Research On Transient Stability Assessment Based On Neural Networks And Support Vector Machines

The transient stability assessment based on ANNs suffers from the unavoidable misclassifications in the boundary region between the two classes due to the complexity of TSA input dimension and the limitation of ANN. If these cases can be found out by some methods, then the reliability of the assessment results will be improved. This paper proposed some methods for finding out sure regions and ambiguous regions defined by lower and upper approximations in rough set theory. An applicable ending-criterion for semi-supervised back-propagation algorithm was proposed and a new rough classifier framework was studied, the assessment results show the effectiveness of the proposed criterion. A new classifier based on support vector machines was studied and applied. At last, an assembling classification schemes was studied by integrating different classifiers to improve the classification reliability. Using the assembled classifier, the boundary cases liable to be misclassified can be picked out, the misclassifications are hence reduced significantly. Liu Yanfang (power system and its automation) Directed by Prof. Gu Xueping...