This paper presents a collaborative direct model predictive control strategy (CDMPC) for back-to-back (BTB) converters to achieve fast dynamic response and low voltage ripple in the DC link. Two MPC controllers are distributed on the machine-side converter (MSC) and grid-side inverter (GSI) according to their models. All control links are integrated into both predictive models, except that the PMSG mechanical speed change is much slower than the electrical state. The power generated by the PMSG is predicted in the MSC's MPC and fed forward to the GSI as an active power reference. To improve the accuracy of the reference power, an active power corrector based on PMSG flux observation and DC voltage deviation is designed on the GSI. This corrector can quickly respond to power changes in the PMSG while maintaining DC voltage stability. At the same time, the prediction accuracy of the DC voltage is further enhanced by designing an interactive model to predict the DC side current of the BTB converters. Both converters are under dual-vector MPC control to achieve better steady state performance. Experiments show that compared with commonly used distributed model prediction methods, the proposed CDMPC achieves faster power response and smaller DC voltage deviation in BTB converters.

Model predictive torque control for permanent magnet synchronous motors is sensitive to parameters and requires weighting factors. Model-free parallel predictive torque control (MF-PPTC) is one of the latest solutions. However, it suffers from a heavy computational burden and hardly achieves the optimization of both torque and flux. In this article, a parameter-free predictive torque and flux control (PF-PTFC) is proposed to improve parameter robustness and eliminate weighting factors. First, a parameter-free incremental stator flux predictive model (ISFPM) is defined by eliminating parameter terms found in conventional predictive models. Based on ISFPM, a new cost function without weighting factors is constructed to automatically compensate for the impact of missing parameter items. Compared with the MF-PPTC, the proposed strategy has a lower computational burden and better steady-state performance. Moreover, the PF-PTFC is combined with an active-disturbance-rejection-based discrete-time integral sliding-mode speed controller to improve speed regulation. The experiments confirm the proposed method's superiority.

In most industrial applications, it is difficult to obtain complete demagnetization fault signals of all conditions with labels for permanent magnet synchronous motor (PMSM), and motors are not allowed to be disassembled, so non-contact diagnostic methods are essential. A non-contact fault diagnosis method using magnetic leakage signal based on wavelet scattering convolution network (WSCN) and semi-supervised deep rule-based (SSDRB) classifier is proposed. Through magnetic equivalent circuit model analysis, the magnetic leakage signal on motor surface is selected as fault signal. To avoid complex signal processing, the symmetrized dot pattern method is introduced to convert fault signals into two-dimensional images. Then, WSCN is applied to extract features from images, and SSDRB classifier is adopted to diagnose demagnetization fault. Finally, faulty motor prototypes are manufactured for experiment. By comparing with other methods, the superiority and effectiveness of the proposed method using a small number of labeled samples under different conditions are verified.

Traditional fault diagnosis methods are easy to be affected by different working conditions. This article proposed a motor fault diagnosis method based on visual knowledge, to reduce the impact of changes in working conditions and improve the feature extraction ability. The mapping relationship between actual faults and image intuitive features by symmetrized dot pattern and scale-invariant feature transform is established in this article. The fault state is obtained by statistics of the matching point with the dictionary templates generated from signals of normal and unnormal motors. Compared with other machine learning algorithms, this method does not need too much data training and learning. The efficiency of this method is validated by experiments, and the data image processing technology has great industrial application value in the field of motor fault detection or monitoring in the age of intelligence.

During the long-term operation of motors, their working conditions are changing due to the industrial demands or declining health status, and traditional diagnosis methods perform poorly in that case. This article proposes a fault diagnosis method based on graph cardinality preserved attention network (GCPAT), which can work under varying working conditions, and can be generalized to the transient state. Diagnosis results are obtained by analyzing signal-converting graphs, which are composed of nodes and edges. First, the vibration signals are converted into symmetrical snowflake images by symmetrized dot pattern (SDP) method. Second, SLIC is developed to make homogeneous super-pixels in SDP images as nodes, and form graphs according to color, texture, and distance features. Finally, the GCPAT is utilized to distinguish motor status. Compared with other state-of-art methods, the results show the out-performance of GCPAT under varying working conditions both in steady and transient state.

To solve the problems of low efficiency and manual dependence of industrial motor winding testing, a joint scanning electromagnetic thermographic (JSET) method and a new quantitative evaluation algorithm are proposed to inspect defects automatically and assess detection capability. We establish a JSET-based defect inspection system including a joint scanning model and induction heating to simulate industrial assembly lines and acquire real-time thermograms. However, the acquired thermograms are misaligned in time and space, which cannot be used for dimension analysis. Therefore, a new 3-D data reconstruction algorithm is proposed to achieve accurate spatial-temporal alignment of the image sequence. Moreover, the parameters (scanning speed and excitation current) of the developed inspection system are optimized through obtaining the maximum inspection quantity. The new quantitative evaluation algorithm can measure the detection capability of different defects types, sizes, and positions by two features of significance and detected area. Experimental results show that the proposed methods can inspect multiple motor winding defects automatically and enhance the inspect efficiency.

