Solar photovoltaic parameter estimation using an improved equilibrium optimizer

Model parameter estimation of the PEMFCs using improved Barnacles Mating Optimization algorithm

Grey wolf optimizer is a novel swarm intelligent algorithm. It has received lots of interest from the heuristic algorithm community for its superior optimization capacity and few parameters. However, it is also easy to trap into the local optimum when solving complex and multimodal functions. In order to boost the performance of GWO, an opposition-based learning grey wolf optimizer (OGWO) is proposed. The opposition-based learning approach is incorporated into GWO with a jumping rate, which can help the algorithm jump out of the local optimum and not increase the computational complexity. What is more, the coefficient a → is dynamically adjusted by the nonlinear function to balance exploration and exploitation. The serial experiments have revealed that the proposed algorithm is superior to the conventional heuristic algorithms, it is also better than GWO and its variants.

Opposition-based learning grey wolf optimizer for global optimization

Proton Exchange Membrane Fuel Cells (PEMFC) is considered a propitious solution for an environmentally friendly energy source. A precise model of PEMFC for accurate identification of its polarization curve and an in-depth understanding of all its operating characteristics attracted the interest of many researchers. In this paper, recent meta-heuristic optimization methods have been successfully applied to evaluate the unknown parameters of PEMFC models, particularly Marine Predators Algorithm (MPA) and Political Optimizer (PO) techniques. The proposed optimization algorithms have been tested on three different commercial PEMFC stacks, namely BCS 500-W, SR-12PEM 500 W, and 250 W stack under various operating conditions. The sum of square errors (SSE) between the results obtained by the application of the estimated parameters and the experimentally measured results of the fuel cell stacks was considered as the objective function of the optimization problem. In order to validate the effectiveness of the proposed methods, the results are compared with those obtained in the literature. Moreover, the I/V curves obtained by the application of MPA and PO showed a clear matching with datasheet curves for all the studied cases. Statistical analysis has been performed to evaluate the robustness of the MPA and PO techniques. Finally, the PEMFC model based on the MPA technique surpasses all compared algorithms in terms of the solution accuracy and the convergence speed. The obtained results confirmed the superiority and reliability of the applied approach of the MPA algorithm. The results prove that the MPA algorithm has a superior performance based on its reliability.

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Tekle Demsas

Papers

Parameter estimation of PEM fuel cells employing the hybrid grey wolf optimization method