Development of rapid and reliable cuckoo search algorithm for global maximum power point tracking of solar PV systems in partial shading condition

The solar photovoltaic output power fluctuates according to solar irradiation, temperature, and load impedance variations. Due to the operating point fluctuations, extracting maximum power from the PV generator, already having a low power conversion ratio, becomes very complicated. To reach a maximum power operating point, a maximum power point tracking technique (MPPT) should be used. Under partial shading condition, the nonlinear PV output power curve contains multiple maximum power points with only one global maximum power point (GMPP). Consequently, identifying this global maximum power point is a difficult task and one of the biggest challenges of partially shaded PV systems. The conventional MPPT techniques can easily be trapped in a local maximum instead of detecting the global one. The artificial neural network techniques used to track the GMPP have a major drawback of using huge amount of data covering all operating points of PV system, including different uniform and non-uniform irradiance cases, different temperatures and load impedances. The biological intelligence techniques used to track GMPP, such as grey wolf algorithm and cuckoo search algorithm (CSA), have two main drawbacks; to be trapped in a local MPP if they have not been well tuned and the precision-transient tracking time complex paradox. To deal with these drawbacks, a Distributive Cuckoo Search Algorithm (DCSA) is developed, in this paper, as GMPP tracking technique. Simulation results of the system for different partial shading patterns demonstrated the high precision and rapidity, besides the good reliability of the proposed DCSAGMPPT technique, compared to the conventional CSA-GMPPT.