Fault Diagnosis and Reconfiguration for Multilevel Inverter Drive Using AI-Based Techniques

A fault diagnostic and reconfiguration method for a cascaded H-bridge multilevel inverter drive (MLID) using artificial-intelligence-based techniques is proposed in this paper. Output phase voltages of the MLID are used as diagnostic signals to detect faults and their locations. It is difficult to diagnose an MLID system using a mathematical model because MLID systems consist of many switching devices and their system complexity has a nonlinear factor. Therefore, a neural network (NN) classification is applied to the fault diagnosis of an MLID system. Multilayer perceptron networks are used to identify the type and location of occurring faults. The principal component analysis is utilized in the feature extraction process to reduce the NN input size. A lower dimensional input space will also usually reduce the time necessary to train an NN, and the reduced noise can improve the mapping performance. The genetic algorithm is also applied to select the valuable principal components. The proposed network is evaluated with simulation test set and experimental test set. The overall classification performance of the proposed network is more than 95%. A reconfiguration technique is also proposed. The proposed fault diagnostic system requires about six cycles to clear an open-circuit or short-circuit fault. The experimental results show that the proposed system performs satisfactorily to detect the fault type, fault location, and reconfiguration.     

Influences of Resistor-Type Superconducting Fault Current Limiter on Power System Transient Stability with Asymmetrical Short-Circuit Faults

The transient stability of a single machine to infinite-busbar power system with resistortype superconducting fault current limiters （SFCL） is analyzed under asymmetrical short-circuit fault conditions. The SFCL is considered to introduce a resistance into the three-phase circuits when faults occur. Based on the power-angle curves for different short-circuit conditions of the single-line to ground, double-line to ground and line to line short-circuit faults, the influences of the SFCLs on transient stability are analyzed in detail. The time-domain simulation of transient stability is carried out to verify the analytical results.     

Fuzzy inference systems implemented on neural architectures formotor fault detection and diagnosis

Motor fault detection and diagnosis involves processing a large amount of information of the motor system. With the combined synergy of fuzzy logic and neural networks, a better understanding of the heuristics underlying the motor fault detection/diagnosis process and successful fault detection/diagnosis schemes can be achieved. This paper presents two neural fuzzy (NN/FZ) inference systems, namely, fuzzy adaptive learning control/decision network (FALCON) and adaptive network based fuzzy inference system (ANFIS), with applications to induction motor fault detection/diagnosis problems. The general specifications of the NN/FZ systems are discussed. In addition, the fault detection/diagnosis structures are analyzed and compared with regard to their learning algorithms, initial knowledge requirements, extracted knowledge types, domain partitioning, rule structuring and modifications. Simulated experimental results are presented in terms of motor fault detection accuracy and knowledge extraction feasibility. Results suggest new and promising research areas for using NN/FZ inference systems for incipient fault detection and diagnosis in induction motors     

Vibration analysis for bearing fault detection and classification using an intelligent filter

This paper proposes an intelligent method based on artificial neural networks (ANNs) to detect bearing defects of induction motors. In this method, the vibration signal passes through removing non-bearing fault component (RNFC) filter, designed by neural networks, in order to remove its non-bearing fault components, and then enters the second neural network that uses pattern recognition techniques for fault classification. Four different categories include; healthy, inner race defect, outer race defect, and double holes in outer race are investigated. Compared to the regular fault detection methods that use frequency-domain features, the proposed method is based on analyzing time-domain features which needs less computational effort. Moreover, machine and bearing parameters, and the vibration signal spectrum distribution are not required in this method. It is shown that better results are achieved when the filtered component of the vibration signal is used for fault classification rather than common methods that use directly vibration signal. Experimental results on three-phase induction motor verify the ability of the proposed method in fault diagnosis despite low quality (noisy) of measured vibration signal.     

Fault mode single-phase operation of a variable frequency inductionmotor drive and improvement of pulsating torque characteristics

Improved reliability and fault tolerant operation of power converter systems are extremely important for industrial AC drives. The paper considers variable frequency variable voltage operation of a three-phase induction motor in single-phase mode for two common faults of a three-phase inverter, i.e., open base drive and device short-circuit. The motor performance has been extensively analyzed in single-phase mode and remedial strategies have been developed to neutralize large second and other lower order harmonic pulsating torques. In a single-phase open loop volts/Hz control made of a faulty three-phase inverter, it has been demonstrated that odd harmonic voltages at appropriate phase angles can be injected to neutralize the low frequency pulsating torques so as to permit smooth drive operation. It has been shown that the pulsating torque can be further reduced by load dependent flux programming rather than operating with constant rated flux     

