Electric vehicles (EVs) could be used to address the issues of environmental pollution and the depletion of non-renewable energy resources. EVs, which are energized by a battery storage system, are becoming attractive because they keep the environment clean. Furthermore, the cost of EVs is becoming cheaper. Thus, EVs will become a significant load on utility distribution system in the future. EV chargers play a significant role in the expansion of EVs. The input current of an EV charger with a high total harmonic distortion (THD) and a high ripple distortion of the output voltage can impact battery life and battery charging time. Furthermore, the high cost and large size of the chargers are considered other issues in EV development. This work presents the complete design process of a universal EV charger with a special focus on its control algorithms. In this regard, a novel control algorithm based on the integration of voltage-oriented control (VOC) and the sinusoidal pulse-width modulation (SPWM) technique is proposed to ensure effective Levels 1, 2, and 3 battery charging. A simulation of the universal EV charger was conducted and assessed in MATLAB–Simulink. Moreover, a laboratory prototype was constructed with a TMS320F28335 digital signal processor (DSP) programmed as the controller to validate its operation and performance. The findings show that the proposed charger is able to provide a controllable and constant charging voltage for a variety of EVs, with an input current of low total harmonic distortion (THD) and an almost unity power factor.

Electric supply is listed as one of the basic amenities of sustainable development in Malaysia. Under this key contributing factor, the sustainable development goal aims to ensure universal access to an affordable, clean, and reliable energy service. To support the generation capacity in years to come, distributed generation is conceptualized through stages upon its implementation in the power system network. However, the rapid establishment growth of distributed generation technology in Malaysia will invoke power quality problems in the current power system network. In order to prevent this, the current government is committed to embark on the development of renewable technologies with the assurance of maintaining the quality of power delivered to consumers. Therefore, this research paper will focus on the review of the energy prospect of both fossil fuel and renewable energy generation in Malaysia and other countries, followed by power quality issues and compensation device under a high renewable penetration distribution network. The issues and challenges of distributed generation are presented, with a comprehensive discussion and insightful recommendation on future work of the distributed generation. In accordance with the addressed highlights in this paper, it would serve as the criterion on upcoming revolution of distributed generation integrated along with the traditional network in Malaysia.

The Dynamic Voltage Restorer (DVR) is a power electronic device that is used to inject 3-phase voltage in series and in synchronism with the distribution feeder voltages in order to compensate for voltage sag. In this paper the operation of a DVR is presented. The power circuit of a DVR together with the control techniques used for compensation is explained. The operation of the Software Phase Locked Loop (SPLL) and its stability are analyzed. The DVR control system is described and verified using simulation. The control algorithm implemented using DSP was tested using generated input voltage signals and the results are also presented. The micro-cycle effect of battery current for balanced and unbalanced sag is also investigated.

Solar power generation is intermittent in nature. It is nearly impossible for a photovoltaic (PV) system to supply power continuously and consistently to a varying load. Operating a controllable source like a fuel cell in parallel with PV can be a solution to supply power to variable loads. In order to coordinate the power supply from fuel cells and PVs, a power management system needs to be designed for the microgrid system. This paper presents a power management system for a grid-connected PV and solid oxide fuel cell (SOFC), considering variation in the load and solar radiation. The objective of the proposed system is to minimize the power drawn from the grid and operate the SOFC within a specific power range. Since the PV is operated at the maximum power point, the power management involves the control of SOFC active power where a proportional and integral (PI) controller is used. The control parameters of the PI controller K_p (proportional constant) and T_i (integral time constant) are determined by the genetic algorithm (GA) and simplex method. In addition, a fuzzy logic controller is also developed to generate appropriate control parameters for the PI controller. The performance of the controllers is evaluated by minimizing the integral of time multiplied by absolute error (ITAE) criterion. Simulation results showed that the fuzzy-based PI controller outperforms the PI controller tuned by the GA and simplex method in managing the power from the hybrid source effectively under variations of load and solar radiation.

Broadcasting, radar, sonar and space telecommunication systems use phased arrays to produce directed signals to be transmitted at the desired angle. This system requires a large number of antenna elements. The presence of faulty element(s) in an array causes asymmetry, which results in a deformed radiation pattern with higher sidelobe levels. Higher sidelobe levels indicate waste of energy by transmitting and receiving signals in unwanted directions. Hence, it is important to develop a method that detects faulty elements and corrects the radiation pattern. To correct the failed radiation pattern, failed elements in an array must be identified first. There have been various studies conducted on linear array failed radiation pattern correction and the finding of faulty elements, but investigation on the planar array is limited. Further, the optimization suggested for linear arrays does not necessarily work for the planar array. In this study, planar array faulty antenna detection was developed with pattern search (PS), simulated annealing (SA), and particle swarm optimization (PSO) methods by reducing the Signal to Noise Ratio (SNR) as the objective function. The analysis was varied for 8 × 8 and 6 × 6 planar arrays with different types of failures. The results were compared to find the best method to identify the faulty element’s location in a planar array. The pattern search method produced outstanding results in finding the faulty element’s locations by providing 100% accuracy for all types of failure, while other methods failed to do the same.

The assessment of grid-connected systems depends on their cost efficiency, reliability, and greenhouse gas (GHG) reduction potential. This study presents a multi-objective optimization framework for designing a grid-connected photovoltaic (PV) and battery energy storage (BES) system integrated with an electric vehicle (EV) for a household in Riyadh, Saudi Arabia. The framework aims to minimize the Cost of Energy (COE) and Loss of Power Supply Probability (LPSP) while maximizing the Renewable Energy Fraction (REF). Additionally, GHG emissions are evaluated as a result of these objectives. The EV operates in Vehicle-to-Home (V2H) mode, enhancing system flexibility and energy management. The optimization process employs two advanced metaheuristic techniques, Multi-Objective Particle Swarm Optimization (MOPSO) and Multi-Objective Harris Hawks Optimization (MOHHO), to identify Pareto front solutions. Fuzzy logic is then applied to determine a balanced compromise among the economically optimal (minimum COE), renewable energy-oriented (maximum REF), and environmentally optimal (minimum GHG emissions) solutions. Simulation results show that the proposed system achieves a COE of USD 0.0554/kWh, a LPSP of 1.96%, and an REF of 92.55%. Although the COE is slightly higher than that of the grid, the system provides significant environmental and renewable energy benefits. This study highlights the potential of integrating dynamic EV management and advanced optimization techniques to enhance the performance of grid-connected systems. The findings demonstrate the effectiveness of combining Pareto-based optimization with fuzzy logic to achieve balanced solutions addressing economic, environmental, and renewable energy objectives, paving the way for sustainable energy systems in urban households.