This paper presents an adaptive maximum power point tracking (MPPT) algorithm. The aim is to dynamically adjust the step length for updating duty ratio (or operating voltage) so as to make full utilization of the output power of photovoltaic (PV) systems, even under the rapidly changing atmospheric conditions. To this end, the average slope in terms of voltage and power is exploited for reducing the harmful effect of noise and error (incurred in measurement or quantization) on the slope. Also, a statistical decision-making scheme is employed for reliably deciding the time instant at which atmospheric conditions actually change. Empirical study has adduced grounds for its dominance over existing references.

First order line seach optimization techniques gained essential practical importance over second order optimization techniques due to their computational simplicity and low memory requirements. The computational excess of second order methods becomes unbearable for large optimization tasks. The only applicable optimization techniques in such cases are variations of first order approaches. This article presents one such variation of first order line search optimization technique. The presented algorithm has substantially simplified a line search subproblem into a single step calculation of the appropriate value of step length. This remarkably simplifies the implementation and computational complexity of the line search subproblem and yet does not harm the stability of the method. The algorithm is theoretically proven convergent, with superlinear convergence rates, and exactly classified within the formerly proposed classification framework for first order optimization. Performance of the proposed algorithm is practically evaluated on five data sets and compared to the relevant standard first order optimization technique. The results indicate superior performance of the presented algorithm over the standard first order method.