Maximum Power Point Tracking of Photovoltaic System Based on Reinforcement Learning

Kuan Yu Chou*, Shu Ting Yang, Chia Shiou Yang, Yon Ping Chen

*Corresponding author for this work

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

Abstract

Maximum power point tracking technique is often used in photovoltaic (PV) system to extract the maximum power at any environment condition. In this paper, a reinforcement learning based variable step size maximum power point tracking (RL MPPT) method is proposed. Q-learning is used as the algorithm of the proposed methods and is implemented by constructing the Q-table (RL-QT MPPT). A Q-network approach (RL-QN MPPT) is also proposed as a more general representation of the RL MPPT method. Implementing of the algorithm doesn't require the information of the actual PV module in advance, and the proposed system is able to track the MPP offline. With smaller ripples and faster tracking speed, the experiment results of the RL-QT MPPT method and the RL-QN MPPT method are presented.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728132792
DOIs
StatePublished - May 2019
Event6th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2019 - Yilan, Taiwan
Duration: 20 May 201922 May 2019

Publication series

Name2019 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2019

Conference

Conference6th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2019
CountryTaiwan
CityYilan
Period20/05/1922/05/19

Keywords

  • Maximum power point tracking (MPPT)
  • photovoltaic (PV) system
  • Q-learning
  • Q-network
  • reinforcement learning

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