摘要
建立了定子水内冷方式发电机定子绕组的动态温度水力模型,通过推导与计算,得到描述定子绕组动态温度水力模型的解析表达式。用径向基函数(RBF)神经网络对模型参数进行辨识,求取了单调连续2个负荷之间定子线棒温度的过渡时间,以此作为监测发电机运行状况的判据。通过对某电厂一台600MW汽轮发电机进行计算,证明了该方法的正确性。用这种方法进行热故障在线监测最大的优点是不受定子线棒温度延迟时间的影响,具有较大的实用价值。
A thermo-hydraulic model for the water-cooled stator bars in large generators is presented. By calculation and deduction, the formula for reflecting the dynamic model is obtained, and the neural network of radial basis function （RBF） is used to identify the model parameters and calculate the transient time of the temperature occurring in water-cooled stator bars from load-1 to load-2. The transient time is used as a tool to judge whether the generator is working in normal condition or in thermo-fault condition. An example based on a 600 MW synchronous generator is used to prove the correctness of this technique. The distinct advantage of this technique is that the judgment result is not affected by the delay time due to the temperature signal.
出处
《电力系统自动化》
EI
CSCD
北大核心
2008年第9期83-87,共5页
Automation of Electric Power Systems
基金
国家自然科学基金资助项目(50677017)~~
关键词
热故障
在线监测
RBF神经网络
温度水力模型
参数辨识
thermo-fault
on-line monitoring
neural network of radial basis function
thermo-hydraulic model
parameter identification
