Search

A new fault diagnosis method for detecting the rotor eccentricity faults including static, dynamic and mixed eccentricity is proposed for brushless doubly‐fed induction machines (BDFIMs). BDFIMs are attractive alternatives for the conventional doubly‐fed induction generator (DFIG) for offshore wind power generation; therefore, paying attention to their fault detection is essential. Existing fault detection methods for conventional induction machines cannot be directly applied to the BDFIM due to its special rotor structure and stator winding configurations as well as complex magnetic field patterns. This article proposes a novel fault detection method based on motor current signal analysis to determine stator current harmonics, induced by the nested‐loop rotor slot harmonics (NRSHs), as fault indices. The analysis is performed under healthy conditions and with different types of rotor eccentricity. Finally, a sensitivity analysis is carried out to confirm the practicability of the proposed technique with various fault intensities and load conditions. Analytical winding function approach, finite element analysis and experimental tests on a prototype D180 BDFIM are used in this study to validate the proposed fault detection technique. [ABSTRACT FROM AUTHOR]

Brushless DC motor (BLDCM) is a synchronous motor that contains a permanent magnet on the rotor and armature windings on the stator. It consists of the following characteristics, such as compact form, high efficient, silent operations and low maintenance. The existing BLDCM control systems have some drawbacks such as, it needs higher requirements for control algorithms, it uses more complicated electronics, it holds high noise and the initial rotor movement is not predictable. In order to overcome these issues, a new control mechanism, namely, carrier frequency rate, duty cycle ratio and pulse position range-space vector pulse width modulation (RRR-SVPWM) technique is proposed in this paper to control the speed of BLDCM. Initially, the unregulated AC from the wind turbine system is given to the input of six-legs insulated gate bipolar transistor (IGBT) circuit for converting the unregulated AC to regulated DC. Here, the pulse generator is used to generate the pulses based on certain parameters, such as input three-phase voltage, phase current and DC voltage. Hence, the proposed RRR-SVPWM technique is applied to reduce the noise and maintain a harmonics in a controlled level by placing a high-speed switches in a circuit. Finally, the speed of the BLDCM is increased and controlled by giving an output regulated AC to the motor. The experimental results evaluate the performance of the proposed system in terms of power factor (PF), displacement PF (DPF) and total harmonic distortion (THD). [ABSTRACT FROM AUTHOR]

In most research works on the disturbance observer (DOB)-based controller, stability conditions are only sufficient and not effective for handling large uncertainties. To deal with large uncertainties, a necessary and sufficient condition for robust stability has been recently provided by utilizing a novel design technique for the Q -filter. Although this design method can deal with large uncertainties, it is not capable of suppressing the effect of sensor noise. To achieve noise suppression as well as disturbance attenuation, this paper proposes a robust controller by utilizing a modified DOB structure. In contrast to the conventional DOB structure, noise suppression ability of the proposed controller can be adjusted by an appropriate choice of the Q-filter. Moreover, a necessary and sufficient condition for the modified DOB system to be robustly stable is newly presented. To show the effectiveness of the proposed controller, the performance is compared with other disturbance attenuation methods through simulation studies. Finally, in order to verify the validity of the proposed method, laboratory experiments are also conducted with brushless dc motor drives, which confirms that the proposed controller is quite useful for attenuating both input disturbance and sensor noise, while DOB controller is not capable of suppressing sensor noise. [ABSTRACT FROM AUTHOR]

Deep belief network (DBN) is a kind of recurrent neural network superposed by several layers of unsupervised restricted Boltzmann machines (RBMs). To solve the defect of RBM during sample training that the fixed learning rate is unfavorable to find the optimal value, a principle of dynamical learning rate was proposed to improve the RBM network so as to increase the accuracy of eigenvector mapping. Moreover, the authors also constructed a DBN with two layers of RBMs, of which the neural networks were self-trained by using contrastive divergence (CD). The improved network was applied to control BLDCM..The experimental results indicate that the improved DBN can effectively accelerate response and also improve the accuracy. [ABSTRACT FROM AUTHOR]

