MPPT for PV systems using deep reinforcement learning algorithms

AVILA, L.; DE PAULA, M.; CARLUCHO, I.; SANCHEZ REINOSO, C.R.

IEEE LATIN AMERICA TRANSACTIONS

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

Lugar: Quartile Q2; Año: 2020 vol. 17 p. 2020 - 2027

1548-0992

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.