Mostafa Ibrahim Marei

The dual active bridge (DAB) converter is one of the most common types of isolated bidirectional power converters in modern spacecraft EPS owing to its galvanic isolation, bidirectionality, soft switching, and good controllability. However, the goal of power transfer maximization often clashes with real-world spacecraft EPS constraints, namely thermal compliance, reliability, and the accuracy of simplified models used for analysis. This paper proposes a physics-informed methodology to derive the practical operating range under single phase shift (SPS) control based on a rigorous piecewise time-domain representation. From this model, the steady-state initial condition, general closed-form RMS current expression, ZVS boundary condition, and ZVS-aware loss model linked to the junction-temperature estimate are derived. The validity domain of the fundamental harmonic approximation (FHA) is evaluated against the exact model across the full (φ, k) space, and a two-dimensional operating map superposing power contours, the ZVS limit, and the thermal limit is presented. For the baseline case study at k = 1.0, the thermal constraint limits the nominal feasible upper phase shift to approximately 35°, while the broader 15–45° range remains useful for design assessment and operation toward 45° requires lower effective resistance and/or improved thermal management. The normalized SPS power-transfer curve retains the same shape under variations in L and f_s, but RMS current, losses, and thermal feasibility must be reassessed for each converter design. The resulting closed-form framework provides a steady-state feasibility-evaluation tool for spacecraft EPS design and offers a computational basis for future supervisory constraint evaluation under varying voltage, load, and thermal conditions.

The increasing penetration of renewable energy sources has increased the need for advanced control strategies capable of maintaining stability under low-inertia, converter-dominated operating conditions. In grid-connected wind energy conversion systems (WECSs), constant power loads (CPLs) exhibit negative incremental impedance characteristics that can amplify DC-link oscillations and complicate the coordination between the electrical and mechanical subsystems. The main contribution of this work is a Soft Actor–Critic (SAC) reinforcement learning algorithm that tunes the outer proportional-integral gains of the machine-side DC-voltage-squared control loop together with the active damping gain, allowing online adaptation of the controller according to the operating condition and disturbance level, thereby improving energy system sustainability. The proposed control framework includes a two-mass shaft model, virtual inertia control, and DC-link load uncertainty in the form of both resistive loads and CPLs. The system is modeled and evaluated using MATLAB/Simulink, and its performance is compared with that of a conventional fixed-gain controller under AC load disturbances and wind speed variations. It has been found that for a 25% load disturbance, the maximum DC-link voltage deviation is reduced by 1.2% under resistive loading and 6.5% under CPL operation. For a 1 m/s reduction in wind speed, the corresponding reductions are 0.8% and 0.9%, respectively. The proposed controller also provides smoother output power and improved damping of the rotor speed and system frequency responses.

The replacement of synchronous generators (SGs) with inertia-less renewable energy sources (RESs) poses a significant challenge to grid stability due to the reduction of system inertia. To prevent grid instability, energy storage systems (ESSs) with frequency-derivative controls are used to emulate inertia. However, the limited lifetime of ESSs, along with their maintenance requirements, large footprint, and high cost, imposes an additional economic burden on microgrids. This paper proposes an enhanced grid-frequency support approach by coordinating two inertia-emulation mechanisms in parallel: (i) inertia support derived from DC-link capacitor dynamics and (ii) inertia support enabled by operating the PV plant with a power reserve. The proposed method enhances the grid support capacity of the PV energy system and energy sustainability through the efficient utilization of available support resources. Moreover, the DC-link voltage is restored smoothly and naturally to its rated value without the need for a complex control algorithm. The dynamic performance of the proposed system is evaluated under different disturbance conditions and different parameter settings. Simulation results using MATLAB/Simulink R2023a show that, under a 7% load increase, the proposed controller improves the frequency nadir by 0.04 Hz and decreases RoCoF by 10% compared with the baseline controller.

This paper presents a novel design optimization strategy for LLC resonant converters that enhances full-load efficiency while operating across wide input and output voltage ranges. Achieving regulation over a wide output voltage range imposes more stringent design constraints on the converter, demanding higher inductance ratio and wider switching frequency range compared to constant output voltage applications. While numerical optimization techniques are effective for determining the optimal parameters of the converter; however, this effectiveness comes at a substantial computational cost. This work establishes a set of closed-form analytical equations that not only constitute a complete, step-by-step procedure for optimal design without reliance on numerical solvers, but also provide a framework for analyzing design trade-offs. The proposed methodology distinguishes itself from conventional approaches by offering a systematic and non-iterative procedure that is computationally efficient for determining an optimal design. The proposed procedure is validated through the simulation of a 495 W LLC converter specified for a wide operational range with a 320–370 V input and a 35–165 V output. The converter achieved the full output voltage range at the worst-case conditions and attained peak efficiency near the full load, while maintaining soft switching across the entire operating range.

