Extracting the maximum power from as solar PV system is a critical task when high changes in light intensity or Partial Shading Condition (PSC) are experienced. The latter case is more difficult as it creates multiple maxima points on P–V curve. In this way, it is obligatory to thoroughly pick a precise Maximum Power Point Tracking (MPPT) method which recognizes adequately the Global Maximum Power Point (GMPP) and tracks it under partial shading. This paper first describes the modeling of PV module and PV characteristics under uniform irradiance as well as effect of PSC on PV characteristics. In the latter sections, a review of conventional and intelligent MPPT methods is done. To tackle the problem of MPPT under PSC, two metaheurisric algorithms namely Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are described briefly. A new optimization method called Cuckoo Search (CS) is implemented in MATLAB SIMULINK tool and tested under three different PSC patterns. A comparative analysis of different MPPT strategies is made after analyzing the results.

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One of the key reasons contributing to a reduction in the performance of a solar photovoltaic (SPV) system is the presence of partial shade on the solar panels. It is necessary to use maximum power point tracking (MPPT) in SPV systems to get around the non-linear behaviour of photovoltaic panels. This research analyses the reduction in power output of SPV systems caused by partial shading. In the latter part of the paper, the improved dynamic behaviour-based ant colony optimization MPPT approach under the effect of module-level partial shading is implemented in MATLAB®/Simulink®. Various partial shading levels were applied to SPV modules to evaluate the system performance. The proposed system follows the maximum power point with 99.9% accuracy and a response time of 2 ms. The addition of a weight coefficient accelerates the convergence of the algorithm.

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