A review of Artificial Intelligence Algorithms for Extracting Maximum Power from the PV system under Partially Shaded Condition

Abstract

The increase in energy demand and soaring prices of fossil fuels together with concerns about environmental issues have generated enormous interest in the utilization of renewable energy sources. In recent years, the solar energy is drawing more attention owing to its advantages such as zero green houses emission & pollution, low maintenance cost, absence of moving parts and freedom from environmental pollution. Due to the high initial cost of Photovoltaic (PV) power generation systems and their low energy conversion efficiency, a PV system is generally operated to extract maximum power from the PV source. The objective of maximum power point tracking (MPPT) is to extract maximum power generated by the PV systems under varying conditions of temperature and solar insolation. A major challenge in PV systems is to analyze the nonlinear current-voltage (I-V) characteristics so as to obtain a unique maximum power point on its power-voltage (P-V) characteristic curve. The process of MPPT is complicated due to the fact that the PV curves vary largely with solar insolation and temperature. Many researchers have proposed and implemented a variety of methods, both conventional and non-conventional, to solve MPPT problem. However, most of conventional tracking methods fail to work properly under partial shaded conditions. Partial shaded SPV modules produce several local maximum power points, making the tracking of Global Maximum Power Point (GMPP) difficult. Hence many researchers have