This study deals with a new version of hill-climbing (HC) algorithm for maximum power peak estimation of the solar photovoltaic (PV) panel, which has self-estimation (SEn) and decision-taking ability. Moreover, it is ...
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This study deals with a new version of hill-climbing (HC) algorithm for maximum power peak estimation of the solar photovoltaic (PV) panel, which has self-estimation (SEn) and decision-taking ability. Moreover, it is based on a single sensor, which is applicable for the PV array-fed battery charging. The working principle of Self-Estimated HC (sehc) algorithm is based on three consecutive operating points on the power-current characteristic. By using perpendicular line analogy (PLA), these points decide direction, and an optimised operating position for next iteration, which is responsible for quick maximum power point tracking as well as improved dynamic performance. Moreover, in every new iteration, the step size is reduced by 90% from the previous step size, which provides an oscillation-free steady-state performance. Owing to a single sensor, the computational burden, as well as calculation complexity of the sehc algorithm is less, so it can be simply implemented on a cheaper microcontroller. The effectiveness of the proposed technique is validated by MATLAB simulation as well as tested on the experimental prototype. Moreover, the performance of sehc algorithm is compared with recent state-of-the-art techniques. The highly suitable and satisfactory results of sehc algorithm show the superiority over the state-of-the-art methods.
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