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A Reinforcement Learning-Based Maximum Power Point Tracking Method for Photovoltaic Array
Joint Authors
Hsieh, Hung-I.
Hsu, Roy Chaoming
Liu, Cheng-Ting
Chen, Wen-Yen
Wang, Hao-Li
Source
International Journal of Photoenergy
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-06-16
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
A reinforcement learning-based maximum power point tracking (RLMPPT) method is proposed for photovoltaic (PV) array.
By utilizing the developed system model of PV array and configuring the environment for the reinforcement learning, the proposed RLMPPT method is able to observe the environment state of the PV array in the learning process and to autonomously adjust the perturbation to the operating voltage of the PV array in obtaining the best MPP.
Simulations of the proposed RLMPPT for a PV array are conducted.
Experimental results demonstrate that, in comparison to an existing MPPT method, the RLMPPT not only achieves better efficiency factor for both simulated weather data and real weather data but also adapts to the environment much fast with very short learning time.
American Psychological Association (APA)
Hsu, Roy Chaoming& Liu, Cheng-Ting& Chen, Wen-Yen& Hsieh, Hung-I.& Wang, Hao-Li. 2015. A Reinforcement Learning-Based Maximum Power Point Tracking Method for Photovoltaic Array. International Journal of Photoenergy،Vol. 2015, no. 2015, pp.1-12.
https://search.emarefa.net/detail/BIM-1066516
Modern Language Association (MLA)
Hsu, Roy Chaoming…[et al.]. A Reinforcement Learning-Based Maximum Power Point Tracking Method for Photovoltaic Array. International Journal of Photoenergy No. 2015 (2015), pp.1-12.
https://search.emarefa.net/detail/BIM-1066516
American Medical Association (AMA)
Hsu, Roy Chaoming& Liu, Cheng-Ting& Chen, Wen-Yen& Hsieh, Hung-I.& Wang, Hao-Li. A Reinforcement Learning-Based Maximum Power Point Tracking Method for Photovoltaic Array. International Journal of Photoenergy. 2015. Vol. 2015, no. 2015, pp.1-12.
https://search.emarefa.net/detail/BIM-1066516
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1066516