摘要
针对传统变压器故障诊断方法的不足,介绍了多种智能诊断方法在基于油中溶解气体分析(dissolved gas-in-oil analysis,DGA)的变压器故障诊断中的应用,包括人工神经网络、模糊理论、专家系统、灰关联分析及其他智能方法。通过对这些智能诊断方法的分析,得出其优缺点及需要改进的方案,为研究人员选择最优油浸式电力变压器故障诊断方法提供参考。最后对基于DGA的变压器故障智能诊断方法进行了展望,并分析了未来的发展方向。
Aiming at the shortcomings of the traditional fault diagnosis method for transformer, this paper introduced the applications of several intelligent methods in the fault diagnosis of power transformer based on dissolved gas-in-oil analysis （DGA）, including the artificial neural network, the fuzzy theory, the expert system, the grey relational analysis and other intelligent methods. This paper analyzed these intelligent diagnosis methods and obtained the relative merits and improved solutions, which could provide a reference for the researchers to choose the optimal fault diagnosis method of oil-immersed power transformer. At last, the DGA-based intelligent fault diagnosis method for transformer was discussed, and its future development direction was analyzed.
出处
《电力建设》
北大核心
2015年第6期34-39,共6页
Electric Power Construction
基金
国家重点基础研究发展计划(973计划)(2013 CB228205)
国家自然科学基金项目(51177051
51477055)
中国南方电网科技项目~~
关键词
变压器
故障诊断
DGA
智能方法
transformer
fault diagnosis
DGA
intelligent methods
