Power Grid Outlier Treatment through Kalman Filter

Abstract

Power grid monitoring is a very challenging task to avoid both disasters and economic damages. This is because many critical services e.g. Health and welfare use the power grid as a reliable utility. Wide Area Monitoring System (WAMS) represents a technological solution to perform power grid monitoring. WAMS is based on two components: Phasor Measurement Unit (PMU) and Phasor Data Concentrator (PDC). PMUs are deployed within the power grid and their purpose is to provide synchronized measurements of both magnitude and angle phase of the electric signal. PDC analyses measurements provided by different PMUs to assess the global status of the power grid. In this paper we propose an enhanced WAMS architecture based on Kalman filter to improve state estimation and fault detection within the power grid, Kalman filter allows to reduce the noise effects on measurements gathered by PMUs. Experimental results show the effectiveness of the proposed solution.