基于集成贝叶斯分类器的暂态稳定评估方法研究

Research on transient stability assessment based on integrated Bayesian classifier

朴素贝叶斯分类器是目前公认的一种简单有效的概率分类方法,具有简单、健壮而且高效的特点,但由于它是建立在属性变量相对类变量独立的假设前提下,而且这个假设在实际问题中往往不能满足,从而影响了其分类精度。针对这个很强的前提假设,提出了基于灰色关联聚类的特征选择方法,在一定程度上放松了这个限制条件;以朴素贝叶斯分类器作为基分类器,采用分类器集成技术中的AdaBoost算法进一步提高分类性能。通过对新英格兰10机39节点系统的仿真计算,结果表明了方法的有效性和正确性。

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.