Fault diagnosis based on equivalent input disturbance for power system

The existing power system fault analysis method is based on the information of power system relay protection and sensor device to determine the location and type of fault. With the continuous expansion of the power system and the increasing complexity of the structure, excessive alarm information makes it difficult to diagnose the power system faults timely and effectively. A fault diagnosis method for power system based on equivalent input disturbance is proposed in this paper. It just uses the electrical quantities on power system, then builds the hierarchical fault diagnosis model. The fault is regarded as a disturbance which could be determined by analyzing disturbance characteristics. Finally, the feasibility of this method could be identified through the simulation results of the IEEE 33 nodes model.

[1]  Ali Mohammad Ranjbar,et al.  Fuzzy rule-based expert system for power system fault diagnosis , 1997 .

[2]  Oscar L. Chac,et al.  AN ON-LINE EXPERT SYSTEM FOR FAULT SECTION DIAGNOSIS IN POWER SYSTEMS , 1997 .

[3]  Gary G. Yen,et al.  Online multiple-model-based fault diagnosis and accommodation , 2003, IEEE Trans. Ind. Electron..

[4]  Anthony G. Pipe,et al.  Application of Multi-Agent Technology to Fault Diagnosis of Power Distribution Systems , 2005, Int. J. Intell. Inf. Technol..

[5]  Fushuan Wen,et al.  Probabilistic approach for fault-section estimation in power systems based on a refined genetic algorithm , 1997 .

[6]  C. Fukui,et al.  An Expert System for Fault Section Estimation Using Information from Protective Relays and Circuit Breakers , 1986, IEEE Transactions on Power Delivery.

[7]  Khashayar Khorasani,et al.  Application of a radial basis function (RBF) neural network for fault diagnosis in a HVDC system , 1998 .

[8]  Lei Wang,et al.  A new framework for power system fault diagnosis , 2012, IEEE PES Innovative Smart Grid Technologies.

[9]  L.A.V. de Carvalho,et al.  Artificial neural networks for power systems diagnosis , 1994, Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94).

[10]  Khashayar Khorasani,et al.  Application of a radial basis function (RBF) neural network for fault diagnosis in a HVDC system , 1996, Proceedings of International Conference on Power Electronics, Drives and Energy Systems for Industrial Growth.

[11]  Bo Hu,et al.  Hierarchical Fault Diagnosis for Power Systems Based on Equivalent-Input-Disturbance Approach , 2013, IEEE Transactions on Industrial Electronics.

[12]  Mingxing Fang,et al.  Improving Disturbance-Rejection Performance Based on an Equivalent-Input-Disturbance Approach , 2008, IEEE Transactions on Industrial Electronics.

[13]  Pierluigi Siano,et al.  Failure Identification in Smart Grids Based on Petri Net Modeling , 2011, IEEE Transactions on Industrial Electronics.

[14]  S. Seker,et al.  Fault section estimation in electrical power systems using artificial neural network approach , 1999, 1999 IEEE Transmission and Distribution Conference (Cat. No. 99CH36333).