Researchers from University of Sharjah Report New Studies and Findings in the Area of Energy (Modwt-xgboost Based Smart Energy Solution for Fault Detection and Classification In a Smart Microgrid)
- Jun 11, 2021 4:28 pm GMT
2021 JUN 10 (NewsRx) -- By a News Reporter-Staff News Editor at Energy Daily News -- Data detailed on Energy have been presented. According to news reporting out of Sharjah, United Arab Emirates, by NewsRx editors, research stated, “Electrical power being the key driver for any technology driven development, an intelligent technology enabled smart grid which ensures reliable, environment-friendly and power quality certainly provides the necessary fillip to the urban intelligence. This study introduces a novel differential approach of microgrid fault detection and classification as a smart grid enabler.”
Our news journalists obtained a quote from the research from the University of Sharjah, “The proposed microgrid protection scheme (MPS) involves an initial phase of pre-processing through anti-aliasing and filtering out of noise of the retrieved system parameters. This is followed by feature extraction process using Maximal Overlap Discrete Wavelet Transform (MODWT) with an abstract wavelet family of mother wavelet ‘FejerKorovkin’ and three level of decomposition. The differential energy calculated for both three-phase current and its zero-sequence current component at each of the decomposition level of MODWT finally serves as input to an Extreme Gradient Boost (XGBoost) based machine learning model to achieve incipient fault detection and classification. The combination of MODWT and XGBoost as an intelligent MPS working upon a pre-processed de-noised system signals, hitherto untried as per the knowledge of the authors, is tested using standard IEC microgrid test model under varied topological configurations, operational modes, fault conditions, etc. The simulation results, so extensively obtained, prove the effectiveness and robustness of the proposed approach of MPS.”
According to the news editors, the research concluded: “The MPS is additionally verified on an IEEE 13 bus microgrid model to reinforce the clam of efficiency.”
This research has been peer-reviewed.
For more information on this research see: Modwt-xgboost Based Smart Energy Solution for Fault Detection and Classification In a Smart Microgrid. Applied Energy, 2021;285:116457. Applied Energy can be contacted at: Elsevier Sci Ltd, The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, Oxon, England. (Elsevier - www.elsevier.com; Applied Energy - http://www.journals.elsevier.com/applied-energy/)
Our news journalists report that additional information may be obtained by contacting Ramesh C. Bansal, University of Sharjah, Dept. of Electrical Engineering, Sharjah, United Arab Emirates. Additional authors for this research include Bhaskar Patnaik, Ranjan K. Jena and Manohar Mishra.
The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.apenergy.2021.116457. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.
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