Shuye Ding

To address the challenges of insulation stress on the secondary winding and low energy efficiency during the miniaturization of Tesla transformers, this article proposes a triple-resonant transformer circuit topology that incorporates a high-voltage diode. Based on the traditional dual-resonant topology, this circuit incorporates a high-voltage diode to introduce an additional self-resonance stage, thereby achieving voltage division that reduces insulation stress on the secondary winding and improves energy efficiency. A prototype of a compact 100-kV high-voltage, high-frequency nanosecond pulse generator has been developed, and by embedding the secondary high-voltage capacitor and high-voltage diode directly within the transformer, the system size is further reduced. The volume of the pulse generator is only $25\times 17\times 27$ cm (11.4 L). Detailed testing of each module showed that the pulse amplitude before sharpening reaches approximately 116 kV, which is 1.67 times the maximum voltage sustained by the Tesla secondary winding. After sharpening by a spark gap switch (SGS), a pulse with an amplitude of 100 kV, a rise time of 8 ns, and a full width at half maximum (FWHM) of 190 ns was generated across a 1.25-k $\Omega $ load. The maximum repetition rate reached 100 Hz, with a peak power density of 0.7 MW/L. This design successfully achieves the miniaturization goal while reducing insulation stress on the secondary winding by 40% and increasing efficiency to 75%.

A loss minimization method based on three-phase asymmetric control is proposed for doubly salient electromagnetic generator (DSEG) systems. The three-phase magnetic circuit of DSEG is asymmetrical because of the parallel structure of the stator poles. The DSEG has the problems of uneven flux density distribution and partial oversaturation. Iron losses are not uniformly distributed in the core and difficult to analyze and calculate. According to the load resistance and conduction angle, the fundamental and harmonic distribution characteristics of the flux density of the core are obtained, and accurate iron loss calculation is realized. The three-phase armature currents in DSEG are also asymmetrical in the armature windings, so the copper losses are not uniformly distributed. In addition, the loss composition of DESG is analyzed, and an online method to calculate copper and iron losses is proposed. The three-phase asymmetric control method is implemented with the copper and iron loss models, and the optimum conduction angle and optimum asymmetric conduction angle are determined through the perturbation observation method. The proposed method is proven to be effective through experiments.

With the increasing prevalence of renewable energy, microgrids play a crucial role in enhancing distributed energy efficiency and system flexibility. However, the intermittent and unpredictable nature of renewable energy generation presents significant challenges for microgrid restoration and stable operation. Black-start technology, a key method for autonomous power restoration, is essential for ensuring reliable microgrid operation. Grid-forming virtual synchronous generators (VSGs), with inherent inertia support and regulation capabilities, autonomously establish the voltage, meeting the power supply demands of black-start processes. However, during the pre-synchronization of multiple distributed energy resources in black-start scenarios, rapid phase-angle adjustments can cause frequency fluctuations due to the coupling between the frequency and phase angle. This coupling often leads to frequency overshoot and decreased system stability. To address this challenge, this paper proposes an enhanced parallel restoration strategy for a multi-source black start. Optimizing phase-angle control reduces the dependency on phase-locked loops (PLLs), mitigates phase-angle difference jumps, and accelerates the pre-synchronization process. Furthermore, a linear active disturbance rejection controller (LADRC) dynamically compensates for frequency fluctuations, effectively decoupling the frequency from the phase angle. This approach improves synchronization accuracy and enhances parallel reliability among multiple distributed energy resources (DERs). Simulation results show that the proposed method suppresses frequency overshoot and system disturbances during a multi-source black start, significantly enhancing microgrid restoration capability and operational stability.

In this paper, a novel dual-three-phase dual-rotor flux-switching permanent magnet (PM) (DRFSPM) machine, building upon conventional FSPM machines, is proposed, where the stator is equipped with dual PMs and dual armature windings, enabling it to operate in various working modes and provide fault tolerance in the event of PM or armature winding faults. Depending on the magnetization directions of the PMs, the proposed DRFSPM machine’s structure can be categorized as 6N-DRFSPM or NS-DRFSPM. In order to assess the electromagnetic performance of the proposed DRFSPM machines with two different magnetizing modes, the topology and operating principle of the two DRFSPM machines are introduced first. Then, the no-load air-gap flux density of the two proposed machines is investigated for a more optimized and purposeful design. Finally, a comparison of the electromagnetic performance between the two proposed DRFSPM machines is conducted by finite-element analysis (FEA), and the FEA-predicted results indicate that the proposed 6N-DRFSPM machine outperforms the NS-DRFSPM machine, as it exhibits a larger back-EMF and average torque and a smaller cogging torque and torque ripple.

