The Auxiliary Power Supply (APS) is widely used to provide power to gate driver circuit (GDC). APS commonly adopts a system structure based either on a transformer or on power over fiber (PoF). APS based on PoF provides superior isolation voltage and enhanced common-mode (CM) noise immunity compared to APS based on transformer, along with the benefits of compact size and lightweight construction by eliminating the magnetic core of the transformer. However, the DC/DC converter in APS based on PoF, which is used to achieve a stable voltage output, adds considerable complexity and increases the overall cost of APS. This paper proposes a converterless APS based on PoF system structure and its closed-loop voltage regulation. The proposed system employs a photovoltaic power converter (PPC) to replace the DC/DC converter, and the closed-loop regulation using a proportional-integral (PI) controller is implemented to achieve constant voltage control for the GDC. Experiments were conducted under two situations to validate the effectiveness of the proposed system structure and its regulation.

In high-frequency transformers, Faraday shields divert the coupling of the common-mode (CM) noise to the ground and contribute to losses in both magnetic fields and electric fields. However, the electrical model is not found in existing research, which brings challenges to the shield design. To address it, an analytical model is proposed to calculate the electrical voltage distribution, CM current, and its generated loss in the Faraday shield. With the method to couple the electric and magnetic fields, the shield is modeled as a resistance-inductance-capacitance-voltage source based circuit. The voltage distribution along the shield is derived to analyze the Electromagnetic Interference (EMI). Moreover, the criteria to determine the electrical losses and to keep the shielding function are given in the possible scenarios of transformer design we foresee, where a quantitative impedance ratio and the corresponding current-shunting ratio are introduced for the shielding-function assessment. The model is applicable to shields with single and multiple turns and layers, arbitrary winding-voltage distributions, and electrical shields of magnetics. With a practical shielding criterion of $\kappa = 10$, the proposed method provides a guideline for shield-thickness design under low-loss insulation conditions.

Conventional common-DC-bus energy routers with single storage units face challenges in port isolation and high storage capacity requirements for renewable grid integration. This paper addresses these limitations through a multiport magnetic network energy router (MNER) replacing the DC bus, combined with a dual-battery interleaved operation strategy. MNER inherently achieves electrical isolation among ports, enhancing system performance. Dual storage units enable interleaved charging/discharging without additional power switches, reducing required capacity while maintaining power quality. Simulations validate the effectiveness and cost advantages over conventional single-storage approaches.

With the rapid advancement of power electronics technology, the power density of power modules has significantly increased, making the junction temperature a critical parameter for ensuring reliable operation. Therefore, accurately predicting junction temperature is crucial for reliable thermal design and condition monitoring of power modules. This article proposes a hybrid physics-based data-driven (HPDD) thermal model for multichip power modules. Firstly, the chip's self-thermal temperature rise (STR) is predicted by the Foster model. Then, based on the information of STR, the chip's coupling-thermal temperature rise (CTR) is predicted by the data-driven model eXtreme Gradient Boosting (XGBoost). Finally, the junction temperature can be predicted based on the STR and CTR. The proposed HPDD thermal model utilizes the XGBoost model to calculate complex CTR, reducing the modeling and computational complexity while significantly decreasing the number of finite-element methods (FEM) simulations and parameter fittings. In addition, it boosts the prediction robustness by reducing the parameter uncertainty of the XGBoost model based on the physical information in STR obtained from the FEM simulation. Simulations and experiments are conducted, and the results confirm the effectiveness of proposed model.

Conventional finite control set model predictive control (FCS-MPC) is widely employed in grid-connected inverters for fast and accurate grid current tracking. However, in practical applications, factors such as parameter mismatch, unmodeled dynamics, and external disturbances frequently cause model mismatch, thereby degrading control performance. To address this problem, this article proposes a data-driven high-order model-free adaptive iterative learning predictive control (HOMFAILPC), which integrates the advantages of model-free adaptive control and FCS-MPC, and incorporates the iterative learning mechanism. The proposed HOMFAILPC aims to overcome the inherent limitations of conventional FCS-MPC regarding model fault tolerance and parametric robustness. Specifically, the proposed framework relies solely on the input-output data of the controlled system, dispensing with the need for any explicit or implicit model information. A high-order iterative data-driven model is first established through dynamic linearization and then integrated with FCS-MPC to form a data-driven predictive control for optimal control actions. The proposed method effectively reduces tracking errors and enhances robustness against parameter drift, model uncertainties, and external disturbances. Simulation and experimental results are presented to validate the feasibility and effectiveness of the proposed approach.

