Journal article
Comprehensive Evaluation of Machine Learning MPPT Algorithms for a PV System Under Different Weather Conditions
Journal of electrical engineering & technology, v 16(1), pp 411-427
01 Jan 2021
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
The rapid growth of demand for electrical energy and the depletion of fossil fuels opened the door for renewable energy; with solar energy being one of the most popular sources, as it is considered pollution free, freely available and requires minimal maintenance. This paper investigates the feasibility of using machine learning (ML) based MPPT techniques, to harness maximum power on a PV system under PSC. In this study, certain contributions to the field of PV systems and ML based systems were made by introducing nine (9) ML based MPPT techniques, by presenting three (3) experiments under different weather conditions. Decision Tree (DT), Multivariate Linear Regression (MLR), Gaussian Process Regression (GPR), Weighted K-Nearest Neighbors (WK-NN), Linear Discriminant Analysis (LDA), Bagged Tree (BT), Naive Bayes classifier (NBC), Support Vector Machine (SVM) and Recurrent Neural Network (RNN) performances are validated and proved using MATLAB SIMULINK simulation software. The experimental results demonstrated that WK-NN performs significantly better when compared with other proposed ML based algorithms.
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Details
- Title
- Comprehensive Evaluation of Machine Learning MPPT Algorithms for a PV System Under Different Weather Conditions
- Creators
- Mpho Sam Nkambule - University of JohannesburgAli N. Hasan - Higher Colleges of TechnologyAhmed Ali - University of JohannesburgJunhee Hong - Gachon UniversityZong Woo Geem - Gachon University
- Publication Details
- Journal of electrical engineering & technology, v 16(1), pp 411-427
- Publisher
- Springer Nature
- Number of pages
- 17
- Grant note
- 2019M3F2A1073164 / Energy Cloud R&D Program through the National Research Foundation of Korea (NRF) - Ministry of Science, ICT
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Engineering Technology
- Web of Science ID
- WOS:000594817900001
- Scopus ID
- 2-s2.0-85097001477
- Other Identifier
- 991022004769904721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Engineering, Electrical & Electronic