Physics-guided Deep Learning for Branch Current Distribution System State Estimation

Abstract

In distribution system state estimation (DSSE), the physics-based approach such as the weighted least squares (WLS) technique suffers from convergence issues, whereas the problem with conventional deep learning (DL) models is the lack of generalizability. To address these aspects, this paper proposes a physics-guided deep learning (PGDL) framework, which incorporates the power system physical laws into the deep learning models for DSSE. The proposed approach has two stages. The first stage leverages the DL models to understand the relation between the measurement data and system states, while the second stage involves physics-guided equations to decode the measurement data from the estimated states. Further, a loss function incorporating the estimated states and estimated measurements is derived to guide the learning process of the considered deep learning model. The required database for DL models is generated by carrying out power flow studies on a modified IEEE 37-node unbalanced distribution test system. Various deep learning models such as multi-layer perceptron (MLP), convolutional neural networks (CNN), and hybrid CNN-MLP are implemented in the proposed PGDL framework for DSSE and their performance metrics are evaluated and compared under different scenarios. The results show that the PGDL approaches achieve better performance compared with their conventional DL models and the basic WLS algorithm.