Parallel operation of multiple household energy storage inverters (HESIs) enables flexible system capacity expansion. However, the absence of a DC point of common coupling (PCC) poses prominent challenges in real-time active power allocation, state-of-charge (SOC) balancing, and communication coordination. This paper establishes the system's impedance model under DC bus signal control, analyzes its decoupling mechanism in a single-unit system and AC-DC coupling characteristics in parallel configurations. To address these issues, a virtual common DC bus balancing control strategy is proposed. By constructing virtual interconnections among all units' DC buses and a virtual DC PCC, AC-DC decoupling of the parallel HESIs system is achieved. Each unit autonomously adjusts output power per unified benchmarks and realizes dynamic inter-unit compensation based on its own characteristics, achieving autonomous active power dispatch with sparse communication. Further, a SOC balancing strategy for distributed energy storage is developed using the virtual DC PCC. The proposed scheme maintains low inter-unit coupling, enhances system efficiency and economy, and reduces control complexity. Simulation and experimental results validate the feasibility and effectiveness of the presented method.

The photon-driven direct-current (dc) motor system offers significant advantages over conventional switching electricity-driven dc motor system, such as reduced intrinsic electromagnetic interference (EMI), voltage ripple, and improved immunity to environmental EMI, making it a more reliable and efficient solution for motor control. However, the conventional photon-driven dc motor system also presents two drawbacks. First, the power supply and communications are separated, resulting in low system integration. Second, the sensors and optical fiber communication require external power source, resulting in high complexity. To address these challenges, this article proposes an optical dialogue photonic converter (ODPC) for photon-driven dc motor system. The proposed ODPC integrates power supply and communication through a single fiber by utilizing an optical dialogue structure, and eliminates the need for external power source. The proposed ODPC enhances system integration, reduces complexity, and improves reliability, effectively overcoming the limitations of the conventional photon-driven dc motor system. An experimental prototype is built to verify the feasibility and performance of the proposed ODPC.

The conventional model predictive control (MPC) has been widely applied for LC-filtered voltage source inverters (VSIs) to regulate the output voltage. To reduce the number of required sensors of the VSI under conventional MPC, the load current is generally observed. However, the performance of MPC and load current observation is easily affected by the accuracy of the model parameters. To eliminate the parametric effect and observe the load current simultaneously, this article proposes a moving-window-adaptive-observer-based data-driven predictive control (MWAO-DDPC) for LC-filtered VSIs, where the proposed MWAO-DDPC not only obtains the observed load current by using the MWAO but also achieves the data-driven voltage prediction by using the observed data-driven model coefficients. The proposed MWAO-DDPC effectively reduces the number of applied sensor and eliminates the parametric effect on voltage prediction. The experimental prototype of LC-filtered VSIs is built to verify the feasibility and performance of the proposed MWAO-DDPC.

The crossing thyristor branches based hybrid modular multilevel converter (CTB-HMMC) can rapidly interrupt dc fault currents with fewer unipolar full-bridge submodules (UFB-SMs), but UFB-SM failures may cause excessive capacitor voltage stress during dc fault clearing. This article proposes an optimal dc fault ride-through control for CTB-HMMC under UFB-SM failures. First, the capacitor voltage stress of healthy UFB-SMs and the dc fault clearing time are analyzed considering different UFB-SM failure ratios and fault distances. Then, the maximum allowable capacitor voltage is taken as the constraint, and the number of additionally bypassed UFB-SMs is minimized to maintain fast fault clearing. Based on the UFB-SM failure distribution in three phases, the proposed optimized method determines the optimal bypass ratios of healthy UFB-SMs and coordinates their operating modes during the dc fault clearing. The fault energy is redistributed among the remaining UFB-SMs, and the maximum capacitor voltage is suppressed without additional hardware. Under a severe UFB-SM failure case, the proposed method restricts the capacitor voltage below 1.5 p.u., reduces the number of additionally bypassed healthy UFB-SMs by 21.4%, and shortens the dc current clearing time by 1.04 ms compared with the conventional method. Simulation and experimental results verify the effectiveness of the proposed method.

The cascaded modular multilevel converter (MMC) and cycloconverter (CCV) machine drive system shows potential in medium/high voltage machine drives due to its low capacitor voltage ripple at low speed. However, the insulated gate bipolar transistors (IGBTs) and diodes of the MMC and the thyristors of the CCV result in power loss and operation cost. In this paper, a power loss reduction operation (PLRO) is proposed to reduce the total device power loss of the cascaded drive system. The proposed PLRO selects the optimized ac-link voltage amplitude according to the machine speed, then the modulation ratios of the MMC/CCV and commutation process of the thyristors in the CCV are jointly adjusted to reduce the total power loss. The effects of ac-link voltage on the modulation and device power losses are discussed, then the selection range of the ac-link voltage is analyzed based on a 6.6 kV/5 MW cascaded drive system. Simulation and experiment results are provided to verify the feasibility of proposed method.

