Adaptive control of DC motor using bacterial foraging algorithm
Applied Soft Computing
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Using the numerical analysis and optimization method to extract solar cells parameters, one recurrent issue refers to the difficulty in initializing the parameters. These methods using solar cells exponential model are sensible to small changes in the data measured. An approach is presented for improving the extracting accuracy of the parameters based on a hybrid genetic algorithm (LS-GA) which combines an adaptive genetic algorithm with a least squares gradient search. The method uses a gradient operator to reduce the influence of the measurement error of experimental data, and searches for the optimum parameters in an approximate parameter scope. The proposed approach of search range estimation is straightforward and easy to use. The experimental results demonstrate that the method needs no prior knowledge of the parameters of interest, and has no limitation condition on the parameter search ranges. The statistical analysis data of LS-GA are better than that of other published methods.