Data-Driven System Theoretic Approach for Identification of Forced Oscillations

Forced oscillations is one of the critical challenges faced by the evolving power systems. In this paper, we present a system theoretic approach based on the concept of controllability Gramian to identify the source of forced oscillation. The Gramians are constructed empirically using time-series simulation data in the training phase. These Gramian matrices are combined with the empirically constructed Gramian obtained from the real-time data with forced oscillation in the formulation of optimization problem to identify the source location. The proposed optimization problem is convex and can be solved efficiently for large scale system. We present the problem formulation, implementation strategies, and case studies for the proposed approach. The proposed data-driven methodology addresses the limitations of existing energy flow method and other data-driven methodologies.