Enhancement of maximum power point tracking of solar photovoltaic systems using modified optimization techniques

Abstract

Among all other sources of renewable energy, the solar Photovoltaic (PV) energy system is mostly preferred as it does not cause pollution, and there is no fuel cost involved and ease of maintenance. The Current-Voltage (I-V) and Power-Voltage (P-V) characteristics of the PV array change according to the change in Sun s irradiation, temperature, and partial shading conditions. The partial shading decreases the available amount of sunlight on the PV panel in a PV array, which causes a number of Local Maximum Power Point (LMPP) and one Global Maximum Power Point (GMPP) in PV array s P-V characteristics. The GMPP needs to be tracked accurately under Partially Shaded Situations (PSS) and changing irradiation conditions. newlineThe traditional methods of Maximum Power Point Tracking (MPPT) include Perturb and Observe (PandO), Hill climbing (HC) and Incremental conductance (INC), and they fail under changing climatic conditions and PSS owing to the low response speed and inability to distinguish between LMPP and GMPP. Various bio-inspired optimization methods developed for MPPT had the weakness of getting stuck at local maximum and/or had greater time for convergence that reduced the solar PV system s efficiency. Therefore, it is a challenge for Global Maximum Power Point Tracking (GMPPT) methods to achieve the global maximum, avoiding getting stuck at local maximums, harvesting maximum possible PV array power during PSS and is achieved by fixing the proper value of the boost DC-DC converter s duty ratio with little time for convergence newline