Salif B. Sissoko
College of Electrical and Electronic Engneering, Huazhong University of Sciences and Technology, Wuhan 430074, Peoples Republic of China
Ahmed N. Abdalla
College of Electrical and Electronic Engneering, Huazhong University of Sciences and Technology, Wuhan 430074, Peoples Republic of China
Jing Zhang
College of Electrical and Electronic Engneering, Huazhong University of Sciences and Technology, Wuhan 430074, Peoples Republic of China
S.J. Cheng
College of Electrical and Electronic Engneering, Huazhong University of Sciences and Technology, Wuhan 430074, Peoples Republic of China
ABSTRACT
The application of Radial Basis Function (RBF) neural networks for Fault Section Estimation (FSE) and fault classification and fault location within faulty section in transmission lines is presented. At the first step, a multi-way graph partitioning method based on weighted minimum degree reordering is proposed for effectively partitioning the original large-scale power system into desired number of connected sub-networks. After partitioning, the proposed scheme for each part of system consists of six RBFNNs, one networks for FSE, one for fault classification and four networks for fault location one for each fault type within the faulty section. For FSE, the relay and circuit breaker states are taken as the input to the distributed FSE system, while the states (faulted or normal) of transmission lines as the outputs. For fault classification, pre-fault and post-fault samples of the three-phase currents and another input from FSE are taken as the input, while faulty phase(s) as the output. For fault location, post-fault samples of both currents and voltages of the three phases and another input from both FSE and fault classification are taken as the input, while the fault locator as the output. To validate the proposed approach simulation studies have been carried out on IEEE 11-bus system in normal and faulty conditions to train and test the RBFNN. Testing results proved that the proposed RBF networks could provide great performance for high speed relaying. It is accurate, fast and reliable.
PDF References Citation
How to cite this article
Salif B. Sissoko, Ahmed N. Abdalla, Jing Zhang and S.J. Cheng, 2007. Graph Partitioning applied to Fault Location in power transmission Lines. Journal of Applied Sciences, 7: 2327-2332.
DOI: 10.3923/jas.2007.2327.2332
URL: https://scialert.net/abstract/?doi=jas.2007.2327.2332
DOI: 10.3923/jas.2007.2327.2332
URL: https://scialert.net/abstract/?doi=jas.2007.2327.2332
REFERENCES
- Bi, T., Y. Ni, C.M. Shen and F.F. Wu, 2002. An on-line distributed intelligent fault section estimation system for large-scale power networks. Electric Power Syst. Res., 62: 173-182.
Direct Link - Coury, D.V. and D.C. Jorge, 1998. Artificial neural network approach to distance protection of transmission lines. IEEE Trans. Power Delivery, 13: 102-108.
CrossRefDirect Link - Dalstein, T. and B. Kulicke, 1995. Neural network approach to fault classification for high speed protective relaying. IEEE Trans. Power Deliv., 10: 1002-1011.
Direct Link - Mahanty, R.N. and P.B. Dutta Gupta, 2004. Application of RBF neural network to fault classification and location in transmission lines. IEE Proc. Generation Transmission and Distribution., 151: 201-212.
CrossRefDirect Link - Sidhu, T.S., H. Singh and M.S. Sachdev, 1995. Design implementation and testing of an artificial neural network based fault direction discriminator for protecting transmission lines. IEEE Trans. Power Deliv., 10: 697-706.
Direct Link - Tageldin, E.M., M.M. El Khairy and H.M. Elghazaly, 2003. Application of the minimal radial basis neural network to fault classification and faulty phase identification on egyptian 500 kv transmission system. Proceedings of the 7th Middle East Power System Conference MEPCON, December 2003, Egypt, pp: 537-543.
- Wang, H. and W.W.L. Keerthipala, 1998. Fuzzy-neuro approach to fault classification for transmission line protection. IEEE Trans. Power Deliv., 13: 1093-1102.
Direct Link - Yingwei, L., N. Sundarrajan and P. Saratchandran, 1998. Performance evaluation of a sequential minimal Radial Basis Function (RBF) neural network learning algorithm. IEEE Trans. Neural Networks, 9: 308-318.
PubMedDirect Link