Evaluation of electrical insulation in three-phase induction motors and classification of failures using neural networks

Guedes, Armando Souza; Silva, Sidelmo M.; Filho, Braz J. Cardoso; Conceicao, Claudio

doi:10.1016/j.epsr.2016.06.016

Cited by 31 publications

(15 citation statements)

References 13 publications

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“…The system consists of a control panel (driver and motor protection), host computer where the classifier and forecast algorithms run in Matlab, the HSCT sensor, heating chamber and an oil or water mist system. The setup allows the motor to be subjected to the main stress agents that can affect the machine insulation, as thermal, moisture and contamination (oil/dirt) [32][33][34]47].…”

Section: Detection and Forecast Resultsmentioning

confidence: 99%

Insulation failures prognosis in electric machines: preventive detection and time to failure forecast

Guedes¹,

Silva²

2020

IET Electric Power Applications

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This work presents a new approach to the on-line evaluation of the insulation of electric machines. Through the proposed system, it is possible to identify the stress agents causing the degradation of the insulation of low-and mediumvoltage machines. In addition, the estimation of the time-to-failure (TF) of the insulation is developed based on linear stochastic models autoregressive moving average and artificial neural networks. The identification of the stress agent and the estimation of the TF give a complete prognosis for the predictive monitoring of the machine insulation.

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Section: Detection and Forecast Resultsmentioning

confidence: 99%

Insulation failures prognosis in electric machines: preventive detection and time to failure forecast

Guedes¹,

Silva²

2020

IET Electric Power Applications

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“…Once the affected phase is identified, (13) and (14) can be expressed as functions of the phase components, as well as the resistive and capacitive components of the current, as…”

Section: Online Insulation Condition Assessmentmentioning

confidence: 99%

“…In the case of mechanical failures, predictive procedures based on vibration analysis are well developed and widely accepted [6][7][8][9][10][11]. For electrical problems, the most common preventive techniques are: (i) measurement of the insulation resistance R I between windings and ground, and calculation of the polarisation index (PI) and absorption index (AI) [12][13][14][15]; (ii) spectral analysis of the stator current, also known as electrical signature analysis [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31]; and (iii) partial discharge analysis (PDA) [32][33][34][35][36][37][38].…”

Section: Introductionmentioning

confidence: 99%

Insulation protection and online stress agent identification for electric machines using artificial intelligence

Guedes

Silva²

2019

IET Electric Power Applications

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Insulation failures are among the most important problems affecting electric machines. Although standard procedures are widely used to assess the insulation condition of these machines, no alternative is available to allow the identification of the stress agents affecting the insulation system. Another limitation of the standard procedures is the fact that they are based on tests that require the interruption of the operation of the machine. This study presents a technique that allows the online assessment and protection of the insulation of low-and medium-voltage electric machines, along with the identification of the stress agents responsible for the degradation of the insulation. The proposed technique is based on the use of a highly sensitive current transformer, an algorithm to identify the most affected phase, and an artificial neural network to indicate the stress agent affecting the machine. The presented experimental results show that the proposed system can identify thermal degradation, moisture absorption and contamination by oil. The low current levels that can be measured by the developed system enable the application of the proposed technique to low-and medium-voltage machines.

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“…After they thought to improve the ANN algorithm to get best results like made by Hadi Fattahi and Habibollah Bazdar to evaluate drilling rate index [17]. Armando Souza Guedesa et al [18] are using this approach to evaluate the electrical insulation in three-phase induction motors. GMDL give amelioration to the ANN classic by the elimination of bad layers and improving the best layers of the network.…”

Section: Computation Of Critical Flashover Voltage Of Polluted Insulamentioning

confidence: 99%

Prediction of critical flashover voltage of polluted insulators under sec and rain conditions using least squares support vector machines (LS-SVM)

Abdelhalim¹,

Zegnini²,

Belkheiri³

2018

Diagnostyka

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This paper describes a methodology that was developed for the prediction of the critical flashover voltage of polluted insulators under sec and rain conditions least squares support vector machines (LS-SVM) optimization. The methodology uses as input variable characteristics of the insulator such as diameter, height, creepage distance, and the number of elements on a chain of insulators. The estimation of the flashover performance of polluted insulators is based on field experience and laboratory tests are invaluable as they significantly reduce the time and labour involved in insulator design and selection. The majority of the variables to be predicted are dependent upon several independent variables. The results from this work are useful to predict the contamination severity, critical flashover voltage as a function of contamination severity, arc length, and especially to predict the flashover voltage. The validity of the approach was examined by testing several insulators with different geometries. A comparison with the Grouping Multi-Duolateration Localization (GMDL) method proves the accuracy and goodness of LS-SVM. Moreover LS-SVMs give a good estimation of results which are validated by experimental tests.

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Evaluation of electrical insulation in three-phase induction motors and classification of failures using neural networks

Cited by 31 publications

References 13 publications

Insulation failures prognosis in electric machines: preventive detection and time to failure forecast

Insulation failures prognosis in electric machines: preventive detection and time to failure forecast

Insulation protection and online stress agent identification for electric machines using artificial intelligence

Prediction of critical flashover voltage of polluted insulators under sec and rain conditions using least squares support vector machines (LS-SVM)

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