Switching Techniques and Intelligent Controllers for Induction Motor Drive: Issues and Recommendations

An image analysis-based two-stage process parameters tuning and Surface Roughness (SR) estimation algorithm is proposed for the laser cleaning application. A Cartesian coordinate robot is utilized to collect image and implement cleaning. Before cleaning, in order to tune the proper laser parameters, first, the environment lighting is controlled for the metal image collection. Second, lots of classification features are computed for the images above. The Gray-Level Co-occurrence Matrix (GLCM) texture features, the concavo-convex region features, the histogram symmetry difference feature, and the imaging thermophysical property features are computed. Third, the initial laser parameters are created randomly and an iteration computation is performed: a Support Vector Machine (SVM) is used to forecast the cleaning effect; its inputs include the classification features and the initial laser parameters; its output is the cleaning effect degree. If the SVM output cannot fulfill user's demand, the laser parameters will be updated randomly. This iteration will be implemented constantly until the SVM output becomes valid. Then the laser cleaning will be performed. When estimating SR for the cleaned metal, multiple image features are calculated for the images after cleaning. The features include the Tamura coarseness, some GLCM features, and the convex region feature. To improve the prediction precision, different feature combinations are used for different cleaning effects. The linear function and the 3-order polynomial function are considered for the SR estimation. After tests, the accuracies of SVM, the SR prediction function, and the integrated SR control and estimation algorithm can be 90.0%, 80.0% and 80.0% approximately.

Section: F Discussionmentioning

confidence: 99%

Automatic Process Parameters Tuning and Surface Roughness Estimation for Laser Cleaning

Liu

Yang

et al. 2020

“…where ϕ k is the kth-order voltage harmonic phase angle. According to (3), the relationship between the amplitude and phase angle of the harmonic with the carrier phase angle η can be deduced, as shown in Fig. 3.…”

Section: B Harmonic Characteristics Of Spwmmentioning

confidence: 99%

“…In recent years, various types of motors for railway traction (as listed in Table 1) have been replacing IMs in newer rail vehicles. By comprehensively considering the aspects and parameters of the motors, the IM, with its excellent performance in terms of speed, reliability, controllability and cost, will remain one of the main choices for the traction motor in a traction propulsion system [3].…”

Section: Introductionmentioning

confidence: 99%

Realization of Smooth Transition in Hybrid PWM Scheme for Induction Motor Control

Chen

Wang

et al. 2019

Hybrid pulse width modulation schemes are widely used in high-power traction converters with a limited switching frequency. During the transition between modulation modes, sudden changes in the voltage harmonics cause the induction motor to enter a transient process, and ultimately lead to torque impacts, current shocks and other adverse effects. To improve the existing solution for such effects, theoretical support is provided in this paper by using an equivalent circuit model and a dynamic model of an induction motor, and a specific implementation scheme for a smooth transition is given considering the third-harmonic frequency component that appears in the phase voltage during the transition. The feasibility and correctness of the scheme are proven by a simulation and an experiment.

“…It deserves to be mentioned that this problem represents that much more loss would be produced during operations, lowering the energy efficiency. Consequently, effective measures to improve the efficiency of the IM-based drive systems are highly required [5][6].…”

Section: Introductionmentioning

confidence: 99%

Low-Switching-Loss Finite Control Set Model Predictive Current Control for IMs Considering Rotor-Related Inductance Mismatch

Tan

et al. 2020

This paper develops a high-performance finite control set model predictive current control (FCS-MPCC) method for induction motors (IM) to ensure the system control performance over the lowswitching-frequency (LSF) range, where the switching loss is low. The new controller is based on tripartite calculations in each control period and sliding mode (SM) rotor-related inductance observer. In detail, firstly, to solve the problem that the control performance of the traditional FCS-MPCC methods is low when they are used at low frequencies, tripartite calculations are employed in each control period to improve the prediction accuracy. By dividing one control period into three equal parts and executing the prediction algorithm in each subsection, the current estimation errors become lower compared to the conventional single-step Euler-discretization controller. Then, considering that the performance of an FCS-MPCC controller highly relies on the machine parameter values, but it is hard to directly obtain the accurate rotor-related inductances (mutual inductance and rotor inductance), this paper uses an online parameter observer based on the sliding mode (SM) principle to diagnose them in real time. It needs to be mentioned that when discussing the stability of the sigmoid-function-based SM observer, a new technique called estimation-error-limitation is initially adopted in this paper to simplify the analysis process. Finally, the proposed algorithms are verified by simulation, which is conducted on a three-phase IM at LSF situations. INDEX TERMS Induction motor, finite control set model predictive control, sliding mode observer, parameter mismatch, low switching frequency.