Accurate emulation of steady-state and dynamic performances of PEM fuel cells using simplified models.

The current effort addresses a novel attempt to extract the seven ungiven parameters of PEMFCs stack. The sum of squared deviations (SSDs) among the measured and the relevant model-based calculated datasets is adopted to define the cost function. A Kepler Optimization Algorithm (KOA) is employed to decide the best values of these parameters within viable ranges. Initially, the KOA-based methodology is applied to assess the steady-state performance for four practical study cases under several operating conditions. The results of the KOA are appraised against four newly challenging algorithms and the other recently reported optimizers in the literature under fair comparisons, to prove its superiority. Particularly, the minimum values of the SSDs for Ballard Mark, BCS 0.5 kW, NedStack PS6, and Temasek 1 kW PEMFCs stacks are 0.810578 V2, 0.0116952 V2, 2.10847 V2, and 0.590467 V2, respectively. Furthermore, the performance measures are evaluated on various metrics. Lastly, a simplified trial to upgrade Amphlett's model to include the PEMFCs' electrical dynamic response is introduced. The KOA appears to be viable and may be extended in real-time conditions according to the presented scenarios (steady-state and transient conditions).

Optimal low voltage ride through of wind turbine doubly fed induction generator based on bonobo optimization algorithm.

The large-scale wind energy conversion system (WECS) based on a doubly fed induction generator (DFIG) has gained popularity in recent years because of its various economic and technical merits. The fast integration of WECS with existing power grids has caused negative influence on the stability and reliability of power systems. Grid voltage sags produce a high overcurrent in the DFIG rotor circuit. Such these challenges emphasise the necessity of the low voltage ride through (LVRT) capability of a DFIG for ensuring power grid stability during voltage dips. To deal with these issues simultaneously, this paper aims to obtain the optimal values of injected rotor phase voltage for DFIG and wind turbine pitch angles for all operating wind speeds in order to achieve LVRT capability. Bonobo optimizer (BO) is a new optimization algorithm that is applied to crop the optimum values of injected rotor phase voltage for DFIG and wind turbine pitch angles. These optimal values provide the maximum possible DFIG mechanical power to guarantee rotor and stator currents do not exceed the rated values and also deliver the maximum reactive power for supporting grid voltage during faults. The ideal power curve of a 2.4 MW wind turbine has been estimated to get the allowable maximum wind power for all wind speeds. To validate the results accuracy, the BO results are compared to two other optimization algorithms: particle swarm optimizer and driving training optimizer. Adaptive neuro fuzzy inference system is employed as an adaptive controller for the prediction of the values of rotor voltage and wind turbine pitch angle for any stator voltage dip and any wind speed.