摘要
采用模糊理论和双向联想记忆神经网络(BAM)相结合的方式对变压器老化进行评价。该方法首先运用模糊理论对变压器的相关特征进行模糊化处理,处理相关参数的不确定性问题,之后采用模糊推理和解模糊过程进行变压器的老化状态评价。在模糊化过程中,因为绝缘油气体和绝缘油的电气特征主观性较强,本文使用BAM神经网络进行训练,从而实现这些规则的模糊化。通过对实际数据的模拟实验,对所提出的方法进行了检验,实验结果与实际情况吻合程度很好,验证了所提出方法的有效性。
A novel method based on fuzzy theory and Bidirectional Associative Memory （BAM） neural networks is proposed to evaluate the transformer aging problem. In this method, first the features of the transformer are fuzzified to deal with their uncertainty problem, and then fuzzy reasoning and defuzzification are performed to evaluate the transformer aging. During the fuzzifying process, because that the electrical characteristics of insulating oil gas and insulating oil are highly personal relied, BAM neural network is used to realize the fuzzification of these two features. To examine the effectiveness of the proposed method, simulation is carried out with real data. Numerical results show that the evaluations of the proposed method highly coincide with the engineering specifications.
出处
《吉林大学学报（工学版）》
EI
CAS
CSCD
北大核心
2013年第5期1331-1337,共7页
Journal of Jilin University:Engineering and Technology Edition
基金
国家自然科学基金项目(61073075
61103092
61163034)
中国博士后科学基金项目(2011M500613
2012T50298)
吉林省科技发展计划项目(20120730
201215022
20121305
12C26212201344)
吉林大学符号计算与知识工程教育部重点实验室项目(93K172011K07)
吉林大学基本科研业务费项目(201200004)
关键词
计算机应用
双向联想记忆神经网络
模糊理论
变化器老化
computer application
bidirectional associative memory neural network
fuzzy theory
transformer aging
