摘要
提出基于遗传算法优化模糊规则库的故障诊断方法,采用模糊故障诊断系统对电力变压器的初期故障进行检测或诊断。采用遗传算法产生优化的模糊规则库,针对缺少数据样本的情况,采用自举法对数据样本进行处理及扩充,使得不同的故障类型有相等的样本数。仿真结果表明:该故障诊断方法提高了故障诊断精度和正确率,对于电力变压器故障诊断有效、可行。
A fault diagnosis technology was presented based on the rule base by optimized genetic algorithm.The fuzzy diagnosis system was employed to detect incipient faults for power transformers.Genetic algorithm was used to obtain the optimized fuzzy rule.For the missing sample data,the bootstrapping technology was introduced to process and expand data samples so that different faults have equivalent samples.The simulation results show that the proposed method improves the accuracy of faults diagnosis and increases the diagnostic correct rate of the fault,and it is effective and feasible.
出处
《中南大学学报(自然科学版)》
EI
CAS
CSCD
北大核心
2013年第3期1018-1023,共6页
Journal of Central South University:Science and Technology
基金
国家自然科学基金资助项目(60604005)
关键词
电力变压器
模糊诊断系统
遗传算法
自举法
power transformers
fuzzy diagnosis system
genetic algorithm
bootstrapping