Least square support vectors machines approach to diagnosis of stator winding short circuit fault in induction motor
| 1 | Ledmased Laboratory, University of Laghouat, 03000, Algeria |
| 2 | Department of Electrical Engineering, Kasdi Merbah University, Ouargla, Algeria |
| 3 | (LACoSERE) University of Laghouat, 03000, Algeria |
| 4 | IREENA, Saint Nazaire, Polytech’Nantes, France |
CORRESPONDING AUTHOR
Submission date: 2020-07-06
Final revision date: 2020-11-04
Acceptance date: 2020-11-07
Online publication date: 2020-11-09
Publication date: 2020-11-09
Diagnostyka 2020;21(4):35–41
KEYWORDS
fault diagnosisinter-turn short circuitleast square support vector machine (LS-SVM))(Induction Motor
TOPICS
Diagnostic systemsDiagnostics of buildingsTechnical diagnosticsDiagnostics of electronic systemsSignal analysis
ABSTRACT
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 MI 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.
REFERENCES (20)
1.
Zhang P, Du Y, Habetler TG, Lu B. A survey on condition monitoring and protection methods for medium-voltage induction motors. IEEE Trans. Ind. Appl. 2011;47:1: 34-46.
2.
Gandhi A, Corrigan T, Parsa L. Recent advances in modeling and online detectionof stator inter-turn faults in electrical motors. IEEE Trans. Ind. Electron. 2011;. 58(5) :1564-1575.
3.
Doğan Z, Selçuk R. A diagnosis of stator winding fault based on empirical mode decomposition in PMSMs. Balkan Journal of Electrical and Computer Engineering. 2020; 8(1):73-80. https://doi.org/10.17694/bajec....
4.
Roshanfekr R, Jalilian A. An approach to discriminate between types of rotor and stator winding faults in wound rotor induction machines. Iranian Conference on Electrical Engineering (ICEE). 2018.
5.
Gao Z, Cecati C, Ding SX. A survey of fault diagnosis and fault –tolerant techniques part I: fault diagnosis with model-based and signal-based approaches. IEEE Trans. Ind. Electron. 2015; 62(6): 3757-3767. https://doi.org/10.1109/TIE.20....
6.
Bhattacharyya S, Sen D, Adhvaryyu S, Mukherjee C. Induction motor fault diagnosis by motor current signature analysis and neural network techniques. Journal of Advanced Computing and Communication Technologies. 2015;3(1):12-18.
7.
Shifat TA, Hur J-W. An improved stator winding short-circuit fault diagnosis using adaboost algorithm. International Conference on Artificial Intelligence in Information and Communication (ICAIIC). 2020.
8.
Dash R, Subudhi B. Stator inter-turn fault detection of an induction motor using neuro-fuzzy techniques. International Journal on Archives of Control Sciences. 2010;20(3):263-276. https://doi.org/10.2478/v10170....
9.
Campos-Delgado DU, Arce-Santana ER, Espinoza-Trejo DR. Edge optimisation for parameter identification of induction motors. IET Electr. Power Appl. 2011; 5(8):668-675.
10.
Wang S, Dinavahi V, Xiao J. Multi-rate real-time model-based parameter estimation and state identification for induction motors. IET Electr. Power Appl. 2013;7(1):77-86. https://doi.org/10.1049/iet-ep....
11.
Hamoudi A, Kouadri B. Stator fault detection in induction machines by parameter estimation using trust region algorithms. Proceedings of the First. Analyses. IEEE Trans. Ind. Informatics. 2014; 10(2):1348-1360. https://doi.org/10.15676/ijeei....
12.
Melin P, Soto J, Castillo O, Soria J. A new approach for time series prediction using ensembles of ANFIS models. Expert Systems with Applications. 2012; 39(3):3494–3506. https://doi.org/10.1016/j.eswa....
13.
Zhang Y, Wang P, Ni T, Cheng P, Lei S. Wind power prediction based on LS-SVM model with error correction. Advances in Electrical and Computer Engineering. 2017; 17(1):3-8. https://doi.org/10.4316/AECE.2....
14.
Suykens J, Gestel JV, Brabanter JD, Moor BD, Vandewalle J. Least square support vector machines. World Scientific Publishers, Singapore, 2002.
15.
Bu WS, Li Z. LS-SVM inverse system decoupling control strategy of a bearingless induction motor considering stator current dynamics. IEEE Access. 2019. https://doi.org/10.1109/ACCESS....
16.
Li K, Cheng G, Sun X, Yang Z. A nonlinear flux linkage model for bearingless induction motor based on GWO-LSSVM, IEEE Access PP(99):1-1 March 2019.
17.
Mahdjoubi A, Zegnini B, Belkheiri M. Prediction of critical flashover voltage of pollutedInsulators under sec and rain conditions using least Squares support vector machines (LS-SVM). Diagnostyka. 2019; 20(1):49–54. https://doi.org/10.29354/diag/....
18.
Jannati M, Idris NRN, Salam Z. A new method for modeling and vector control of unbalanced induction motors. Energy Conversion Congress and Exposition (ECCE). 2012: 3625–3632.
19.
Bessedik SA, Hadi H. Prediction of flashover voltage of insulators using least squares support vector machine with particle swarm optimisation. Electric Power Systems Research. 2013; 104:87-92. https://doi.org/10.1016/j.epsr....
20.
Bessedik SA, Djekidel R, Ameur A. Performance of different kernel functions for LS-SVM-Grey Wolf Optimiser to estimate flashover voltage of polluted insulators. IET Science, Measurement & Technology. 2018;12(6):739-745. https://doi.org/10.1049/iet-sm....
RELATED ARTICLE
„Podniesienie poziomu naukowego i poziomu umiędzynarodowienia czasopisma Diagnostyka oraz upowszechniania informacji o wynikach badań lub prac rozwojowych poprzez udział uznanych zagranicznych recenzentów w ocenie publikacji - zadanie finansowane w ramach umowy 745/P-DUN/2017. ze środków Ministra Nauki i Szkolnictwa Wyższego przeznaczonych na działalność upowszechniającą naukę”
© 2006-2023 Journal hosting platform by Bentus
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.