Machine Learning Taxonomy for Multi Area Network Control

Machine Learning Taxonomy for Multi Area Network Control

Registration ID: IJNRD_204057

Published ID: IJNRD2303495

DOI: http://doi.one/10.1729/Journal.35846

Shelja , Pawan Kumar

Machine learning, network type, training technique, load frequency control, Mutli area control

Machine learning have an inherent attribute of learning from the data which may be numerical data or categorical data in order to imitate human intelligence for increasing the performance of the system. All this is attributed to the availability of different types of machine learning algorithm Each type of machine learning model has its strengths and weaknesses, and the choice of model depends on the nature of the data and the specific problem at hand. So generalization is always worked upon for the selection of the machine learning model. In this paper, authors have systematically developed a framework for machine learning based control for a very practical problem of supply-load balance as applicable to electrical power system. The identified problem is regression based. The paper thoroughly analyzes different combinations of machine learning algorithm along with different network architecture for achieving the desired results in the selected multi-area load frequency control problem with greater accuracy and high speed.

Published Paper   E-Certificate

"Machine Learning Taxonomy for Multi Area Network Control", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.8, Issue 3, page no.e724-e733, March-2023, Available :https://ijnrd.org/papers/IJNRD2303495.pdf

Volume 8 Issue 3, March-2023

Pages : e724-e733

Paper Reg. ID: IJNRD_204057

Published Paper Id: IJNRD2303495

Downloads: 000121160

Research Area: Engineering

Country: -, -, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2303495.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2303495

DOI: http://doi.one/10.1729/Journal.35846

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

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This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition

Call For Paper - Volume 10 | Issue 8 | August 2025

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