Partitioned modal extraction applied to power system wide-area measurements using empirical orthogonal functions

Abstract

Abstract This paper presents a numerical algorithm in order to identify and extract the inter-area modal inestability presented in large interconnected power systems, which significantly decreases the computational burden and gives a distributed application in the analysis of collected data from wide-area measurement systems (WAMS) using the empirical orthogonal functions. The proposed algorithm is based on a spatio-temporal statistical identification method using both empirical orthogonal functions (EOF) analysis and partitioned network data approximation. Firstly, identification process is carried out separating the power system network into a number of subnetworks either geographically distributed or by containing the same load types. Then, EOF analysis is applied to the local data array which contains the space-time response for each subnetwork where modal extraction is determined. Afterward, the derived modal approximation sets are used to compute global predominant modes by correlation of its temporal coefficients, which yields to a refined global approximation of local and inter-area oscillations, and a model-order reduction removing spurious modes. An illustrative example involving a transmission system is presented in order to validate the proposed methodology. © 2015 Elsevier B.V. All rights reserved.