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

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 wi...

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Bibliographic Details
Published in:Sustainability 2023-03, Vol.15 (6), p.5575
Main Authors: Kumar, Dilip, Chauhan, Yogesh Kumar, Pandey, Ajay Shekhar, Srivastava, Ankit Kumar, Kumar, Varun, Alsaif, Faisal, Elavarasan, Rajvikram Madurai, Islam, Md Rabiul, Kannadasan, Raju, Alsharif, Mohammed H
Format: Article
Language:English
Subjects:
Algorithms
Alternative energy sources
Artificial intelligence
Diodes
Electricity
Energy development
Energy resources
Fuzzy logic
Genetic algorithms
Incremental conductance
Irradiance
Learning algorithms
Machine learning
Mathematical optimization
Neural networks
Optimization
Optimization algorithms
Photovoltaic cells
Pollen
Search algorithms
Solar energy
Squirrels
Sustainability
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Summary: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.
ISSN:2071-1050
2071-1050
DOI:10.3390/su15065575