摘要
油中溶解气体分析(DGA)是对变压器进行故障诊断最方便、最有效的方法之一,其中静态分析方法取得了较多的研究成果。实际中故障信息的出现具有时序特征,趋势信息可以反映故障的状态及发展,将基于时序特征的动态分析方法与传统的静态分析方法相结合,可以对故障发展及危害进行更为全面准确的描述。提出了基于时序特征和参数估计诊断变压器故障的方法,采用最小二乘参数估计算法识别特征气体的变化趋势,采用滑动窗口方法实现在线分析,利用递推最小二乘估计算法减小运算复杂度。以实际变压器油中气体测量数据进行实验,结合静态三比值和灰色关联度分析方法,对变压器进行故障诊断,实验结果证明了方法的有效性。
Dissolved gas-in-oil analysis （DGA） is the most effective and convenient method in power transformer fault diagnosis, and more research results were acquired by using static analysis methods. Actually, the fault information produced by operating power transformer has temporal characteristics which can reflect the condition and evolution of faults. Integrating this kind of dynamic temporal information analysis with traditional static analysis can make general and exact description for the fault evolution and harm. A fault diagnosis method for＇transformer based on temporal characteristics and parameter estimation are presented, which uses least square curve fitting to identify the varying trend of characteristic gas. Further, sliding window model and recursive least square estimation is used for online circumstances. Simulated experiments are implemented using real DGA data and integrating the static analysis results of three gas ratios and grey correlation degree analysis, and the experimental results show the effectiveness of proposed method.
出处
《电工技术学报》
EI
CSCD
北大核心
2008年第12期48-54,共7页
Transactions of China Electrotechnical Society
