This paper focuses on the application of digital twins in the field of electric motor fault diagnosis. Firstly, it explains the origin, concept, key technology and application areas of digital twins, compares and analyzes the advantages and disadvantages of digital twin technology and traditional methods in the application of electric motor fault diagnosis, discusses in depth the key technology of digital twins in electric motor fault diagnosis, including data acquisition and processing, digital modeling, data analysis and mining, visualization technology, etc., and enumerates digital twin application examples in the fields of induction motors, permanent magnet synchronous motors, wind turbines and other motor fields. A concept of multi-phase synchronous generator fault diagnosis based on digital twins is given, and challenges and future development directions are discussed.

As an important hub equipment in the power system, the power transformer has increasingly higher requirements for its reliability and safety with the rapid development of the power industry. However, the vibrations and noise generated during its operation not only cause a series of issues such as loose windings, core wear, and fatigue of fasteners, but also lead to anxiety and unease among nearby residents, severely affecting their physical and mental health. Therefore, the generation mechanism and control measures of vibration and noise in power transformers have attracted extensive attention from scholars. The magnetostrictive effect of silicon steel sheets is one of the important factors in the causes of transformer vibration and noise. This paper focuses on the magnetostrictive effect of silicon steel sheets. Firstly, the micro mechanism of magnetostriction is introduced. Secondly, based on current research status, the vibration and noise mechanism caused by magnetostriction are summarized, and the influencing factors, measurement, modeling, and noise reduction methods based on magnetostriction are summarized. Finally, based on the current research status and future development trends, prospects are proposed, aiming to provide references for subsequent research on transformer vibration and noise.

Autonomous navigation and target search for unmanned aerial vehicles (UAVs) have extensive application potential in search and rescue, surveillance, and environmental monitoring. Reinforcement learning (RL) has demonstrated excellent performance in real-time UAV navigation through dynamic optimization of decision-making strategies, but its application in large-scale environments for target search and obstacle avoidance is still limited by slow convergence and low computational efficiency. To address this issue, a hybrid framework combining RL and artificial potential field (APF) is proposed to improve the target search algorithm. Firstly, a task scenario and training environment for UAV target search are constructed. Secondly, RL is integrated with APF to form a framework that combines global and local strategies. Thirdly, the hybrid framework is compared with standalone RL algorithms through training and analysis of their performance differences. The experimental results demonstrate that the proposed method significantly outperforms standalone RL algorithms in terms of target search efficiency and obstacle avoidance performance. Specifically, the SAC-APF hybrid framework achieves a 161% improvement in success rate compared to the baseline SAC model, increasing from 0.282 to 0.736 in obstacle scenarios.

The state-of-charge (SOC) estimation accuracy is closely associated with the estimation method and the battery parameter identification performance. The battery parameter identification method based on forgetting factor recursive least squares (FFRLS) has the advantages of high parameter identification accuracy and fast dynamic response speed. On this basis, the performance of two SOC estimation methods, the extended Kalman filter (EKF) and adaptive extended Kalman filter (AEKF) are compared and studied. The results show that AEKF has better steady-state and dynamic SOC estimation performance, but the estimation accuracy and dynamic response performance are still not objective. To further improve the performance of SOC estimation, a joint SOC estimation method based on FFRLS-AEKF is proposed, and the SOC estimation experimental results with FFRLS-AEKF and AEKF are conducted. The experimental results show that the proposed joint SOC estimation method based on FFRLS-AEKF has a better steady-state and dynamic performance of SOC estimation. The maximum absolute error of the proposed algorithm is 4.97%. As the battery working time increases, the SOC estimation accuracy continues to converge to the true value, and the average absolute error is reduced to 2.5%. The proposed method and theoretical analysis are proven to be correct and feasible.

In this paper, a dual-channel RF-DC microwave rectifier circuit is designed with a 2:1 power distribution ratio in a Wilkinson power splitter. The rectifier circuit works at 2.45 Ghz. After impedance matching and tuning, the structure is able to broaden the dynamic power range of the rectifier circuit while maintaining maximum rectifier efficiency. Compared with the HSMS2820 rectifier branch, this design enhances the power dynamic ranges of 60% efficiency and 50% efficiency by 4 dBm and 3 dBm, respectively. Compared with the HSMS2860 rectifier branch, for the efficiency of 60% and efficiency of 50%, the power dynamic range is expanded by 5 dBm and 2 dBm, respectively. This shows that the technology is helpful for improving the stability of energy conversion at the receiver end of microwave wireless energy transmission systems. Finally, the rationality of this conclusion is verified by establishing a mathematical model.

In this paper, an exact linearization algorithm and nonlinear control scheme of the inductive power transfer (IPT) system for electric vehicles (EVs) are proposed, considering a variation in system operating point. For ease of modeling, the secondary side of IPT system is equivalent to a reflected reactor and resistor on primary side at different operating frequencies. By utilizing the exact linearization method, the nonlinear model is accurately transformed into a linear one at whole operating points. Besides, to acquire constant currentnvoltage (CV/CC) charging and zero voltage switching (ZVS) operating for EVs, the controllers are designed and optimized in the linear space, and then inverse mapped to the nonlinear space. This avoids designing different controllers for each operating point. Compared with a traditional PI controller, the nonlinear control scheme suggested in this paper enables the system to obtain a fixed dynamic response even if the operating point of the IPT system changes. Finally, practical results obtained from a hardware prototype are included. They confirm the performances of the system and indicate that the proposed nonlinear control scheme can automatically maintain CCnCV output and ZVS operation with a constant response time of 10 ms.