摘要
故障诊断对于事故后系统快速恢复正常运行具有重要的意义。该文提出应用新型径向基函数 (RadialBasisFunc tion ,RBF)神经网络解决故障诊断问题 ,文中将正交最小二乘 (Orthogonalleastsquare)算法扩展用于优化RBF神经网络参数。并应用传统的BP神经网络解决同样的问题以进行比较。在 4母线测试系统中的计算机仿真结果证明 ,在解决故障诊断这一类问题时 ,RBF神经网络优于BP神经网络模型 。
Fault section estimation is of great importance to the restoration of power systems. In this paper, the application of Radial Basis Function Neural Network (RBF NN) to fault section estimation is addressed. The orthogonal least square algorithm has been extended to optimize the parameters of RBF NN. A classical Back Propagation Neural Network (BP NN) has been developed to solve the same problem for comparison. Computer test is conducted on a 4 bus test system and the test results show that the RBF NN is quite effective and superior to BP NN in fault section estimation.
出处
《中国电机工程学报》
EI
CSCD
北大核心
2002年第2期73-78,共6页
Proceedings of the CSEE
关键词
电网
故障诊断
电力系统
神经网络
fault section estimation
radial basis function neural network
orthogonal least square algorithm
power systems
