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 (CFRP) 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) non-destructive 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 SNR and shape identification. As a new machine vision inspection method, TWRI will play an important role in the OCM of wind turbine blade.

In the back electromotive force (EMF)-based sensorless control of interior permanent magnet synchronous motor (IPMSM), the inverter nonlinearity and flux linkage spatial harmonics will possibly give rise to (6k±1)th harmonics in the estimated back-EMF, especially the 5th and 7th harmonics. Those harmonics will consequently introduce (6k)th harmonic ripples to the estimated rotor position, especially the 6th harmonic component. In order to solve this problem, a bilinear recursive least squares (BRLS) adaptive filter is proposed and integrated into a sliding mode position observer to suppress the dominant harmonic components in the estimated back-EMF and as a result, the accuracy of the estimated rotor position can be greatly improved. A unique feature of the BRLS adaptive filter is its ability to track and suppress the specified harmonic components in different steady state and dynamic operational conditions. The proposed method can compensate harmonic ripples caused by the inverter nonlinearity and machine spatial harmonics at the same time; this method is also robust to machine parameter variation and the BRLS algorithm itself is machine parameter independent. The implementation of the proposed BRLS filter in the sensorless control of IPMSM is explained in details in this paper. The enhanced drive performances using the BRLS filter have been thoroughly validated in different steady state and dynamic operational conditions on a 1.5kW IPMSM sensorless drive.

Conventional model-free predictive current control (MFPCC) using an extended state observer (ESO) based on ultra-local model significantly decreases the sensitivity to partial motor parameters. However, the performance and stability of ESO will be degraded in the face of amplified high-frequency (HF) perturbation caused by inductance parameter mismatch. To solve the above problem, an enhanced ESO-based MFPCC method is proposed in this article. On the basis of constructing the discrete equation of ESO, an accurate and fast calculation of inductance parameter is proposed, which is real-time convergence to the actual value by deadbeat control principle, and fed back to the lumped disturbance and current observation calculation. It effectively eliminates the effects of HF perturbation on ESO. Simultaneously, the inductance and compensated lumped disturbance obtained from ESO are provided to the MFPCC command voltage calculation module, further improving the parameter robustness of MFPCC. The stability of the proposed ESO is ensured by the design of observer gain. Experiments are carried out on 1-kW surfaced permanent magnet synchronous machines test bench. Compared with the conventional ESO-based MFPCC method, the proposed method is superior in dynamic response, steady-state performance, and parameter robustness.

When applying the permanent magnetic synchronous motors (PMSM) to refrigerant system, the suppression of speed ripples of the PMSM in low‐feed range becomes the focus of consideration. The authors start with the generation mechanism and the periodical characteristics of the speed ripples to propose an adaptive control method. In the proposed method, the authors place a frequency variable resonance controller in parallel with the conditional proportion and integral (PI) controller to form a PI‐resonance (PI‐RES) controller. The compensation torque current generated by the resonance controller and the main reference current generated by the PI controller constitute the reference torque current. Because of the existence of the compensation torque current, the electromagnetic torque can follow the variation of the load torque much better, thus the speed ripples are suppressed. Performance indices of the conventional PI controller and the PI‐RES controller are compared and evaluated through experimental investigations. The results validate the effectiveness of the proposed method.

This paper focuses on speed control in a permanent-magnet synchronous motor (PMSM). To promote the speed tracking performance and anti-disturbance property of PMSM speed regulation system against various unknown disturbances, such as parameter mismatch, load torque variation, friction force, and so on, an improved integral sliding mode control (IISMC) method based on a new variable rate reaching law (NVRRL) and an adaptive reduced-order proportional integral observer (AROPIO) is proposed. The NVRRL method can reduce chattering, and convergence time of the system state at the same time compared to the existing sliding mode reaching law. An anti-windup sliding mode surface is proposed to overcome the overshoot problem of the integral sliding mode surface when the reference speed is large and rises in steps or large ramps. To further improve the system tracking accuracy and anti-disturbance ability, an AROPIO is used to estimate lumped disturbances and provides feedforward compensation for the IISMC. The simulation and experimental results show that the proposed IISMC+AROPIO method not only achieves high speed tracking accuracy and fast convergence speed but also achieves strong anti-interference ability.

In order to solve the problems of low efficiency and small area of existing eddy current thermography industrial nondestructive testing (NDT), 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 can not 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 (ICA) and non-negative matrix factorization (NMF), 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.