The moving-average-filter-based quasi-type-1 phase-locked loop (MAF-QT1 PLL) can provide zero steady-state phase error in the presence of frequency drifts, achieving a high filtering capability. However, in the presence of an MAF and frequency drift, MAF-QT1 PLL cannot achieve a fast transient response and frequency-dependent characteristic under adverse grid conditions. To overcome this drawback of the MAF-QT1 PLL, this paper proposes a frequency-adaptive improved moving-average-filter-based quasi-type-1 PLL (FAIMAF-QT1 PLL). Aiming at the shortcoming of the slow dynamic response of the MAF-QT-1 PLL, a correction link is introduced, and an improved moving-average-filter-based quasi-type-1 PLL (IMAF-QT1 PLL) is obtained. To improve the anti-interference ability of the IMAF-QT1 PLL in response to grid frequency changes, a FAIMAF-QT1 PLL and a corresponding digital implementation scheme are proposed. The regulator parameter setting method is proposed based on the small-signal model of the FAIMAF-QT1 PLL. Simulation and experimental results show that the FAIMAF-QT1 PLL can accurately track the grid voltage phase in the presence of power grid frequency fluctuations, phase angle jumps, distorted harmonic injections and unbalanced voltage drops and has good steady-state and dynamic performance.

This paper addresses the challenge of diagnosing open-circuit faults in power devices within three-level Neutral Point Clamped (NPC) converters. An enhanced one-dimensional Convolutional Neural Network (1D-CNN) based fault diagnosis method is proposed. The method begins with the acquisition of fault data, including three-phase voltages on the DC side and three-phase output currents. A fault feature matrix is constructed using the Complementary Ensemble Empirical Mode Decomposition (CEEMD) algorithm, followed by energy percentage feature extraction. This matrix is then processed by the enhanced 1D-CNN framework, which effectively detects both single and dual open-circuit faults in power devices. To address single open-circuit faults, a fault-tolerant control strategy based on space vector pulse width modulation (SVPWM) is introduced, ensuring continuous operation by adjusting the space vector upon fault detection. Additionally, a redundant power unit is employed to maintain consistent output voltage and current amplitude during fault-tolerant operations. The proposed method’s effectiveness is validated through simulation and experimental results, demonstrating its capability to accurately locate faulty devices and enable fault-tolerant operation. This research presents a reliable solution for open-circuit fault diagnosis, improving the resilience and operational efficiency of three-level NPC converters.

The reliability of SiC MOSFETs plays a critical role in ensuring the stability and safety of power electronic converters, highlighting the need for a precise and data-driven approach to predict their Remaining Useful Life (RUL). This study focuses on developing an accurate RUL prediction model by identifying the on-state drain-source voltage (V_{ds_on}) as the critical failure parameter in power cycling experiments for SiC MOSFETs. Based on experimental data, a hybrid prediction model, Autoformer-Regularized Extreme Learning Machine (RELM), is proposed and validated for RUL prediction. Experimental results demonstrate that the Autoformer-RELM model reduces MAPE by 26.84% compared to Autoformer, while achieving 55.56% and 57.45% improvements over Informer and LSTM, respectively. The Autoformer-RELM model outperforms traditional approaches by effectively handling nonlinearity and long-term dependencies, resulting in significantly improved prediction accuracy. In addition to advancing RUL prediction methodologies, this research provides meaningful insights into the reliability analysis and practical applications of SiC MOSFETs and other power devices.

This paper, regarding a five-level nested neutral point piloted (NNPP) converter as the research object, analyzes the working mechanism of an NNPP five-level converter and determines the switching commutation circuit and switching principles. To solve the problem of common-mode voltage (CMV) in NNPP five-level converters, a control strategy based on fast model predictive control (MPC) is proposed to effectively suppress common-mode voltage. The control method proposed in this paper is divided into two main parts. First, 19 zero common-mode voltage (ZCMV) vectors are selected from 125 voltage vectors, and 19 ZCMV vectors are optimized to select the best ZCMV vector. Next, according to the switching principles, the effective switching combinations are selected as the effective candidate control set, and proper switching combinations of the optimal ZCMV vector are determined. The effectiveness and validity of the proposed suppression strategy based on fast MPC in terms of the steady state and dynamic performance is confirmed by simulation and experimental results of the five-level NNPP converter.

The phase-locked loop of grid-connected inverters affects their output impedance characteristics and stability. Based on the NPC, three-level LCL grid-connected inverter, this article first analyzes the frequency characteristics of the improved moving average filter phase-locked loop (IMAF-PLL). Then, analyzing the influence of the neutral point control on the inverter impedance modeling, the harmonic linearization impedance modeling method combined with the symmetrical component method is used to establish a positive and negative sequence impedance model of the NPC three-level LCL grid-connected inverter. Furthermore, the frequency characteristics of the IMAF-PLL and the impedance model of the NPC three-level LCL grid-connected inverter are verified by simulations. Finally, the impedance-based analysis method is used to study the stability of the grid-connected system under different grid impedance conditions when using different phase-locked loops, and the theoretical analysis was validated experimentally.