A comprehensive review of maximum power point tracking algorithms for photovoltaic systems

In recent decades, Photovoltaic (PV) energy has made significant progress towards meeting the continuously increasing world energy demand. Besides that, the issue of conventional fossil fuels depletion as well as environmental pollution both contribute to the growth of PV technology. However, the deployment and implementation of photovoltaic systems remain as a great challenge, since the PV material cost is still very high. The low PV module conversion efficiency is another factor that restricts the wide usage of PV systems, therefore a power converter embedded with the capability of maximum power point tracking (MPPT) integrated with PV systems is essential to further the technology. This paper provides a comprehensive review of the available MPPT techniques, both the uniform insolation and partial shaded conditions. In order to appreciate the knowledge of MPPT concepts, several types of PV cell equivalent models are explained too. Conventional MPPT techniques have proven the ability to track the maximum power point (MPP) under uniform solar irradiance. However, under rapidly changing environments and partially shaded conditions, conventional techniques have failed to track the true MPP. For this reason, stochastic based methods and artificial intelligence have been developed with the ability to seek the true MPP under multiple peaks with good convergence speed. This paper analyses and compares both conventional and stochastic MPPT techniques based on the true MPP tracking capability, design complexity, cost consideration, sensitivity to environmental change and convergence speed. Comparatively, the stochastic algorithms and artificial intelligence show excellent tracking performance. The research on MPPT techniques is ongoing towards achieving a better performance in terms of the ease of implementation, low system cost and better tracking efficiency.