Summary This paper introduces a novel dynamic semiempirical model for the proton exchange membrane fuel cell (PEMFC). The proposed model not only considers the stack output voltage but also provides valid waveforms of component voltages, such as the no‐load, activation, ohmic, and concentration voltages of the PEMFC stack system. Experiments under no‐load, ramping load, and dynamic load conditions are performed to obtain various voltage components. According to experimental results, model parameters are optimised using the lightning search algorithm by providing valid theoretical ranges of parameters to the lightning search algorithm code. In addition, the correlation between the vapour and water pressures of the PEMFC is obtained to model the component voltages. Finally, all component voltages and the stack output voltage are validated by using the experimental/theoretical waveforms mentioned in previous research. The proposed model is also compared with a recently developed semiempirical model of PEMFC through particle swarm optimisation. The proposed dynamic model may be used in future in‐depth studies on PEMFC behaviour and in dynamic applications for health monitoring and fault diagnosis.

For the applications related to the medium/high-power/voltage, Multilevel inverters are widely accepted and commercially used. The performance of MLI compare to the conventional two-level inverters is significantly superior due to the insignificant amount of harmonic distortion, lower filter size, requirement of low voltage rating devices, lower electromagnetic interference, etc. However, there are a few disadvantages such as an increased number of components, a complex modulation and control strategy, and issues related to the voltage balancing of capacitors. The present paper proposes a new topology with a lower voltage rating component to improve the performance by remedying the mentioned disadvantages. Compared with existing inverter topologies, (especially higher levels), this topology requires fewer components, fewer dc sources, and gate drives. Further, voltage stress is also low. The overall costs and complexity are therefore greatly reduced, especially for higher voltage levels. The proposed topology has been compared with other similar topologies and the comparison proves the better structure of the proposed topology. To show the working of the proposed topology, a prototype has been developed and tested for a different operating condition with two different modulation techniques. All the results show the adequate performance of the inverter topology at the different real-time environment. INDEX TERMS Multilevel inverter, PWM technique, higher level, reduced switch count.

a b s t r a c tThis paper investigates the application of model free control (MFC) strategy to optimally control the oscillation of single body heaving waves energy converter (WEC). The aim of the proposed controller is to maximize the electrical energy output of the device. The MFC is an off-line fixed structure control approach that combines between simplicity and insensitivity to system uncertainties and/or operational conditions. The proposed controller is based on a classical linear compensator, which is designed and tuned using only the well-known dynamics of the WEC. Therefore, any extra unknown or partially known dynamics are taken care of using the ultra-local model (ULM). Simulation results show that the MFC performance is superior to that of the base linear compensator, in terms of reference tracking capability and robustness towards uncertainties. Also other well-established control strategies are used to further validate the proposed controller.

For the large-scale promotion of electric vehicles (EV), reliable fast-charging stations (FCS) demand high priority among EV users. However, unplanned locations of charging stations (CSs) and station capacity determination have adverse effects on the operation and the performance of powerdistribution network. In this study, we developed an optimal FCS-planning model considering the aspects of EV users' convenience, station economic benefits, the impact on distribution systems and the effect on environment. A queuing-theory-based CS sizing algorithm that benefits EV users as well as improves CS capacity utilization was proposed. The proposed planning model was verified through a case study using real road network data by employing multi-objective binary and non-dominated sorting genetic algorithm. In addition, to evaluate the efficiency of the proposed sizing algorithm, sensitivity analyses for different EV penetration levels and station utilization were conducted. The simulation results show that the proposed CSallocation model is beneficial in terms of achieving the satisfaction of EV users, cost savings, better station utilization, and less impact on power grids and the environment. Finally, to validate the effectiveness of the proposed planning model, a comparative study with one of the previous work on CS planning is also performed. The results demonstrate that the proposed charging station sizing method is highly efficient in optimizing EV users' satisfaction and for better station utilization.

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