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2024-08-30
Stator Winding Interturn Short-Circuit Fault Detection in WRIM Using Rise and Fall Times of Stator Currents
By
Progress In Electromagnetics Research C, Vol. 147, 109-116, 2024
Abstract
One of the major challenges of today's rotating machine manufacturing industries is finding effective techniques to prevent early mechanical or electrical failure. Efficient troubleshooting methods must be developed for rotating electrical machines, such as three-phase and multiphase electrical induction or synchronous machines. A novel method for fault detection in a Wound Rotor Induction Machine (WRIM) is presented in this paper. Its originality lies in the determination of current rise and fall times in healthy and InterTurn short-Circuit Fault (ITSCF) cases. The method is based on using the two-current (isd, isq) sigmoid transform (ST) of Park's vector approach. A WRIM with a nominal power of 0.3 kW is used for the analytical and experimental studies. The type of fault detection being studied is short circuit InterTurns on one phase of the stator winding. The results are promising because the methodology used is simple, fast, and accurate for diagnosing this type of fault, and can detect a low number of short-circuit InterTurns in the stator winding.
Citation
Habachi Bilal, Svetlana Dyagileva, Nicolas Heraud, Eric Jean Roy Sambatra, and Blaise Ravelo, "Stator Winding Interturn Short-Circuit Fault Detection in WRIM Using Rise and Fall Times of Stator Currents," Progress In Electromagnetics Research C, Vol. 147, 109-116, 2024.
doi:10.2528/PIERC24061905
References

1. Cherif, Hakima, Abdelhamid Benakcha, Ismail Laib, Seif Eddine Chehaidia, Arezky Menacer, Bassel Soudan, and A. G. Olabi, "Early detection and localization of stator inter-turn faults based on discrete wavelet energy ratio and neural networks in induction motor," Energy, Vol. 212, 118684, Dec. 2020.

2. Ranzinger, Lukas, Stephanie Uhrig, and Stefan Tenbohlen, "Analysis and modeling the frequency response of rotating machines regarding fault diagnosis using SFRA," IEEE Transactions on Energy Conversion, Vol. 39, No. 1, 747-756, 2023.

3. Athikessavan, Subash Chandar, Elango Jeyasankar, and Sanjib Kumar Panda, "Inter-turn fault detection of induction motors using end-shield leakage fluxes," IEEE Transactions on Energy Conversion, Vol. 37, No. 4, 2260-2270, Dec. 2022.

4. Fu, Yang, Zixu Ren, Shurong Wei, Yao Xu, and Fangxing Li, "Using flux linkage difference vector in early inter-turn short circuit detection for the windings of offshore wind DFIGs," IEEE Transactions on Energy Conversion, Vol. 36, No. 4, 3007-3015, Dec. 2021.

5. Nazemi, Mohammadhossein, Xiaodong Liang, and Farhad Haghjoo, "Convolutional neural network-based online stator inter-turn faults detection for line-connected induction motors," IEEE Transactions on Industry Applications, Vol. 60, No. 3, 4693-4707, 2024.

6. Husari, Fatimatelbatoul and Jeevanand Seshadrinath, "Stator turn fault diagnosis and severity assessment in converter-fed induction motor using flat diagnosis structure based on deep learning approach," IEEE Journal of Emerging and Selected Topics in Power Electronics, Vol. 11, No. 6, 5649-5657, Dec. 2023.

7. Alipoor, Ghasem, Seyed Jafar Mirbagheri, Seyed Mohammad Mahdi Moosavi, and Sérgio M. A. Cruz, "Incipient detection of stator inter‐turn short‐circuit faults in a doubly-fed induction generator using deep learning," IET Electric Power Applications, Vol. 17, No. 2, 256-267, 2023.

8. Husari, Fatimatelbatoul and Jeevanand Seshadrinath, "Early stator fault detection and condition identification in induction motor using novel deep network," IEEE Transactions on Artificial Intelligence, Vol. 3, No. 5, 809-818, Oct. 2022.

