The estimation of the photovoltaic (PV) cell/module model parameters could lead to accomplish a diagnostic tool and to estimate several factors which affect the health state of a PV generator. In this context, it is crucial to look for an extraction technique which performs this evaluation precisely and quickly. Due to the nonlinear and implicit nature of the PV cell/module, significant computational effort is required to obtain all the parameters; therefore, in this context different metaheuristic algorithms are proposed. For the identification of the meaningful parameters of PV cell/module models, illuminated current-voltage (I–V) curves, under real conditions of PV cells temperature and incident irradiance, are employed. Considering several PV cell/module models, the goodness of the proposed algorithms is analyzed by means of statistical errors, convergence speed, and unknown parameters precision. Then these algorithms are tested and validated using a daily set of measured I–V curves, specifically for each one both the whole set of measured data and a reduced set around the maximum power point are used.

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In order to extract efficient power generation, a wind turbine (WT) system requires an accurate maximum power point tracking (MPPT) technique. Therefore, a novel robust variable-step perturb-and-observe (RVS-P&O) algorithm was developed for the machine-side converter (MSC). The control strategy was applied on a WT based permanent-magnet synchronous generator (PMSG) to overcome the downsides of the currently published P&O MPPT methods. Particularly, two main points were involved. Firstly, a systematic step-size selection on the basis of power and speed measurement normalization was proposed; secondly, to obtain acceptable robustness for high and long wind-speed variations, a new correction to calculate the power variation was carried out. The grid-side converter (GSC) was controlled using a second-order sliding mode controller (SOSMC) with an adaptive-gain super-twisting algorithm (STA) to realize the high-quality seamless setting of power injected into the grid, a satisfactory power factor correction, a high harmonic performance of the AC source, and removal of the chatter effect compared to the traditional first-order sliding mode controller (FOSMC). Simulation results showed the superiority of the suggested RVS-P&O over the competing based P&O techniques. The RVS-P&O offered the WT an efficiency of 99.35%, which was an increase of 3.82% over the variable-step P&O algorithm. Indeed, the settling time was remarkably enhanced; it was 0.00794 s, which was better than for LS-P&O (0.0841 s), SS-P&O (0.1617 s), and VS-P&O (0.2224 s). Therefore, in terms of energy efficiency, as well as transient and steady-state response performances under various operating conditions, the RVS-P&O algorithm could be an accurate candidate for MPP online operation tracking.

Summary To get efficient current/voltage (I/V) polarization curves, the parameter identification of the proton exchange membrane (PEM) fuel cells (FCs) model based on experimental datasets and meta‐heuristic algorithms remains an active research field during the past few years. Meanwhile, estimating those parameters accurately is still a challenge. In this work, a new hybridized approach is presented to identify the PEMFC model parameters denominated the artificial bee colony differential evolution shuffled complex (ABCDESC) optimizer. In the proposed algorithm, the double execution of the probabilities evaluation and the selection strategy of ABC optimizer enables to have better exploitation phase without getting stuck into the local optimum. The sum of squared errors (SSE)‐based objective function is used to perform the optimization as it is the well used in the literature. In order to assess this new hybridized approach, a comparative study with the latest published techniques is carried out using six typical test benchmarking PEMFCs modules extensively utilized in the literature. In this context, the reached SSE values and the standard deviations among other challenging methodologies are very competitive with the best convergence speed and reduced number of function evaluations. The ABCDESC algorithm reaches a standard deviation/CPU run time (STD/CRT) of 8.4690e−16/0.298 second, 1.3275e−16/0.290 second, 1.9721e−14/0.356 second, 4.3889e−15/0.343 second, 1.4388e−12/0.49 second, and 1.6398e−17/0.326 second for 250 W, BCS 500 W, NedStack PS6, Ballard Mark V, Horizon H‐12, and Modular SR‐12 stacks; respectively. The comparison results indicate the successful use of the proposed ABCDESC optimizer to characterize the PEMFC model accurately.