Implementation of Forecasting-Aided State Estimation Algorithm for Distribution System Application

Abstract

State Estimation (SE) is a vital component of the Supervisory Control and Data Acquisition (SCADA) system used today in power networks. In traditional SE methods, such as Weighted Least Squares (WLS), the state variables of the grid (voltage magnitudes and phase angles) are estimated from a snapshot of the meters embedded in the network (i.e. the last measurements available). New approaches to the SE problem, known as Forecasting-Aided State Estimation (FASE), take advantage of past states in order to improve the estimation and endow the system with forecasting capabilities. The application of FASE to the low voltage grid in the context of the Smart Grid paradigm is an alluring area of research. In this work, a FASE algorithm using Kalman filters is developed and applied to a distribution network. The algorithm is implemented in Matlab and is assessed in the context of test feeders using quasi-static time series data. The performance of the new algorithm is compared with a traditional WLS implementation