9. Attallah, Omneya, Rania A. Ibrahim, and Nahla E. Zakzouk, "Fault diagnosis for induction generator-based wind turbine using ensemble deep learning techniques," Energy Reports, Vol. 8, 12787-12798, Nov. 2022.

10. Husari, Fatima and Jeevanand Seshadrinath, "Sensitive inter-tum fault identifcation in induction motors using deep learning based methods," 2020 IEEE International Conference on Power Electronics, Smart Grid and Renewable Energy (PESGRE2020), 1-6, Cochin, India, Jan. 2020.

11. Indiran, Raja Rajeswari and Albert Alexander Stonier, "Inter-turn short-circuit faults detection and monitoring of induction machines using WPT-fuzzy logic approach based on online condition," Journal of Circuits, Systems and Computers, Vol. 32, No. 01, 2350001, 2023.

12. Saucedo-Dorantes, Juan Jose, Arturo Yosimar Jaen-Cuellar, Angel Perez-Cruz, and David Alejandro Elvira-Ortiz, "Detection of inter-turn short circuits in induction motors under the start-up transient by means of an empirical wavelet transform and self-organizing map," Machines, Vol. 11, No. 10, 958, Oct. 2023.

13. Hamatwi, Ester, Paul Barendse, and Azeem Khan, "An investigation into the diagnosis of interturn winding faults in a scaled-down DFIG using the MCSA and DWT of the stator and rotor current," 2021 IEEE Energy Conversion Congress and Exposition (ECCE), 3797-3804, Vancouver, BC, Canada, Oct. 2021.

14. Hussein, Ameer M., Adel A. Obed, Rana H. A. Zubo, Yasir I. A. Al-Yasir, Ameer L. Saleh, Hussein Fadhel, Akbar Sheikh-Akbari, Geev Mokryani, and Raed A. Abd-Alhameed, "Detection and diagnosis of stator and rotor electrical faults for three-phase induction motor via wavelet energy approach," Electronics, Vol. 11, No. 8, 1253, Jan. 2022.

15. Almounajjed, Abdelelah and Ashwin Kumar Sahoo, "Wavelet-based multi-class support vector machine for stator fault diagnosis in induction motor," Transactions of the Institute of Measurement and Control, Vol. 45, No. 2, 261-273, 2023.

16. Akhil Vinayak, B., K. Anjali Anand, and G. Jagadanand, "Wavelet-based real-time stator fault detection of inverter-fed induction motor," IET Electric Power Applications, Vol. 14, No. 1, 82-90, 2020.

17. Aswad, Raya A. K. and Bassim M. H. Jassim, "Detection and localization of the stator winding inter-turn fault in induction motors based on parameters estimation using genetic algorithm," Journal of the Institution of Engineers (India): Series B, Vol. 103, No. 2, 405-414, 2022.

18. Tomczyk, Marcin, Ryszard Mielnik, Anna Plichta, Iwona Gołdasz, and Maciej Sułowicz, "Application of genetic algorithm for inter-turn short circuit detection in stator winding of induction motor," Energies, Vol. 14, No. 24, 8523, 2021.
doi:10.3390/en14248523

19. Dongare, Ujwala, Bhimrao Umre, and Makarand Ballal, "Stator inter-turn short-circuit fault diagnosis in induction motors applying VI loci-based technique," Energy Reports, Vol. 9, 1483-1493, Oct. 2023.

20. Dongare, Ujwala V., Bhimrao S. Umre, and Makarand S. Ballal, "Voltage-current locus-based stator winding inter-turn fault detection in induction motors," International Journal of Circuit Theory and Applications, Vol. 51, No. 6, 2889-2911, 2023.

21. Noussaiba, Lazar Amat Ellah and Ferdjouni Abdelaziz, "ANN-based fault diagnosis of induction motor under stator inter-turn short-circuits and unbalanced supply voltage," ISA Transactions, Vol. 145, 373-386, 2024.

