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1

Detection of partial rotor bar rupture of a cage induction motor using least square support vector machine approach

Birame M’hamed, Bessedik Sid Ahmed, Benkhoris Mohamed Fouad

Diagnostyka

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2021

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Vol. 22, No. 1

57--63

EN

Squirrel cage induction motors suffer from numerous faults, for example cracks in the rotor bars. This paper aims to present a novel algorithm based on Least Squares Support Vector Machine (LS-SVM) for detection partial rupture rotor bar of the squirrel cage asynchronous machine. The stator current spectral analysis based on FFT method is applied in order to extract the fault frequencies related to rotor bar partial rupture. Afterward the LS-SVM approach is established as monitoring system to detect the degree of rupture rotor bar. The training and testing data sets used are derived from the spectral analysis of one stator phase current, containing information about characteristic harmonics related to the partial rupture rotor bar. Satisfactory and more accurate results are obtained by applying LS-SVM to fault diagnosis of rotor bar.

2

Least square support vectors machines approach to diagnosis of stator winding short circuit fault in induction motor

Birame M’hamed, Taibi Djamel, Bessedik Sid Ahmed, Benkhoris Mohamed Fouad

Diagnostyka

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2020

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Vol. 21, No. 4

35--41

EN

Various approaches have been proposed to monitor the state of machines by intelligent techniques such as the neural network, fuzzy logic, neuro-fuzzy, pattern recognition. However, the use of LS-SVM. This article presents an automatic computerized system for the diagnosis and the monitoring of faults between turns of the stator in IM applying the LS-SVM least square support vector machine. in this study for the detection of short circuit faults in the stator winding of the induction motor. Since it requires a mathematical model suitable for modelling defects, a defective IM model is presented. The proposed method uses the stator current as input and at the output decides the state of the motor, indicating the severity of the short-circuit fault.

3

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

Mahdjoubi Abdelhalim, Zegnini Boubakeur, Belkheiri Mohammed

Diagnostyka

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2019

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Vol. 20, No. 1

49--54

EN

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.

4

A new approach using Least Squares Support Vector Machines (LS-SVM) to predict Furan in power transformers

Mahdjoubi A., Zegnini B., Belkheiri M.

Przegląd Elektrotechniczny

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2014

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R. 90, nr 2

142--145

EN

LS-SVM present recently more efficiency in different industrial applications like medicine, engineering and power systems. This paper describes a methodology that was developed for the prediction of Furan in power transformers. The methodology uses as input variables such as the dissolved gases (CO and CO2). The approach presents the advantage that can reduce the time vs. laboratory tests. The validity of the approach was examined by testing several power transformers. LS-SVM gives a good estimation of results which are validated by experimental tests.

PL

W artykule opisano metodologię prognozowania obecności Furanu w transformatorach energetycznych. Na wejściu podawane są takie parametry jak ilość rozpuszczonych gazów CO i CO2. Metoda opiera się na wykorzystaniu algorytmów LS-SVM.

5

Odwzorowanie dynamiki pracy regulatora PID zaimplementowanego w sterowniku PLC za pomocą Least Squares- Support Vector Machines

Sałat R., Awtoniuk M.

Przegląd Elektrotechniczny

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2012

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R. 88, nr 5a

227-230

PL

Artykuł ma na celu przedstawienie metody umożliwiającej odwzorowanie dynamiki pracy algorytmu PID zaimplementowanego w sterowniku PLC przy wykorzystaniu LS-SVM. W artykule opisano rodzaje algorytmów PID zaimplementowanych w sterownikach PLC, jak również omówiono w skrócie różnice między SVM a LS-SVM. Główny nacisk położono na proces doboru cech i ich wpływ na zdolności uczące i generalizacyjne. Przedstawiono wyniki uczenia i testowania sieci LS-SVM odwzorowującej działanie rzeczywistego algorytmu PID w PLC.

EN

The following paper presents a new approach to the dynamics mapping of the PID controller implemented in the PLC using the LS-SVM. The article describes the types of PID algorithms implemented in the PLC. The differences between SVM and LS-SVM are also briefly discussed. The process of features selection and their impact on learning ability and testing is mainly emphasized. The results of learning and testing of the LSSVM mapping of the work of the PID controller are demonstrated.

6

Displacement Self-sensing of Bearingless Switched Reluctance Motors Based on LS-SVM

Zhu Z., Sun Y., Huang Y., Ji X.

Przegląd Elektrotechniczny

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2012

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R. 88, nr 12a

310-313

EN

To achieve the rotor radial displacement self-sensing for a bearingless switched reluctance motor (BSRM), a new displacement estimation method using least squares support vector machine (LS-SVM) was proposed. Firstly, the working principle and mathematic of a 3-phase 12/8 pole BSRM was introduced in brief. Then taking advantage of LS-SVM with better solution for small-sample learning problem and strong generalization ability, two LS-SVMs were trained off-line to obtain two efficient nonlinear mapping structures to express the dynamic behavior of BSRM. The LSSVM training data set is comprised of representative experimental data with current {i | i = (isa1, isa2, ima)} and rotor position θ as inputs and the corresponding displacements {D | D=(α , β )}as outputs. As well as giving a detailed explanation of the new method, simulation and experimental results were presented. It shows that the proposed LS-SVM-based displacement self-sensing method has high precision and operation efficiency.

PL

W artykule przedstawiono uczący się estymator przesunięcia dla bezłożyskowego silnika o przełączanej reluktancji (BSRM), wykorzystujący metodę LS-SVM (ang. Least Square Support Vector Machines). Opisano zasadę działania i model matematyczny silnika BSRM 3- fazowego 12/8 biegunowego. W celu uzyskania efektywnej struktury mapowania nieliniowego do określenia stanów dynamicznych, zastosowano dwa algorytmy, które zostały nauczone offline. Estymator poddano weryfikacji symulacyjnej i eksperymentalnej.