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A Comprehensive Review of MPPT Techniques Based on ML Applicable for Maximum Power in Solar Power Systems

Document Type : Research Article

Authors

1 Department of EEE, School of Engineering, IIMT University, P. O. Box: 250001, Meerut, UP, India.

2 Department of R&D, School of Engineering, IIMT University, P. O. Box: 250001, Meerut, UP, India.

3 Department of ECE, IIMT College of Engineering, AKTU Lucknow, P. O. Box: 201306, Greater Noida, UP, India.

Abstract

Solar power energy continues to be a renewable and sustainable source of energy in the coming year due to its cleaner nature and abundant availability. Maximum Power Point Tracking (MPPT) is a technique used in solar power systems to extract maximum power from photovoltaic (PV) modules by tracking the operating point of the modules. MPPT is essential for achieving optimal power output from a solar panel, particularly in variable weather conditions. Traditional MPPT techniques are subject to limitations in handling the partial shading conditions (PSC). To ensure the tracking of maximum power point while boosting the MPPT's overall efficacy and performance, Machine Learning must be integrated into MPPT. As per the reviewer work, ML techniques have the potential to play a crucial role in the development of advanced MPPT systems for solar power systems operating under partial shading conditions and to compare the performance of existing ML-MPPT in terms of accuracy, response time, and efficacy. These review papers technically analyze the result of ML-MPPT techniques and suggest the optimum ML-MPPT tactics that are Q learning, Bayesian Regularization Neural Network (BRNN), and Multivariate Linear Regression Model (MLIR) to achieve optimum outcomes in MPPT under PSC. Further, these techniques can offer efficiency greater than 95%, tracking duration less than 1sec, and error threshold of 0.05. In the future, the reviewer may propose simulation work to compare the optimal algorithms.

Keywords

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References
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Journal of Renewable Energy and Environment
Volume 11, Issue 1
January 2024
Pages 28-37
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History
  • Receive Date: 28 February 2023
  • Revise Date: 24 March 2023
  • Accept Date: 14 April 2023
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