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Global Maximum Power Point Tracking of PV Systems under Partial Shading Condition: A Transfer Reinforcement Learning Approach

1
State Grid Suzhou Power Supply Company, Suzhou 215004, China
2
Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, China
3
College of Engineering, Shantou University, Shantou 515063, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(13), 2769; https://doi.org/10.3390/app9132769
Received: 11 June 2019 / Accepted: 2 July 2019 / Published: 9 July 2019
(This article belongs to the Special Issue Standalone Renewable Energy Systems—Modeling and Controlling)
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Abstract

This paper aims to introduce a novel maximum power point tracking (MPPT) strategy called transfer reinforcement learning (TRL), associated with space decomposition for Photovoltaic (PV) systems under partial shading conditions (PSC). The space decomposition is used for constructing a hierarchical searching space of the control variable, thus the ability of the global search of TRL can be effectively increased. In order to satisfy a real-time MPPT with an ultra-short control cycle, the knowledge transfer is introduced to dramatically accelerate the searching speed of TRL through transferring the optimal knowledge matrices of the previous optimization tasks to a new optimization task. Four case studies are conducted to investigate the advantages of TRL compared with those of traditional incremental conductance (INC) and five other conventional meta-heuristic algorithms. The case studies include a start-up test, step change in solar irradiation with constant temperature, stepwise change in both temperature and solar irradiation, and a daily site profile of temperature and solar irradiation in Hong Kong. View Full-Text
Keywords: photovoltaic systems; MPPT; partial shading condition; transfer reinforcement learning; space decomposition photovoltaic systems; MPPT; partial shading condition; transfer reinforcement learning; space decomposition
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Ding, M.; Lv, D.; Yang, C.; Li, S.; Fang, Q.; Yang, B.; Zhang, X. Global Maximum Power Point Tracking of PV Systems under Partial Shading Condition: A Transfer Reinforcement Learning Approach. Appl. Sci. 2019, 9, 2769.

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