Machine learning soft computing fuzzy set to identify ubiquitous integrated network state at different AM operations
by K. Harinadha Reddy
International Journal of Ad Hoc and Ubiquitous Computing (IJAHUC), Vol. 38, No. 4, 2021

Abstract: Issues arising during the operation like interruption, disorder and uncertainty of network need to be solved. Among many conditions, island state needs to be controlled for providing proper safety and security integrated energy network. Also, converter devices and human being's functional operation in a domain of integrated energy resource environment demand the safe network conditions. The proposed fuzzy sets are associated to recognise the state of the said network, and also continuously updating by the variations of voltage, frequency control parameters. This paper presents a k-nearest neighbour machine learning (knn-ML) to obtain the upper limit (UL) and lower limit (LL) of parameters for continuous fuzzy-based state estimation. In a proposed knn-ML method, effective countenance neural network training has been addressed to get better and successful recognition and identification of integrated network state. Effective outcomes of the proposed work are: 1) continuous control of parameters at every abnormal mode (AM) of network function; 2) proper updating of the control at every conditional instants of network operation. Effective outcome has been obtained with knn-ML soft computing algorithm.

Online publication date: Wed, 22-Dec-2021

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