Bearing damage analysis in a large oil-ring-lubricated inductionmachine

In this paper, an alternative method of online detection capable of discerning machine failure modes resulting in shaft current is proposed. The relationship between shaft current and fault conditions such as asymmetrical flux due to joints in the lamination segments, broken rotor bars, air-gap eccentricity, saturation, and slot harmonics are investigated. The diagnostic equipment used in the investigation of a three-phase medium-voltage squirrel-cage induction machine is described. The experimental results using the alternative method of online detection of shaft current are presented     

FMM神经网络结合CART的感应电动机故障诊断方法

针对感应电动机存在多种故障问题,提出一种融合模糊极小-极大（FMM）神经网络和分类回归树（CART）的电机故障诊断方法（FMM-CART）,对转子断条、定子绕组和电压失衡三种常见电机故障进行诊断。通过采集电机三相的电流信号,并进行功率谱分析,提取特定谐波信号作为FMM-CART模型的输入特征。训练过的FMM神经网络根据输入特征计算置信因子,CART根据置信因子构建决策树,最终输出诊断结果。实验结果表明,FMM-CART能有效的诊断各种电机故障,且具有较少的检测时间和较低的网络复杂度。     

Induction machine fault detection using SOM-based RBF neural networks

A radial-basis-function (RBF) neural-network-based fault detection system is developed for performing induction machine fault detection and analysis. Four feature vectors are extracted from power spectra of machine vibration signals. The extracted features are inputs of an RBF-type neural network for fault identification and classification. The optimal network architecture of the RBF network is determined automatically by our proposed cell-splitting grid algorithm. This facilitates the conventional laborious trial-and-error procedure in establishing an optimal architecture. In this paper, the proposed RBF machine fault diagnostic system has been intensively tested with unbalanced electrical faults and mechanical faults operating at different rotating speeds. The proposed system is not only able to detect electrical and mechanical faults, but the system is also able to estimate the extent of faults.     

基于小波神经网络的三相整流电路的故障诊断

应用带动量项和自适应学习率的小波神经网络解决了应用神经网络诊断三相整流电路时收敛速度慢,搜索空间局部极小,易引起振荡等问题。首先根据不同晶闸管的故障输出波形的不同,使用Multisim软件对三相整流电路的故障进行仿真模拟,然后用波形采集数据制作的样本对网络进行训练,最后训练好的网络可用于故障诊断。仿真表明,提出的方法比现有方法的收敛速度快,诊断误差小。     

双三相感应电机仿真建模新方法

多相电机相比传统三相电机,具有较高的可靠性,较低的转矩脉动等优势,在船舶电力推动和低压大功率方向有很好的发展潜力。目前对多相电机的仿真建模研究多基于信号流模式。本文以双三相感应电机为例,在分析其数学模型的基础上,提出了双三相感应电动机基于线性变压器和受控电流源仿真建模的新方法。所搭建的双三相感应电机模型可以作为Matlab/Simul i nk电力模块系统模块库中的元件进行直接调用。仿真结果证明了模型的准确性和有效性。     

Induction machine condition monitoring with higher order spectra

This paper describes a novel method of detecting and unambiguously diagnosing the type and magnitude of three induction machine fault conditions from the single sensor measurement of the radial electromagnetic machine vibration. The detection mechanism is based on the hypothesis that the induction machine can be considered as a simple system, and that the action of the fault conditions are to alter the output of the system in a characteristic and predictable fashion. Further, the change in output and fault condition can be correlated allowing explicit fault identification. Using this technique, there is no requirement for a priori data describing machine fault conditions, the method is equally applicable to both sinusoidally and inverter-fed induction machines and is generally invariant of both the induction machine load and speed. The detection mechanisms are rigorously examined theoretically and experimentally, and it is shown that a robust and reliable induction machine condition-monitoring system has been produced. Further, this technique is developed into a software-based automated commercially applicable system     

Efficiency Analysis of PWM Inverter Fed Three-Phase and Dual Three-Phase High Frequency Induction Machines for Low/Medium Power Applications

A performance analysis of three-phase and dual three-phase (DTP) induction pulsewidth modulation (PWM) inverter-fed motor drives is conducted in this paper. The focus is on the efficiency performance of high-frequency DTP machines compared to their three-phase counterparts in low/medium power applications. For this purpose, a DTP machine, having two sets of stator three-phase windings spatially shifted by 30 electrical degrees (asymmetrical six-phase winding configuration), was tested for both six-phase and three-phase winding configurations under the same magnetic conditions. Simulation and experimental results are presented to evaluate the efficiency performance of three-phase and dual-three induction motor drives employing PWM voltage source inverters.     

