Summary In this article, a recently developed bio‐inspired based manta rays foraging optimizer (MRFO) is attempted for reliable and accurate extraction of the model uncertain parameters of proton exchange membrane fuel cells (PEMFCs). The parameter estimation is formulated as a non‐linear optimization problem subject to set of restrictions. The great development and tremendous revolution of computation heuristic‐based algorithms are the impetus of the authors to apply the MRFO to solve this constrained optimization problem resulting in a precise PEMFC model. Three case studies of typical field PEMFC stacks namely Ballard type Mark V, NedStack type PS6, and Horizon type H‐12. Various I to V datasets are demonstrated to appraise the performance of MRFO among other recent optimizers available in the literature. To be objective and for sake of quantifications, the best scores of minimum fitness values are 0.8533, 2.1360, and 0.0966 for the later said PEMFC stacks, correspondingly. At a later stage, production of various characteristics under varying operating conditions such as changeable cell temperature and regulating pressures are established using the generated best values of PEMFCs model. Further calculations of statistical indices are performed to validate the robustness of obtained results by the MRFO. Through comprehensive performance assessments, it can be confirmed that MRFO is very promising tool for the effective extraction of PEMFCs' model and suggested to be applied for solving other engineering problems.

This paper addresses a new attempt of the AEFA to define the uncertain model parameters of TDM of PV units. Two commercial PV modules are investigated with intensive simulations and necessary analysis. The parameters of AFEA based TDM are validated thru the empirical dataset points. Necessary performance assessments are made which signify the AEFA results compared to others. Dynamic simulations of MPP is performed.

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Inductive power transfer (IPT) technology offers a promising solution for electric vehicle (EV) charging. It permits an EV to charge its energy storage system without any physical connections using magnetic coupling between inductive coils. EV inductive charging is an exemplary option due to the related merits such as: automatic operation, safety in harsh climatic conditions, interoperability, and flexibility. There are three visions to realize wireless EV charging: (i) static, in which charging occurs while EV is in long-term parking; (ii) dynamic (in-motion), which happens when EV is moving at high speed; and (iii) quasi-dynamic, which can occur when EV is at transient stops or driving at low speed. This paper introduces an extensive review for IPT systems in dynamic EV charging. It offers the state-of-the-art of transmitter design, including magnetic structure and supply arrangement. It explores and summarizes various types of compensation networks, power converters, and control techniques. In addition, the paper introduces the state-of-the-art of research and development activities that have been conducted for dynamic EV inductive charging systems, including challenges associated with the technology and opportunities to tackle these challenges. This study offers an exclusive reference to researchers and engineers who are interested in learning about the technology and highlights open questions to be addressed.

Frequency control represents a critically significant issue for the enhancement of the dynamic performance of isolated micro grids. The micro grid system studied here was a wind-diesel system. A new and robust optimization technique called the mine blast algorithm (MBA) was designed for tuning the PID (proportional-integral-differential) gains of the blade pitch controller of the wind turbine side and the gains of the superconducting magnetic energy storage (SMES) controller. SMES was implemented to release and absorb active power quickly in order to achieve a balance between generation and load power, and thereby control system frequency. The minimization of frequency and output wind power deviations were considered as objective functions for the PID controller of the wind turbine, and the diesel frequency and power deviations were used as objective functions for optimizing the SMES controller gains. Different case studies were considered by applying disturbances in input wind, load power, and wind gust, and sensitivity analysis was conducted by applying harsh conditions with varying fluid coupling parameter of the wind-diesel hybrid system. The proposed MBA-SMES was compared with MBA (tuned PID pitch controller) and classical PI control systems in the Matlab environment. Simulation results showed that the MBA-SMES scheme damped the oscillations in the system output responses and improved the system performance by reducing the overshoot by 75% and 36% from classical and MBA-based systems, respectively, reduced the settling time by 45% compared to other systems, and set the final steady-state error of the frequency deviation to zero compared to other systems. The proposed scheme was extremely robust to disturbances and parameter variations.

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