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Application of a novel fuzzy neural network to real-time transient stability swings prediction based on synchronized phasor measurements

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4 Author(s)
Chih-Wen Liu ; Dept. of Electr. Eng., Nat. Taiwan Univ., Taipei, Taiwan ; Mu-Chun Su ; Shuenn-Shing Tsay ; Yi-Jen Wang

The ability to rapidly acquire synchronized phasor measurements from around a power system opens up new possibilities for power system protection and control. In this paper, the authors develop a novel class of fuzzy hyperrectangular composite neural networks which utilize synchronized phasor measurements to provide fast transient stability swings prediction for use with high-speed control. Primary features of the method include constructing a fuzzy neural network for all fault locations, using a short window of realistic-precision post-fault phasor measurements for the prediction, and testing robustness to variations in the operating point. From simulation tests on a sample power system, it reveals that the proposed tool can yield a highly successful prediction rate in real-time

Published in:

Power Systems, IEEE Transactions on  (Volume:14 ,  Issue: 2 )

Date of Publication:

May 1999

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