Non-invasive detection of winding short-circuit faults in salient-pole synchronous machine

Cuevas, Mauricio; Romary, Raphaël; Lecointe, Jean‐Philippe; Morganti, Fabrice; Jacq, Thierry

doi:10.1109/iemdc.2017.8002102

Cited by 12 publications

(7 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Consequently, a magnetic imbalance was created, as one of the poles produced a lower magnetomotive force than the others. Under such conditions, a harmonic corresponding to the rotating frequency is expected to appear in the stray flux of the stator [14,15].…”

Section: Rotor Inter-turn Faulty Conditionsmentioning

confidence: 99%

“…The stray flux analysis may be a reliable method to detect early faults in such electrical machines. Past works suggest that inter-turn faults in both the rotor and the stator can be detected reliably [14,15].…”

Section: Introductionmentioning

confidence: 99%

“…Throughout the literature, the most frequently used flux sensors to measure the stray flux in electrical machines are coreless coils, normally known as research coils [1,2,[4][5][6][7][8][9]. Some authors use rectangular-shaped coils [14,15]. Only a few authors use other types of sensors such as giant magneto resistive (GMR) [1] or tunneling magneto resistive sensors (TMR) [12,13].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Stray Flux Sensor Core Impact on the Condition Monitoring of Electrical Machines

Tian

Platero

Gyftakis

et al. 2020

Sensors

View full text Add to dashboard Cite

The analysis of the stray flux for electrical machine condition monitoring is a very modern and active research topic. Thanks to this technique, it is possible to detect several types of failures, including stator and rotor inter-turn faults, broken rotor bars and mechanical faults, among others. The main advantages are that it involves a non-invasive technique and low-cost monitoring equipment. The standard practice is to use coreless flux sensors, with which the stray flux of the machine is not perturbed and there are no problems due to saturation or nonlinear behavior of the iron. However, the induced voltage in the coreless coil sensor may be very low and even, in some cases, have a similar amplitude to the noise floor. This paper studies the use of iron core stray flux sensors for condition monitoring of electrical machines. The main advantage of iron core flux sensors is that the measured electromotive force is stronger. In the case of large machines in noisy environments, this can be crucial. Two different types of iron core stray flux sensors and a coreless flux sensor are tested. A comparison of the three sensors is presented. Extensive experimental testing with all sensors shows the superiority and greater sensitivity of sensors with core versus the coreless ones.

show abstract

Section: Rotor Inter-turn Faulty Conditionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Stray Flux Sensor Core Impact on the Condition Monitoring of Electrical Machines

Tian

Platero

Gyftakis

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…The fault detection in which there is no need to direct access to the faulted part has been investigated in some literature and some processes are proposed. In the articles, the fault detection is carried out using acoustic signals [2][3][4][5], vibration signals [6][7][8][9][10][11], some other types of signals [12][13][14][15], and the combination of some signals [16][17][18][19][20]. The KNN method is utilised to classify the short-circuit fault in the main winding and auxiliary coil of the single-phase induction motor using acoustic signals [2].…”

Section: Introductionmentioning

confidence: 99%

“…Short-circuit fault on synchronous generator filed winding is investigated in [15], and twin signal sensing is proposed for the fault detection. A fault detection method for internal turn-to-turn short-circuit fault on the winding of the rotor and the coil of the stator by external magnetic flux density and external housing vibration signals is proposed in [16]. The hypoid gear fault has been evaluated utilising sound and vibration signals and a differential waveform has to detect the fault [17].…”

Section: Introductionmentioning

confidence: 99%

Machine‐learning approach for fault detection in brushless synchronous generator using vibration signals

Rahnama

Vahedi

Mohammad‐Alikhani

et al. 2019

IET Science, Measurement & Technology

View full text Add to dashboard Cite

In order to maintain continuous production and to avoid the maintenance cost increment in power plants, it is important to monitor the condition of equipment, especially the generator. Regarding the impossibility of direct access to rotating diodes in brushless synchronous generators, the condition monitoring of these elements is very important. In this paper, a novel fault detection method is proposed for the diode rectifier of brushless synchronous generator. At the first stage of this method, the vibration signals are recorded and feature extraction is performed by calculating the relative energy of discrete wavelet transform components. Multiclass support vector machine (MSVM) is used for classification, and the best mother wavelet and number of decomposition level are chosen based on classification performance. To enhance the performance of the classification, a modified sequential forward subset selection approach is included by which the best statistical features are selected. In this approach, besides selecting the best subset of statistical features, the classification parameter is tuned according to the selected subset to achieve the best performance. The result of the proposed method is eventually compared with those results of classification performance using conventional subset selection. Experimental results show that the proposed method can detect rectifier faults effectively.

show abstract

An overview of various faults detection methods in synchronous generators

Mostafaei

Faiz

2021

IET Electric Power Appl

View full text Add to dashboard Cite

Synchronous generator (SG) plays a vital and critical role in the power system by supplying electric power to consumers. Various faults in SGs can cause some catastrophic events such as power disruption or blackout. These faults can be classified into two electrical and mechanical faults. Short circuit in stator windings and field winding are electrical fault while bearing, static/dynamic eccentricity, and broken damper bars faults are mechanical one. Unlike the induction machines, there are no much researches in SGs condition monitoring owing to its complex behaviour against the faults. Herein, the SG modelling approaches are presented briefly to elaborate shortcoming and challenging issues in the modelling, and then a comprehensive review of various electrical and mechanical fault detection methods is presented.

show abstract

Non-invasive detection of winding short-circuit faults in salient-pole synchronous machine

Cited by 12 publications

References 11 publications

Stray Flux Sensor Core Impact on the Condition Monitoring of Electrical Machines

Stray Flux Sensor Core Impact on the Condition Monitoring of Electrical Machines

Machine‐learning approach for fault detection in brushless synchronous generator using vibration signals

An overview of various faults detection methods in synchronous generators

Contact Info

Product

Resources

About