22. Rajamany, Gayatridevi, Sekar Srinivasan, Krishnan Rajamany, and Ramesh K. Natarajan, "Induction motor stator interturn short circuit fault detection in accordance with line current sequence components using artificial neural network," Journal of Electrical and Computer Engineering, Vol. 2019, No. 1, 4825787, Dec. 2019.

23. Namdar, Ali, Haidar Samet, Mehdi Allahbakhshi, Mohsen Tajdinian, and Teymoor Ghanbari, "A robust stator inter-turn fault detection in induction motor utilizing Kalman filter-based algorithm," Measurement, Vol. 187, 110181, Jan. 2022.

24. Prakash, R. B. R., Madhusudana rao Ranga, A. Pandian, and P. Srinivasa Varma, "Induction machine stator winding failure detection using motor current signature analysis," IOP Conference Series: Materials Science and Engineering, Vol. 993, No. 1, 012084, 2020.

25. Saied, Basil and Ahmed Jadaan Ali, "Fault prediction of deep bar cage rotor induction motor based on FEM," Progress In Electromagnetics Research B, Vol. 53, 291-314, 2013.

26. Rehman, Attiq Ur, Yu Chen, Guorui Huang, Yan Yang, Shuang Wang, Yihan Zhao, Yong Zhao, Yonghong Cheng, and Toshikatsu Tanaka, "Stator inter-turns short circuit fault detection in DFIG using empirical mode decomposition method on leakage flux," 2020 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD), 184-187, Xi’an, China, Oct. 2020.

27. Ghanbari, Teymoor and Abbas Mehraban, "Stator winding fault detection of induction motors using fast Fourier transform on rotor slot harmonics and least square analysis of the Park's vectors," IET Electric Power Applications, Vol. 18, No. 3, 356-366, 2024.

28. Bilal, Habachi, Nicolas Heraud, and Eric Jean Roy Sambatra, "An experimental approach for detection and quantification of short-circuit on a doubly fed induction machine (DFIM) windings," Journal of Control, Automation and Electrical Systems, Vol. 32, No. 4, 1123-1130, 2021.

29. Xu, Zhao, Changhua Hu, Feng Yang, Shyh-Hao Kuo, Chi-Keong Goh, Amit Gupta, and Sivakumar Nadarajan, "Data-driven inter-turn short circuit fault detection in induction machines," IEEE Access, Vol. 5, 25055-25068, 2017.

30. Chen, Xinglong, Peng Qin, Yongyi Chen, Jianjian Zhao, Wenhao Li, Yao Mao, and Tao Zhao, "Inter-turn short circuit fault diagnosis of PMSM," Electronics, Vol. 11, No. 10, 1576, 2022.

31. Bilal, Habachi, E. J. Sambatra, Nicolas Heraud, Jean-Marie Razafimahenina, and Svetlana Dyagileva, "Detection of inter-turn short-circuit on a doubly fed induction machine with dq axis representation-application to different power levels," Progress In Electromagnetics Research B, Vol. 95, 23-40, 2022.
doi:10.2528/PIERB22011207

32. Marques Cardoso, A. J., S. M. A. Cruz, and D. S. B. Fonseca, "Inter-turn stator winding fault diagnosis inthree-phase induction motors, by Park's vector approach," IEEE Transactionson Energy Conversion, Vol. 14, No. 3, 595-598, Sep. 1999.

33. Parra, A. P., M. C. A. Enciso, J. O. Ochoa, and J. A. P. Penaranda, "Stator fault diagnosis on squirrel cage induction motors by ESA and EPVA," 2013 Workshop on Power Electronics and Power Quality Applications (PEPQA), 1-6, Bogota, Colombia, Jul. 2013.

34. Cruz, S. M. A. and A. J. M. Cardoso, "Stator winding fault diagnosis in three-phase synchronous and asynchronous motors, by the extended Park’s vector approach," IEEE Transactions on Industry Applications, Vol. 37, No. 5, 1227-1233, Sep.-Oct. 2001.