摘要
针对现有的智能变电站过程层网络故障分类所面临的效率低下、数据集噪音等问题,提出一种基于ANP-SVM的过程层网络故障分类算法。该算法首先利用改进的分离间隔法对SVM进行核参数以及误差参数的优化选取,然后将经过抗噪声处理的样本数据输入优化后的SVM,从而使得分类更加精确、高效。实验结果表明,本算法在过程层网络故障分类中具有较好的性能。
Aiming at the inefficiency and data set noise of the existing process layer network fault classification in intelligent substation, this paper proposes an ANP-SVM based process layer network fault classification algorithm.Firstly, the improved separation interval method is used to optimize the selection of kernel parameters and error parameters of SVM, and then the anti-noise sample data is input into the optimized SVM, which makes the classification more accurate and efficient.The experimental results show that the algorithm has good performance in the process layer network fault classification.
作者
张开延
潘杨
娄季朝
ZHANG Kai-yan;PAN Yang;LOU Ji-chao(Yangtze University, Jingzhou 434023, China;Guodian Changyuan Jingzhou Thermal Power Co., Ltd., Jingzhou 434000, China;Wuhan Branch, Guodian Science and Technology Research Institute Co., Ltd., Wuhan 430070, China;School of Computer Science, Wuhan University, Wuhan 430072, China)
出处
《计算机与现代化》
2019年第7期72-77,103,共7页
Computer and Modernization