基于LMBP神经网络的惯性器件故障预报方法研究

利用Levenberg-Marquardt(L-M)算法优化计算BP权值调整量,将L-M算法与传统的BP网络相结合开发出一种快速收敛的LMBP网络,并在此基础上提出了基于LMBP神经网络的时间序列预测方法。最后利用该方法对某惯性器件进行故障预报,通过仿真实验证明了该方法的有效性。     

Nonlinear parameter estimation of steady-state induction machinemodels

The use of modern identification techniques in determining the values of the steady-state equivalent circuit parameters of a three-phase squirrel-cage induction machine is discussed. The identification procedure is based on the steady-state phase current versus slip and input power versus slip characteristics. The proposed identification algorithm is of a nonlinear kind. The machine parameters are obtained as the solution of a minimization of least-squares cost function of the difference between calculated and experimental steady-state characteristics. Simulation, as well as experimental results concerning the application of the proposed algorithm to the modeling of a 1.5 kW wound-rotor three-phase induction machine, are presented     

基于PLC的电气配电柜自动控制系统研究

本文基于PLC实现了配电柜及其连接线路上短路故障的监测、定位和切除。通过监测配电柜的绝缘电阻实现了对配电柜的绝缘智能监控和异常报警,通过故障定位模块实现了对故障位置的快速定位,并通过PLC控制将故障线路及时切除,保证了配电系统的正常运行。     

A Stochastic-Based FPGA Controller for an Induction Motor Drive With Integrated Neural Network Algorithms

This paper applies stochastic theory to the design and implementation of field-oriented control of an induction motor drive using a single field-programmable gate array (FPGA) device and integrated neural network (NN) algorithms. Normally, NNs are characterized as heavily parallel calculation algorithms that employ enormous computational resources and are less useful for economical digital hardware implementations. A stochastic NN structure is proposed in this paper for an FPGA implementation of a feedforward NN to estimate the feedback signals in an induction motor drive. The stochastic arithmetic simplifies the computational elements of the NN and significantly reduces the number of logic gates required for the proposed NN estimator. A new stochastic proportional-integral speed controller is also developed with antiwindup functionality. Compared with conventional digital controls for motor drives, the proposed stochastic-based algorithm enhances the arithmetic operations of the FPGA, saves digital resources, and permits the NN algorithms and classical control algorithms to be easily interfaced and implemented on a single low-complexity, inexpensive FPGA. The algorithm has been realized using a single FPGA XC3S400 from Xilinx, Inc. A hardware-in-the-loop (HIL) test platform using a Real Time Digital Simulator is built in the laboratory. The HIL experimental results are provided to verify the proposed FPGA controller.     

MATLAB电力系统仿真技术在水利电气自动化中的应用

水利系统中,电气自动化设备是其重要组成部分.而在进行电气系统设计时,往往需要知晓电力系统潮流、系统稳定性等重要性能,而有时又要对系统进行故障预测、诊断与分析,继电保护系统等的计算与分析,因此针对其电气自动化系统进行有效的仿真分析工具,对深入研究和设计水利电气自动化系统起到了积极的推动工作用.MATLAB电力系统仿真技术...     

Speed-sensorless vector control of an induction motor using neuralnetwork speed estimation

In this paper, a novel speed estimation method of an induction motor using neural networks (NNs) is presented. The NN speed estimator is trained online by using the error backpropagation algorithm, and the training starts simultaneously with the induction motor working. The estimated speed is then fed back in the speed control loop, and the speed-sensorless vector drive is realized. The proposed NN speed estimator has shown good performance in the transient and steady states, and also at either variable-speed operation or load variation. The validity and the usefulness of the proposed algorithm are thoroughly verified with experiments on fully digitalized 2.2 kW induction motor drive systems     

Analytical Prediction of the Short-Circuit Current in Fault-Tolerant Permanent-Magnet Machines

This paper describes an analytical technique that can be utilized to predict the short-circuit current in a fault-tolerant permanent-magnet machine under partial-turn short-circuit fault conditions. It has been shown that the current in partially short-circuited turns is dependent on their relative position in the slot where the phase winding is accommodated, and the slot-leakage flux associated with these turns has a significant influence on the short-circuit current when a remedial action has been applied. An analytical model that quantifies the variation of the slot-leakage flux as a function of the relative position of partially short-circuited turns has been developed. Both finite-element analysis and experimental results demonstrate the effectiveness of the proposed technique for predicting the short-circuit current.      

模拟电路故障诊断的发展现状与展望

阐述了模拟电路故障诊断的意义，介绍了模拟电路故障诊断的发展现状，以及应用神经网络、模糊理论和小波分析后发展的趋势。分析了存在的问题，并提出了解决方法。