A Novel Hybrid MPPT Approach for Solar PV Systems Using Particle-Swarm-Optimization-Trained Machine Learning and Flying Squirrel Search Optimization

Dilip Kumar (), Yogesh Kumar Chauhan, Ajay Shekhar Pandey, Ankit Kumar Srivastava, Varun Kumar, Faisal Alsaif, Rajvikram Madurai Elavarasan, Md Rabiul Islam, Raju Kannadasan and Mohammed H. Alsharif ()
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Dilip Kumar: Department of Electrical Engineering, Institute of Engineering and Technology, Dr. Rammanohar Lohia Avadh University, Ayodhya 224001, India
Yogesh Kumar Chauhan: Department of Electrical Engineering, Kamla Nehru Institute of Engineering and Technology, Sultanpur 228118, India
Ajay Shekhar Pandey: Department of Electrical Engineering, Kamla Nehru Institute of Engineering and Technology, Sultanpur 228118, India
Ankit Kumar Srivastava: Department of Electrical Engineering, Institute of Engineering and Technology, Dr. Rammanohar Lohia Avadh University, Ayodhya 224001, India
Varun Kumar: Department of Electrical Engineering, Kamla Nehru Institute of Engineering and Technology, Sultanpur 228118, India
Faisal Alsaif: Department of Electrical Engineering, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia
Rajvikram Madurai Elavarasan: Research & Development Division (Power & Energy), Nestlives Private Limited, Chennai 600091, India
Md Rabiul Islam: School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Wollongong, NSW 2522, Australia
Raju Kannadasan: Department of Electrical and Electronics Engineering, Sri Venkateswara College of Engineering, Sriperumbudur, Chennai 602117, India
Mohammed H. Alsharif: Department of Electrical Engineering, College of Electronics and Information Engineering, Sejong University, Seoul 05006, Republic of Korea

Sustainability, 2023, vol. 15, issue 6, 1-29

Abstract: In this paper, a novel hybrid Maximum Power Point Tracking (MPPT) algorithm using Particle-Swarm-Optimization-trained machine learning and Flying Squirrel Search Optimization (PSO_ML-FSSO) has been proposed to obtain the optimal efficiency for solar PV systems. The proposed algorithm was compared with other well-known methods viz. Perturb & Observer (P&O), Incremental Conductance (INC), Particle Swarm Optimization (PSO), Cuckoo Search Optimization (CSO), Flower Pollen Algorithm (FPA), Gray Wolf Optimization (GWO), Neural-Network-trained Machine Learning (NN_ML), Genetic Algorithm (GA), and PSO-trained Machine Learning. The proposed algorithm was modelled in the MATLAB/Simulink environment under different operating conditions, for example, with step changes in temperature, solar irradiance, and partial shading. The proposed algorithm improved the efficiency up to 0.72% and reduced the settling time up to 76.4%. The findings of the research highlight that PSO_ML-FSSO is a potential approach that outperforms all other well-known algorithms tested herein for solar PV systems.

Keywords: DC–DC converter; MPPT algorithm; solar photovoltaic system (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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