The nonlinear characteristics of inductors, especially those with soft magnetic powder cores, are often perceived as undesirable and should be typically either avoided or mitigated. However, this letter presents an innovative method to take advantage of the nonlinear behavior of inductors in interleaved dc–dc converters. By analyzing the total current shape after interleaving, a novel single-sensor current balancing method is proposed, which is realized by using peak current sampling and exploiting the inductor's nonlinearity. The proposed method not only achieves low-cost current balancing with just one current sensor but also reduces the hardware volume and complexity of the converter. In addition, it also provides inspiration for various current balancing applications. Experimental validations for an interleaved dc–dc converter confirm the effectiveness of the proposed current balancing scheme.

With the high penetration of renewable energy sources, the inertia of microgrids has declined. In dc microgrids, the lack of inertia leads to bus voltage fluctuations, which may threaten system stability. To mitigate this issue, virtual inertia can be provided by energy storage units via emulating the inertial properties akin to virtual dc motors. Although the fast voltage change can be restrained, the principle of virtual inertia remains elusive and there is no suitable implementation for V-I droop. With these considerations, in this article, the voltage regulation issue is analysed from an impedance perspective, and it is revealed as transient current sharing between the dc-link capacitor and equivalent converter impedance. On this basis, an inertia emulation strategy is proposed based on capacitor current regulation and droop gain modification. With the proposed strategy, the equivalent converter impedance is reshaped in the medium-frequency range to absorb the transient current, and the capacitor current is suppressed. Consequently, the virtual inertia is provided to improve voltage regulation. Remarkably, in the proposed strategy, no additional control loops or measurement is required, and it seamlessly integrates with existing V-I droop control methods. Then, the proposed inertia emulation is validated with hardware-in-loop and experimental test results.

Voltage and current balance are essential in an interleaved three-level dc–dc converter. In this article, a simultaneous voltage and current self-balance mechanism based on near-critical conduction mode (CRM) for an interleaved three-level dc–dc converter is discovered, which can realize current and voltage balancing automatically without sensors. First, the zero-voltage switching (ZVS) and resonance transition generated by near-CRM are analyzed in detail. Second, the actual equivalent duty cycle is formulated to disclose its relationship to the unbalanced capacitor voltages and unbalanced inductor currents. It is found that the additional equivalent duty cycles in near-CRM are created and affected by the resonance around the valley and peak currents, so that some feedback loops are naturally formed to adjust the unbalanced voltages and currents. Then, by constructing the 3D relationship and natural feedback loops, the simultaneous voltage and current self-balance mechanism is proposed, where the additional equivalent duty cycles are simultaneously compensated by the unbalanced voltages and currents to counteract the unbalanced components. Finally, a three-phase interleaved three-level dc–dc converter is tested to experimentally verify the voltage and current self-balance mechanism based on near-CRM .

Conventional optimization approaches for dual active bridge converters (DAB) involve separate analysis and experimental verification stages, which may lead to suboptimal experiment results due to unaccounted parameters. This article presents a data-driven control parameters optimization method for DAB. The theoretical analysis and experimental verification of power loss serve as the source and target domains, respectively. By employing a large-scale set of simulation samples, we train an artificial neural network to evaluate power loss under various operating conditions. The insights gleaned from the pretrained source domain model are subsequently transferred to a target domain model (TDM) through transfer learning fine-tuning on a small scale of experiment samples. The TDM is utilized within a mathematical software to explore optimal control parameters, striking a balance between precision and calculation complexity. Experimental results from a 2.4-kW 400-V DAB prototype demonstrate that the proposed peak efficiency searching method progressively enhances the accuracy of the power loss model through the accumulation of experimental data. Outperforming conventional AI-based optimization methods, our approach utilizes a TDM based on real-world experimental data, effectively guiding the search for optimal control parameters, and ensuring the attainment of actual peak efficiency.

DC microgrid clusters are effective solutions for integrating multiple autonomous subgrids at the same and different voltage levels. In such a system, global power management is an essential concern, but it is always degraded by system structures and line impedances of distributed generation units (DGUs). To solve this problem, a decentralized multilayer master-slave control strategy is proposed. In the selected master DGU, an ac signal is injected into the output voltage, and power information is carried by its frequency and consequently broadcast to other slave DGUs via power lines. In slave DGUs, the output power is regulated to track the demodulated power information, which generates voltage offset to realize power sharing in a single subgrid. The interlinking converter is exploited for interconnection between two subgrids, it is also used to transmit the broadcast power information from one subgrid to another one, which therefore ensures global power management throughout the dc microgrid cluster. All the control algorithms rely on only local measurement, and information transmission is implemented via existing power lines, no dedicated communication network is required. The effectiveness of the proposed strategy is validated by hardware-in- loop and experimental results.

Three-level boost (TLB) converters are a promising interface of bipolar DC microgrids. With different power of the positive and negative DC buses, the conventional modulation method used in TLB converters fails to have zero voltage switching (ZVS) for all switches, and the peak current is also high, leading to the risk of inductor saturation. In this article, an optimized modulation (OM) method is proposed to achieve ZVS for all switches even under unbalanced loads. The OM method features the same valley current at both the high and low output power sides, contributing to wider ZVS range as well as smaller peak-to-peak current. The modulation degree of freedom in TLB converters under unbalanced-load conditions is analyzed in detail for the first time, and the advantages of the proposed OM method are compared with its counterparts in terms of the ZVS range, the peak and valley current, and the efficiency. The effectiveness and advantages of the proposed OM method are finally validated by experiment.