This paper proposes two intelligent torque distribution strategies based on particle swarm optimization (PSO) and fuzzy logic control (FLC) to provide convenient torque allocation that maximizes hybrid electric vehicle (HEV) propulsion power. PSO torque distribution strategy uses torque transfer ratio (TTR) as a fitness function to select the best torque candidates and differential arrangements that maximize HEV propulsion torque. A proposed FLC controller with adequate membership functions is designed to ensure convenient torque vectoring across vehicle wheels. A new coordinated switching strategy is proposed in this paper to address the undesired transient ripples occurring during drivetrain commutations and power source switchings. The proposed coordinated switching strategy controls the switching period duration through transition functions fitting the transient dynamics of power sources. In non-uniform surfaces, intelligent torque allocation strategies converted $84\sim 86$ % of the generated torque into propulsion torque whereas the equal torque distribution strategy yielded a torque transfer ratio of 50%. Thanks to the proposed coordinated switching strategy, DC bus voltage ripples were reduced to a narrow band of $\pm ~5\text{V}$ , transient power ripples were limited to a narrow band of 600 W and torque jerks were almost suppressed. Real-time simulation using the RT LAB platform confirms that the proposed coordinated switching strategy has reduced transient torque overshoot from 69% to almost zero and this is expected to improve HEV driving comfort.

The paper addresses the ongoing and continuous interest in photovoltaic energy systems (PESs). In this context, the study focuses on an isolated photovoltaic system with hybrid battery-supercapacitor storage (HBSS). The integration of supercapacitors (SCs) in this system is particularly important because of their high specific power density. In photovoltaic (PV) systems, multi-storage systems use two or more energy storage technologies to enhance system performance and flexibility. When batteries and supercapacitors are combined in a PV system, their benefits are maximized and offer a more reliable, efficient, cost-effective energy storage option. In addition, effective multi-storage power management in a PV system needs a solid grasp of the energy storage technologies, load power demand profiles, and the whole system architecture. This work establishes a battery-supercapacitor storage system (HBSS) by combining batteries and supercapacitors. The primary objective is to devise a novel management algorithm that effectively controls the different power sources. The algorithm is designed to manage the charge and discharge cycles of the hybrid battery-supercapacitor energy storage system (HBSS), thereby guaranteeing that the state of charge (SOC) for both batteries and supercapacitors is maintained within the specified range. The proposed management algorithm is designed to be simple, efficient, and light on computational resources. It efficiently handles the energy flow within the HBSS, optimizing the usage of both batteries and supercapacitors based on real-time conditions and energy demands. The proposed method ensures their longevity and maximizes their performance by maintaining the SOC of these energy storage components within the specified limits. Simulation results obtained from applying the management strategy are found to be satisfactory. These results show that the proposed algorithm maintains the SOC of batteries and supercapacitors within the desired range, leading to improved energy management and enhanced system efficiency.

This paper presents a comprehensive exploration of a hybrid energy system that integrates wind turbines with photovoltaics (PVs) to address the intermittent nature of electricity production from these sources. The necessity for such technology arises from the sporadic nature of electricity generated by PV cells and wind turbines. The envisioned outcome is an emissions-free, more efficient alternative to traditional energy sources. A variety of optimization techniques are utilized, specifically the Particle Swarm Optimization (PSO) algorithm and Electric Eel Foraging Optimization (EEFO), to achieve optimal power regulation and seamless integration with the public grid, as well as to mitigate anticipated loading issues. The employed mathematical modeling and simulation techniques are used to assess the effectiveness of EEFO in optimizing the operation of grid-connected PV and wind turbine hybrid systems. In this paper, the optimization methods applied to the system’s architecture are described in detail, providing a clear understanding of the intricate nature of the approach. The efficacy of these optimization strategies is rigorously evaluated through simulations of diverse operating scenarios using MATLAB/SIMULINK. The results demonstrate that the proposed optimization strategies are not only capable of precisely and swiftly compensating for linked loads, but also effectively controlling the energy supply to maintain the load’s power at the desired level. The findings underscore the potential of this hybrid energy system to offer a sustainable and reliable solution for meeting power demands, contributing to the advancement of clean and efficient energy technologies. The results demonstrate the capability of the proposed approach to improve system performance, maximize energy yield, and enhance grid integration, thereby contributing to the advancement of renewable energy technologies and sustainable energy systems.

Power transformers have great importance in power system networks. Any malfunctions in the power transformers cause a system disconnection, which leads to lost profits for the electricity utilities. Transformer malfunctions can result from various stresses like electrical, thermal, or mechanical pressures acting on the insulation system, typically composed of insulating oil and paper. Dissolved Gas Analysis (DGA) is widely adopted to identify transformer faults. While traditional DGA methods such as the IEC Code, Rogers Ratio, and Duval triangle exist, their diagnostic accuracies are often lacking. So, optimization techniques are applied to augment the artificial intelligence of conventional DGA, aiming to significantly enhance the accuracy in diagnosing faults in power transformers. Still, it individually does not give high diagnostic accuracy. Therefore, a transformer fault diagnosis intelligent system (TFDIS) was developed in this work to increase the high analytical accuracy of recent DGA methods based on comparing the output of four DGA methods such as code tree 2020, modified IEC, and Rogers’ ratio method, and Neural pattern recognition. The intelligent system developed a diagnostic accuracy (89.12%), higher than the highest diagnostic accuracy created by neural pattern recognition (86.01%).