Research on transient stability assessment based on integrated Bayesian classifier
Haijun Liu · Journal of North China Electric Power University · 2010
The naive bayes classifier is now recognized as the probability of a simple and effective classification method,with simple,stuggy and highly effective characteristic.But because it is the establishment under the attribute variable relative kind of variable independent supposition premise,moreover this supposition often cannot satisfy in the actual problem,thus affecting its classified precision.In view of this very strong premise supposition,this paper proposed feature selection method based on the gray connection cluster,relaxed this limiting condition to a certain extent;Takes the base classifier by the naive bayes classifier,uses the AdaBoost algorithm in the classifier integration technology to further enhance the classifying performance.Based on the New England 10 machine 39 node system's simulation computation,the results showe that this article method is valid and correct.