IET Power Electronics最新文献

This article presents a magnetic structure for coupled inductor filters that simplifies the adjustment of the coupling factor between the windings and the target mutual and self-inductances to achieve a zero-ripple input current. The proposed structure utilizes a low-permeability ferrite polymer in the core gap. This design significantly reduces the fringing flux and restricts the leakage flux path, allowing for precise tuning of the magnetic flux path and simplifying the circuit design. The proposal also decreases the required number of turns in each of the coupled windings, increasing the overall power density. The necessary equations to design the proposed structure are determined and validated through FEA simulation and experimental testing on a step-up converter incorporating the designed coupled inductor. The results demonstrate the effectiveness of the proposed structure in achieving significant reductions in inductance requirements, enabling an increased power density while maintaining zero-ripple operation under varied conditions.

Inductive power transfer (IPT) technology has found widespread applications across various fields. As demand for IPT systems increases, single-stage IPT systems are gradually becoming inadequate to meet the increasing requirements. Therefore, two-stage IPT systems that incorporate front-end or rear-end DC/DC converter have been developed. However, the DC/DC converter in these cascaded two-stage IPT systems introduce considerable energy losses. This paper aims to mitigate the energy losses by building several high efficiency two-stage IPT system with proposed partial power processing feature. The paper first presents the proposed overall structure, followed by the development of an equivalent circuit model and a detailed power analysis. Subsequently, simulation and experimental results are provided to validate the performance of the proposed system. Under a 40 V input and 120 W output, the system achieves a maximum efficiency of 90.52%. It is then extended to a high-power scenario, where, with a 100 V input and 500 W output, the maximum efficiency reaches 95.29%. Compared to conventional two-stage IPT systems, the proposed system only processes partial power through the DC/DC converter, and hence offers reduced losses and diminished stresses on components. Therefore, a more compact design with enhanced power density and reduced overall system cost can be achieved.

As the power conversion device between new energy generation systems and the power grid, the high-frequency resonance self-stability of the current loop of grid-following (GFL) converters has been widely researched. However, this paper finds that, in addition to the current loop, the power loop of GFL converters also has high-frequency resonance self-stability issues when adopting a closed-loop control strategy. Therefore, this paper researches the high-frequency resonance mechanism in the power loop first, and the research results show that the resonance in the power loop is induced by power-current loop interaction, which results from the mismatched control parameters between the power loop and the current loop. Based on this, a self-stability design method is proposed in this paper. This method reduces power-current loop interaction by designing the power loop control parameters based on existing current loop constraints, thereby suppressing the high-frequency resonance in the power loop. Finally, the experimental results validate the feasibility and effectiveness of the theoretical analysis and the proposed design method.

Although the stable operation of grid-forming inverters (GFMIs) in weak grids is well-documented, undesirable oscillations tend to arise when cascaded multi-loop control architectures are employed, particularly under strong-grid conditions. In this work, a sequence impedance model of the GFMI is developed, through which the closed-loop feedback control of the inductor current is identified as the main contributor to oscillations. Accordingly, a stabilisation strategy for GFMIs, combining inductor current feedforward and coupling-enhancing techniques, is proposed for strong-grid applications. Finally, a 2 kW experimental prototype was constructed to validate the stability enhancement method, thereby providing practical verification.

Ensuring a seamless transition between grid-connected and islanded modes of operation in microgrids is a critical challenge due to transient disturbances during the transition between the corresponding control strategies (grid-forming and grid-following control strategies), affecting system stability and reliability. This paper presents an advanced control strategy that relies on a linear quadratic regulator-based smooth transition regulator (LQR-STR) to enhance the smoothness and reliability of microgrid mode transitions. Unlike previous LQR-based approaches, the proposed method integrates the full control loops in the smooth regulator design, including the virtual impedance loop, and voltage and current feedforward coefficients for the grid-forming controller, and active/reactive power control loops for the grid-following controller. The impact of imperfect islanding detection and premature reconnection is analysed, demonstrating the robustness of the proposed method under varying transition conditions. The effectiveness of the proposed approach is validated through detailed simulations in MATLAB/Simulink and through a real-time embedded system using the NI sbRIO GPIC Evaluation Kit. The simulation and experimental results show significant improvements in reducing transient overshoots, enhancing voltage and frequency stability and ensuring reliable mode transitions.

The increasing adoption of electric vehicles (EVs) necessitates efficient and eco-friendly charging solutions. Solar-powered EV charging offers a sustainable alternative to grid-dependent systems by reducing carbon emissions. However, the intermittent nature of solar irradiance demands robust maximum power point tracking (MPPT) algorithms to ensure optimal power extraction. Conventional MPPT methods often face challenges like slow convergence and limited tracking accuracy. To address this, the proposed study introduces a deep learning-based MPPT framework using long short-term memory (LSTM) networks for intelligent, data-driven control of a boost converter in a solar-powered EV charging system. The LSTM model is optimized employing stochastic gradient descent with momentum and trained using irradiance and temperature hourly data obtained from NASA/POWER for Jaipur city, India. The controller's performance is benchmarked against traditional algorithms, INC, PSO and ANN. Results show that the LSTM-based MPPT achieved superior tracking efficiency (97.63%), low current ripple (0.21%), and minimal prediction error (RMSE: 0.59%). Afterwards, this LSTM-tuned solar system is employed to charge a 5 kW EV through a boost and a dual active bridge converter. The entire system is validated in MATLAB/Simulink and implemented in real-time on an OPAL-RT OP4512 platform, confirming its effectiveness for intelligent and reliable solar-powered EV charging.