摘要
短路电抗法是检测电力变压器绕组变形的有效方法之一,在线检测变压器短路电抗变化对随时获取绕组的健康状况具有重要意义。为了实现变压器绕组的在线监测,基于一台模型变压器,以变压器回路方程作为参数辨识模型,利用递推的最小二乘算法对绕组漏电感参数进行实时辨识。分析和计算了测量系统产生的误差,提出了采用类似模糊神经网络的方法对PT和CT进行补偿。通过对比在线测量值与离线测量值的结果,表明该测量系统具有良好的准确性与稳定性,变压器的运行状态对测量结果影响很小,能够为实际运行中的变压器的绕组状况提供实时准确的指示。
The short-circuit-reactance method is one of effective measures to detect the transformer′s winding deformation.Online detecting the short-circuit-reactance has a great significance in winding′s state.In order to realize on-line monitoring the transformer winding,based on a model transformer,an on-line identification algorithm for transformer leakage inductance is proposed with recursive least-square algorithm.The measuring system errors are analyzed and calculated,a method like fuzzy neural network is put forward to compensating error of PT and CT.Comparing the online and offline measuring value,it shows that the measuring system is satisfactory for detecting short-circuit reactance variations with high degree of accuracy and good stability,and the running condition of transformer will not influence the results,and it can be used for detecting deformation of the transformer windings.
出处
《高压电器》
CAS
CSCD
北大核心
2010年第12期41-44,共4页
High Voltage Apparatus
关键词
漏电感
参数辨识
在线监测
误差分析
leakage reactance
parameters identification
on-line detecting
error analysis
