Deep Q-Network Based Global Maximum Power Point Tracking for Partially Shaded PV System

Kuan Yu Chou, Chia Shiou Yang, Yon Ping Chen

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

Abstract

Under the sun insolation in the daytime, the partially shaded effect would easily happen in a photovoltaic (PV) array due to clouds, trees, buildings, etc. To deal with the partially shaded effect, this paper proposes solar global maximum power point tracking (GMPPT) method based on Deep Q-Network. Maximum power point tracking (MPPT) is often used to achieve the maximum power in the PV system. The Perturbation and Observation (PO) method is one of the most popular MPPT techniques in practice. However, due to the use of fixed step size, the PO method may cause undesired oscillation around the maximum power point (MPP). With the partially shaded effect, the characteristic P-V curve of a PV array may possess multi-peaks, which often results in tracking of a local maximum, not the expected global maximum. Demonstrated by experiment results, the proposed Deep Q-Network based solar GMPPT method indeed can track the global MPP faster and more precisely without oscillation.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728173993
DOIs
StatePublished - 28 Sep 2020
Event7th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020 - Taoyuan, Taiwan
Duration: 28 Sep 202030 Sep 2020

Publication series

Name2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020

Conference

Conference7th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020
CountryTaiwan
CityTaoyuan
Period28/09/2030/09/20

Keywords

  • deep Q-network
  • Global maximum power point tracking (GMPPT)
  • partially shaded photovoltaic (PV) system
  • reinforcement learning

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