摘要
为提高变压器故障诊断的准确性,进行了利用粗糙集和神经网络来诊断变压器故障的研究。首先将连续属性的决策表离散化,部分属性采用基于油中溶解气体分析知识的方法离散化,部分属性采用自然算法和等频划分算法离散化;然后用粗糙集属性约简方法对离散后的决策表进行属性约简以获取最小决策表,约简后的最小决策表反映了变压器油中溶解气体的5种比值与故障的关系,是对IEC三比值法的扩展;最后用最小决策表训练BP神经网络,并用测试数据对训练后的BP神经网络进行检验。结果表明该方法比IEC三比值法有更高的故障判断准确率,结合粗糙集和神经网络诊断变压器故障可约简变压器故障诊断决策表,简化神经网络的结构,提高故障诊断的准确率。
Rough Set is a tool to extract knowledge from a vast volume of data. BP neural network has nonlinear characteristic and the ability of self organization and self-learning. A new method combining Rough Set and BP neural network is used for transformer fault diagnosis. Rough Set is just suitable for discrete data, parts of continuous attributes of decision table are discretizated based on transformer DGA knowledge, and parts of continuous attributes of decision table are discretizated with Nave algorithm and Equal Frequency Intervals algorithm. The decision table is reduced according to the Rough Set theory and the minimal diagnostic table rules are gotten. It reflects relation of five-ratio of dissolved gases and transformer fault dlagnosis, and it is a kind of improvement of the IEC threeratio transformer diagnosis method. BP neural network has three layers： one input layer, one hidden layer and one output layer, nodes of input layer is five, nodes of hidden layer is twenty, nodes of output layer is five. The BP neural network is trained by the minimal diagnostic table, the actual malfunction diagnostic ability is proved and fault diagnosis accuracy is higher compared with IEC three-ratio diagnosis. Combining Rough Set theory with neural network, the method is applied in the transformer fault diagnosis, it reduces transformer decision table of fault diagnosis, simplifies the structure of neural network, improves the diagnostic accuracy.
出处
《高电压技术》
EI
CAS
CSCD
北大核心
2007年第8期122-125,共4页
High Voltage Engineering
基金
南昌大学科学基金(Z03325)。~~
关键词
变压器
神经网络
粗糙集
离散化
故障诊断
最小决策表
transformer
neural network
rough set
discretization
fault diagnosis
the minimal diagnostic table
