A direct adaptive neural control for maximum power point tracking of photovoltaic system. (May 2015)

• Record Type: Journal Article
• Authors: Dounis, Anastasios I.; Kofinas, P.; Papadakis, G.; Alafodimos, C.
• Is Part Of: Solar energy. Volume 115(2015)
• Page Start: 145
• Page End: 165
• Publication Date: 2015-05
• Subjects: Direct adaptive neural control -- Maximum power point tracking -- Photovoltaic system -- Online learning algorithm; Solar energy -- Periodicals; Solar engines -- Periodicals; 621.47
• DOI: 10.1016/j.solener.2015.02.004
• Languages: English
• ISSNs: 0038-092X

Abstract

Graphical abstract: Highlights: The proposed DANC improves the performance of solar energy conversion efficiency. Online simple and fast learning algorithm is utilized. The new method provides possibilities for straightforward digital implementation. Measurements of solar radiation and cell temperature are not needed. We conduct tests on different characteristics photovoltaic sources. Abstract: This paper represents a novel direct adaptive neural control (DANC) method for maximum power point tracking (MPPT) of photovoltaic (PV) systems. A DC/DC buck converter to regulate the output power of the photovoltaic system is considered. The direct adaptive neural control scheme operates on MPP and improves the performance of solar energy conversion efficiency. The online adaptation procedure is based on learning law of the δ rule and only the system output error is required. The prime contributions of this study are a simple and effective solution for MPPT, the fast learning and the straightforward digital implementation. The performance of direct adaptive neural controller on photovoltaic sources with different characteristics is assessed. The simulation results confirm the feasibility and effectiveness of the DANC. The proposed MPPT control system is compared to the conventional perturbation and observation (P&O) method.