Most Relevant Measurements for State Estimation According to Information Theoretic Criteria

Abstract

This work presents a methodology for selecting the most relevant measurements for real-time power system monitoring. A genetic algorithm is employed to find the meter plan, composed of relevant, real-time measurements and pseudo-measurements that present the best compromise between investment costs and state estimation performance. This is achieved by minimizing both the number of real-time measurements in the power network and the degradation of the estimated states. Performance measures based on the Information Theory are investigated. Simulation results illustrate the performance of the proposed method.