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Sustainability
Volume 13
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10.3390/su13010410
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Article

An Evaluation on Wind Energy Potential Using Multi-Objective Optimization Based Non-Dominated Sorting Genetic Algorithm III

by
Senthilkumar Subramanian
1,*,
Chandramohan Sankaralingam
1,
Rajvikram Madurai Elavarasan
2,
Raghavendra Rajan Vijayaraghavan
3,
Kannadasan Raju
4 and
Lucian Mihet-Popa
5,*
1
Department of Electrical and Electronics Engineering, College of Engineering, Anna University, Chennai 600025, India
2
Clean and Resilient Energy Systems Laboratory, Texas A&M University, Galveston, TX 77553, USA
3
Research and Development Laboratory, Innovate Educational Institute, Chennai 600069, India
4
Department of Electrical and Electronics Engineering, Sri Venkateswara College of Engineering, Chennai 602117, India
5
Faculty of Electrical Engineering, Ostfold University College, No-1757 Halden, Norway
*
Authors to whom correspondence should be addressed.
Sustainability 2021, 13(1), 410; https://doi.org/10.3390/su13010410
Submission received: 3 December 2020 / Revised: 30 December 2020 / Accepted: 31 December 2020 / Published: 5 January 2021
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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.
Keywords: dominating and non-dominated sorting; genetic algorithm; multi-objective optimization (MOO); single-objective optimization; wind energy system dominating and non-dominated sorting; genetic algorithm; multi-objective optimization (MOO); single-objective optimization; wind energy system

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MDPI and ACS Style

Subramanian, S.; Sankaralingam, C.; Elavarasan, R.M.; Vijayaraghavan, R.R.; Raju, K.; Mihet-Popa, L. An Evaluation on Wind Energy Potential Using Multi-Objective Optimization Based Non-Dominated Sorting Genetic Algorithm III. Sustainability 2021, 13, 410. https://doi.org/10.3390/su13010410

AMA Style

Subramanian S, Sankaralingam C, Elavarasan RM, Vijayaraghavan RR, Raju K, Mihet-Popa L. An Evaluation on Wind Energy Potential Using Multi-Objective Optimization Based Non-Dominated Sorting Genetic Algorithm III. Sustainability. 2021; 13(1):410. https://doi.org/10.3390/su13010410

Chicago/Turabian Style

Subramanian, Senthilkumar, Chandramohan Sankaralingam, Rajvikram Madurai Elavarasan, Raghavendra Rajan Vijayaraghavan, Kannadasan Raju, and Lucian Mihet-Popa. 2021. "An Evaluation on Wind Energy Potential Using Multi-Objective Optimization Based Non-Dominated Sorting Genetic Algorithm III" Sustainability 13, no. 1: 410. https://doi.org/10.3390/su13010410

APA Style

Subramanian, S., Sankaralingam, C., Elavarasan, R. M., Vijayaraghavan, R. R., Raju, K., & Mihet-Popa, L. (2021). An Evaluation on Wind Energy Potential Using Multi-Objective Optimization Based Non-Dominated Sorting Genetic Algorithm III. Sustainability, 13(1), 410. https://doi.org/10.3390/su13010410

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