A power conversion system needs high efficiency for modern-day applications. A DC–DC isolated bidirectional dual active bridge-based converter promises high efficiency and reliability. There are several converter topologies available in the market claiming to be the best of their type, so it is essential to choose from them based on the best possible result for operation in a variety of applications. As a result, this article examines the characteristics, functionality, and benefits of dual active bridge-based DC–DC converter topologies and the other members of the family, as well as their limits and future advances. A high-frequency transformer is also an important device that is popular due to high leakage inductance in dual active bridge (DAB) converters. Therefore, a detailed review is presented, and after critical analysis, minimized leakage inductance in the toroidal transformer is obtained using the ANSYS Maxwell platform. Furthermore, this work includes a comprehensive examination of the control approaches for DAB converters, which is important for selecting the most appropriate technique for a certain application. The outcome of ANSYS Maxwell is integrated with a DAB-based boost inverter in the MATLAB/Simulink environment, and the results are validated with the help of an experimental prototype.

This paper proposed a reinforcement learning (RL) based energy management system of pelagic islands network microgrids (PINMGs) by ship swapping under the influence of environmental impacts. In addition, the day-ahead standard scheduling by proposing a novel method to maximize the usage of renewable energy (RE) proposes the energy-sharing structure between islands. Energy sharing among islands plays an important role in electrifying remote islands, which need energy due to the unavailability of renewable energy resources to meet local demand. The two-stage cooperative multi-agent deep RL has been presented with the deep Q-learning (DQN) based approach with central RL and island agents (IA) distributed over numerous islands to overcome this challenge. The deep RL-based approaches efficiently learn and optimize their behaviors through several epochs compared with other machine learning or conventional methods due to their in-depth learning capability. Hence, the centralized RL-based problem using dueling DQN was solved to schedule charge battery sharing from resource-rich islands (SI) to load island networks (LIN). In addition, the case study has compared the accuracy among different DQN methods and further scheduling based on the dueling DQN because of its accurate tracking. Due to fluctuating swapping demand and charging patterns, the need for LIN is also stochastic. Hence, the simulation results, including energy scheduling through the ship, are validated by maximizing RE usage through sharing between several islands, and the usefulness of the proposed algorithm is verified through state and action perturbation to verify the robustness.

Solar photovoltaic PV plants worldwide are continuously monitored and carefully protected to ensure safe and reliable operation through detecting and isolating faults. Faults are very common in modern solar PV systems which interrupt normal system operation adversely affecting the performance of the PV systems. When undetected, faults not only cause significant reduction in the efficiency and life span of the PV system, but also result in damage and fire hazards compromising their reliability. Therefore, early fault detection and diagnosis of photovoltaic plants is a necessity for safe and reliable operation required for growing solar PV systems. Unfortunately, several recent fire incidents have been reported recently caused by undetected faults in solar PV systems. Motivated by this challenge, this paper, utilizing a proposed fuzzy logic algorithm, presents a novel technique for detecting and classifying faults in solar PV systems. Furthermore, the proposed method introduces fault indexing as a performance indicator that measures the degree of deviation from the normal operating conditions of the photovoltaic system. Various signatures of each fault scenario are identified in the shape of corresponding current-voltage trajectories and their extracted parameters. The effectiveness of the proposed technique is evaluated both in simulation and experimentally using a 5 kW grid connected solar array. It is demonstrated that the proposed technique is capable of diagnosing the occurrence of different faults with more than 98% accuracy.

High-frequency transformers are becoming an essential component in the integration of power resources that rely on power electronic converters; their efficiency and performance are influenced by parasitic characteristics in the interface. In this article, the design of a high-frequency toroidal transformer has been explained in detail using the ANSYS Maxwell platform. Various parameters, such as leakage inductance, magnetic flux density, magnetic field strength and uniform magnetic flux line are analyzed using Finite element analysis. High-frequency transformers using a toroidal core with different winding configurations are examined and all parameters obtained through simulation are validated by an analytical approach. Analysis of each design is based on its leakage inductances, which will aid in the appropriate selection of transformers as a function of their operating frequency. This analysis is expected to guide designers to optimize the high-frequency transformer parameters based on practical applications. The optimized parameters are then applied for a dual active bridge (DAB) converter within MATLAB/Simulink to verify the design process. A prototype has been built to validate the simulation and design procedure. The results obtained from both simulation and experiments are compared and show great correlation.

The dual active bridge (DAB) converter is widely used in applications such as energy storage systems (ESSs), charging stations, and solid-state transformers. However, input power fluctuations, such as those encountered in photovoltaic systems or fuel cells, may reduce the system’s damping and cause oscillations in the converter’s output voltage. To minimize ripples under various operating conditions, this paper proposes an optimal inductor-assisted disturbance observer-based disturbance rejection control (DOB-DRC) technique. The components of the high-frequency transformer are precisely modeled, including the series inductance, which is optimally placed for maximum power transfer. The DOB-DRC is designed for voltage regulation with minimum ripples and is compared with other control algorithms, such as current predictive control (CPC), output current feedforward control (OCFFC), and virtual direct power control (VDPC). Experimental results demonstrate that the proposed control strategy achieves fast dynamic performance, minimum overshoot, and steady-state error.