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

Ghosh, Eshaan; Mollaeian, Aida; Kim, Seog; Tjong, Jimi; Kar, Narayan C.

doi:10.1109/tmag.2017.2703624

Cited by 20 publications

(10 citation statements)

References 10 publications

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“…Magnetic flux sensors can be installed at various locations inside electrical machines. Here, these positions are categorised as follows (see Figure 4): � IPS1: a search coil (SC) around a stator tooth in induction machines (IMs) [4,[40][41][42][43][44] and permanent magnet machines (PMMs) [45][46][47][48][49][50][51][52], � IPS2: an SC which its straight section goes inside the slot under the stator slot wedge and comes out of the machine through holes in stator housing [53],…”

Section: Internal Magnetic Fluxmentioning

confidence: 99%

“…Magnetic flux sensors can be installed at various locations inside electrical machines. Here, these positions are categorised as follows (see Figure 4): IPS1 : a search coil (SC) around a stator tooth in induction machines (IMs) [4, 40–44] and permanent magnet machines (PMMs) [45–52], IPS2 : an SC which its straight section goes inside the slot under the stator slot wedge and comes out of the machine through holes in stator housing [53], IPS3 : a Hall‐effect flux sensors (HEFSs)/flux‐gate magnetometer (FGM)/tunnelling magnetoresistor (TMR) on a stator tooth between the extension of two coils coming out of stator slots [54], IPS4 : an Fibre Bragg Grating (FBG)‐Terfenol‐D sensor in the stator slot wedge [32], and IPS5 : a HEFS/FGM/TMR on the stator bore inside the airgap. …”

Section: Magnetic Flux Monitoring Techniquesmentioning

confidence: 99%

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Airgap and stray magnetic flux monitoring techniques for fault diagnosis of electrical machines: An overview

Mazaheri‐Tehrani

Faiz

2021

IET Electric Power Appl

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Flux-based fault diagnosis methods have attracted ever-increasing attention from technical communities because of their potential diagnostic capabilities and ability to overcome weaknesses of the traditional methods such as motor current signature analysis (MCSA). This study reviews recent literature on these methods and addresses their strengths and limitations. Different types of flux sensors that can be used for fault diagnosis applications are also discussed. Flux-based approaches are classified based on the type of utilised flux into internal (airgap flux) and external (stray flux) methods and compared with each other. The most common faults in both induction machines (IMs) and permanent magnet machines (PMMs), including inter-turn short-circuit (ITSC), airgap eccentricity (AGE), rotor broken bars (BRB), and irreversible demagnetisation (IDM) faults, are considered in this review. Finally, research gaps are identified, and some suggestions for future research in this area are provided.

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Section: Internal Magnetic Fluxmentioning

confidence: 99%

Section: Magnetic Flux Monitoring Techniquesmentioning

confidence: 99%

Airgap and stray magnetic flux monitoring techniques for fault diagnosis of electrical machines: An overview

Mazaheri‐Tehrani

Faiz

2021

IET Electric Power Appl

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“…There is a rising interest in developing condition monitoring systems based on artificial intelligence techniques such as as support vector machine (SVM) [ 13 ], artificial neural networks (ANN) [ 14 ], Naïve Bayes classifier, Ensemble, k-nearest neighbours (KNN) or decision trees as they can determine not only the presence or absence but also the severity degree of given fault, which improves the reliability. In fact, they have been used to develop condition monitoring systems able to detect different types of faults such as broken rotor bar [ 15 , 16 ], stator short circuit [ 17 , 18 ] or bearing faults [ 14 , 16 , 19 ], among others.…”

Section: Introductionmentioning

confidence: 99%

A Review of Techniques Used for Induction Machine Fault Modelling

Terron-Santiago

Martínez-Román

Puche-Panadero

2021

Sensors

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Over the years, induction machines (IMs) have become key components in industry applications as mechanical power sources (working as motors) as well as electrical power sources (working as generators). Unexpected breakdowns in these components can lead to unscheduled down time and consequently to large economic losses. As breakdown of IMs for failure study is not economically feasible, several IM computer models under faulty conditions have been developed to investigate the characteristics of faulty machines and have allowed reducing the number of destructive tests. This paper provides a review of the available techniques for faulty IMs modelling. These models can be categorised as models based on electrical circuits, on magnetic circuits, models based on numerical methods and the recently proposed in the technical literature hybrid models or models based on finite element method (FEM) analytical techniques. A general description of each type of model is given with its main benefits and drawbacks in terms of accuracy, running times and ability to reproduce a given fault.

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“…However, it is hard to get a precise parametric fault model. Ghosh et al 6 propose harmonic compensation based on the deep neural network for stator interturn fault to ensure the continuity of the IM operation. Salmasi 7 propose the FTC for current and speed sensors without the need for the IM parameters.…”

Section: Introductionmentioning

confidence: 99%

Global fault‐tolerant control approach for vector control of an induction motor

Gouichiche

Safa

Chibani

et al. 2020

Int Trans Electr Energ Syst

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In this research article, a global fault-tolerant control technique (GFTC) for field-oriented control of asynchronous (or induction) motor is addressed. It aims to present an FTC scheme that deals with the most common failures, which can alter the induction machine. Two classes of failures are treated here, machine failures such as stator winding fault and broken bars fault, and sensor failures such as current and speed sensors faults. Each failure has its own FTC system. To achieve a better design, a diagnosis algorithm based on sliding mode observers and current spectral analysis is set to detect and isolate such fault in a suitable time taking the right action to eliminate or offset the effect of this failure. This is done by providing a new threshold selection technique adopted in this work. Moreover, experimental results based on DSpace 1104 board are provided for the exploration of different scenarios showing the FTC scheme validity.

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

Cited by 20 publications

References 10 publications

Airgap and stray magnetic flux monitoring techniques for fault diagnosis of electrical machines: An overview

Airgap and stray magnetic flux monitoring techniques for fault diagnosis of electrical machines: An overview

A Review of Techniques Used for Induction Machine Fault Modelling

Global fault‐tolerant control approach for vector control of an induction motor

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