Power Transformers Fault Diagnosis Based on Fuzzy-RBF Neural Network

Abstract:

Dissolved Gas Analysis (DGA) is a popular method to detect and diagnose different types of faults occurring in power transformers. Improved three-ratio is an effective method for transformer fault diagnosis used in recent years. This paper applies appropriate Artificial Neural Networks (ANN) to resolve the online fault diagnosis problems for oil-filled power transformer based on improved three-ratio. Because of the characteristic of improved three-ratio boundary is too absolute, a method using fuzzy math theory to deal with the data of the neural network input is also proposed. A major kind of neural network, i.e. radial basis function neural network (RBFNN), is used to model the fault diagnosis structure. In addition, to improve the convergence speed, an improved gradient descent algorithm is used in training RBFNN. Through on-line monitoring the concentrations of the dissolved gases, the proposed diagnostic system can offer a way to interpret the incipient faults. The simulation diagnosis demonstrates the effectiveness and veracity of the proposed method.

• Periodical: Advanced Materials Research (Volumes 614-615)
• Pages: 1303-1306
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.614-615.1303
• Online since: December 2012
• Authors: Hui Da Duan, Xin Yao
• Keywords: Dissolved Gas Analysis, Fault Diagnosis, Fuzzy, Radial Basis Function Neural Network (RBFNN)

References

[1] J.J. Kelly, Transformer fault diagnosis by dissolved-gas analysis, IEEE Trans. on Industry Applications, Vol. 16, No. 6, pp.777-782, (1980).

DOI: 10.1109/tia.1980.4503871

[2] Roers, R., IEEE and IEC codes to interpret incipient faults in transformer, using gas in oil analysis. IEEE Trans. On Electr. Insul., Vol. 13, No. 5, pp.349-354, (1978).

DOI: 10.1109/tei.1978.298141

[3] D.K. Cao, gas analysis diagnosis and fault inspection in transformer oil, China Electric Power Press,2005.

[4] EY. Zhang, X. Ding, Y. Liu and P. J. Griffin, An artificial neural network approach to transformer fault diagnosis, IEEE Trans. On PWRD, Vol. 11, No. 4, pp.1836-1841, (1996).

DOI: 10.1109/61.544265

[5] Z. Wang, Y. Liu, and P. J. Griffin, A combined ANN and expert system tool for transformer fault diagnosis, IEEE Trans. on Power Delivery, Vol. 13, No. 4, pp.1224-1229, (1998).

DOI: 10.1109/61.714488

[6] Su, Q., Mi, C., Lai, L.L., and Austin, P., A Fuzzy Dissolved Gas Analysis Method for the Diagnosis of Multiple Incipient Faults in a Transformer, IEEE Transactions on Power Systems, vol. 15, No.2 p.593 – 598, (2000).

DOI: 10.1109/59.867146

[7] Zhang, S.L., A Fuzzy Logic and Neural Network Approach to Power Transformer Incipient Fault Detection, B Eng Final-year Project Thesis, National University of Singapore, (2003).

[8] Lin, C.E., Ling, J.M., and Huang, C.L., An Expert System for Transformer Fault Diagnosis Using Dissolved Gas Analysis, IEEE Transactions on Power Delivery, vol. 8, no. 1, pp.231-238,(1993).

DOI: 10.1109/61.180341

[9] Z. Wang, Y. Liu, and P. J. Griffin, A combined ANN and expert system tool for transformer fault diagnosis, IEEE Trans. on Power Delivery, Vol. 13, No. 4, pp.1224-1229,(1998).

DOI: 10.1109/61.714488

[10] Park, J. and Sandberg, I.W., Approximation and radial-basis-function networks.Neural Computation, Vol. 13, No.5, pp.305-316,(1993).

DOI: 10.1162/neco.1993.5.2.305