基于径向基网络的同杆双回路故障类型判定  

Fault identification in double-circuit on same pole using neural network

作　　者：钱鑫[1] 张光辉[2] 施围[1] 

机构地区：[1]西安交通大学电气工程学院,陕西西安710049 [2]西安理工大学自动化学院,陕西西安710048

出　　处：《继电器》2002年第3期8-12,共5页Relay

摘　　要：由于同杆双回线发生故障时两回线之间存在非线性耦合 ,故用传统方法无法对同杆双回线进行故障判定 ,而径向基神经网络可以通过故障线路相关电压和电流对线路故障进行准确分类 ,网络输入样本在不同故障发生地点、发生时间、过渡电阻情况下得到。仿真结果表明该网络能够进行同杆双回路故障判定。The problems encountered by conventional techniques in fault type classification in double-circuit transmission line is presented in this paper; these arise principally due to the mutual coupling is nonlinear. However a RBF(radial basis function)neural network provides the ability to classify the fault type by identifying different patterns of the associated voltages and currents, which is tested under different fault location, inception angle and resistance. All simulation results show that the neural network is well suited for double-circuit.

关 键 词：电力系统 人工神经网络 径向基网络 同杆双回路 故障类型 判定 

分 类 号：TM773[电气工程—电力系统及自动化]