Variable-structure torque control of induction motors using space vector modulation

Compared with field orient control (FOC), direct torque control (DTC) is known to provide fast and robust response for induction motors. However, while offering high dynamic performance, classical DTC produces notable torque, flux, and current ripples, and operates with a variable inverter switching frequency. In this paper, a novel torque control scheme for induction motors is proposed. The stator flux and the electrical torque are directly controlled by variable-structure controllers, and stator voltage vectors calculated by the variable-structure controllers are applied to the motor by means of space vector modulation. The proposed scheme therefore provides smooth, fast and robust regulations of the electrical torque and the stator flux. Moreover, the implementation of the scheme is simple. Theoretical analysis shows the asymptotical convergence of electrical torque and stator flux tracking. Simulation and experimental results are provided to evaluate the performance of the proposed scheme.     

A robust principal component analysis-based approach for detection of a stator inter-turn fault in induction motors

Health condition monitoring of induction motors is important because of their vital role and wide us in a variety of industries. A stator inter-turn fault (SITF) is considered to be the most common electrical failure according to statistical studies. In this paper, an algorithm for the detection of an SITF is presented. It is based on one of the blind source separation techniques called principal component analysis (PCA). The proposed algorithm uses PCA to discriminate between the faulty components of motor current signatures and motor voltage signatures from other components. The standard deviation of one of the decomposed vectors is used as a statistical SITF criterion. The proposed criterion is robust to non-fault conditions including voltage quality problems and large mechanical load changes as well as harmonic contaminants in the voltage supply. In addition, with a straightforward and low computational burden in the fault detection process, the proposed method is computationally efficient. To evaluate the performance of the proposed method, large numbers of practical and simulation scenarios are considered, and the results confirm the good performance, high degree of accuracy, and good convergence speed of the proposed method.     

基于空间矢量法的感应电机定子线圈故障检测方法研究

提出了一种基于空间矢量法的异步电机定子线圈短路故障检测方法，导出了与线圈短路故障相关的故障特征，定义了表征故障程度的灵敏因子λ。实验结果证实，该方法可准确检测出反映异步电机在定子线圈短路故障时的故障特征，其应用于异步电机定子线圈故障检测，准确可靠，方法切实可行。     

共振解调与小波降噪在电机故障诊断中的应用

针对异步电机形成复合故障时电流频谱存在的故障频率成分难以准确分离的问题,结合小波降噪算法与共振解调技术,提出一种异步电机复合故障分离方法.依托小波优良的时频局部化特性,有效地区分信号中的突变部分和噪声,实现信号的降噪;利用软件方法实现共振解调,构造带通滤波器提取共振信息.利用Hilbert变换进行解调分析得到包含故障特征信息的低频包络信号,经过低通滤波、频谱分析后实现异步电机耦合故障分离和故障特征提取.实验结果表明,该方法使复合故障情况下的异步电机电流信号的故障特征频率更容易识别和分离.     

Fault detection in induction motors using Hilbert and Wavelet transforms

In this work, a new on-line method for detecting incipient failures in electrical motors is proposed. The method is based on monitoring certain statistical parameters estimated from the analysis of the steady state stator current (for broken bars, saturation, eccentricities, and bearing failures) or the axial flux signal (for coil short-circuits in the stator windings). The approach is based on the extraction of the envelop of the signal by Hilbert transformation, pre-multiplied by a Tukey window to avoid transient distortion. Then a wavelet analysis (multi-resolution analysis) is performed, which makes the fault diagnosis easier. Finally, based on a statistical analysis, the failure thresholds are determined. Thus, by monitoring the mean value estimate it is possible to detect an incipient failure condition on the machine.     