Optimizing energy conversion in photovoltaic (PV) systems is crucial for maximizing energy conversion efficiency and ensuring reliable operation. Achieving this requires that the PV array consistently operates at the Global Maximum Power Point (GMPP). Conventional Maximum Power Point Tracking (MPPT) algorithms, such as Perturb and Observe (P&O) and Incremental Conductance (INC), perform effectively under uniform irradiance but fail to track the GMPP under partial shading conditions (PSCs), resulting in energy losses and degraded system efficiency. To overcome this limitation, this paper proposes a hybrid MPPT method that integrates the Crayfish Optimization Algorithm (COA), a bio-inspired metaheuristic, with the P&O technique. The proposed approach combines the global exploration ability of COA with the fast convergence of P&O to ensure accurate and stable GMPP identification. The algorithm is validated under multiple irradiance patterns and benchmarked against established MPPT methods, including voltage-source and current-source region detection, Improved Variable Step Perturb and Observe and Global Scanning (VSPO&GS), and a hybrid Particle Swarm Optimization (PSO)-P&O method. Simulation studies performed in MATLAB/Simulink demonstrate that the proposed technique achieves higher accuracy, faster convergence, and enhanced robustness under PSCs. Results show that the proposed method reliably identifies the global peak, limits steady-state oscillations to below 1%, restricts maximum overshoot to 0.5%, and achieves the fastest settling time, stabilizing at the new power point significantly faster following major step changes, thereby enhancing overall PV system performance.

This paper introduces an off-board charger designed for electric vehicles (EVs), capable of charging a wide range, from low-voltage to high-voltage EVs. The charger utilizes an interleaved AC-DC converter, where each supply phase connects to an interleaved buck-boost converter via an isolating transformer, followed by a bridge rectifier. To maintain balanced current operation, the control strategy incorporates three distinct control loops. The outer loop is responsible for regulating the output voltage of the charger, while the middle cascaded loop manages the battery charging current. The final control loop ensures that the grid current remains sinusoidal with a unity power factor. Additionally, a phase shift scheme for the interleaved switches has been proposed to minimize output current ripple. Moreover, an inductive current equalizer has been implemented to address varying current stress on the interleave inductances caused by mismatched inductance values. Furthermore, the charger features a modular design that allows it to function effectively even in the event of a failure in one of the boost submodules. The performance of the charger, along with its ability to operate as a level ‘3’ charger, has been evaluated using PSCAD/EMTDC simulation software and a scaled-down experimental prototype.

A newly developed hybrid maximum power point tracker (MPPT) utilizes a modified simulated annealing (SA) algorithm in conjunction with an adaptive hill climbing (HC) technique to optimize the extraction of the maximum power point (MPP) from photovoltaic (PV) systems. This innovative MPPT improves the ability to harvest maximum power from the PV system, particularly under rapidly fluctuating weather conditions and in situations of partial shading. The controller combines the rapid local search abilities of HC with the global optimization advantages of SA, which has been modified to retain and retrieve the maximum power achieved, thus ensuring the extraction of the global maximum. Furthermore, an adaptive HC algorithm is implemented with a variable step size adjustment, which accelerates convergence and reduces steady-state oscillations. Additionally, an offline SA algorithm is utilized to fine-tune the essential parameters of the proposed controller, including the maximum and minimum step sizes for duty cycle adjustments, initial temperature, and cooling rate. Simulations performed in Matlab/Simulink, along with experimental validation using Imperix-Opal-RT, confirm the effectiveness and robustness of the proposed controller. In the scenarios that were tested, the suggested HC–SA reached the global maximum power point (GMPP) of approximately 600 W in about 0.05 s, whereas the traditional HC stabilized at a local maximum close to 450 W, and the fuzzy-logic MPPT attained the GMPP at a slower rate, taking about 0.2 s, with a pronounced transient dip before settling with a small steady-state ripple. These findings emphasize that, under the operating conditions examined, the proposed method reliably demonstrates quicker convergence, enhanced tracking accuracy, and greater robustness compared with the other MPPT techniques.

This paper proposes a soft switching isolated phase-shifted full-bridge DC-DC converter using active clamping circuit for fuel cell power generation applications. An active clamping circuit is added to the primary side of the high frequency transformer to clamp voltage spikes and recycle the energy trapped in the leakage inductor. The presented converter has the advantages of a small number of components, simple circuit configuration, reduced voltage stress and a wide input voltage range. The soft switching operation of the presented converter allows for high switching frequency with high power conversion efficiency. Analysis, simulation, operation principles and a design example of the proposed converter are conducted using PSIM simulation software. Various operation modes and their equivalent circuits during each operating cycle are investigated in detail. A down scale 1.2 kW of simulation and 350 W of experimental prototypes are developed to validate the wide output voltage gain of the proposed converter employing a wide range low input voltage to maintain a regular output voltage with a maximum power conversion efficiency of 98% and 96.5% at full load are achieved due to the ability of the proposed converter to minimize switching losses rated power conditions. The results from simulation and experimentation are used to validate the operation and modeling and to strengthen the viability and efficiency of the suggested converter topology.