摘要
提出了一种利用径向基函数神经网络(RBFNN)预测大扰动后发电机转子转角值的方法,来实时判断系统暂态稳定状态,并对相干发电机组的辨识进行了研究。在故障后将相量测量单元(PMU)同步采样的前六个周期的发电机的转子角度和电压等数据作为神经网络的输入,以预测系统未来的状态。该方法还可以实时判断发电机的同步状态。在测试系统上对该方法以不同运行条件进行了评估,实验结果证明所提出的径向基函数神经网络对扰动后的转子转角值具有良好的预测性能,适合于实时应用。
A method using radial basis function neural nework(RBFNN) to predict the rotor angle of the generator after large disturbances is proposed to judge the transient and stable state of the system in real time,and to identify the coherent generator set.Research.After a fault,the phasor measurement unit(PMU) synchronously sampled the rotor angle and voltage of the generator in the first six cycles as the input of the neural network to predict the future state of the system.This method can also judge the synchronization status of the generator in real time.The method is evaluated under different operating conditions on the test system.The experimental results prove that the proposed radial basis function neural network has good predictive performance on the rotor angle value after disturbance,and is suitable for real-time applications.
作者
刘辉
郑剑
刘行行
LIU Hui;ZHENG Jian;LIU Xing-xing(Hebei Jiyan Energy Science and Technology Research Institute Co.,Ltd.,Shijiazhuang,Hebei 050000,China;State Grid Hebei Shijiazhuang Power Supply Company,Shijiazhuang,Hebei 050000,China)
出处
《计算技术与自动化》
2022年第4期7-11,共5页
Computing Technology and Automation
关键词
故障预测
暂态分析
人工神经网络
fault prediction
transient analysis
artificial neural network
