The dc solid-state transformer (dcSST) plays a vital role in interconnecting diverse dc sources and loads in dc microgrids. However, output voltage regulation and submodule power balance control have always been two essential control challenges of the dcSST. To address these challenges, this article proposes a multiagent soft actor-critic-based active disturbance rejection control (MASAC-ADRC) method. The ADRC method is used for uncertainty estimation and compensation in modular dcSST. Specifically, the controller gains of the ADRC method are optimized adaptively by the MASAC method, thus enhancing the adaptability to changing conditions from the environment. Compared with the existing controller gain optimization methods, the proposed method does not rely on predetermined datasets. Instead, it provides a tailored strategy, employing a neural network to map optimal ADRC parameters from the measured states. Through the incorporation of diverse environmental scenarios, encompassing variant input voltage and output power, the MASAC-ADRC method achieves superior dynamic performance. Experimental validation underscores the efficacy of the proposed algorithm, showcasing its superiority over alternative approaches. The proposed method yields enhancements exceeding 50% in dynamic performance metrics such as overshoot, settling time, and mean square error when compared with existing methods.

The cascaded H-bridge (CHB) low-capacitance static compensator (LC-StatCom) has a limited operating range in the inductive region compared to a conventional StatCom due to the inherent large oscillations on the capacitor voltages. This limitation constitutes a great drawback of CHB LC-StatComs. This article presents an effective hardware-free solution to overcome this drawback. The proposed solution is based on the injection of an optimal third-harmonic circulating current to suitably shape the capacitor voltages aiming at avoiding overmodulation. Considering all the effects of the third-harmonic circulating current, the article proposes a reference design that guarantees a prescribed safe operating voltage limit, thus increasing the operational range of the LC-StatCom. The proposed approach takes into account the inherent induced oscillation on the capacitor voltages. This approach has been verified by simulation and experimentally with a laboratory prototype, corroborating the excellent performance that agrees with the theoretical analysis.

This article proposes a physics-informed deep transfer reinforcement learning (PIDTRL) approach for power balance control and triple phase shift (TPS) modulation method for the input-series output-parallel dual active bridge (ISOP-DAB) converter-based auxiliary power module (APM) in electric aircraft. The approach involves three stages: 1) centralized training of deep reinforcement learning agents to balance power and reduce current stress in the ISOP-DAB converter; 2) effective knowledge transfer from a source simulation system to a target experimental system using minimal experimental data, providing a scalable solution without extensive data reliance; and 3) deployment of multiple agents for online control in the ISOP-DAB converter. The proposed method adaptively determines optimal modulation variables (duty cycles and phase shifts) in stochastic and uncertain environments without requiring accurate model information. The experimental results validate the effectiveness of the proposed PIDTRL algorithm.

This article presents a discontinuous modulation (DM) strategy for static compensators (StatComs) based on a cascaded H-bridge (CHB) converter with a star configuration. The proposed DM strategy considers the capacitor voltage oscillations at twice the fundamental frequency and the effect of zero-sequence voltage injection on the capacitor voltages. Considering these effects is especially important in CHB-StatComs with large capacitor voltage ripples (low-capacitance StatComs), where the assumption of a constant dc-link voltage, which is the basis of conventional DM strategies, does not apply. This article also describes a coherent set of steady-state waveforms for CHB-StatComs under DM. In addition to the well-known benefit of reducing the switching losses, the proposed DM also reduces the dc-link capacitors size and extends the operating range. The viability of the proposed DM strategy is verified experimentally on a small-scale prototype. In addition, simulation results are obtained using a real-scale system to study feasibility of the DM under unbalanced conditions.

In this letter, a hybrid method of fault detection using data and models, based on easy knowledge transfer learning, is proposed. The proposed method is applied for multiple battery converters, where new systems that are integrated into a microgrid are trained using the knowledge acquired by the existing systems during the offline phase. The new Target classifier can detect both open-circuit faults and current sensor faults with a 60% dataset reduction. The effectiveness of the method has been experimentally corroborated in a microgrid with two three-phase two-level converters, one Source, and one Target. Different values in terms of voltage, capacity, and power rating of the batteries, are tested using hardware in the loop. The detection accuracy is 99.1%.

The cascaded H-bridge low-capacitance static compensator (StatCom) deliberately reduces the capacitor size while allowing large oscillations in capacitor voltages. When a capacitor deteriorates, its capacitance decreases or/and its equivalent series resistance (ESR) increases, compromising StatCom performance and safety. For this reason, the condition monitoring of the capacitors is important to schedule maintenance before failure. This letter introduces a cost-effective and nonintrusive method to monitor the condition of capacitors online. The proposed method offers real-time monitoring without requiring additional hardware or signal injection. The method uses averaged low-frequency signals instead of instantaneous high-frequency signals that can be corrupted by undesired high-frequency noise. Specifically, the method takes advantage of the inherent twice-fundamental-frequency oscillations on the capacitor voltage and current to identify the ESR and capacitance values. The method takes into account the influence of antialiasing filters required for sampling process. The letter shows experimental results that validate the fast and accurate performance of the proposed capacitor condition monitoring.