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

Senthilkumar Subramanian, Chandramohan Sankaralingam, Rajvikram Madurai Elavarasan, Raghavendra Rajan Vijayaraghavan, Kannadasan Raju and Lucian Mihet-Popa
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Senthilkumar Subramanian: Department of Electrical and Electronics Engineering, College of Engineering, Anna University, Chennai 600025, India
Chandramohan Sankaralingam: Department of Electrical and Electronics Engineering, College of Engineering, Anna University, Chennai 600025, India
Rajvikram Madurai Elavarasan: Clean and Resilient Energy Systems Laboratory, Texas A&M University, Galveston, TX 77553, USA
Raghavendra Rajan Vijayaraghavan: Research and Development Laboratory, Innovate Educational Institute, Chennai 600069, India
Kannadasan Raju: Department of Electrical and Electronics Engineering, Sri Venkateswara College of Engineering, Chennai 602117, India
Lucian Mihet-Popa: Faculty of Electrical Engineering, Ostfold University College, No-1757 Halden, Norway

Sustainability, 2021, vol. 13, issue 1, 1-29

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 (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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