Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/4132
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dc.contributor.authorSatapathy, Subhalaxmi-
dc.contributor.authorKumar, Vagesh-
dc.contributor.authorRai, Shekha-
dc.date.accessioned2023-12-18T04:41:49Z-
dc.date.available2023-12-18T04:41:49Z-
dc.date.issued2023-09-
dc.identifier.citation1st International Conference on Intelligent Computation and Analytics on Sustainable Energy and Environment (ICICASEE), Online Mode, 21-23 September 2023en_US
dc.identifier.urihttp://hdl.handle.net/2080/4132-
dc.descriptionCopyright belongs to proceeding publisheren_US
dc.description.abstractThe 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.en_US
dc.subjectPMUen_US
dc.subjectK-Medoids-LSTMen_US
dc.subjectK-Means-ANNen_US
dc.subjectRPCAen_US
dc.subjectPDCen_US
dc.subjectESPRITen_US
dc.subjectModes Evaluationen_US
dc.titleA Review on Mode Estimation in Wide Area Monitoring of Smart Grids using Degraded PMU Dataen_US
dc.typeArticleen_US
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