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Fault location technique using GA-ANFIS for UHV line

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Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper presents an improved approach for locating and identifying faults for UHV overhead Transmission line by using GA-ANFIS. The proposed method uses one end data to identify the fault location. The ANFIS can be viewed either as a Fuzzy system, neural network or fuzzy neural network FNN. The integration with neural technology enhances fuzzy logic system on learning capabilities are proposed to analyze the UHV system under different fault conditions. The performance variation of two controllers in finding fault location is analyzed. This paper analyses various faults under different conditions in an UHV using Matlab/simulink. The proposed method is evaluated under different fault conditions such as fault inception angle, fault resistance and fault distance. Simulation results confirm that the proposed method can be used as an efficient for accurate fault location on the transmission line.
Rocznik
Strony
247--262
Opis fizyczny
Bibliogr. 15 poz., rys., tab., wykr.
Twórcy
autor
  • Suguna College of Engineering Coimbatore Coimbatore, India
autor
  • Institute of Technology Coimbatore Coimbatore, India
Bibliografia
  • [1] Rao S.S., Switchgear and Protection. 10th Edition, KHANNA Publishers, Delhi (1994).
  • [2] Carpenter W.M., IEEE Guide for Protective Relay Applications to Transmission Lines. IEEE Std, C37. 113 (1999).
  • [3] Sachdev M., Agarwal R., A technique for estimating transmission line fault locations from digital impedance relay measurements. IEEE Trans. Power Del. 3(1): 121-129 (1998).
  • [4] Izykowski J., Molag R., Rosolowski E., Saha M., Accurate location of faults on power transmission lines with use of two-end unsynchronized measurements. IEEE Trans. Power Del. 21(2): 627-633 (2006).
  • [5] Girgis A.A., Hart D.G., Peterson W.L., A new fault location technique for two-and three-terminal lines. IEEE Trans. Power Del. 7(1): 98-107 (1992).
  • [6] Khorashadi-Zadeh H., Aghaebrahimi M.R., A novel approach to fault classification and fault location for medium voltage cables based on artificial neural network. International journal of computational intelligence 2(1): 1304-2386 (2005).
  • [7] Jyh-Shing Roger Jang, ANFIS: Adaptive Network Based Fuzzy Inference System. IEEE Transaction on systems 23(3) (1993).
  • [8] Mohamed D.F., A New Design of an Intelligent Digital Distance Protective Relay. PhD Dissertation Submitted to the Office of GraduateStudies of Cairo University (2007).
  • [9] Vasilic S., Fuzzy Neural Network Pattern Recognition Algorithms For Classification Of The events In Power System Network. Ph. D. Dissertation Submitted to the Office of Graduate Studies of Texas A&M University (2004).
  • [10] Abeer Galal Saad, Digital Relaying of High Voltage Transmission Lines by Artificial Neural Networks. Master Dissertation Submitted to the Office of Graduate Studies of Cairo University (2004).
  • [11] Dash P.K., Pradhan A.K., Panda G., A Novel Fuzzy Neural Network Based Distance Relaying Scheme, IEEE Transactions on Power Delivery 15(3): 902-907 (2000).
  • [12] Coury D.V., Jorge D.C., Artificial Neural Network Approach to Distance Protection IEEE. Transactions on Power Delivery 13(1): 102-108 (1998).
  • [13] Sadeh J., Afradi H., A new and accurate fault location algorithm for combined transmission ines using adaptive network-based fuzzy inference system. Journal of Electric Power System Research 79(11): 1538-1545 (2009).
  • [14] Nan Zhang, M. Kezunovic, Coordinating fuzzy ART neural networks to improve transmission line fault detection and classification. IEEE PES, General Meeting, San Francisco (2005).
  • [15] Yeo S.M. et. al., A novel algorithm for fault classification in transmission lines using a combined adaptive network and fuzzy inference system. Electrical Power and Energy Systems 25: 747-758 (2003). Brought
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-738b115c-c8b8-44be-8a2b-a10d0e6315c2
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