A PMU-Based Data-Driven Approach for Enhancing Situational Awareness in Building A Resilient Power Systems

Abstract

A data-driven approach for improving wide-area situational awareness (SA) in modern power systems for building a more resilient grid is proposed in this article. SA is of paramount importance when it comes to accurate sensing of events for better visualization of the power system dynamics. Various events uniquely excite particular states of the power systems resulting in a maximum change in the entropy for certain power system components. A new conceptual framework utilizing structure preserving energy function (SPEF) for identifying the specific component of the energy function and correlating it with the appropriate event has been suggested. Cubature Kalman filter-unknown input based dynamic state estimation technique has been employed for the extraction of states and unknown inputs from the phasor measurement unit (PMU) measurements for the derivation of the SPEF-based event indicators. In order to demonstrate the efficacy of the proposed technique, it has been validated on the IEEE 39-bus system and also on actual PMU data obtained from the Indian power grid. The results indicate the suggested technique to be accurate and robust to noise and thus can be a promising tool for real-time power system monitoring.