Maximum Power Point Tracking of Photovoltaic Systems Using Deep Q-networks

Kangshi Wang, Dou Hong, Jieming Ma, Ka Lok Man, Kaizhu Huang, Xiaowei Huang

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

1 Citation (Scopus)


A photovoltaic (PV) generator exhibits nonlinear current-voltage characteristics and its maximum power point varies with incident atmospheric conditions. Therefore, maximum power point tracking (MPPT) control is required to maximize the output power of the PV generator. In this paper, deep Q-network based reinforcement learning strategy is proposed to optimize MPPT process for the photovoltaic system. The proposed system uses a novel control method which introduces agent to interface with the environment and finally gets the strategy of maximum reward accordingly. Simulations and experiments show the feasibility and effectiveness of the proposed system. Compared with the traditional perturb and observe (PO) and incremental conductance (InC) methods, this method prominently saves tracking steps.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE 18th International Conference on Industrial Informatics, INDIN 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages4
ISBN (Electronic)9781728149646
Publication statusPublished - 20 Jul 2020
Event18th IEEE International Conference on Industrial Informatics, INDIN 2020 - Virtual, Warwick, United Kingdom
Duration: 21 Jul 202023 Jul 2020

Publication series

NameIEEE International Conference on Industrial Informatics (INDIN)
ISSN (Print)1935-4576


Conference18th IEEE International Conference on Industrial Informatics, INDIN 2020
Country/TerritoryUnited Kingdom
CityVirtual, Warwick


  • deep Q-network
  • maximum power point tracking
  • photovoltaic systems
  • reinforcement learning

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