Self-Adaptive Incremental Conductance Algorithm for Swift and Ripple-Free Maximum Power Harvesting From PV Array

This paper deals with a new version of an incremental conductance algorithm for maximum power harvesting (MPH) from the solar photovoltaic array, which has inherent decision taking and self-adaptive ability. The working principle of a self-adaptive incremental conductance (SAInC) algorithm is based on three consecutive operating points on the power–voltage characteristic. These points smartly detect the dynamic condition, as well as under normal condition, search the maximum power peak (MPP) zone. Moreover, using triangular analogy, it decides the optimum operating position for next iteration, which is responsible for quick MPP tracking as well as good dynamic performance. Here, in every new iteration, the step-size is reduced by 90% from the previous step-size, which provides an oscillation-free steady-state performance. The effectiveness of the proposed technique is validated by MATLAB simulation as well as tested on an experimental system. Moreover, performance of an SAInC algorithm is compared with the popular and recent state-of-the-art methods. The satisfactory dynamic and steady-state performances with low complexity as well as low computational burden of the SAInC algorithm show the superiority over state-of-the-art methods.