Pulsating torque exists in interior permanent magnet (IPM) machines due to non-sinusoidal flux density distribution around the air-gap. These torque pulsations are reflected as speed ripple, noise and vibration, which degrade the IPM drive performance and should be minimized. Firstly, the paper proposes a mathematical model of IPM machine that takes into account space harmonics of inductances and flux linkages related to permanent magnets as a function of rotor position. The space harmonics are analyzed by the 2D finite-element method (FEM) and the motor parameters of the model are determined. Then, the pulsating torque is compensated by generating an inverse torque component through the stator current, and this procedure to predetermine the adequate current reference is based on maximum torque per ampere characteristic (MTPA). Because of the periodic nature of torque ripple, this paper presents a current control scheme that solves bandwidth limits of conventional PI controllers by means of multiple reference frames. Two different motor structures have been studied and verification for them is given.

This paper describes the development of a mobile robot capable of clearing such obstacles as counterweights, anchor clamps, and torsion tower. The mobile robot walks on overhead ground wires in 500KV power tower. Its ultimate purpose is to automate to inspect the defect of power transmission line. The robot with 13 motors is composed of two arms, two wheels, two claws, two wrists, etc. Each arm has 4 degree of freedom. Claws are also mounted on the arms. An embedded computer based on PC/104 is chosen as the core of control system. Visible light and thermal infrared cameras are installed to obtain the video and temperature information, and the communication system is based on wireless LAN TCP/IP protocol. A prototype robot was developed with careful considerations of mobility. The new sensor configuration is used for the claw to grasp the overhead ground wires. The bridge is installed in the torsion tower for the robot easy to cross obstacles. The new posture plan is proposed for obstacles cleaning in the torsion tower. Results of experiments demonstrate that the robot can be applied to execute the navigation and inspection tasks.

During the last three decades, the remarkable dynamic features of microelectromechanical systems (MEMS) and nanoelectromechanical systems (NEMS), and advances in solid-state electronics hold much potential for the fabrication of extremely sensitive charge sensors. These sensors have a broad range of applications, such as those involving the measurement of ionization radiation, detection of bio-analyte and aerosol particles, mass spectrometry, scanning tunneling microscopy, and quantum computation. Designing charge sensors (also known as charge electrometers) for electrometry is deemed significant because of the sensitivity and resolution issues in the range of micro- and nano-scales. This article reviews the development of state-of-the-art micro- and nano-charge sensors, and discusses their technological challenges for practical implementation.

Fault diagnosis of the planetary gearbox (PGB) of wind turbines (WTs) plays an important role in the normal operation of WTs. Current studies commonly focus on the diagnosis of fault types of WT PGBs. Nevertheless, in addition to identifying the fault type, the current severity of the fault is also instructive for the maintenance and repair of WT PGBs. Thus, a novel optimized stacked diagnosis structure (OSDS) is proposed for the identification of fault type and severity. Compressed sensing is adopted to implement the compressed sampling of original vibration signals collected by the wireless sensor. Then, the compressed samples are input into first- and second-layer deep belief networks (DBNs) for the separate identification of fault type and severity. In order to realize the best feature extraction performance of DBNs, every single DBN in the OSDS is optimized with the chaotic quantum particle swarm optimization (CQPSO) algorithm. For OSDS, which is a hierarchical diagnosis system, the misdiagnosis results of the first layer will bring irreversible influence to the diagnosis of the second layer. That is to say, an incorrect fault type diagnosis will mean that these signals are wrongly classified, making them unable to judge the severity of the fault. Because the first-layer DBN is optimized with PGB historical data and the CQPSO algorithm, it shows an excellent performance in identifying fault types. Therefore, the diagnostic performance of OSDS has not been affected by the absence of diagnosis, and still shows an excellent recognition performance of fault type and severity in the experiment. This verifies its excellent role in the fault diagnosis of WT PGBs.