MPPT for PV systems using deep reinforcement learning algorithms

Luis Avila, Mariano De Paula, Ignacio Carlucho, Carlos Sanchez Reinoso

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

This work proposes the use of reinforcement learning (RL) techniques with deep-learning models to address the maximum power point tracking (MPPT) control problem of a photovoltaic (PV) array. We implemented the deep deterministic policy gradient (DDPG) method, the inverted gradient (IGDDPG) method and the delayed twins (TD3) method to solve the MPPT control problem. Several simulation experiments were performed in the OpenAI Gym platform aiming to evaluate the performance of the proposed control strategies, under different operating conditions in terms of temperature and solar irradiance. The obtained results show that the use of deep reinforcement learning (DRL) achieves a successful performance for the MPPT control problem with a fast response and a stable behavior. Moreover, the algorithms do not require any previous knowledge about the dynamic behavior of the photovoltaic array.
Original languageEnglish
Pages (from-to)2020-2027
Number of pages8
JournalIEEE Latin America Transactions
Issue number12
Publication statusPublished - 1 Dec 2019

Keywords / Materials (for Non-textual outputs)

  • MPPT
  • Deep RL
  • PV systems
  • Open AI Gym


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