A reinforcement learning approach for MPPT control method of photovoltaic sources. (August 2017)

• Record Type: Journal Article
• Authors: Kofinas, P.; Doltsinis, S.; Dounis, A.I.; Vouros, G.A.
• Is Part Of: Renewable energy. Volume 108(2017)
• Page Start: 461
• Page End: 473
• Publication Date: 2017-08
• Subjects: Photovoltaic systems -- Maximum power point tracking -- On line learning -- Reinforcement learning MPPT control method; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2017.03.008
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: Photovoltaic arrays are the means to convert solar power into electricity, and a significant way to generate renewable and clean energy. To be efficient, a photovoltaic must generate constantly the maximum possible power and under different environmental conditions. Finding the maximum generated power has been a known issue in the industry using methods of classic control theory with very good results. However, those solutions are case-specific resulting to increased set-up effort. This work proposes a universal RLMPPT control method based on a reinforcement learning (RL) method that tracks and adjusts the maximum power point of a photovoltaic source without any prior knowledge. A Markov Decision Process (MDP) model for the Maximum Power Point Tracking (MPPT) photovoltaic process is defined and an RL algorithm is proposed and evaluated on a number of photovoltaic sources. The proposed RLMPPT control method has the advantage of being applicable to different PV sources with minimum set-up time. To evaluate the RLMPPT control method performance, a number of simulations run under different environmental and operating conditions and a comparison with the conventional method of Perturb and Observe (P&O) is performed. Results show quick response and close to optimal behavior without requiring any prior knowledge. Graphical abstract: Highlights: A novel universal Reinforcement Learning Maximum Power Point Tracking is proposed. The RLMPPT controller requires knowledge of only two parameters. The RLMPPT controller operates effectively under variable environmental conditions. The proposed controller can operate under dynamic electrical loads. The RLMPPT controller shows better results in terms of speed & power than the P&O.