In order to predict the performance of designed electric machines, and also based on which the high efficiency of the drive systems can be obtained, the accurate calculation of the iron loss is necessary. Taking modeling accuracy versus modeling complexity into account, this paper proposes a general and accurate iron loss calculation method considering harmonics based on the Loss Surface(LS) hysteresis model and Finite Element Method(FEM). Compared with the previous triangular wave excitation, this paper uses the data under sinusoidal wave excitation to establish the LS model, which is more convenient for engineering application. A specimen is tested for different flux density with harmonics, the extensive results verify the modeling method. Due to a powerful hysteresis model, iron loss calculation can be realized by directly integration of hysteresis loops. A separated iron loss calculation package is developed, and it is combined with the commercial finite element software to compute the iron loss distribution. The method that combines the LS model and FEM is proved feasible by comparing the calculated iron loss with the measured one for the Epstein frame.

Due to the use of multiple observers and controllers in multi-sensor fault-tolerant control of PMSM drive systems, the algorithm is complex and the system control performance is affected. In view of this, the paper studies multi-sensor fault diagnosis and active fault-tolerant control strategies based on a composite sliding mode observer. With the mathematical model of PMSM built, a design method of the composite sliding mode observer is proposed. A single observer is used to observe and estimate various state variables in the system in real time, which simplifies the implementation of observer-related algorithms. In order to improve the diagnostic accuracy of different types of sensors under different faults, a method for determining fault thresholds is proposed through global search for the maximum residual value. Based on this, a fault diagnosis and active fault-tolerant control strategy is proposed to realize fast switching and reconstruction of feedback signals of closed-loop control systems under different faults of multiple sensors, thus restoring the system performance. Finally, the effectiveness of the proposed algorithm and control strategy is verified by simulation experiments

In view of the complex calculation and limited fault tolerance capability of existing neutral point shift control algorithms, this paper studies the fault-tolerant control method for sub-module faults in modular multilevel converters on the basis of neutral point compound shift control strategy. In order to reduce the calculation complexity of shift parameters in the traditional strategy and simplify its implementation, an improved AC side phase voltage vector reconstruction method is proposed, achieving online real-time calculation of the modulation wave adjustment parameters of each phase required for fault-tolerant control. Based on this, a neutral point DC side shift control method is proposed to further improve the fault tolerance capability of the modular multilevel converter (MMC) system by compensating the fault phase voltage with non-fault phase voltage. By means of the compound shift control strategy of the DC side and AC side of the neutral point, an optimal neutral point position is selected to ensure that the MMC system output line voltage is symmetrical and the amplitude is as large as possible after fault-tolerant control. Finally, the effectiveness and feasibility of the proposed control strategy are verified by simulation and low-power MMC experimental system testing.

The traction main circuit serves not only to supply power to the EMUs train set but also plays a crucial role in the initiation, speed regulation, and braking of the train. Therefore, diagnosing the operational status of the traction main circuit is of paramount importance. This paper initiates the modeling of the traction main circuit for the CRH2 EMUs train set, followed by an analysis of the primary components’ faults within the traction main circuit. Voltage and current signals from the transformer’s primary side, traction winding, as well as the stator current and speed signals of the traction motor, are collected. Fault characteristics are then extracted using FFT and wavelet analysis methods. Subsequently, employing the Product-based Neural Network (PNN), the transformer, converter, and motor are subjected to fault diagnosis. Experimental results validate the high accuracy of the proposed fault diagnosis method presented in this paper.