摘要
针对油浸式变压器的故障诊断问题,提出了基于置信规则库(belief rule base,BRB)的有效诊断模型,通过模型结构简化和模型参数优化来提高BRB建模的效率和精度.首先,合理约减故障气体类型和减少训练模型参数以实现对BRB模型结构的简化.其次,提出一种具有自适应更新策略的人群搜索算法(seeker optimization algorithm with adaptive update strategy,AUS-SOA),对简化BRB模型的参数进行优化.然后,根据简化模型和优化参数建立AUS-SOA-BRB诊断模型.实验结果表明,AUS-SOA-BRB模型具有较高的诊断精度,也验证了所提建模方法的有效性.
Aiming at the fault diagnosis of oil-immersed transformers,a method of establishing an effective diagnostic model was proposed based on belief rule base(BRB).The efficiency and accuracy of BRB model was improved by simplifying the model structure and optimizing the model parameters.First,the structure of BRB model was simplified by reasonably reducing the types of faulty gas and reducing the parameters of the training model.Second,a seeker optimization algorithm with adaptive update strategy(AUS-SOA)was proposed to optimize the parameters of the simplified BRB model.Third,an AUS-SOA-BRB diagnostic model was established based on the simplified model and the optimized parameters.Results show that the AUS-SOA-BRB model has higher diagnostic accuracy,and verifies the effectiveness of the proposed modeling method.
作者
胡蓉
易照云
钱斌
HU Rong;YI Zhaoyun;QIAN Bin(School of Information Engineering and Automation,Kunming University of Science and Technology,Kunming 650500,China)
出处
《北京工业大学学报》
CAS
CSCD
北大核心
2021年第9期1000-1010,共11页
Journal of Beijing University of Technology
基金
国家自然科学基金资助项目(61963022,51665025)。
关键词
置信规则库
专家系统
模型参数
油浸式变压器
故障诊断
人群搜索算法
belief rule base(BRB)
expert system
model parameters
oil immersed transformer
fault diagnosis
seeker optimization algorithm
