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This paper presents transient stability assessment of a large practical power system using two artificial neural network techniques which are the probabilistic neural network (PNN) and the least squares support vector machine (LS-SVM). The large power system is divided into five smaller areas depending on the coherency of the areas when subjected to disturbances. This is to reduce the number of data...
For enhancing fault diagnosis precision, the wavelet packet analysis and least squares support vector machine are combined effectively. First, the signals are decomposed in arbitrary minute frequency bands by use of wavelet packet analysis technique. Doing energy calculation in these frequency bands to form eigenvectors is more reasonable. And then a least squares support vector machine fault diagnosis...
Analog fault diagnosis has been an active area of research since the mid-1970s, now many diagnosis methods use neural networks. But it needs lots of fault samples and it is also not easy to train the neural network. We have presented a analog-circuit fault diagnosis method based on LS-SVM. To reduce the fault feature vectors to train LS-SVM, we use the energy of high frequency of wavelet transform...
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