Wasserstein Adversarial Learning for Identification of Power Quality Disturbances With Incomplete Data

Abstract

Power quality disturbances (PQDs) have adverse impacts on safe operation and reliability of modern integrated power system so it is of great necessity to identify them. Existence of missing measurement data hinders accurate identification of potential PQDs and the inevitable discrepancy after data recovery vitiates the current detection methods. Besides, the related research is lacked. In this study, a novel unified framework of Wasserstein adversarial learning (WAL) is proposed on identifying PQDs with incomplete data for the first time. It consists of Wasserstein adversarial imputation (WAI) and Wasserstein adversarial domain adaptation (WADA). WAI minimizes the improved Wasserstein distance between the data distributions of observed and generated PQD parts to impute missing values. During this process, PQD characteristics can be well recovered. Then, WADA leverages the Wasserstein domain discrepancy between the feature distributions of source labeled complete and target unlabeled imputed PQDs to capture domain-invariant features. Thus, labels of target imputed PQDs can be predicted accurately. Experimental verification demonstrates that the proposed WAI and WADA outperform other typical methods with better imputation results and higher classification accuracy. Constrained Wasserstein loss empowers the proposed deep learning models with excellent convergence and gradient stability.