Sim-to-Real Deep Reinforcement Learning for Maximum Power Point Tracking of Photovoltaic Systems

Kangshi Wang*, Jieming Ma, Ka Lok Man, Kaizhu Huang, Xiaowei Huang

*Corresponding author for this work

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

2 Citations (Scopus)

Abstract

The maximum power points (MPPs) of photovoltaic (PV) panels vary with atmospheric conditions (solar irradiance, ambient temperature, shading conditions, etc.). Simulation platforms are widely used by engineers and scientists to create models, analyze data, and develop algorithms for the maximum power point tracking (MPPT) of PV systems. However, the behavior, which the algorithm develops in simulations, is usually specific to the characteristics of the models. Strategies that succeed in simulations may not be victoriously transferred to the real world because the modeling errors and sensor noise. In this paper, we create a dynamic model of the PV system only based on the data-sheet from manufacturers. A sim-to-real transfer strategy is proposed for the maximum power point tracking of PV systems. By dynamically randomizing the environments for the agent during the training, the proposed policy can adapt to very different atmospheric conditions. Simulations show that the proposed strategy can maintain a similar level of performance when deployed on the real PV panels.

Original languageEnglish
Title of host publication21st IEEE International Conference on Environment and Electrical Engineering and 2021 5th IEEE Industrial and Commercial Power System Europe, EEEIC / I and CPS Europe 2021 - Proceedings
EditorsZbigniew M. Leonowicz
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665436120
DOIs
Publication statusPublished - 2021
Event21st IEEE International Conference on Environment and Electrical Engineering and 2021 5th IEEE Industrial and Commercial Power System Europe, EEEIC / I and CPS Europe 2021 - Bari, Italy
Duration: 7 Sept 202110 Sept 2021

Publication series

Name21st IEEE International Conference on Environment and Electrical Engineering and 2021 5th IEEE Industrial and Commercial Power System Europe, EEEIC / I and CPS Europe 2021 - Proceedings

Conference

Conference21st IEEE International Conference on Environment and Electrical Engineering and 2021 5th IEEE Industrial and Commercial Power System Europe, EEEIC / I and CPS Europe 2021
Country/TerritoryItaly
CityBari
Period7/09/2110/09/21

Keywords

  • deep reinforcement learning
  • dynamics randomization
  • maximum power point tracking
  • photovoltaic systems

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