摘要
电力变压器运行可靠性直接关系到电力系统的安全及供电可靠性,为提高电力变压器故障诊断的准确率,由在线监测变压器油中溶解气体组分含量分析,提出了基于人工免疫和模糊C均值聚类分析方法有效结合的变压器故障诊断算法,通过对电力变压器油中的溶解气体进行分析,实现对变压器的故障诊断。重点研究了基于人工免疫网络的变压器故障样本数据处理、基于模糊C均值聚类对变压器故障的识别,以及仿真实验。实验结果表明:提出的算法能有效对变压器故障类型进行分类,该算法在变压器故障诊断中有较好的应用前景。
The reliability of the power transformer operation is closely linked to the security of a power system and the reliability of power supply. For the sake of the improvement of transformer on-line fault diagnosis accu- racy, it is significant to put forward an algorithm based on an artificial immune system and fuzzy C-means clus- tering by using the online monitoring of transformer oil dissolved gas composition content. The particular em- phasis was paid on the introduction of data processing of transformer faults, the identification of transformer fault types on the basis of fuzzy C-means analysis, and the simulation experiment. The result of experiments shows that the algorithm can classify transformer fault types effectively, and it has a preferable application prospect in the transformer fault diagnosis.
出处
《河南理工大学学报（自然科学版）》
CAS
北大核心
2015年第3期379-383,共5页
Journal of Henan Polytechnic University（Natural Science）
基金
国家自然科学基金资助项目(61104079)
河南省产学研基金资助项目(132107000027)
