Fault Diagnosis for Transmission Lines Systems Using ANFIS Techniques

  • Azriyenni Azhari Zakri Universitas Riau, Indonesia
DOI: https://doi.org/10.31258/ijeepse.1.1.17-20
Abstract viewed: 242 times
PDF downloaded: 183 times
Keywords: ANFIS, Classification, Estimate, Fault, Transmission Lines


This paper presents a fault diagnosis for long transmission lines using Adaptive Neuro-Fuzzy Inference System (ANFIS). The electric power transmission system is a link power generation and distribution. If a failure occurs as long the transmission line could be estimation caused of undesired fault power delivery to consumer come not go well. Therefore, it would need to provide an alternative solution to solve this problem. The objectives of this paper are classification and estimate of a fault into the transmission line by using application of ANFIS. The systems have been put forward and tested on simulated data transmission lines into different faults. The results test given to contribute to an alternate technique where it has good performance for fault diagnosis in the transmission lines.


[1] Aastha Aggarwal, H.M., Rajneesh Sharma, "Feature Extraction using EMD and Classification through Probabilistic Neural Network for Fault Diagnosis of Transmission Line," International Conference on Power Electronics. Intelligent Control and Energy Systems (ICPEICES), 2016.

[2] Fan YU, S.Z., Baolong LIU, "Based on the Wavelet Function of Power Network Fault Location," TELKOMNIKA. 11(4): p. 1924-1929, 2013.

[3] L. de Andrade, T.P.d.L., "Fault Location for Transmission Lines Using Wavelet," IEEE Latin America Transactions, 12(6), 2014.

[4] Sunil Singh, D.N.V, "Intelligent Techniques for Fault Diagnosis in Transmission lines An Overview," International Conference on Recent Developments in Control, Automation and Power Engineering (RDCAPE), 2015.

[5] Mahdi Raoofat, A.M., Alireza Abunasri, “Fault Location in Transmission Lines Using Neural Network and Wavelet Transform,” International Congress on Electric Industry Automation (ICEIA), Shiraz University, Iran. International Congress on Electric Industry Automation (ICEIA), Shiraz University. Iran.

[6] Reddy, M.J. and D.K. Mohanta, "Adaptive-neuro-fuzzy inference system approach for transmission line fault classification and location incorporating effects of power swings," IET Generation, Transmission & Distribution, 2(2): p. 235, 2008.

[7] Yuanyuan Chai, L.J., and Zundong Zhang, "Mamdani Model-based Adaptive Neural Fuzzy Inference System and its Application," World Academy of Science, Engineering, and Technology 3 (27), 2009.

[8] Kevin Warwick, A.E.a.R.A., "Artificial Intelligence Techniques in Power Systems," IET Power and Energy series 22. London, United Kingdom: The Institution of Engineering and Technology, 2008.

[9] N. Sarikaya, K.G.a.C.Y., "Adaptive Neuro-Fuzzy Inference System For The Computation Of The Characteristic Impedance And Effective Permittivity Of The Micro Coplanar Strip Line," Progress In Electromagnetics Research B. p. 225–237, 2008.

[10] Zadeh, H.K, "Fuzzy-Neuro Approach to Investigating Transformer Inrush Current," 2006.

How to Cite
A. Azhari Zakri, “Fault Diagnosis for Transmission Lines Systems Using ANFIS Techniques”, IJEEPSE, vol. 1, no. 1, pp. 17-20, Sep. 2018.