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© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Wind energy is an abundant renewable energy resource that has been extensively used worldwide in recent years. The present work proposes a new Multi-Objective Optimization (MOO) based genetic algorithm (GA) model for a wind energy system. The proposed algorithm consists of non-dominated sorting which focuses to maximize the power extraction of the wind turbine, minimize the cost of generating energy, and the lifetime of the battery. Additionally, the performance characteristics of the wind turbine and battery energy storage system (BESS) are analyzed specifically torque, current, voltage, state of charge (SOC), and internal resistance. The complete analysis is carried out in the MATLAB/Simulink platform. The simulated results are compared with existing optimization techniques such as single-objective, multi-objective, and non-dominating sorting GA II (Genetic Algorithm-II). From the observed results, the non-dominated sorting genetic algorithm (NSGA III) optimization algorithm offers superior performance notably higher turbine power output with higher torque rate, lower speed variation, reduced energy cost, and lesser degradation rate of the battery. This result attested to the fact that the proposed optimization tool can extract a higher rate of power from a self-excited induction generator (SEIG) when compared with a conventional optimization tool.

Details

Title
An Evaluation on Wind Energy Potential Using Multi-Objective Optimization Based Non-Dominated Sorting Genetic Algorithm III
Author
Senthilkumar Subramanian 1   VIAFID ORCID Logo  ; Sankaralingam, Chandramohan 1 ; Rajvikram Madurai Elavarasan 2   VIAFID ORCID Logo  ; Raghavendra Rajan Vijayaraghavan 3 ; Kannadasan Raju 4   VIAFID ORCID Logo  ; Mihet-Popa, Lucian 5   VIAFID ORCID Logo 

 Department of Electrical and Electronics Engineering, College of Engineering, Anna University, Chennai 600025, India; [email protected] 
 Clean and Resilient Energy Systems Laboratory, Texas A&M University, Galveston, TX 77553, USA; [email protected] 
 Research and Development Laboratory, Innovate Educational Institute, Chennai 600069, India; [email protected] 
 Department of Electrical and Electronics Engineering, Sri Venkateswara College of Engineering, Chennai 602117, India; [email protected] 
 Faculty of Electrical Engineering, Ostfold University College, No-1757 Halden, Norway 
First page
410
Publication year
2021
Publication date
2021
Publisher
MDPI AG
e-ISSN
20711050
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2524985635
Copyright
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.