An analysis of the field acceleration method and of vector control of induction motors

The two-axis theory and vector control based on this theory are well known. On the other hand, Yamamura has proposed the phase segregation method (spiral vector method) for the analysis of induction motors along with the field acceleration method (FAM). In this paper these analytical and control methods are compared. We begin by analyzing the equations for phase segegation, which contain information derived from the three-phase stator and rotor equations. We next demonstrate that the vector control system for constant rotor flux is obtained from the type T-I type field acceleration method when the transient term is zero. By By using the equivalent circuit for a T-I transient, we readily obtain the desired vector control system, because the circuit is identical to the steady-state circuit when the rotor flux is constant. An analytical solution of the transient response for the vector control system is obtained for arbitrary initial conditions. Finally, the stability of type T-II FAM is discussed, with special emphasis on the effect of changes in stator resistance, by computing the torque transfer function. When the stator resistance is correctly estimated, pole-zero cancellation occurs on the imaginary axis.     

用矢量控制原理的异步电动机在线监测与故障诊断方法——原理分析

利用传动领域的矢量控制概念，将异步电动机的三相定子电流通过坐标变换，转换到同步旋转的（M，T）坐标系中，M轴的方向与转子磁链矢量的方向重合，在新坐标系中电流分量iM为励磁分量，主要与定子外加电压的频率，大小及故障程度有关；ir分量为转矩分量，主要与负载大小有关，对iM分量进行频谱分析既可达到文献[1]所证明的特征频率提取效果，还可避免波动性负载造成的误诊断，从总结的两种求取坐标转换角度的方案看出，该方法物理意义明确，适用于不进行速率调节的异步电动机。     

An artificial immune system approach for fault detection in the stator and rotor circuits of induction machines

In this paper, an artificial immune system approach to the detection and diagnosis of faults in the stator and rotor circuits of an induction machine is presented. The proposed technique requires the measurement of two stator currents to compute their αβ representation before and after a fault condition. It is verified that for different faults, different patterns are generated by the vector αβ representation, helping to construct a characteristic image of the operating condition of the induction machine.A pattern recognition algorithm inspired by how the immune system operates throughout the body is proposed to identify and classify the fault condition. According to the proposed methodology, there is no need to know the details of machine operation in a certain regime and all phenomena and effects resulting from the machine operating in this regime are taken into account. Several experimental results obtained on 2.2 kW and 3.2 kW three-phase induction machines are presented and discussed to validate the methodology, verifying its good performance in preventive fault detection.     

Diagnosis of rotor asymmetries in induction motors based on the transient extraction of fault components using filtering techniques

The aim of this paper is to present and validate a methodology for diagnosing rotor asymmetries in cage motors, based on the analysis of the stator startup current. The method consists of the extraction of a harmonic component introduced by this fault – the left sideband component – from the stator startup current. Two alternative techniques developed by different research groups are proposed for the transient extraction of this component; the digital low-pass filtering (DLPF) and the discrete wavelet transform (DWT). Both approaches are applied to three different industrial motors ranging from 1.1 to 450 kW. A detailed explanation of the physical basis of the method and comments related to the application scope of the approach are also given. The results show the robustness of both approaches for the reliable diagnosis of the fault and suggest a clear potentiality for extending the methodology to the detection of other types of faults introducing components dependant on the slip.     

Support vector classification and regression for fault location in distribution system using voltage sag profile

Fault location identification is an important task to provide reliable service to the customer. Most existing artificial intelligence methods such as neural network, fuzzy logic, and support vector machine (SVM) focus on identifying the fault type, section, and distance separately. Furthermore, studies on fault type identification are focused on overhead transmission systems and not on underground distribution systems. In this paper, a fault location method in the distribution system is proposed using SVM, addressing the limitations of existing methods. Support vector classification (SVC) and regression analysis are performed to locate the fault. The method uses the voltage sag data during a fault measured at the primary substation. The type of fault is identified using SVC. The fault resistance and the voltage sag for the estimated fault resistance are identified using support vector regression (SVR) analysis. The possible faulty sections are identified from the estimated voltage sag data and ranked using the Euclidean distance approach. The proposed method identifies the fault distance using SVR analysis. The performance of the proposed method is analyzed using Malaysian distribution system of 40 buses. Test results show that the proposed method gives reliable fault location.