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.

How to extract the fault feature and how to design the classification algorithm are the two most critical problems in power electronic circuits (PECs) fault diagnosis. Based on a kernel entropy component analysis theory, combining an extreme learning machine classification algorithm, this paper explores the feasibility of applying an ensemble approach, called KECA-ELM, to deal with the hard fault and soft fault diagnosis in a superbuck converter circuit (SCC). This approach can reduce the influence of the complex correlation between the data on the accuracy of fault classification. Furthermore, it can compress the feature dimension of the data while maintaining the feature discriminating power and reduce the computation in the classification stage. We record the signal from the output of the circuit, analyze their static and dynamic electrical performance, and then select the representative feature parameters. These feature parameters are combined into feature vectors which can reflect the health status of the PECs. Finally, simulation and physical experiment are presented in the SCC to demonstrate the fault classification ability of the proposed approach.

Conventional cabled seafloor observatories (CSOs) power in-situ instruments via wet-mated or dry-mated direct electrical contact (DEC) connectors to achieve long-term and real-time observation. However, the DEC connectors have high risks of water leakage and short circuits in power feeding, especially under high water pressure. This paper proposes a highly compatible underwater station based on inductive wireless power transfer (IPT) technology to address the above reliability issue. A novel energy transmitter with runway-structure coils is applied to the proposed underwater station to cover a sufficient power feeding area so that various in-situ equipment can be powered with desirable misalignment tolerance. The magnetic field distribution is analyzed by both derivation and finite element analysis (FEA) methods, and the principal parameters of the transmitter are further optimized and compared with both the mixed-integer sequential quadratic programming (MISQP) algorithm and the evolutionary algorithm (EA) for better performance. The same results show a reliable optimization process. The WPT circuit characteristics are also investigated to power different loads and improve the power transmission efficiency. The output power decreases, and the transmission efficiency rises and then decreases as the load increases. In addition, receivers with higher mutual inductance have better transmission performance. A prototype of the underwater station has been tested both in air and in water, and the experimental results have proven it can power multiple seafloor observation instruments stably and achieve compatibility requirements. The maximum output power of the station prototype for testing is 100 W, and the maximum overall transmission efficiency is 61%.

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.

Autonomous underwater vehicles (AUVs) are important mobile equipment for ocean observation. Subsea docking stations are used to charge AUV batteries to increase their cruise duration. Here, a universal wireless charging platform with a novel transmitter is developed to address the issues of poor compatibility existing in conventional docking stations. In order to guarantee a sufficient charging area and reduce copper losses, the proposed transmitter is designed to have a bulged‐structure surrounding coil and a centre coil in coaxial. Moreover, the parameters of the transmitter that impact the magnetic flux density are analysed and optimized by non‐linear programming by Quadratic Lagrangian (NLPQL) algorithm to achieve a field with desirable intensity and uniformity. The characteristics of the circuits under single‐load and double‐load conditions have been tested in air and tank experiments. The results verify that the proposed platform achieves both the universality and misalignment tolerance requirements.

The maintenance of scientific cabled seafloor observatories (CSOs) is not only extremely difficult but also of high cost for their subsea location. Therefore, the cable fault detection and location are essential and must be carried out accurately. For this purpose, a novel on-line fault location approach based on robust state estimation is proposed, considering state data gross errors in sensor measurements and the influence of temperature on system parameter variation. The circuit theory is used to build state estimation equations and identify the power system topology of faulty CSOs. This method can increase the accuracy of fault location, and reduce the lose form shutting down a faulty CSO in traditional fault location methods. It is verified by computer simulation and the laboratory prototype of a planned CSO in the East China Sea, and the fault location error is proved to be less than 1 km.