Soft magnetic materials are widely used for magnetic components in power electronic converters. Their hysteresis effect leads to the nonlinear impedance characteristics of the magnetic cores, which can cause loss increase, waveform distortion, and electromagnetic interference issues in the converter. Due to the nonlinear behavior and the diversity of material characteristics, current Preisach hysteresis modeling remains challenging in parameter identification and probability distribution function (PDF) modeling procedure. In parameter identification, the classical methods rely on complex and restrictive double integration or repeated measurements. In PDF modeling, conventional PDF forms are constrained by a few degrees of freedom. To achieve high modeling precision, PDF forms with higher degrees of freedom are required. It also restricts the universality of the PDF, so different PDFs are needed for different magnetic materials. To address these two issues, a parameter identification approach based on the geometric interpretation of integration is proposed, which requires only a single B-H curve for ferrite. It avoids repeated testing and improves the identification efficiency significantly. Moreover, a generalized PDF form with more independent adjustable parameters is proposed. It can encompass different PDFs and model different hysteresis loop shapes of various magnetic materials. Experiments on different ferrites and silicon steels are conducted for verification. Compared with the conventional method, the proposed approach demonstrates significant improvements in accuracy, universality, simplification, and rapidity across different materials and shapes of magnetic cores.

The quad-port magnetic network energy router (QPMNER) is a promising solution for energy dispatch in DC microgrids with its high efficiency and flexible power control capability. For QP-MNER systems, effective multi-port power control is the core premise to ensure reliable and stable operation, while the inherent nonlinear power coupling between ports has become a key bottleneck restricting its control performance. To solve this problem, this paper proposes a moving-discretized-control-set model predictive control (MDCS-MPC) strategy for QP-MNER. The proposed MDCS-MPC achieves independent power regulation for each port by designing adaptive candidate phase-shift angles and predicting the corresponding port voltages and currents, which effectively coordinates the power flows of the whole system and greatly improves the dynamic performance. The feasibility and superiority of the proposed MDCS-MPC are fully verified by experimental tests based on a QP-MNER hardware prototype.

Modular multilevel converters (MMCs) are susceptible to the open-circuit faults of power switches in submodules (SMs). During the fault location interval, the MMC is subject to severe performance degradations, including overcurrent, power fluctuations, and current distortion. These threaten the operating quality and reliability of the MMC, especially under medium-voltage scenarios with stringent operating demands. As a solution, this article proposes a “performance-oriented” seamless fault-riding-through (FRT) strategy, which ensures that the MMC performs as effectively as a fault-free system during the fault location. The seamless FRT operation is achieved by the coordination of the proposed model adaptive predictive control (MAPC) and simultaneous fault location. In the proposed MAPC, the correction factor is estimated to adaptively acquire the precise predictive model of MMC despite the fault occurrence to minimize the tracking errors of control targets. Moreover, the fault location can be achieved with reduced time, even with existing methods. The experiments are conducted to validate the effectiveness of the proposed seamless FRT strategy, which significantly enhances the transient MMC performance quality and reliability for both single and multiple faults.

Data-driven regression compensation control effectively mitigates the inherent nonlinearities in grid-connected inverters. However, the structural limitations inherent to the single-data regression model challenge the balance between minimal data requirements and adaptive optimization, thereby constraining compensation precision and generalization capability. To overcome the generalization limitations of the single-data model, this paper proposes a collaborative compensation control strategy based on multi-data regression models, which employs adaptive weighting for dynamic model fusion. This method integrates techniques for operating interval and boundary feature extraction to construct multi-data regression models using lightweight offline regression. Real-time matching of operating features enables dynamic model selection and weight assignment, thereby accurately reconstructing the compensation signal in extrapolation regions beyond the training data boundaries. Furthermore, by integrating with a low-order controller, a hybrid control architecture merging the mechanism-based and data-driven paradigms is established, significantly enhancing the adaptive compensation performance. Experimental results validate the effectiveness and superiority of the proposed strategy.

With the increasing integration of renewable energy, low-frequency AC transmission has become a competitive candidate for high-voltage long-distance power transmission. The bipolar modular AC-AC converter (BMAC), known for its simple structure and independent control of active and reactive power, can be used in such systems. However, the switching devices in the numerous full-bridge submodules (SMs) are prone to open-circuit faults. In this study, a diagnostic method for open-circuit faults is put forward for BMAC based on the 3-sigma criterion. A fault is identified if the SM capacitor voltage lies outside the preset range. The proposed approach avoids the need for precise mathematical models and additional hardware. Simulation results confirm the validity of the presented method.

With the growing proportion of renewable energy in power systems, the dedicated energy consumption technologies are required for grid stability. Electric spring (ES) is a novel power electronic equipment for solving the power fluctuation issues by categorizing consumer loads into critical loads (CLs) and adjustable non-CLs (NCLs) and transfer the power fluctuation of CL to NCL. The typical ES topology with voltage source is studied in this article, and a deadbeat predictive power control strategy of ES system is proposed, in which the system's active power and reactive power are controlled, respectively, by applying the calculated ES reference voltage to the inverter, with good dynamic performance, no steady-state error, no need for parameter tuning, and no phase-locked loop (PLL). To address parameter sensitivity issues inherent in model-based control, an integrated online parameter identification method based on recursive least squares (RLSs) is implemented. Finally, the proposed control strategy is validated by both simulation and experimental results.