Experimental Evaluation of Moth-Flame Optimization Based GMPPT Algorithm for Photovoltaic Systems Subject to Various Operating Conditions
Nadir Boutasseta, Mohammed Salah Bouakkaz, Nadir Fergani, Issam Attoui, Ahmed Bouraiou, Ammar Neçaïbia
Abstract
Photovoltaic solar energy conversion systems efficiency depends mainly on environmental conditions and operating modes. The challenge is how to ensure maximum energy transfer between the photovoltaic (PV) array and the load in different operating conditions. When photovoltaic arrays are subject to partial shading, the power–voltage characteristic curve is characterized by multiple local maximum power points. In this paper, a Global Maximum Power Point Tracking (GMPPT) method based on the bio-inspired Moth-Flame Optimization (MFO) algorithm is implemented to search for the GMPP and hence increase energy conversion efficiency of PV arrays subject to uniform and partial shading operating conditions with better transient performance. A detailed implementation description of the proposed approach in a Digital Signal Processor (DSP) is provided with additional implementation of conventional PSO and P&O MPPT algorithms. An innovative characterization technique is used to scan the I–V characteristic curve at the considered operating condition to assess the actual operating points that will be consequently used to correctly evaluate the performance of the implemented algorithms. The simulation and experimental implementation of the MFO based GMPPT show higher effectiveness and accuracy with faster convergence when compared to conventional approaches both in uniform and partial shading condition.