A modular multilevel converter (MMC)-based multiport power electronic transformer (PET) topology with shared medium-frequency transformer (MFT) is proposed in this paper, where the proposed PET is suitable for the hybrid AC/DC distribution network because of the existence of four essential voltage ports, including medium-voltage AC and DC ports, lowvoltage AC and DC ports. Moreover, a multi-frequency modulation and the design of voltage decoupling circuit are introduced. One MFT is shared by three-phase MMC arms, which contributes to the compact structure and reduced cost of PET. The operating principle, applied control strategy and parameter design of proposed PET are introduced in detail. The simulation and experimental results are provided to verify the feasibility of PET.

As the key equipment in dc distribution grids, dc transformer (DCT) suffers from low power density caused by a large number of submodules (SMs). Hence, this article proposes a novel DCT for the interconnection of medium-voltage dc (MVdc) and low-voltage dc (LVdc) distribution grids, which adopts a series-connected half-bridge SM branch (SSB) on MV-side to achieve low voltage stress of devices and reduce the number of SMs. Quasi square wave modulation strategy and phase-shifted control are employed to realize bidirectional power transmission control and zero-voltage switching (ZVS) for all switches. Furthermore, in order to ensure low current stress and ZVS within wide voltage range, a certain number of SMs are constantly inserted or bypassed to achieve voltage matching. Besides, smooth transition control strategy is employed to avoid current spikes caused by abruptly bypassing or inserting SMs. Based on the theoretical analysis, design consideration and control strategy are elaborated. Finally, a 500 kW/6–12 kV/750 V simulation model and a 2 kW prototype are built to verify the proposed DCT.

The soft normally open point (SNOP) is a critical apparatus in AC distribution grid to increase the distributed generation (DG) penetration. The existing converters for SNOP are limited by either the utilization of abundant switching devices and passive components or the voltage coupling between two AC systems. Therefore, a multiport AC–AC–DC converter for SNOP is proposed in this brief, where two cascaded H-bridge (CHB) converters with a LC series resonant circuit in parallel connection with each phase-leg are adopted in back-to-back (BTB) structure. The two three-phase CHB converters are connected by three medium-frequency transformers (MFTs). Moreover, the mixed-frequency modulation method and voltage decoupling principle are introduced. Therefore, less switching devices and passive components are used for the multiport AC–AC–DC converter. In addition, the voltages between two AC systems are completely decoupled, and the electrical isolation is ensured. The operating principle, control strategy and parameter design of multiport AC–AC–DC converter are introduced in detail in this brief. Finally, the experimental results are provided to verify the feasibility of proposed multiport AC–AC–DC converter.

Detailed temperature field distribution of high-power insulated gate bipolar transistor (IGBT) modules is essential information for reliability analysis and thermal design of power electronic systems. This article proposes a dynamic mode decomposition (DMD) based reduced-order temperature field prediction method for IGBT module. The IGBT temperature field (ITF) snapshot data obtained from finite element method simulation are decomposed to obtain the dynamic modes. And then, the dominant modes among dynamic modes are chosen to establish a reduced-order model, which can quickly predict the ITF. The proposed DMD based RTFP method only requires a small number of sample snapshots to capture the dynamic modes of ITF and selects a small number of dominant modes to predict the ITF, and accordingly it improves the computational efficiency and reduces the disk space occupation. In addition, it does not require complex mathematical models and can obtain detailed ITF distribution. Finally, the accuracy of the proposed method is verified by simulation and experiment.

The stability and economic dispatch efficiency of photovoltaic (PV) microgrids is influenced by various internal and external factors, and they require a well-designed optimization plan to enhance their operation and management. This paper proposes a multi-objective coordinated control and optimization system for PV microgrids. To address the challenges of slow convergence and local optima in traditional PV microgrid scheduling methods, this study introduced an improved multiple objective particle swarm optimization (IMOPSO) algorithm that integrates an adaptive inertia weight adjustment strategy based on optimal similarity and a multi-directional iterative Pareto solution archive update mechanism. A tri-objective optimization model is formulated to minimize operational costs, environmental pollution, and grid output fluctuation variance, with decision-making supported by the Entropy Weight TOPSIS method. The proposed algorithm is validated through a practical case study of a PV microgrid located in Suzhou, China, and the results demonstrate that IMOPSO achieves a 4.4% reduction in total operational costs under time-of-use pricing (from 50.73 USD to 48.49 USD) and a 4.6% reduction under fixed pricing (from 54.93 USD to 52.38 USD), alongside a maximum safety variance reduction of 45% (from 22.16 to 12.15). The Pareto front distribution exhibits enhanced diversity and uniformity compared to the original MOPSO. While single-objective optimization yields lower costs in isolated scenarios (e.g., 28.50 USD for economic cost minimization), it significantly compromises environmental performance (20.44 USD) and grid stability (14.05 variance). In contrast, IMOPSO ensures coordinated control and effectively balances economic efficiency, environmental sustainability, and operational safety. This study provides a robust framework for multi-objective coordinated control and microgrid scheduling, advancing sustainable energy transition.

As the excitation system is the core control component of a synchronous condenser system, its fault diagnosis is crucial for maximizing the reactive power compensation capability of the synchronous condenser. To achieve accurate diagnosis of excitation system faults, this paper proposes a novel adaptive grey wolf optimizer (AGWO) to optimize the initial weights and biases of the fuzzy neural network (FNN), thereby enhancing the diagnostic performance of the FNN model. Firstly, an improved nonlinear convergence factor is introduced to balance the algorithm’s global exploration and local exploitation capabilities. Secondly, a new adaptive position update strategy that enhances the interaction capability of the position information is proposed to improve the algorithm’s ability to jump out of the local optimum and accelerate the convergence speed. In addition, it is demonstrated that the proposed AGWO algorithm has global convergence. By selecting real fault waveforms of the excitation system for case validation, the results show that the proposed AGWO has a better convergence performance compared to the grey wolf optimizer (GWO), particle swarm optimization (PSO), whale optimization algorithm (WOA), and marine predator algorithm (MPA). Specifically, compared to the FNN and GWO-FNN models, the AGWO-FNN model improves average diagnostic accuracy on the test set by 4.2% and 2.5%, respectively. Therefore, the proposed AGWO-FNN effectively enhances the accuracy of fault diagnosis in the excitation system and exhibits stronger diagnostic capability.