Aida Mollaeian

Analytical Investigation Into Magnet Eddy Current Losses in Interior Permanent Magnet Motor Using Modified Winding Function Theory Accounting for Pulsewidth Modulation Harmonics

The study of the interaction between an interior permanent magnet (IPM) motor and a pulsewidth modulated (PWM) converter is increasingly becoming important, especially in the case of high-speed drives owing to the losses caused by modulation. Considering the significance of machine-converter interaction, this paper proposes a novel method to calculate magnet eddy current losses in an IPM used in traction applications by incorporating both PWM harmonics as well as rotor geometry and design parameters. The voltage harmonics from the PWM are incorporated into a series of steps in order to calculate the stator magnetomotive force (MMF) using the modified winding function theory. The rotor MMF and, subsequently, the magnet losses are computed using a magnetic circuit model incorporating the harmonics from the stator MMF as well as the flux barrier and the rotor geometry. The results have been demonstrated analytically for various cases of modulation and PWM input parameters to study the dependence of magnet losses on converter properties. The analytical results have been validated by finite-element analysis and experimental investigations.

A Novel Control Strategy for Online Harmonic Compensation in Parametrically Unbalanced Induction Motor

Inverter-fed motor drive operating an unbalanced induction motor (IM) has high harmonic content which inflicts large torque ripple on the load. Dead short circuit and incipient or partial short circuit in motor stator windings lead to asymmetry in the machine parameters. Consequently, an imbalance in the voltage supply worsens the condition deteriorating the optimal performance of the drive-based motor due to the injection of both increased time and spatial harmonics. It is of primary importance that these discrepancies are taken care of while modeling a more fault tolerant, reduced harmonics drive system. This paper proposes a novel control strategy to minimize torque ripple by considering the time harmonics produced due to imbalance in inverter voltage and parameters of the faulty IM, and the estimated space harmonics from the measured magnetic flux density in a transient magnetic phenomenon. The proposed control strategy has been implemented on an unbalanced aluminum-rotor IM with online monitoring of unhealthy conditions and feeding it to the harmonic compensation block of the drive system.

Reduction of space harmonics in induction machines incorporating rotor bar optimization through a coupled IPSO and 3-D FEA algorithm

An induction machine with skewed rotor bars has enhanced starting performance and reduced current harmonic distortion. In a conventional skewed induction machine (IM) modeling, the axial magnetic flux variation is usually ignored in machine performance analysis in spite of significant parameter variation in the axial direction. Furthermore, the selection of optimal skew angle is vital to keep the output torque constant, especially in electric vehicle applications. In this paper, a 7.5 hp IM with one slot pitch skew angle is considered as an initial model and space harmonic reduction has been investigated by considering axial variation of the air-gap flux density. An analytical model incorporating spatial harmonics due to slotting has been implemented in parallel with a 3-D FEA model towards improvements in skew angle and rotor bar geometry selection using improved swarm intelligence technique. The air gap flux density distortion and torque ripple due to space harmonic reduction for improved design are compared with that of the initial design.

A Novel Parallel Modelling-Wavelet Based Mechanical Fault Detection Using Stator Current Signature of Induction Machine under Variable Load Conditions

Induction Machine (IM) fault detection techniques such as Fast Fourier Transform (FFT) which is a popular steady-state analysis method is recognized to be highly dependent on the IM loading and speed conditions. Nonetheless, implementing an FFT or even Short Time Fourier Transform (STFT) will result in low resolution frequency characteristics especially under a variable speed and loading conditions. Consequently, fault detection and classification under variable loading and speed conditions is quite inconvenient. Since, mechanical faults are one of the major breakdowns, which occur in IMs, it needs to be addressed to prevent breakage andfault extension. This paper investigates and detects faults under variable loading and speed conditions by studying the Motor Current Signature Analysis (MCSA) using a novel developed parallel technique based on the discrete wavelet transform (DWT).The proposed model input would be MCSA with the similar drive, loading condition and constraints for both healthy and faulty electric motors and the Discrete Wavelet Transform (DWT) is used as a detection technique. The proposed technique uses the DWT to the IM’s stator current to extract the desired features including Min, Max, Standard deviation, and Energy of the signal as a specific vector for each electric motor. In addition, different mechanical faults including Rotor broken bar(s), eccentricity and bearing have been detected using the proposed technique. Then, the accuracy of proposed parallel model using DWT is verified using experimental set-up of the parallel machines under variable loading and speed conditions for different mechanical faults. Finally the results of the proposed technique for all mechanical faults is tabulated and presented.

Parameter identification of permanent magnet synchronous machine based on metaheuristic optimization

Understanding the significance of precise dynamic modeling of electrical machines and the importance of parameter determination for the same, this manuscript proposes a new method of identifying variable inductances and damper parameters of a line-start interior permanent magnet synchronous machine (LSIPMSM) through an off-line improved particle swarm optimization (IPSO). An improved dynamic machine model incorporating the dependence of inductances on magnetizing currents has been developed. Through the combination of experimental test methods conducted on the inverter connected LSIPMSM under varied operating conditions and IPSO algorithm, parameters such as stator and magnetizing inductances and damper parameters have been identified for all conditions. Though conducted on LSIPMSM, the modeling and identification procedures presented in this paper are also applicable to IPMSM and surface magnet PSM with simplified variations. Comparison results of experiments with conventional and improved models are also presented for validation.

