In order to improve the performance of traditional phase-shifted full-bridge converters and reduce the output current ripple, an improved zero voltage and zero current switching phase-shifted full-bridge PWM converter is proposed in this article. By adding a simple auxiliary circuit, the leading-leg switches realize zero voltage switching and the lagging-leg switches realize zero current switching. The primary current is reset during the freewheeling period, so the efficiency of the converter is improved. What is more, the improved converter can effectively reduce the output current ripple. The added auxiliary circuit does not include any lossy components or saturable reactors. The improved phase-shifted full-bridge converter has many advantages, including simple circuit topology, easy implementation of soft switching, high efficiency, and low output current ripple. The operational principle and analysis of the proposed converter are presented in this article. An 800 W experimental prototype is built for experimental verification.

The integration of renewable power sources with power grids presents many challenges, such as synchronization with the grid, power quality problems and so on. The shunt active power filter (SAPF) can be a solution to address the issue while suppressing the grid-end current harmonics and distortions. Nonetheless, available SAPFs work somewhat unpredictably in practice. This is attributed to the dependency of the SAPF controller on nonlinear complicated equations and two distorted variables, such as load current and voltage, to produce the current reference. This condition will worsen when the plant includes wind turbines which inherently produce 3rd, 5th, 7th and 11th voltage harmonics. Moreover, the inability of the typical phase locked loop (PLL) used to synchronize the SAPF reference with the power grid also disrupts SAPF operation. This paper proposes an improved synchronous reference frame (SRF) which is equipped with a wavelet-based PLL to control the SAPF, using one variable such as load current. Firstly the fundamental positive sequence of the source voltage, obtained using a wavelet, is used as the input signal of the PLL through an orthogonal signal generator process. Then, the generated orthogonal signals are applied through the SRF-based compensation algorithm to synchronize the SAPF’s reference with power grid. To further force the remained uncompensated grid current harmonics to pass through the SAPF, an improved series filter (SF) equipped with a current harmonic suppression loop is proposed. Concurrent operation of the improved SAPF and SF is coordinated through a unified power quality conditioner (UPQC). The DC-link capacitor of the proposed UPQC, used to interconnect a photovoltaic (PV) system to the power grid, is regulated by an adaptive controller. Matlab/Simulink results confirm that the proposed wavelet-based UPQC results in purely sinusoidal grid-end currents with total harmonic distortion (THD) = 1.29%, which leads to high electrical efficiency of a grid-connected PV system.

In order to improve the safety and reliability of airborne power supply and distribution system, a digital twin of a phase-shift full-bridge current doubling rectifier converter based on gallium nitride metal-oxide-semiconductor field-effect transistor (GaN MOSFET) is built. The digital twin can be regarded as virtual symmetry of the real converter. The particle swarm optimization (PSO) algorithm is applied to compare the voltage and current waveforms of the real converter with relative waveforms of its digital model established in simulation software, and the interior parameters of the model are constantly updated until the waveforms of the digital twin coincide with that of the converter. Applications of this digital twin are discussed through experimental verification, and the results show that the digital twin can deduce the parameter value of key components of the converter in the case of soft faults. In the case of hard faults, a back propagating artificial neural net (BP ANN) is trained by data collected from the digital twin, and the BP ANN is able to identify different running states of the converter. The outcomes of this paper present a non-intrusive and high real-time method of condition monitoring and fault diagnosis, which is beneficial to improve the reliability of airborne HVDC power supply system.

Pulsed power loads (PPLs), such as high-power radar, impose significant challenges on the dynamic stability and energy efficiency of the onboard power system due to their high-frequency and highly transient characteristics. To ensure stable operation under complex flight conditions, this article proposes a decentralized control strategy for a hybrid power supply system (HPSS) in more electric aircraft (MEA), based on virtual impedance matching. By designing a coordinated virtual inductor–resistor–capacitor droop controller, the strategy decouples load power into low-, medium-, and high-frequency components, enabling full-range dynamic power allocation among the fuel cell (FC), lithium battery (BAT), and supercapacitor (SC). To address power surplus issues when generator output exceeds load demand across the full flight envelope, a frequency-based regenerative energy recovery method is incorporated into the proposed strategy, enabling the BAT and SC to absorb excess energy across distinct frequency bands. In addition, a virtual switch-based adaptive control mechanism ensures autonomous power redistribution between the remaining sources in the event of a partial source failure. The proposed strategy is validated through simulation and hardware-in-the-loop (HIL) testing under both normal and fault conditions. The results show that dc bus voltage fluctuations are limited to 3.04% during normal operation and 4.67% under single-source disconnection, both of which comply with the stability requirements of aerospace electrical standards.

For recycling waste batteries, high-gain DC-DC provides a great solution. In this article, the design and optimization of a high-gain converter–synchronous rectification low-inductance current secondary boost converter (SLIC-QBC) is studied. The optimization objective of this article is to propose an automatic design method for passive components and the switching frequency of the converter to improve efficiency and power density. A machine learning-integrated optimization method is proposed to minimize the converter mass and power loss of the converter. In this method, first, a component-based automatic design model is built with embedded SLIC-QBC simulation. Then, a series of design schemes is generated within a large parameter range, and training data for machine learning are collected. Support vector machine (SVM) and artificial neural network (ANN) are used to validate the converter design scheme, where ANN establishes the mapping relationship from design parameters to optimization objectives. After the optimization, an experimental prototype is built for experimental verification. The simulation and experimental results verify the practicability of the proposed method.

The evolution of Hybrid Power Supply Systems (HPSSs) has extended from ground-based microgrids to the safety-critical domain of More Electric Aircraft (MEA). This paper presents a comprehensive review of dynamic power allocation strategies, bridging the gap between mature ground-based control theories and the stringent operational requirements of aerospace systems. Strategies are systematically classified into centralized, decentralized, and distributed architectures based on control structures. Evaluations indicate that centralized strategies, while effective in microgrids, achieve global optimality but face reliability constraints in airborne environments. In contrast, decentralized strategies based on virtual impedance ensure the high reliability and “plug-and-play” modularity essential for avionics yet often yield suboptimal coordination. Consequently, distributed cooperative control is identified as the most promising paradigm to bridge this gap, synthesizing optimization with fault tolerance. Finally, critical challenges in adapting these technologies to aviation—spanning algorithmic determinism and airworthiness certification—are discussed, and future trends in hybrid intelligence and digital twin-based verification are outlined for next-generation airborne energy systems.