An Adaptive Maximum Power Point Tracker for Photovoltaic Arrays Using an Improved Soft Computing Algorithm

This paper presents an improved version of the firefly algorithm (FA) by which a maximum power point (MPP) tracker was developed to track down the global maximum power point (GMPP) of a partially shaded photovoltaic module array (PVMA). As the first step, our team developed a high-voltage step-up converter where a coupled inductor was used to store the energy so that the duty cycle can be reduced so as to raise the voltage gain. The single-peaked P-V output characteristic curve of a PV array turns out to contain multiple peaks when the array is partially shaded. As a consequence, conventional MPP trackers occasionally track down a local maximum power point (LMPP), instead of the desired GMPP, and the output power of the array falls accordingly. Therefore, an improved version of the FA is proposed as a way to ensure that the GMPP can be tracked down in a more efficient way. Using the Matlab software, the MPP tracking performance of the proposed tracker was finally simulated in five scenarios. As it turned out, the proposed converter provided a high voltage gain at a relatively low duty cycle, and the improved version of the FA outperformed the original in terms of tracking time.