Copyright of Automatika: Journal for Control, Measurement, Electronics, Computing & Communications is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper proposes a new operation and control strategy for Power-Assisted Wheelchairs (PAW) using one brushless DC (BLDC) motor. The conventional electrical wheelchairs are too heavy and large for one person to move because they have two electric motor wheels. On the other hand, the proposed PAW system has a small volume and is easy to move due to the presence of a single wheel motor. Unlike the conventional electric wheelchairs, this structure for a PAW does not have a control joystick to reduce its weight and volume. To control the wheelchair without a joystick, a special control system and algorithm are needed for proper operation of the wheelchair. In the proposed PAW system uses only one sensor to detect the acceleration and direction of PAW's movement. By using this sensor, speed control can be achieved. With a speed control system, there are three kinds of operations that can be done on the speed of a PAW: the increment of PAW speed by summing external force, the decrement of PAW speed by subtracting external force, and emergency breaking by evaluating the time duration of external force. The validity of the proposed algorithm is verified through experimental results. [ABSTRACT FROM AUTHOR]

This study proposes an adaptive neuro-fuzzy inference system (ANFIS)-based rotor position controller for brushless direct current (BLDC) motors to improve the control performance of the drive under transient and steady-state conditions. The dynamic response of a BLDC motor to the proposed ANFIS controller is considered as standard reference input. The effectiveness of the proposed controller is compared with that of the proportional integral derivative (PID) controller and fuzzy PID controller. The proposed controller solves the problem of nonlinearities and uncertainties caused by the reference input changes of BLDC motors and guarantees a fast and accurate dynamic response with an outstanding steady-state performance. Furthermore, the ANFIS controller provides low torque ripples and high starting torque. The detailed study includes a MATLAB-based simulation and an experimental prototype to illustrate the feasibility of the proposed topology. [ABSTRACT FROM AUTHOR]

This paper investigates chaos control for the brushless DC motor (BLDCM) system by adaptive dynamic surface approach based on neural network with the minimum weights. The BLDCM system contains parameter perturbation, chaotic behavior, and uncertainty. With the help of radial basis function (RBF) neural network to approximate the unknown nonlinear functions, the adaptive law is established to overcome uncertainty of the control gain. By introducing the RBF neural network and adaptive technology into the dynamic surface control design, a robust chaos control scheme is developed. It is proved that the proposed control approach can guarantee that all signals in the closed-loop system are globally uniformly bounded, and the tracking error converges to a small neighborhood of the origin. Simulation results are provided to show that the proposed approach works well in suppressing chaos and parameter perturbation. [ABSTRACT FROM AUTHOR]

Presents the table of contents for this issue of the periodical. [ABSTRACT FROM AUTHOR]

Aiming at servo control of high-performance permanent magnet brushless motor, the physical structure, the mathematical model and the principle of permanent magnet brushless motor were researched. After the introductions of the principle of traditional PID controller and the control system of permanent magnet brushless motor, the defects of brushless motor control system based on traditional PID controller were analyzed and summarized, an intelligent control strategy of permanent magnet brushless motor based on Neuron PID was proposed. Firstly, the characteristics of Neuron PID controller were expounded. Then the algorithm of the Neuron PID control system was designed and implemented, the Neuron PID controller was used in control of speed loop of permanent magnet brushless, utilizing the self-learning ability of Neuron PID, control parameters can be trained and adjusted in real time. At last, simulation of permanent magnet brushless motor control system based on traditional PID and Neuron PID were given with Simulink, both the results of simulation were compared. The results indicate that the system can improve the response of speed and the rotational stability of brushless motor. [ABSTRACT FROM AUTHOR]

In order to design a low-cost, small-size and light-weight brushless DC motor (BLDCM) controller, the paper introduces the advantages of BLDCM and discusses related position detecting methods, compares the control methods of BLDCM with position sensors with that without position sensors, and analyzes the operation status of BLDCM controller. The paper designs a sensorless BLDCM controller based on back-EMF, and employs a double-closed-loop speed regulating system to get good dynamic performance. Besides, based on the excellent performance of MCU in interruption processing, the designed controllr could achieve real-time current monitoring and fast over-current protection. The paper also designs a communication protocol, on the basis of RS232, to strengthen the communication stability between PC and motor controller and to monitor the status of speed regulating system in real time. Finally, experiment is designed and cnried out to validate the proposed system. [ABSTRACT FROM AUTHOR]

In, conventional 3-stage start-up method of sensorless brushless direct current motor (BLDCM), the rotor is likely to jitter because rotor position cannot be obtained, and the motor is apt to lose step when it starts with load. These defects limit its use in engineering applications. In order to achieve smooth start in specific direction and guarantee start-up success rate with load, a start-up method based on improved inductance method and electromotive force (EMF) integration is proposed applying different voltage vectors according to rotor position interval judged by inductance method and determining integrator start-up time according to rotor initial position and the EMF. Experiments show that the method guarantees smooth acceleration and increases start-up success rate with load. [ABSTRACT FROM AUTHOR]