a b s t r a c tIn this paper, a new approach based on adaptive Differential Evolution Technique (DET) is used to extract the parameters of solar cell models accurately. The adaption is achieved through crossover and mutation factor. It is indicated that the optimization with an objective function can minimize the difference between the estimated and measured values. In order to verify the performance of the proposed system, three different solar cell models: single diode model, double diode model, and photovoltaic module are used to extract the parameters. The analysis is performed by using the voltage and current data sets. The result shows that the proposed DET outperforms these other methods: chaos particle swarm optimization (CPSO), genetic algorithm (GA), harmony search algorithm (HSA), and artificial bee swarm optimization (ABSO). Furthermore, the DET technique is practically validated by two different solar cell types such as monocrystalline and multi-crystalline and modules. The performance of solar cell models has been verified and the outcome shows that it is an optimal method which suits the parameter extraction of solar cells and modules.

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Summary With the latest development in the area of power electronics, the photovoltaic (PV) cell can be made to operate at the optimum peak point with increased system efficiency. To maximize the power on photovoltaic cells under various conditions, optimum power point tracking (OPPT) methods such as conventional and soft computing methods are used. But it is not providing accurate and efficient output due to its randomness, fixed step size, and poor convergence. In this paper, the adaptive differential evolution (ADE) algorithm is introduced in the solar module to obtain the maximum power, and it has the ability to reach the optimum peak with the shorter time period. An Apriori method is used in the proposed ADE algorithm, wherein mutation factor and crossover are used as control parameters to increase the speed. The ruggedness of the ADE algorithm is tested under different shading condition such as no shading, 30% shading, and 50% shading condition. Extensive simulation has been carried out using PV solar module, and the analysis has been tabulated and compared with the existing results. Various statistical metrics such as root mean square error, the relative error, tracking efficiency, standard deviation, and efficiency are used to evaluate the effectiveness and validate the feasibility of the proposed method. Further, hardware has been implemented and tested with this algorithm.