Abstract:
To increase the efficiency of photovoltaic (PV) generators, tracking of the maximum power point (MPP) of each PV module is necessary. The output power of photovoltaic module depends on solar irradiance and ambient temperature. Also, the phenomenon of partial shading has a direct impact on the output power of photovoltaic installations and leads to a malfunction of the MPP tracking (MPPT). Under partial shading conditions, the P-V characteristic of the photovoltaic panel presents several maxima. In this case, the conventional MPPT methods do not give good results, hence the idea of studying other approaches for optimizing and tracking the MPP. In this context, this thesis presents a contribution to the study and implementation into reconfigurable circuit of MPPT controllers based on three metaheuristics of optimization: bat algorithm (BA), particle swarm optimization (PSO) and differential evolution (DE) algorithm. These three algorithms are used to design intelligent MPPT controllers that can efficiently handle the multimodal characteristic curves of photovoltaic systems under partially shaded conditions. Furthermore, these controllers are implemented into a reconfigurable FPGA circuit. The performances of the proposed methods are verified by simulation and by experiments and the results confirm the high accuracy of these algorithms for an optimal management of the energy available at the output of the photovoltaic panels subjected to partial shaded conditions.
