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

Autor:	Kostas Bavarinos, Anastasios Dounis, Panagiotis Kofinas
Jazyk:	angličtina
Rok vydání:	2021
Předmět:	maximum power point tracking reinforcement learning q-learning state–action-reward-state–action evolutionary algorithms optimization Technology
Zdroj:	Energies, Vol 14, Iss 2, p 335 (2021)
Druh dokumentu:	article
ISSN:	14020335 1996-1073
DOI:	10.3390/en14020335
Popis:	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.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/5cf5cd5293d84435a9b563c1a09d5daf Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
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