The doubly salient electromagnetic generator (DSEG) is a competitive candidate for more electric aircraft applications due to its high reliability. This paper proposes a comprehensive loss management method based on current regulations with angular position control (APC) for DSEG systems. The loss components are divided and analyzed to accurately determine the best conducting angle in the APC method for minimized total loss. An efficiency regression model based on random forest (RF) is also proposed to address the uncertainty of loss distribution and the coupling of multiple losses. The Bayesian optimization (BO) algorithm is used to optimize the RF parameters and improve its prediction accuracy. The efficiency optimization method is proposed based on the BO-RF efficiency model. The loss distribution is investigated based on analysis and experiments. A control method is proposed to reduce the overall losses and achieve optimal efficiency for the DSEG system by regulating the winding currents, which effectively predicts the optimal conducting angle for maximum efficiency in wide operation ranges.

The mismatch problem is commonly happened in photovoltaic (PV) systems due to partial shading conditions(PSCs). Distributed maximum power point tracking (DMPPT) architectures can be used to solve such problem. Reinforcement learning (RL) method which is one of the advanced artificial intelligence(AI) methods is proposed to improve the tracking speed. However, the drawbacks such as the lack of limited adaptability and exploration-exploitation trade-off theory make the RL method low in efficiency. Therefore, this article combines the Beta method and $\varepsilon$ - greedy algorithm with the RL method to address this problem. The simulation and experimental tests have been carried out and the result shows the efficiency of the proposed RL method is up to 96.85%, which verifies the superiority of the proposed scheme.

In order to solve the frequency modulation (FM) problem caused by load in the virtual synchronous generator (VSG) system under an islanding state, this paper proposes a frequency modulation method based on active disturbance rejection control (ADRC). Firstly, design a nonlinear function using the design rules of nonlinear functions. Then, the extended state observer (ESO) and nonlinear feedback control law (NLSEF) of ADRC are designed based on the nonlinear function. By introducing ADRC into the power–frequency control loop of VSG, error-free frequency modulation of VSG is achieved. Finally, the superiority of the proposed method was verified through experimental comparison.

Photovoltaic (PV) power generation is considered to be a clean energy source. Solar modules suffer from nonlinear behavior, which makes the maximum power point tracking (MPPT) technique for efficient PV systems particularly important. Conventional MPPT techniques are easy to implement but require fine tuning of their fixed step size. Unlike conventional MPPT, the MPPT based on reinforcement learning (RL-MPPT) has the potential to self-learn to tune step size, which is more adaptable to changing environments. As one of the typical RL algorithms, the Q-learning algorithm can find the optimal control strategy through the learned experiences stored in a Q-table. Thus, as the cornerstone of this algorithm, the Q-table has a significant impact on control ability. In this paper, a novel Q-table of reinforcement learning is proposed to maximize tracking efficiency with improved Q-table update technology. The proposed method discards the traditional MPPT idea and makes full use of the inherent characteristics of the Q-learning algorithm such as its fast dynamic response and simple algorithm principle. By establishing six kinds of Q-tables based on the RL-MPPT method, the optimal discretized state of a photovoltaic system is found to make full use of the energy of the photovoltaic system and reduce power loss. Therefore, under the En50530 dynamic test standard, this work compares the simulation and experimental results and their tracking efficiency using six kinds of Q-table, individually.

The doubly salient electromagnetic generator (DSEG) is sought in aircraft applications because of its simple structure and high reliability. Angular position control and advanced angle control have been proposed for active rectifiers in DSEGs, but the selection of the maximum output points under this scheme requires several simulations and experiments. This study proposes a maximum output performance model to automatically select the maximum output point for enhancing the power capacity. On this basis, a three-phase nine-state (TPNS) control for DSEG systems based on the active rectifier is also proposed. In contrast to conventional control strategies, the TPNS strategy could improve the power capacity of the DSEG system. Furthermore, losses were suppressed and system efficiency was enhanced. The proposed model and TPNS strategy were verified by finite element analyses and experimental results.