Early artificial neural networks diagnosis using admittance model of a three-phase induction machine with inter-turn short-circuit fault
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10751-024-02120-8.pdf
Reference15 articles.
1. Tallam, R.M., Habetler, T.G., Harley, R.G.: Transient model for induction machines with stator winding turn faults. IEEE Trans. Ind. Appl. 38(3), 632–637 (2002). https://doi.org/10.1109/TIA.2002.1003411
2. Lu, Q., Breikin, T., Wang, H.: Modelling and fault diagnosis of stator inter-turn short circuit in doubly fed induction generators. IFAC Proceedings Volumes (IFAC-PapersOnline), pp. 1013 (2011). https://doi.org/10.3182/20110828-6-IT-1002.02217
3. Berzoy, A., Mohamed, A.A.S., Mohammed, O.: Complex-Vector Model of Interturn Failure in Induction Machines for Fault Detection and Identification. IEEE Trans. Ind. Appl. 53(3), 2667–2678 (2017). https://doi.org/10.1109/TIA.2016.2632068
4. Husari, F., Seshadrinath, J.: 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, PESGRE 2020 (2020). https://doi.org/10.1109/PESGRE45664.2020.9070334
5. Lashkari, N., Poshtan, J., Azgomi, H.F.: Simulative and experimental investigation on stator winding turn and unbalanced supply voltage fault diagnosis in induction motors using Artificial Neural Networks. ISA Trans. 59, 334–342 (2015). https://doi.org/10.1016/j.isatra.2015.08.001
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