Forecasting Aided Distribution Network State Estimation Using Mixed μPMU-RTU Measurements

Abstract

Distribution network state estimation is the backbone of energy management systems, whose accuracy and adaptability are very important for the advanced application software in distribution networks. This article proposes a novel forecasting-aided state estimation method for distribution networks with the mixed measurements of microphasor measurement unit (μPMU) and remote terminal unit (RTU). First of all, the data imputation techniques for RTU with a longer update period are proposed to handle asynchronous characteristics and improve computational accuracy using historical and current measurements. Then, the measurement equations are built to process the different types of measurement data from μPMU and RTU. The cubature Kalman filter is adopted to ensure the numerical stability of state forecasting, measurement forecasting, and filter correction. Finally, the IEEE 33- and 37-node systems are applied to verify the effectiveness of the proposed method, which can get an accurate state under mixed measurement.