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Currently, AC motors are a key element of industrial and commercial drive systems. During normal operation, the machines may become damaged, which may pose a threat to the users. Therefore, it is important to develop a fault detection method that allows for the detection of a fault at an early stage. Among the currently used diagnostic systems, applications based on deep neural structures are dynamically developed. Despite many examples of applications of deep learning methods, there are no formal rules for selecting the network structure and parameters of the training process. Such methods would make it possible to shorten the implementation process of deep networks in diagnostic systems of AC machines. The article presents a detailed analysis of the influence of deep convolutional network hyperparameters and training procedures on the precision of the interturn short-circuits detection system. The studies take into account the direct analysis of phase currents through the convolutional network for induction motors and permanent magnet synchronous motors. The research results presented in the article are an extension of the authors' previous research. [ABSTRACT FROM AUTHOR]

This paper presents the design and implementation of an application-specific integrated circuit (ASIC) for a discrete-time current control and space-vector pulse-width modulation (SVPWM) with asymmetric five-segment switching scheme for AC motor drives. As compared to a conventional three-phase symmetric seven-segment switching SVPWM scheme, the proposed method involves five-segment two-phase switching in each switching period, so the inverter switching times and power loss can be reduced by 33%. In addition, the produced PWM signal is asymmetric with respect to the center-symmetric triangular carrier wave, and the voltage command signal from the discrete-time current control output can be given in each half period of the PWM switching time interval, hence increasing the system bandwidth and allowing the motor drive system with better dynamic response. For the verification of the proposed SVPWM modulation scheme, the current control function in the stationary reference frame is also included in the design of the ASIC. The design is firstly verified by using PSIM simulation tool. Then, a DE0-nano field programmable gate array (FPGA) control board is employed to drive a 300W permanent-magnet synchronous motor (PMSM) for the experimental verification of the ASIC. [ABSTRACT FROM AUTHOR]

Dual Y shift 30° six-phase motors are expected to be extensively applied in high-power yet energy-effective fields, and a harmonic-suppressing control strategy plays a vital role in extending their prominent features of low losses and ultra-quiet operation. Aiming at the suppression of harmonic voltages, this paper proposes a six-phase space vector pulse width modulation method based on an optimization model, namely OM-SVPWM. First, four adjacent large vectors are employed in each of 12 sectors on a fundamental sub-plane. Second, the optimization model is constructed to intelligently determine activation durations of the four vectors, where its objective function aims to minimize the synthesis result on a harmonic sub-plane, and its constraint condition is that the synthesis result on the fundamental sub-plane satisfies a reference vector. Finally, to meet the real-time requirement, optimum solutions are obtained by using general central path following algorithm (GCPFA). Simulation and experiment results prove that, the OM-SVPWM performs around 37% better than a state-of-the-art competitive SVPWM in terms of harmonics suppression, which promise the proposed OM-SVPWM conforms to the energy-effective direction in actual engineering applications. [ABSTRACT FROM AUTHOR]

A large number of permanent magnet synchronous motors (PMSMs) are used to drive coal conveyer belts in coal enterprises. Sensorless energy conservation control has important economic value for these enterprises. The key problem of sensorless energy conservation control for PMSMs is how to decompose the stator current through estimating the rotor position and speed accurately. Then a double closed loop control for stator current and speed is formed to make the stator current drive the motor as an entire torque current. In this paper, the proposed startup estimation algorithm can utilize the current model of PMSM as reference model to estimate the rotor speed and position in the startup stages. It is not dependent on the back electromotive force (EMF) which is used by the general estimation algorithm. However, the resistance will change with the temperature shift of stator windings, and these changes will cause the reference current model to be inaccurate and influence the rotor speed and position estimation precision. Thus, startup estimation algorithm switches to the proposed operation estimation algorithm which is based on the robust sliding mode theory and is not dependent on the motor parameters. The advantages of startup estimation algorithm and operation estimation algorithm are combined to form a hybrid observer. This hybrid observer realizes the accurate estimation of the rotor speed and position from start-up to operation. The stator current is precisely decomposed. The excitation current is controlled to 0. Meanwhile, the double closed-loop control of current and speed is achieved. The stator current is as entire torque current to drive motor. The closed-loop control, which is based on the proposed rotor position and speed estimation algorithm, achieve the most efficient conversion of electrical energy. [ABSTRACT FROM AUTHOR]

The general method to suppress the axial force of the permanent magnet synchronous motor (PMSM) direct-drive turbine is to increase the number of balance devices, such as balance disks and special bearings, to counteract its influence, but this also leads to complex system structure and higher mechanical losses. Aiming to solve the above issue, this paper presents a novel PMSM structure with a conical-rotor (CR). Due to its adaptive equilibrium of axial force and simple structure of rotor with turbine, the CR-PMSM can help improve the system efficiency. Both surface-type and interior-type motors are analyzed, and the axial magnetic force of CR-PMSM is studied in detail. The 3-D finite-element method (FEM) is used to model and simulate the machine, and the magnetic-field distribution, axial magnetic force and driving performance are obtained. Also, the control rule of d-axis current is analyzed to achieve the adaptive equilibrium of axial force. A 2.0 kW, 6000 r/min prototype motor is fabricated and tested to validate the theory. [ABSTRACT FROM AUTHOR]