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Wide area transient stability prediction using on-line Artificial Neural Networks

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5 Author(s)
Hashiesh, Fahd ; Dept. of Electr. Power & Machines Eng., Ain Shams Univ., Cairo ; Mostafa, Hossam E. ; Mansour, Mohamed M. ; Khatib, A.-R.
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This paper proposes a real-time wide area protection system which incorporates artificial neural networks (ANN) for transient stability prediction. The ANN makes use of the advent of phasor measurements units (PMU) for real-time prediction. Rate of change of bus voltages and angles for six cycles after fault tripping and/or clearing is used to train a two layers ANN. Coherent groups of generators which swing together is identified through an algorithm based on PMU measurements. A remedial action scheme (RAS) is applied to counteract the system instability by splitting the system into islands and initiate under-frequency load shedding actions. The potential of the proposed approach is tested using New England 39-bus system.

Published in:

Electric Power Conference, 2008. EPEC 2008. IEEE Canada

Date of Conference:

6-7 Oct. 2008