A Review on Mode Estimation in Wide Area Monitoring of Smart Grids using Degraded PMU Data

Abstract

The addition of non-conventional energy sources and the evolving landscape of the electrical industry have brought about fresh operative challenges and uncertainties within the power system. As a consequence, the power system’s stability has been significantly compromised. In order to tackle these challenges, wide area monitoring systems (WAMSs) has been introduced by the power industry and to convey exact time phasor measurements it employs Phasor Measurement Units (PMUs) in conjunction with the Global Positioning System (GPS).This advancement has enabled the extraction of modal information in real-time. This review paper presents a comprehensive investigation of techniques for accurate mode estimation in wide-area monitoring ofsmart grids using degraded PMU data. The paperdiscusses three efficient methods i.e., K-Medoids-Long ShortTerm Memory (K-Medoids-LSTM), K- Means-Artificial Neural Network (K-Means-ANN), and Robust Principal Component Analysis (RPCA) with Probabilistic Distributional Clustering (PDC) Algorithm, which address combines outlier detection and missing data imputation and Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT) is employed as a mode estimator.