A fault warning for inter-turn short circuit of excitation winding of synchronous generator based on GRU-CNN

Synchronous generators are important components of power systems and are necessary to maintain its normal and stable operation.To perform the fault diagnosis of mild inter-turn short circuit in the excitation winding of a synchronous generator,a gate recurrent unit-convolutional neural network(GRU-CNN)model whose structural parameters were determined by improved particle swarm optimization(IPSO)is proposed.The outputs of the model are the excitation current and reactive power.The total offset distance,which is the fusion of the offset distance of the excitation current and offset distance of the reactive power,was selected as the fault judgment criterion.The fusion weights of the excitation current and reactive power were determined using the anti-entropy weighting method.The fault-warning threshold and fault-warning ratio were set according to the normal total offset distance,and the fault warning time was set according to the actual situation.The fault-warning time and fault-warning ratio were used to avoid misdiagnosis.The proposed method was verified experimentally.

核电多相星形无刷励磁机定子励磁绕组匝间短路故障特征分析

相比于常规的三相绕组,多相星形电枢绕组的联接方式更为复杂灵活。以3n(n=2,3,…)相P对极的星形无刷励磁机为研究对象,从定、转子间磁势的性质及其相互作用入手,通过理论推导得出在定子励磁绕组中感应出的谐波电流特性。以某6相星形励磁机为例,依据每相各分支绕组相对空间位置、三相绕组相对空间位置分析定子励磁绕组匝间短路故障的特征。理论分析与仿真结果均表明,6相星形无刷励磁机正常运行时励磁电流中主要含有直流分量与6倍次谐波分量,当发生定子励磁绕组匝间短路故障后,励磁电流中还会出现3/4及其倍数次特征谐波电流。