A uniform implementation scheme for evolutionary optimization algorithms and the experimental implementation of an ACO based MPPT for PV systems under partial shading

Partial shading is one of the important issues in maximum power point (MPP) tracking (MPPT) for photovoltaic (PV) systems. Multiple peaks on the power-voltage (P-V) curve under partial shading conditions can result in a conventional MPPT technique failing to track the global MPP, thus causing large power losses. Whereas, evolutionary optimization algorithms exhibit many advantages when applying them to MPPT, such as, the ability to track the global MPP, no requirement for irradiance or temperature sensors, system independence without knowledge of the PV system in advance, reduced current/voltage sensors compared to conventional methods when applied to PV systems with a distributed MPPT structure. This paper presents a uniform scheme for implementing evolutionary algorithms into the MPPT under various PV array structures. The effectiveness of the proposed method is verified both by simulations and experimental setup. The implementation of the ant colony optimization (ACO) based MPPT is conducted using this uniform scheme. In addition, a strategy to accelerate the convergence speed, which is important in systems with partial shading caused by rapid irradiance change, is also discussed.