Search

This paper introduces a novel index for static eccentricity (SE) fault diagnosis in synchronous reluctance motors (SynRMs). Although SynRMs with rotor barriers under SE have been modeled in a few papers, any indices for the fault detection have not yet been reported. The proposed index is a specific frequency pattern in the motor current, which can also determine the fault severity. A novel analytical magnetic field investigation is applied to ascertain the proposed index. An accurate nonlinear numerical method is proposed for the motor inductances calculation. The model considers the rotor flux barriers, magnetic saturation, and the stator slots effect. The air gap flux density and the motor current are then achieved. The fast Fourier transform is exploited as a signal-processing tool to calculate motor current spectra. Then, a two-dimension time-stepping finite element method is used to attest of the proposed numerical model. Effectiveness of the proposed index is verified by simulation and experimental tests. [ABSTRACT FROM AUTHOR]

In this paper, speed and torque control modes (SCM and TCM) of synchronous reluctance motor (SynRM) drives are proposed based on emotional controllers and space vector modulation under an automatic search of the maximum-torque-per-ampere (MTPA) strategy. Furthermore, in order to achieve an MTPA strategy at any operating condition, after recognition of transient state by two new indicators, a search algorithm changes the stator flux magnitude automatically. The indicators operate based on slip effect generated at transient conditions in a SynRM with cage. The performance of the proposed controller is compared with an optimized conventional PI controller under different operating conditions. Simulation and experimental results demonstrate the benefits of the proposed approach. This structure shows superiority of the proposed method, such as fast dynamic, simple implementation and robustness to parameter variations and external disturbances in both TCM and SCM. In addition, the proposed MTPA strategy shows a reliable and fast response to operating point change. [ABSTRACT FROM AUTHOR]

In this paper, a well-known adaptive input-output feedback-linearization (AIOFL) technique is used for speed and torque-tracking control of synchronous reluctance motor drive. This controller is capable of estimating motor two-axis inductances (Ld, Lq) simultaneously. The overall stability of the proposed control and the persistency of excitation condition are proved based on Lyapunov theory. In addition, the maximum rate of change of torque control scheme is applied to generate the motor dand q-axis reference currents which are needed for AIOFL controllers. Another contribution of this paper is to estimate the rotor speed and position. For low-speed estimation, we have to eliminate the current and voltage sensors' dc offsets, detect the stator resistance, and take into account the voltage drop of the inverter power switches. We solve these problems by using a simple technique for eliminating the voltage sensors and a simple method for online estimation of the stator resistance and modeling the voltage drop of the inverter power switches. It is worthwhile to mention that the current sensors' dc offsets, seen on the measured currents, are negligible. Finally, the validity of the proposed method is verified by experimental results. [ABSTRACT FROM AUTHOR]

Control issue in electrical drives mainly deals with the dynamic behavior, which is based on the deviation of variables. The deviations play a straightforward role in presenting the various capabilities and merits of the electric drive systems. This paper describes, for the first time, a deviation-based torque control of synchronous reluctance motor (SynRM) drives with no need to know the motor parameters. The proposed control system is designed by using a normalized deviation model to derive linear and simple relationships amongst different machine signals. Therefore, the commonly used proportional–integral current regulators are replaced by novel deviation equations. As a result, the proposed approach provides facilities to electric drives, including control system simplicity, parameter independency, and no need for controller tuning. The theoretical findings are verified by those experiments. The obtained results are reported for a typical SynRM drive. In addition, performance comparison of the control system with a general PI controller-based field-oriented control scheme is carried out. It is expected that the proposed approach contributes to other electrical drives to simplify the machine equations, reduce control complexities, such as the number of conventional controllers, and overcome the problem of machine parameter dependency. [ABSTRACT FROM AUTHOR]

This paper proposes an enhanced brain emotional learning-based intelligent controller for synchronous reluctance motor (SynRM) drives. The controller is based on an emotional learning and decision making mechanism in the brain via emotional cues and sensory inputs. Furthermore, the enhanced controller improves learning process in the amygdala to avoid internal instability. In spite of the development for interior-stability, the proposed controller could keep its ability to deal with challenges related to SynRM drives. The proposed controller is also contributed with a speed deviation control. The new deviation controller is based on model but parameter-free. The updated system is implemented in real-time by a PC-based three-phase 370 W laboratory SynRM. The proportional-integral (PI) controllers used in a standard rotor field-oriented control structure are replaced with those of the proposed method. The speed and d-axis stator current references are accurately tracked. The achieved performances by the proposed controllers are compared with those of optimized conventional PI controller in different situations. Considering the results, the enhanced system shows superiority over the traditional system in terms of fast dynamics, easy tuning, and robustness against disturbances and parameter variations. [ABSTRACT FROM AUTHOR]