CAJ | 학술논문

针对变压器故障的复杂性、模糊性以及模糊集理论、神经网络和粗糙集理论的优缺点，利用粗糙集理论的属性约简和规则生成能力和模糊神经网络在模式识别方面具有容错和分类优势。采用粗集理论对采集到的变压器油中溶解气体数据形成的规则进行约简处理，建立精简的规则集，根据规则集建变压器故障诊断的神经网络模型，采用自适应遗传算法优化神经网络连接的权值，通过仿真验证了该网络较好的诊断性能。
침대변압기고장적복잡성、모호성이급모호집이론、신경망락화조조집이론적우결점，이용조조집이론적속성약간화규칙생성능력화모호신경망락재모식식별방면구유용착화분류우세。채용조집이론대채집도적변압기유중용해기체수거형성적규칙진행약간처리，건립정간적규칙집，근거규칙집건변압기고장진단적신경망락모형，채용자괄응유전산법우화신경망락련접적권치，통과방진험증료해망락교호적진단성능。
Considering the complexity and Ambiguity of transformer fault diagnosis and the advantages and disadvantages of fuzzy set theory, neural network and of rough set theory, this article makes fully using the attribute reduction and rule generation of rough sets and pattern recognition and fault-tolerant Category advantages of fuzzy neural network. Using rough set theory dealing the reduction rules from collected data about gas dissolved in oil into a streamlined set of rules, transformer fault diagnosis model of the neural net-work is built in accordance with the Streamlining rules set, connection weights of neural network are optimized by genetic algorithm. At last the method is proved having superior diagnostic performance.