In this paper, we use a nonlinear model of a grid-connected Archimedes wave swing (AWS) to explore the actual generation potential of this wave energy conversion system. Our model has never been used before for a grid-connected AWS. The control system employs six proportional-integral (PI) controllers to maximize the energy harvest from waves, to minimize the generator power losses, and to maintain both the grid and DC link voltages at their reference values. The PI controller parameters were selected using a hybrid augmented grey wolf optimizer and cuckoo search (AGWO-CS) algorithm. The results obtained from MATLAB Simulink for different types of grid fault, with successful and unsuccessful reclosing of the circuit breakers, were compared with results from the particle swarm optimization (PSO) and COOT algorithms. To verify the accuracy of our control system, a grid-connected AWS was tested experimentally using a real-time RT-LAB simulator combined with OP4510. The results validated the efficiency and superiority of the control system using AGWO-CS and the results from simulation and experiment were similar.

In the past ten years, railway level-crossing accidents have noticeably escalated in an indisputably preposterous manner, this devastating snag opened the floodgates for the frustrating death of a numerous number of the third world’s citizens, especially in Egypt. To tackle with this problem, a fully intelligent control system is required, which must be automated without human intervention. So, in this research, a new proposed level-crossing tracking system is designed and introduced. The system comprises a high-performance induction motor (IM) fed from photovoltaic (PV) array, the boom barrier (gate) with its mechanism – as a load – buck–boost converter, inverter, and two smart PI-controllers. The first one is designed to regulate the duty cycle of the converter to its optimum value required to balance between maximum power point tracking (MPPT) and keeping dc-link voltage of the inverter at a minimum level needed to maintain the motor internal torque at rated value. The second PI-controller is designed for speed control of indirect field-oriented vector-control (IFO-VC) IM. The proposed design problems of MPPT, dc-link voltage and speed controllers are solved as optimization problems by bacteria-foraging optimization (BFO) algorithm to search for the optimal PI-parameters. The simulation test results are acquired when using the battery-less PV-array with and without the proposed controllers. Also, results are obtained when applying several prescribed speed trajectories to test the robustness against PV-irradiance fluctuations and motor-dynamic disturbances. From these results, the proposed intelligent controllers are robust compared to classical Ziegler–Nichols (ZN) PI-controllers and also when the motor is directly fed from PV generator without converter

The aim of this work is to design speed and current controllers of a brushless dc (BLDC) motor to drive a hybrid electric-bike. The system is fed from two hybrid sources for driving the motor and charging of storage elements; one is a photovoltaic (PV) generator as a green and neat source; and the other is a human-powered pedal dc-generator. The proposed design of the controllers is formulated as an optimization problem to overcome the most static and dynamic fluctuations of the system. The ant-colony optimization (ACO) algorithm is employed to search for the optimal proportional-integral-derivative (PID) parameters of the proposed controllers by minimizing the time domain of the objective function. The performance of the system is analysed when using the proposed controller with and without storage elements. Extensive simulation results are provided to validate the effectiveness and robustness of the proposed approach against system dynamics and PV-fluctuations. The obtained results confirm the better performance of the system with the proposed controllers for several speed trajectories of the drive compared to the classical PID-controllers.

The development and commercialization of hybrid electric vehicles (HEVs) and electric vehicles (EVs) are actively being realized due to environmental concerns and rising oil prices. But, EV technology with battery has faced many problems up to now. One of the most important problems is rapidly battery-charging from a clean and reliable source. So, this research proposes a convenient way to charge batteries of EVs and HEVs from a neat power supply. The paper introduces a public off-board battery-charging station fed from on-grid (OG) photovoltaic (PV) array. The proposed 100 kW-contactless charging station is modelled, and simulated. It comprises two main sections: station-side (stationary) section and EV (moving) section. The first section includes PV panels, maximum power point tracking (MPPT)-converter, on-grid 3-level interface inverter, and high-frequency compensated resonant converter. The other section consists of the rectifier, switched mode power converter, and battery-charging controller. An inductive-coupled power (ICP) is transferred from the station to the batteries through an air-core transformer (ACT) with large air-gap. Its primary winding is placed on the station ground and the secondary winding is in the EV. Modelling of the ACTs accomplishes several requirements such as air-gap, have good tolerance to misalignment, light-weight, durable and cost effective as possible. Smart and intelligent controllers are proposed for MPPT, interface on-grid inverter and battery-charging converter. Simulation results are obtained using a Simulink/m-file software package helping in modelling of battery-charging public station.