基于遗传支持向量机和灰色人工免疫算法的电力变压器故障诊断  

Power Transformer Fault Diagnosis Based on Genetic Support Vector Machine and Gray Artificial Immune Algorithm

作　　者：郑蕊蕊[1] 赵继印[1] 赵婷婷[2] 李敏[3] 

机构地区：[1]吉林大学通信工程学院,吉林省长春市130022 [2]浙江大学理学院,浙江省杭州市310058 [3]大连民族学院机电信息工程学院,辽宁省大连市116600

出　　处：《中国电机工程学报》2011年第7期56-63,共8页Proceedings of the CSEE

基　　金：国家中小企业创新基金(07C26212200168);中央高校基本科研业务费专项资金(DC10010103);辽宁省自然科学基金项目(20082174)~~

摘　　要：针对电力变压器单一故障和多故障诊断问题,模拟生物免疫系统,提出一种两级分类器级联的诊断算法。采用遗传算法优化支持向量机核函数参数的电力变压器故障和正常状态初分类器,和以灰关联度度量抗体与抗原之间亲和力的灰色人工免疫算法,设计了动态疫苗机制的高频变异操作。根据不同的故障类型,训练生成最佳记忆抗体集。采用5近邻综合决策法,根据最佳记忆抗体集诊断电力变压器故障类型。实验表明,遗传支持向量机和动态疫苗机制的灰色人工免疫算法相结合的电力变压器故障诊断算法,对电力变压器单一故障和多故障都能够有效地分类,提高了电力变压器故障诊断的准确率和速度。A two classifier cascade power transformer fault diagnosis algorithm was proposed to solve the problem of both single and multiple power transformer fault diagnosis. The diagnosis algorithm simulated biological immune system. Support vector machine classified fault or normal state of power transformer as the first classifier, Genetic algorithm optimized kernel function parameter of support vector machine. Gray relation grade calculated affinity between antibody and antigen in artificial immune algorithm, High-frequency variation based on dynamic vaccine mechanism generated a new antibody. Best memory antibody set was trained according to different fault types. Five neighbors integrated decision making method diagnosed power transformer fault representation based on the best memory antibody set. Experiments indicate that power transformer fault diagnosis algorithm combines genetic support vector machine with gray artificial immune and dynamic vaccine mechanism can effectively classifies single and multi-fault of power transformer and raise power transformer fault diagnosis accuracy and diagnosis speed.

关 键 词：电力变压器 故障诊断 支持向量机 遗传算法 灰色人工免疫算法 动态疫苗 

分 类 号：TM41[电气工程—电器]