A novel PSO strategy for improving dynamic change partial shading photovoltaic maximum power point tracker

Photovoltaic (PV) energy systems are very important electric generation sources in modern power systems. The generated power from the PV array is a function in its terminal voltage. Tracking the maximum power needs a DC/DC converter to control the PV array terminal voltage. The boost converter is used in this paper for this purpose. Due to the multiple peaks in the power versus voltage (P-V) characteristics of PV array a smart optimization technique is required to work as a maximum power point tracker (MPPT). The particle swarm optimization (PSO) is a superior technique to track the global peak (GP) and avoid getting trapped in one of the local peaks (LPs). Despite the superiority of PSO, it suffers from some shortcomings in the application of MPPT of PV systems such as its sluggishness convergence, its inability to catch the new GP in case of acute change in shading pattern, and the possibility of getting trapped in one of the LPs. All these shortcomings are solved in this paper using a new adaptive PSO (NA-PSO) strategy. This new strategy solved these problems by starting the duty ratio at an equal distance between each other and force the particles with lower generated power to work around the one with the highest generated power. This newly proposed technique reduced the convergence time by 50% and reduced the failure rate to zero. Also, the generated energy is increased by 10.4% compared to the conventional PSO. The results collected from the NA-PSO strategy show its superiority in reducing the convergence time and failure rate and increasing the generated power, and the system efficiency, especially in the dynamic variation of the shading pattern compared to the conventional PSO.