In this paper, an open-circuit (OC) fault diagnosis algorithm for power switch in the motor drive inverter system is proposed, which uses signal normalization preprocessing to deal with the current fluctuation caused by the load change. For the proposed fault diagnosis algorithm, firstly, the three-phase currents are sampled by the sliding window, which can improve the timeliness of diagnosis. Then, the sampled data is preprocessed by the variable modulus length normalization to eliminate the influence of the load change. Especially in the transient state, the information of the data feature is retained and the impact of load change is removed. Moreover, the frequency domain feature of the data is extracted by the DFT. Finally, the dimension of the features is reduced by the PCA method and its contribution graph is used to locate the fault IGBT. Moreover, the method can overcome the misjudgment of IGBT failure caused by transient currents when the load changes in the drive system. The simulation and experiment results show its effectiveness and timeliness in the OC fault diagnosis of the inverter.

This article presents a open-circuit fault diagnosis method for a sensorless vector control system based on the voltage–current hybrid model flux observer. The error voltage, which is obtained after a PI module, is used as a diagnostic factor. This PI module is designed to correct the deviation between the voltage model flux observer and the current model flux observer. The error voltage is not affected by load and speed variation. First, the activation function is used to separate fault information and normal information, and the feature function is defined to describe the signal feature in healthy and faulty conditions. Second, a second-order moving mean filtering method is proposed to reduce the storage of the error voltage data and increase the timeliness of diagnosis. Finally, the fault is diagnosed and located through logical calculations. Compared with conventional methods, this method features the timeliness of fault diagnosis in a closed-loop system and robustness with respect to speed and load changes. It reduces the storage and operation burden such that it can be easily implanted into the control algorithm as a subroutine. Simulations and experiments show the accuracy and superiority of the proposed fault diagnosis method for voltage source inverter fed sensorless vector controlled drives.

In order to improve the speed regulation performance of induction motors, a multi-modulation times space vector pulse width modulation (MMT-SVPWM) is proposed for the three-level neutral point clamped (NPC) converter. This method can eliminate the dead time effect (DTE), which is caused by the addition of the dead time (DT) in the SVPWM. This paper firstly analyzes the deviation of the output voltage which is caused by the DTE in the SVPWM, and then proposed the MMT-SVPWM. The generation of driving signals in this method is different from the ideal SVPWM. According to the directions of three-phase currents, the driving signals of a pair of complementary power switches are obtained by comparing a pair of multiple modulation times with the triangular carrier. Thus, this method directly eliminate the DTE, which is different from the SVPWM with DT (DT-SVPWM). The output voltage vector is the same as that of the ideal SVPWM, and there is no zero-current clamping effect. Finally, the simulation and experimental results show that the proposed method effectively eliminates the DTE and improves the output currents of the three-level NPC converter.

Wind power is of strategic importance for reducing carbon dioxide emissions, minimizing environmental pollution, and enhancing the sustainability of energy supply. Health monitoring of wind turbines is a crucial technology to ensure the quality of grid-connected power. Insufficient labeled data and class imbalance problems are two critical issues for intelligent fault diagnosis of wind turbines. In this article, an intelligent fault diagnosis method based on stacked capsule autoencoders is proposed to address the issues of inadequate labeled data and class imbalance. A prior knowledge-based convolution layer is applied to optimize the initialization of capsules, making it more conducive to learning spectral information. The pose representations of parts and objects can be improved, and a method for embedding spectral templates is proposed. The stacked capsule autoencoder in this study can learn partial templates unsupervised through likelihood estimation and establish the mapping between capsules and fault types. The experimental results, obtained from the CWRU dataset and a private dataset from a wind turbine drive-train simulation platform, demonstrate that the proposed method is robust to imbalanced and small-sized datasets. It can perform stable and effective unsupervised training by utilizing a sufficient amount of normal class data to expedite learning convergence.

The optimal control of fuzzy systems with constraints is still an open problem. Our focus concerns the optimal control problem of fuzzy systems derived from receding horizon control (RHC) schemes. We consider methods to numerically compute the value function for general fuzzy systems. The numerical method that is developed using the finite difference with sigmoidal transformation is a stable and convergent algorithm for the Hamilton-Jacobi-Bellman (HJB) equation. An optimization procedure is developed to increase the calculation accuracy with less computation time. A parallel-processing method is employed in the optimization procedure. The optimization results are applied to the controller design of general fuzzy dynamic systems. Employing the principle of conventional RHC schemes, RHC-form controllers are designed for some classes of fuzzy dynamic systems. The basic ideas are as follows. First, the value function is calculated by numerical methods. Then, the value function is used as controller-design parameters to redesign RHC controllers for fuzzy systems, which is motivated by the inverse Lyapunov function design method. It is proven that the closed-loop system is asymptotically stable. An engineering implementation of the controller redesign scheme is discussed. Meanwhile, the parallel-processing framework that can improve the closed-loop performance is also introduced.