Waveform matching approach for fault diagnosis of a high-voltage transmission line employing harmony search algorithm

An accurate and effective technology for fault diagnosis of a high-voltage transmission line plays an important role in supporting rapid system restoration. The fault diagnosis of a high-voltage transmission line involves three major tasks, namely fault-type identification, fault location and fault time estimation. The diagnosis problem is formulated as an optimisation problem in this work: the variables involved in the fault diagnosis problem, such as the fault location, and the unknown variables such as ground resistance, are taken into account as optimisation variables; the sum of the discrepancy of the approximation components of the actual and expected waveforms is taken as the optimisation objective. Then, according to the characteristics of the formulated optimisation problem, the harmony search, an effective heuristic optimisation algorithm developed in recent years, is employed to solve this problem. Test results for a sample power system have shown that the developed fault diagnosis model and method are correct and efficient.

[1]  M. Kezunovic,et al.  A novel method for transmission network fault location using genetic algorithms and sparse field recordings , 2002, IEEE Power Engineering Society Summer Meeting,.

[2]  S. Pati,et al.  Wavelet fuzzy combined approach for fault classification of a series-compensated transmission line , 2004, IEEE Transactions on Power Delivery.

[3]  P.B.D. Gupta,et al.  Application of RBF neural network to fault classification and location in transmission lines , 2004 .

[4]  Zong Woo Geem,et al.  A New Heuristic Optimization Algorithm: Harmony Search , 2001, Simul..

[5]  B. Das,et al.  Fuzzy-logic-based fault classification scheme for digital distance protection , 2005, IEEE Transactions on Power Delivery.

[6]  G.B. Gharehpetian,et al.  Fault current reduction in distribution systems with distributed generation units by a new dual functional series compensator , 2008, 2008 13th International Power Electronics and Motion Control Conference.

[7]  G.B. Gharehpetian,et al.  Power control strategy of parallel inverter interfaced DG units , 2008, 2008 13th International Power Electronics and Motion Control Conference.

[8]  N.S.D. Brito,et al.  Fault detection and classification in transmission lines based on wavelet transform and ANN , 2006, IEEE Transactions on Power Delivery.

[9]  K. Lee,et al.  A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice , 2005 .

[10]  Fushuan Wen,et al.  Fault section estimation in power systems using a genetic algorithm , 1995 .

[11]  M. Fesanghary,et al.  An improved harmony search algorithm for solving optimization problems , 2007, Appl. Math. Comput..

[12]  Fu Yang,et al.  A Heuristic Particle Swarm Optimization Method and its Application in Power Network Planning , 2008, 2008 First International Conference on Intelligent Networks and Intelligent Systems.

[13]  M. Fesanghary,et al.  Combined heat and power economic dispatch by harmony search algorithm , 2007 .

[14]  T.S. Sidhu,et al.  A comprehensive analysis of an artificial neural-network-based fault direction discriminator , 2004, IEEE Transactions on Power Delivery.