电力系统保护与控制2011，Vol.39Issue(1)：96-99,122,5.
基于改进蚁群算法的变压器诊断数据的约简
The transformer diagnosis data reduction based on improved ant colony algorithm
田冰冰 1刘念 1刘琨 1姜刚2
作者信息
- 1. 四川大学电气信息学院,四川,成都,610065
- 2. 孝感电业局,湖北,孝感,432100
- 折叠
摘要
Abstract
Most algorithms for transformers fault diagonosis can't extract effective information from increasing data, thus it can't diagnose fault fast and accurately. In order to improve the speed of fault diagnosis on the basis of the principle of ant colony algorithm and the information entropy theory of fuzzy rough set, the local search strategy, ant's internal state, pheromone updating and state transition rules of the ant colony model are modified. An improved ant colony algorithm for the reduction of the diagnosis data is proposed. The transformer fault diagnosis experiment indicates that the proposed algorithm has higher diagnosis accuracy rate in data reduction and has fast diagnosis speed compared with traditional algorithm.关键词
蚁群算法/数据约简/模糊粗糙集/信息熵/变压器Key words
ant colony algorithm/ data reduction/ fuzzy rough set/ information entropy/ transformer分类
信息技术与安全科学引用本文复制引用
田冰冰,刘念,刘琨,姜刚..基于改进蚁群算法的变压器诊断数据的约简[J].电力系统保护与控制,2011,39(1):96-99,122,5.
