Forecasting-Aided State Estimation With Deep Learning-Generated Pseudo Measurements

The state estimation of an active distribution network (ADN) in the absence of enough measurements, and with the presence of distributed energy resources (DERs) located behind-the-meter (BTM), is a significant challenge. The operational philosophy of power distribution networks (DNs) is also changing from “generation following demand” to “demand following generation.” Consequently, emerging demand and generation patterns are difficult to handle through standard state estimators. This article introduces a profiling framework that characterizes responsive/flexible demands and BTM DERs to represent nonconventional load power. The impact of these nonconventional load power on DN monitoring and the challenges of system observability are addressed using a deep learning-based forecaster. This forecaster utilizes weather and other relevant input features to tackle the irregularities in demand profiles. Additionally, the framework includes proposing a strategy for time-varying smoothing parameters in forecasting-aided state estimation (FASE) to address the uncertainties associated with loads and the outputs of DERs. The framework is validated on a modified IEEE-123 bus unbalanced three-phase DN for demonstrating improved accuracy of the estimation.