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.

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.

Accurate fault diagnosis and remaining useful life (RUL) prediction are critical for health management before fatal system failures occur. Reasonable stage prediction of RUL can avoid the waste of computing power. However, the difficult-to-measure multiple metrics of real systems cause the starting and end point of RUL to be challenging to determine effectively. Based on the data distribution and digital twin technique, this paper proposes a method to collect the steady-state output signal to determine the beginning and end points of the stable equilibrium state system (SET) RUL. The combination of DT technology can accurately reproduce the actual operating state and measurable characteristics of the system, update the component degradation model by collecting the current component parameters in real-time after the starting point of RUL and inject the future parameter state of the component into DT. The DT is equivalent to a multi-indicator acquisition device, which solves the problem of the physical system’s difficulty in acquiring multiple indicators determines the remaining life of the system. Achieving a reasonable starting point and accurate endpoint remaining life prediction under the dual constraints of data and mechanism. The Superbuck converter is used as an example for experiments, and the results prove that the method can reasonably determine the system’s start and end points to predict the remaining life. The comparison of the three methods proves the superior performance of the method in this paper.

Healthy DC/DC converters in undersea observation networks are essential for ensuring that these systems operate reliably over decades. Any performance degradation or failures in the DC/DC converters can result in significant, potentially incalculable damage. To prevent such issues, continuous anomaly monitoring during operation is critical for synchronized and successful mission completion. This paper introduces a method for DC/DC converter anomaly monitoring based on the complete output signal distribution. A specialized signal collection device is integrated into the monitoring system to capture and preprocess output signals, enabling reliable data acquisition in challenging environments like undersea operations. The method leverages Gaussian Process Regression (GPR) to determine monitoring thresholds and Gaussian Mixture Modelling (GMM) to detect anomalies. The robustness of the method is validated using high-order Superbuck converters, with comparative analysis against other monitoring methods, demonstrating its superior performance. Experimental results confirm that this method effectively identifies anomalous conditions across various types and orders of DC/DC converters, regardless of signal type, ensuring versatility and reliability in critical applications.