An Inverse Approach for Interturn Fault Detection in Asynchronous Machines Using Magnetic Pendulous Oscillation Technique

This study proposes a novel inter-turn fault diagnosis method for a permanent magnet synchronous machine. Search coils are set on every stator tooth to measure the tooth fluxes. The high-order harmonics produced by the inverter are adopted to diagnose the inter-turn fault existence and locate the fault tooth. The fault severity coefficient is proposed on the basis of theoretical analysis and can identify the fault severity. A comparative analysis of a traditional method based on co-simulation shows that the proposed method is sensitive and that the fault severity coefficient is accurate, intuitive and independent of the operating condition of the motor. An experimental platform is set up, and it validates the effectiveness of the proposed method.

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“…Averaging the results of the two frequencies yields (see (25)) . Integrating the parameters of the adopted motor into (25) yields…”

Section: Implementation Of Diagnosis Of Fault Existence and Locationmentioning

confidence: 99%

Inter‐turn fault diagnosis of permanent magnet synchronous machine based on tooth magnetic flux analysis

Zhang

Huang

Yang

et al. 2018

IET Electric Power Applications

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“…It indicates an anomaly when its aggregated index Q exceeds the corresponding threshold Q α , as defined in Equations (3) and (4). In this phase, fault parameters θ are solved based on an optimization formulation (LSQ) of fault parameters about the mechanism model of distillation composed of measurable variables y, manipulated variables u, disturbance ω, and state variables x, as shown in Equation (5). r = y meas − y sim (1)…”

Section: Obtaining Fault Parameters By the Lsq Algorithmmentioning

confidence: 99%

“…For example, a hybrid fault detection and diagnosis scheme was implemented in a two-step pattern, that is, neural networks were activated to deduce the root reason for a fault state after the fault-related section of a plant was located by a Petri net [4]. A fault diagnosis technique was proposed based on multiple linear models, in which several linear perturbation models suitable for various operation regimes were identified by a Bayesian approach and then combined with a generalized likelihood ratio method to perform fault identification tasks [5]. A fault detection and diagnosis scheme, which uses one tier of a nonlinear rigorous model and another tier of a linear simplified model to monitor the distillation process and identify abnormal parameters, respectively, was developed to consider the accuracy and speed of nonlinear and linear models simultaneously [6].…”

mentioning

confidence: 99%

A Hybrid Inverse Problem Approach to Model-Based Fault Diagnosis of a Distillation Column

et al. 2020

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Early-stage fault detection and diagnosis of distillation has been considered an essential technique in the chemical industry. In this paper, fault diagnosis of a distillation column is formulated as an inverse problem. The nonlinear least squares algorithm is used to evaluate fault parameters embedded in a nonlinear dynamic model of distillation once abnormal symptoms are detected. A partial least squares regression model is built based on fault parameter history to explicitly predict the development of fault parameters. With the stripper of Tennessee Eastman process as example, this novel approach is tested for step- and random-type faults and several factors affecting its efficiency are discussed. The application result shows that the hybrid inverse problem approach gives the correct change of fault parameter at a speed far faster than the base approach with only a nonlinear model.

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“…Winding in this phase has two parts-shorted turns and unfaulted turns. Machine equations in abc variables for a symmetrical motor with turn fault in one winding can be expressed as [34][35][36]. Here, we assumed that the leakage inductance of the shorted turns is βL ls , where L ls is the per-phase leakage inductance and the fault impedance is resistive…”

Section: Mathematical Modeling Of Stator Winding Turn Faultmentioning

confidence: 99%

Induction Motor Stator Interturn Short Circuit Fault Detection in Accordance with Line Current Sequence Components Using Artificial Neural Network

Rajamany

Srinivasan

Rajamany

et al. 2019

Journal of Electrical and Computer Engineering

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The intention of fault detection is to detect the fault at the beginning stage and shut off the machine immediately to avoid motor failure due to the large fault current. In this work, an online fault diagnosis of stator interturn fault of a three-phase induction motor based on the concept of symmetrical components is presented. A mathematical model of an induction motor with turn fault is developed to interpret machine performance under fault. A Simulink model of a three-phase induction motor with stator interturn fault is created for extraction of sequence components of current and voltage. The negative sequence current can provide a decisive and rapid monitoring technique to detect stator interturn short circuit fault of the induction motor. The per unit change in negative sequence current with positive sequence current is the main fault indicator which is imported to neural network architecture. The output of the feedforward backpropagation neural network classifies the short circuit fault level of stator winding.

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An Inverse Approach for Interturn Fault Detection in Asynchronous Machines Using Magnetic Pendulous Oscillation Technique

Cited by 30 publications

References 22 publications

Inter‐turn fault diagnosis of permanent magnet synchronous machine based on tooth magnetic flux analysis

Inter‐turn fault diagnosis of permanent magnet synchronous machine based on tooth magnetic flux analysis

A Hybrid Inverse Problem Approach to Model-Based Fault Diagnosis of a Distillation Column

Induction Motor Stator Interturn Short Circuit Fault Detection in Accordance with Line Current Sequence Components Using Artificial Neural Network

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