Evaluation of Local Anisotropy of Magnetic Response From Non-Oriented Electrical Steel by Magnetic Barkhausen Noise

Non-oriented electrical steels are indispensable materials for use in electric motors as magnetic cores. It is desired that the magnetic properties of the steel sheets be optimal and uniform in all the directions in the sheet plane. Thus, knowing the magnetic properties of the steel sheets in all the directions is crucial for the design of the electric motors. However, the magnetic properties of non-oriented electrical steels are usually measured by standard Epstein frame method, which normally only gives the overall magnetic properties in the rolling and transverse directions, and the magnetic properties in other directions are usually not known. In this research, magnetic Barkhausen noise (MBN) technique is utilized to characterize the local magnetic response of the processed non-oriented electrical steel. By rotating the MBN sensor to all the directions in the sheet plane, the local magnetic responses are obtained. The measured MBN is then directly compared to the crystallographic texture (texture factor) measured in the same direction. In this way, the local magnetic response of the steel sheet can be correlated to the crystallographic texture. It was found that magnetic Barkhausen noise technique was able to detect the difference in magnetic response induced by magnetocrystalline anisotropy if the effect of the residual stress can be eliminated. This would provide a potential technique for the characterization of magnetic properties of non-oriented electrical steel.

Intelligent flux predictive control through online stator inter-turn fault detection for fault-tolerant control of induction motor

Inter-turn insulation failure in induction motor results in complete or developing winding short circuit. Stator winding fault leads to an unbalance in the three phases of the motor leading to a faulty induction motor with increased time and space harmonics of flux. This can lead to uneven distribution of air gap flux and increase in torque ripple. The condition is worsened due to an unbalance in the voltage supply depreciating the optimal performance of the drive-based It is of primary importance that the aforementioned discrepancies are taken care of while modelling a more fault tolerant motor drive system with faster processing and lower response time. This paper proposes a novel control technique to reduce the unbalance in the motor due to stator fault by taking into account the air-gap flux developed in the motor and harmonics generated. An improved swarm optimization algorithm has been used in order to efficiently predict the flux reference for the stator-flux controlled motor drive. The proposed detection scheme has been implemented on an aluminum-rotor induction motor with incipient stator inter-fault with the help of online monitoring of unhealthy conditions and using it as a feedback for the drive system, thereby a robust online detection of fault and a stable fault control system.

DNN predictive magnetic flux control for harmonics compensation in magnetically unbalanced induction motor

Space and time harmonics in an induction motor (IM) increases due to motor fault and eccentricity leading to voltage unbalance and harmonic distortion.

3-D Sub-Domain Analytical Model to Calculate Magnetic Flux Density in Induction Machines With Semiclosed Slots Under No-Load Condition

In this paper, a novel 3-D sub-domain analytical model is developed to determine magnetic flux distribution in single-cage induction machines (IMs) with skewed rotor bars under no-load condition in an effort to more detailed analysis of spatial harmonics. The proposed model, along with an optimization algorithm, is as an alternative solution to finite-element analysis (FEA) in optimizing the geometry of IMs. The analytical method is based on the resolution of 3-D Laplace and Poisson’s equations in cylindrical coordinates using the separation of variables method to calculate the magnetic vector potential for corresponding sub-domain. The proposed model includes the effect of slotting and tooth tips for the stator and rotor slots, which is usually neglected in a 2-D analysis due to the complexity of differential equations. Also, the proposed 3-D model can be used for any slot-pole combination in addition to considering the asymmetrical effect in the axial direction, which is a source of noise, vibration, and excessive losses in IMs. To evaluate the performance of the proposed 3-D analytical model, calculated magnetic-field distribution is compared with the results obtained from the 3-D FEA.

DNN-Based Predictive Magnetic Flux Reference for Harmonic Compensation Control in Magnetically Unbalanced Induction Motor

A stator inter-turn fault occurring in one of the phases of a three-phase induction motor (IM) gives rise to high harmonics distortion in air-gap flux density, increased torque ripple, temperature rise in the stator windings, and mechanical vibrations due to varying magnetic forces and magnetic noise. The fault leads to a change in the electromagnetic field generated when compared to that during the normal motor operation. An incipient stator fault leads to variation of machine’s parameters, causing malfunction of the motor drive. Hence, it is of significant importance to detect the incipient fault before complete motor breakdown occurs. In this paper, a novel magnetic flux reference predictive method for control has been presented by using a harmonic compensation block in coordination with deep neural network (DNN) as a feedforward method to continue safe operation of motor after occurrence of incipient stator fault. This method takes into account both time and space harmonics discrepancies produced due to the fault. The proposed method has been implemented on a 7.5 hp IM using online observer of unhealthy conditions and compensated using DNN predictive methodology.