A Novel Method Based on Particle Swarm Optimization Support Vector Neural Network for Transformer Fault Diagnosis
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-4399-5_51
Reference13 articles.
1. Kaur, K., Bhalla, D., Singh, J.: Fault diagnosis for oil immersed transformer using certainty factor. IEEE Trans. Diele. Elec. Insul. 485–494 (2023)
2. Stringer, A.D., Thompson, C.C., Barriga, C.I.: Analysis of historical transformer failure and maintenance data: effects of era, age, and maintenance on component failure rates. IEEE Trans. Indus. Appl. 55, 5643–5651 (2019)
3. Meng, K., Dong, Z.Y., Wang, D.H., Wong, K.P.: A self-Adaptive RBF neural network classifier for transformer fault analysis. IEEE Trans. Pow. Syst. 25, 1350–1360 (2010)
4. Taha, I.B.M., Mansour, D.A.: Novel power transformer fault diagnosis using optimized machine learning methods. Intel. Aut. Sof. Com. 28, 739–752 (2021)
5. Rogers, R.R.: IEEE and IEC codes to interpret incipient faults in transformers, using gas in oil analysis. IEEE Trans. Elec. Insul. EI-13, 349–354 (1978)
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