采用改进人工鱼群优化粗糙集算法的变压器故障诊断  

Transformer Fault Diagnosis Using Improved Artificial Fish Swarm with Rough Set Algorithm

作　　者：陈小青[1] 刘觉民[1] 黄英伟[2] 付波[2] 

机构地区：[1]湖南大学电气与信息工程学院,长沙410082 [2]湖北工业大学电气与电子工程学院,武汉430068

出　　处：《高电压技术》2012年第6期1403-1409,共7页High Voltage Engineering

基　　金：湖南省科技计划(2011CK3067;2011GK3132)~~

摘　　要：传统的人工智能方法对变压器大量的不完备故障信息不能有效地分析,或在故障数据的离散化过程中由于区间分割不当而无法正确诊断故障甚至误诊。为此,提出了一种基于改进人工鱼群优化粗糙集的变压器故障诊断方法。该方法首先将变压器溶解气体分析(DGA)的值作为条件属性,将故障类型作为决策属性,建立故障决策表,利用鱼群的聚群寻优行为对决策表中的连续属性数据进行离散化;然后采用粗糙集理论对离散化后的决策表进行约简,建立故障诊断规则决策表,大大简化了决策表属性约简的难度,使诊断变得更加简便。最后通过实例验证表明:该方法能够有效地对样本进行离散和约简,与传统方法相比,提高了故障诊断的正确率。Facing a large number of incomplete fault data, the traditional artificial intelligence methods cannot effectively and timely analyze or accurately diagnosed because of the ill-conditioned problem caused by inefficient discretization approaches. We presented a method based on rough set theory integrated with improved artificial fish swarm algorithm {AFSA} for fault diagnosis of transformer. Firstly, the values of dissolved gas analysis {DGA） in oil were taken as conditional attributes and the type faults were taken as decision attributes. Various relations between fault and symptom were connected, and decision table was established. Then, the improved artificial fish swarm algorithm was used to discrete continuous attribute, and the rough set theory was used to reduce the decision table. Finally, the simplified decision rules were got, which greatly simplified the difficulty of diagnosis. The experimental results indicate that the method increases the diagnosis accuracy compared with the traditional algorithm.

关 键 词：变压器 故障诊断 溶解气体分析(DGA) 人工鱼群算法(AFSA) 粗糙集 数据约简 决策表 

分 类 号：TM407[电气工程—电器]