Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/4018
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dc.contributor.authorSahoo, Manoranjan-
dc.contributor.authorReshma, Varnika-
dc.contributor.authorRai, Shekha-
dc.date.accessioned2023-05-25T11:09:13Z-
dc.date.available2023-05-25T11:09:13Z-
dc.date.issued2023-04-
dc.identifier.citation8th International Conference for Convergence in Technology (I2CT 2023), Pune, India, 07-09 April 2023en_US
dc.identifier.urihttp://hdl.handle.net/2080/4018-
dc.descriptionCopyright belongs to proceeding publisheren_US
dc.description.abstractDue to heavy communication network congestion, malicious attacks, malfunction of phasor measurement units (PMUs) or Phasor data concentrators (PDCs), and other factors, PMUs data collected at PDC may comprise of nonlinear treads with missing values and outliers which results in degradation in the performance of estimator, thereby resulting in system stability and sustainability issues. So, in the proposed work, a novel K-Means-ANN based technique has been proposed to overcome this detrimental effect. So, in the proposed technique, an unsupervised K-Mean clustering algorithm has been implemented for the detection and removal of the outliers and, thereby the Artificial Neural Network (ANN) is exploited for missing data imputation. Finally, total-least square-estimation-of-signal-parameters via rotational invariance technique (TLS-ESPRIT) is applied for mode estimation. To validate the robustness of the proposed approach over the other approaches, comparative study is carried out on synthetic signal with different modes of oscillation and noise levels through Monte Carlo simulations. This technique is also validated on two area data and real probing data obtained from Western Electricity Co-ordinating Council (WECC).en_US
dc.subjectPMUen_US
dc.subjectK-Means-ANNen_US
dc.subjectTLS-ESPRITen_US
dc.subjectModes Estimationen_US
dc.titleAK-Means-ANN Based Technique for Identification of Oscillatory Modes in Power Systemen_US
dc.typeArticleen_US
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