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Maximum Power Point Tracking Based on Reinforcement Learning Using Evolutionary Optimization Algorithms

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  • Kostas Bavarinos

    (Industrial Design and Production Engineering, University of West Attica, 250 Thivon & P. Ralli Str, 12241 Egaleo, Greece)

  • Anastasios Dounis

    (Biomedical Engineering, University of West Attica, Ag. Spyridonos 17, 12243 Egaleo, Greece)

  • Panagiotis Kofinas

    (Industrial Design and Production Engineering, University of West Attica, 250 Thivon & P. Ralli Str, 12241 Egaleo, Greece
    Biomedical Engineering, University of West Attica, Ag. Spyridonos 17, 12243 Egaleo, Greece)

Abstract

In this paper, two universal reinforcement learning methods are considered to solve the problem of maximum power point tracking for photovoltaics. Both methods exhibit fast achievement of the MPP under varying environmental conditions and are applicable in different PV systems. The only required knowledge of the PV system are the open-circuit voltage, the short-circuit current and the maximum power, all under STC, which are always provided by the manufacturer. Both methods are compared to a Fuzzy Logic Controller and the universality of the proposed methods is highlighted. After the implementation and the validation of proper performance of both methods, two evolutionary optimization algorithms (Big Bang—Big Crunch and Genetic Algorithm) are applied. The results demonstrate that both methods achieve higher energy production and in both methods the time for tracking the MPP is reduced, after the application of both evolutionary algorithms.

Suggested Citation

  • Kostas Bavarinos & Anastasios Dounis & Panagiotis Kofinas, 2021. "Maximum Power Point Tracking Based on Reinforcement Learning Using Evolutionary Optimization Algorithms," Energies, MDPI, vol. 14(2), pages 1-23, January.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:2:p:335-:d:477465
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    References listed on IDEAS

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