hand, key factors affecting the accuracy of the solution operator discretization (SOD)-based eigenvalue computation methods for DCPPS are identified by matrix perturbation analysis, which provide a guidance for selecting suitable parameters to precondition the DCPPS. The study results on the 16-generator 68-bus test system validate the effectiveness of the two applications of matrix perturbation theory. [ABSTRACT FROM AUTHOR]

To efficiently analyze the stability of large delayed cyber-physical power systems (DCPPS) incorporating wide-area damping controllers, an iterative infinitesimal generator discretization-based method (IIGD) for computing critical eigenvalues of the system is presented. IIGD contains three core techniques to guarantee efficiency and scalability. First, the sparsity of the infinitesimal generator's discretized matrix, which possesses identical spectrum to DCPPS, is explored by reformulating its blocks into Kronecker products. Especially, the dominant block is factorized as sum of Kronecker products of constant Lagrange vectors and system state matrices, which lays the basis of further utilizing the sparsities in the augmented state matrices of DCPPS. Second, the shift-invert preconditioning technique is applied to transform the required eigenvalues into those dominated in moduli. Third, the inverse iteration of the discretized matrix involved in sparse eigenvalue computation is iteratively achieved by utilizing the induced dimension reduction method (IDR( s )). Subsequently, the discretized matrix–vector product embedded in the method is efficiently implemented by exploiting the unique property of Kronecker product and the inherent sparsities in augmented system state matrices. The correctness, accuracy, efficiency and scalability of IIGD are extensively studied and thoroughly validated on the two-area four-machine test system and a real-life large transmission grid. [ABSTRACT FROM AUTHOR]