Power electronic systems (PESs) are pivotal in power generation, transmission, and diverse industrial applications. With the increasing shift toward digitalization, digital twin (DT) technology has emerged as a transformative enabler, enhancing the informatization and intelligence of PESs. This paper offers a comprehensive overview of DT applications for PESs, highlighting core features of real-time synchronization, accurate mapping, seamless data interaction, and high-fidelity modeling. Four prevalent DT modeling approaches are reviewed, with a detailed discussion of their respective strengths and challenges in the context of PESs. The paper further examines the role of DT across three critical lifecycle phases—design, control, and maintenance—illustrating how DT optimizes and drives innovation at each phase. By reviewing over 170 publications, this study identifies key trends, implementation challenges, and research gaps, offering valuable insights into the state-of-the-art and future directions for DT in PESs. This work aims to serve as a foundational reference for researchers and practitioners, fostering a deep understanding of the potential of DT and providing a comprehensive grasp of the application in academics and the industry.

The auxiliary converter is the power source of the vehicular electronic system, the failure of which may cause inestimable economic loss. As the most age-affected component in the converter, the online health monitoring for output aluminum electrolytic capacitor (AEC) is of great significance. Throughout the full-life period of AEC, the capacitance ( $C$ ) will fall and the equivalent series resistance (ESR) will rise. This article proposes an online proactive health monitoring method for output capacitors of a 1-kW vehicular auxiliary converter. The operating mechanism of the two-level Boost-LLC converter is analyzed and the ESR calculation model is derived in detail. A novel ripple measurement approach and a peak current calculation method are proposed. Both ripple measurement and current approximation are confirmed to be practical and applicable. Finally, the online health monitoring platform is built. The experimental results can show the effectiveness of this method. The monitored ESR can indicate the healthy degree for the output capacitors in the auxiliary converter. The influences of switching frequency and ambient temperature are verified to express the superiority of the online monitoring scheme.

This paper proposes a detection and classification method of recessive weakness in Superbuck converter through wavelet packet decomposition (WPD) and principal component analysis (PCA) combined with probabilistic neural network (PNN). The Superbuck converter presents excellent performance in many applications and is also faced with today’s demands, such as higher reliability and steadier operation. In this paper, the detection and classification issue to recessive weakness is settled. Firstly, the performance of recessive weakness both in the time and frequency domain are demonstrated to clearly show the actual deterioration of the circuit system. The WPD and Parseval’s theorem are utilized in this paper to feature the extraction of recessive weakness. The energy discrepancy of the fault signals at different wavelet decomposition levels are then chosen as the feature vectors. PCA is also employed to the dimensionality reduction of feature vectors. Then, a probabilistic neural network is applied to automatically detect and classify the recessive weakness from different components on the basis of the extracted features. Finally, the classification accuracy of the proposed classification algorithm is verified and tested with experiments, which present satisfying classification accuracy.

Tailplane control system (TCS) is a key component to ensure pitch maneuverability and horizontal stability in flight control. However, due to inherent sealing, time-varying, and uncertainty, conventional control methods involve enormous challenges to guarantee optimal operation of the TCS. This article proposes a digital twin-based predictive control method, called twin predictive control (TPC), to explore tailplane optimal control under complex conditions. First, a digital twin of the TCS is established as a predictive model, and twin adaptation is set up to overcome parameter time-varying and uncertainty for supporting accurate prediction. Then, an optimization model is constructed combining tracking error and control adjustment. Based on model transformation, the optimization objective is theoretically proved convex and coupled with the active-set method to enable efficient optimization solving. Finally, the TPC method, integrating the digital twin and the optimization model, is implemented into a physical experimental system to verify the effectiveness of the proposed method. The experimental results and comparisons show that the TPC method can significantly improve tracking and antiinterference performance. Furthermore, it shows merits in excessive adjustment suppression.

Automotive auxiliary converter empowers the electronic system, the failure of which will affect the system performance. As a critical component in the feedback loop of the converter, real-time health monitoring for optocoupler is of great importance. The degraded optocoupler mainly shows a falling current transfer ratio (CTR). This article proposes an online proactive CTR monitoring method for optocoupler via voltage measurement of critical nodes. The monitored CTR can indicate the health condition of the optocoupler. First, the principle of a boost- LLC converter is analyzed in detail. Then, the influences of degraded CTR on the feedback loop are derived. An online proactive CTR monitoring scheme is introduced. The realization of voltage measurement, signal filtering, wireless communication, and time synchronization are in two independent embedded systems. CTR will be calculated in the local server and be stored in the local database. The proposed method can monitor CTR for both unaged and aged optocouplers. Effects of switching frequency and ambient temperature are also incorporated for online CTR monitoring. Experimental results validate the superiority of this online proactive monitoring method. The method could be extended and further evaluate the health condition of the overall automotive auxiliary converter.

This paper highlights the critical role of reliable DC-DC converter operation in ensuring the stability of power conversion systems, especially in extreme environments like underwater observation networks. Addressing the common challenge in DC-DC converter fault diagnosis—overlooking the periodic characteristics of electrical signals during feature extraction from large datasets—we propose an innovative diagnostic method that combines Adaptive Wavelet Transform (AWT) and an Enhanced Classifier Generative Adversarial Network (ECGAN). First, AWT dynamically selects and optimizes WBFs to accurately capture degradation features in the output voltage signals. Then, a Compound Rainbow Convolution Block (CRConvBlock) is used to enhance the time-frequency representation of these signals, integrating temporal and frequency information for improved feature extraction. Furthermore, the proposed ECGAN model is guided by an Adaptive Loss Optimization Framework (ALOF) that dynamically adjusts training weights to balance sample quality and classification accuracy. Experimental validation in four different circuits demonstrates the high accuracy and robustness of the method, highlighting its potential for practical engineering applications.