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Multi-Objective Optimal Power Flow Solutions Using Improved Multi-Objective Mayfly Algorithm (IMOMA)

Department of Electrical and Electronics Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D, Institute of Science and Technology, Chennai 600062, Tamil Nadu, India

E-mail Address: vijayk252@gmail.com

Corresponding author.

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S. Ramesh

Department of Electrical and Electronics Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D, Institute of Science and Technology, Chennai 600062, Tamil Nadu, India

E-mail Address: rameshsme@gmail.com

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K. Karunanithi

Department of Electrical and Electronics Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D, Institute of Science and Technology, Chennai 600062, Tamil Nadu, India

E-mail Address: k.karunanithiklu@gmail.com

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, and

S. P. Raja

School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India

E-mail Address: avemariaraja@gmail.com

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https://doi.org/10.1142/S0218126623502006Cited by:5 (Source: Crossref)

Abstract

This paper realizes the implementation of Improved Multi-objective Mayfly Algorithm (IMOMA) for getting optimal solutions related to optimal power flow problem with smooth and nonsmooth fuel cost coefficients. It is performed by considering Simulated Binary Crossover, polynomial mutation and dynamic crowding distance in the existing Multi-objective Mayfly Algorithm. The optimal power flow problem is formulated as a Multi-objective Optimization Problem that consists of different objective functions, viz. fuel cost with/ without valve point loading effect, active power losses, voltage deviation and voltage stability. The performance of Improved Multi-objective Mayfly Algorithm is interpreted in terms of the present Multi-objective Mayfly Algorithm and Nondominated Sorting Genetic Algorithm-II. The algorithms are applied under different operating scenarios of the IEEE 30-bus test system, 62-bus Indian utility system and IEEE 118-bus test system with different combinations of objective functions. The obtained Pareto fronts achieved through the implementation of Improved Multi-objective Mayfly Algorithm, Multi-objective Mayfly Algorithm and Nondominated Sorting Genetic Algorithm-II are compared with the reference Pareto front attained by using weighted sum method based on the Covariance Matrix-adapted Evolution Strategy method. The performances of these algorithms are individually analyzed and validated by considering the performance metrics such as convergence, divergence, generational distance, inverted generational distance, minimum spacing, spread and spacing. The best compromising solution is achieved by implementing the Technique for Order of Preference by Similarity to Ideal Solution method. The overall result has shown the effectiveness of Improved Multi-objective Mayfly Algorithm for solving multi-objective optimal power flow problem.

This paper was recommended by Regional Editor Takuro Sato.

Keywords:

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