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Adaptive detection of generator out-of-step conditions in power systems using an artificial neural network

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5 Author(s)
Abdelaziz, A.Y. ; Dept. of Electr. Power & Machines, Ain Shams Univ., Cairo, Egypt ; Irving, M.R. ; Mansour, M.M. ; El-Arabaty, A.M.
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The application of artificial neural networks (ANN) to power systems has resulted in an overall improvement of solutions in many implementations. This paper presents a new approach for adaptive out-of-step detection of synchronous generators based on neural networks. The paper describes the ANN architecture adopted as well as the selection of the input features for training the ANN. A feedforward model of the neural network based on the stochastic backpropagation training algorithm has been used to predict the out-of-step condition. Due to power network configuration changes, the performance of the protective relays can vary. Consequently, an adaptive out-of-step prediction strategy is suggested in this paper. The capabilities of the proposed strategy have been tested through computer simulation for a typical case study. The results reveal acceptable classification performance.

Published in:

Control '96, UKACC International Conference on (Conf. Publ. No. 427)  (Volume:2 )

Date of Conference:

2-5 Sept. 1996