info:eu-repo/semantics/article
MPPT for PV systems using deep reinforcement learning algorithms
Fecha
2019-12Registro en:
Avila, Luis Omar; de Paula, Mariano; Carlucho, Ignacio; Sánchez Reinoso, Carlos Roberto; MPPT for PV systems using deep reinforcement learning algorithms; Institute of Electrical and Electronics Engineers; IEEE Latin America Transactions; 17; 12; 12-2019; 2020-2027
1548-0992
CONICET Digital
CONICET
Autor
Avila, Luis Omar
de Paula, Mariano
Carlucho, Ignacio
Sánchez Reinoso, Carlos Roberto
Resumen
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.