Barely visible impact damage (BVID) is inevitable during either fabrication or lifetime of a carbon fiber reinforced plastic (CFRP) component. These flaws are usually difficult to be detected from the surface by visual inspection or machine vision based on a charge-coupled device or CMOS. In order to solve the problems of low efficiency, low sensitivity, and small detection area of the existing nondestructive testing (NDT) for BVID in CFRP, this paper proposes for the first time the integrated nonlinear ultrasound (NU) and vibrothermography (VT) NDT based on the shared excitation sources. The experimental system was built after introducing the principle of shared excitation based NU&VT NDT. The CFRP plates with 5, 15, and 25 J visible impact damage (VID) as well as 12 and 16 J BVID were tested using the integrated NU&VT. Experimental studies after signal processing have shown that all VID and BVID could be detected by the integrated NU&VT NDT, and the defection capability has a significant improvement after fast Fourier transform. The proposed method could provide a visualized and effective means for quality control and inspection of large-scaled and complex shape key components in manufacturing process and in service.

Defects can affect the generation efficiency and service life of photovoltaic (PV) cells and modules, and even can cause serious damage to grid connected PV power generation station. This paper proposes active electromagnetic induction infrared thermography defect detection methods for PV cells and modules, which have the advantages of noncontact, rapid, full-field, subtle, and quantitative detection. First, the mechanism of infrared radiation after electromagnetic induction of PV cells is described, and a digital EIIT system is established. Then, the thermal image sequences of PV cells and modules are obtained under pulse and lock-in modes of excitation. Fast Fourier transform, independent component analysis, and principal component analysis are used to deal with the thermal image sequences. Finally, the visual detection of defects, including scratches, hot spots, microcracks, surface impurities, and broken grids in PV cells and modules are realized. The experimental results have shown that the proposed method can distinguish the background noise and the defects very well, thus providing a reliable and rapid inspection means for the research, testing, manufacturing, service, and maintenance of PV cells and modules.

In order to solve the problems of low efficiency and small area of existing eddy current thermography industrial nondestructive testing, this work realizes in-field-of-view (FOV) dynamic scanning eddy current pulsed thermography (DSECPT) with the help of blind source separation (BSS) algorithms for continuous detection of large-scaled industrial components. The principle of FOV-DSECPT, including finite-length inductive heating of mobile coil, the reconstruction of transient temperature response, and feature extraction, is investigated. The original thermal images cannot be used for depth analysis; thus, new data reconstruction method with good adaptivity for manual movement was proposed. The emerging BSS algorithms, including independent component analysis and nonnegative matrix factorization, are employed to process the reconstructed data. Through experimental studies, images using various features from classical and BSS algorithms were compared. The proposed FOV-DSECPT could provide a visualized and effective means for quality control and inspection of large-scaled key components in both manufacturing and service processes.

Condition monitoring, nondestructive testing, and fault diagnosis are currently considered crucial processes for on-condition maintenance (OCM) to increase the reliability and availability of wind turbines and reduce the wind energy generation cost. Carbon fiber reinforced plastics (CFRPs) have been increasingly used to fabricate wind turbine blades. Delamination-type damage is inevitable during manufacture or in-service of a CFRP blade. This inner (subsurface) flaw, usually difficult to be detected by artificial visual inspection or machine vision based on CCD or CMOS, severely degrades the load-bearing capacity of a blade. Induction infrared thermography (IIT) is an emerging infrared machine vision inspection technology, which has the capability of insight to CFRP based on electromagnetic induction and heat conduction. This paper introduces photothermal thermal-wave radar (TWR) nondestructive imaging (NDI) to IIT, based on cross-correlation (CC) pulse compression and matched filtering and applies TWR principles to CFRP imaging inspection and diagnosis. The experimental studies carried out under the transmission mode have shown that TWR B-scan and phasegram can be used to inspect and diagnose subsurface delaminations in CFRP with improved signal-to-noise ratio (SNR) and shape identification. As a new machine vision inspection method, TWRI will play an important role in the OCM of the wind turbine blade.

This paper is about an electro-thermal model to estimate the semiconductors junction temperature of bidirectional quasi Z-source inverter. Firstly, three types of averaged loss models are presented and compared. In the presented model the impact of the output current waveforms on the loss calculation is considered. Then, based on the instantaneous power loss and developed thermal model, junction temperatures of power devices are estimated. Finally, this paper discusses the factor that influences the junction temperatures: output frequency. It was found that extended model C can be directly presented the temperature peak value and fluctuation amplitude in faster simulation speed. Good agreement between model B and model C. Simulation results are shown to verify the operation and theoretical analysis.