At present, an automatic-mechanic contact tap-changer is widely used in power system, but it can not frequently operate. In addition, arc will occur when the switch changes. In order to solve these two problems, this ...At present, an automatic-mechanic contact tap-changer is widely used in power system, but it can not frequently operate. In addition, arc will occur when the switch changes. In order to solve these two problems, this paper presented an automatic on-load voltage-regulating distributing transformer which employed non-contact solid-state relay as tap-changer, and mainly introduced its structure, basic principal, design method of each key link and experimental results. Laboratory simulation experiments informed that the scheme was feasible. It was a smooth and effective experiment device, which was practical in application.展开更多
为解决传统特高压直流保护对高阻故障检测准确率不高、故障检测时间过长以及故障选极不完善的问题,提出基于长短时记忆(long short term memory,LSTM)循环神经网络(recurrent neural network,RNN)的特高压直流输电线路继电保护故障检测...为解决传统特高压直流保护对高阻故障检测准确率不高、故障检测时间过长以及故障选极不完善的问题,提出基于长短时记忆(long short term memory,LSTM)循环神经网络(recurrent neural network,RNN)的特高压直流输电线路继电保护故障检测方法。首先,基于快速傅里叶变换分析特高压直流输电系统暂态故障特征,使用相模变换和小波变换提取出故障特征量作为输入数据。其次,将输入数据输入到LSTM-RNN中进行前向传播,对系统故障特征进行深度学习,同时使用反向传播方式更新网络参数,将深层的特征量输入到Softmax分类器中进行分类,把故障识别分成区外故障、母线故障和线路故障,故障分类为正极故障、负极故障和双极故障,并输出识别结果。最后,在PSCAD/EMTDC仿真条件下,搭建特高压直流输电模型。验证结果表明:所提的方法在特高压直流输电线路继电保护的故障检测、故障选极上具有更好的效果,相比于人工神经网络、卷积神经网络、支持向量机,故障识别准确率分别提升4.71%、6.57%、9.32%。展开更多
文摘At present, an automatic-mechanic contact tap-changer is widely used in power system, but it can not frequently operate. In addition, arc will occur when the switch changes. In order to solve these two problems, this paper presented an automatic on-load voltage-regulating distributing transformer which employed non-contact solid-state relay as tap-changer, and mainly introduced its structure, basic principal, design method of each key link and experimental results. Laboratory simulation experiments informed that the scheme was feasible. It was a smooth and effective experiment device, which was practical in application.
文摘为解决传统特高压直流保护对高阻故障检测准确率不高、故障检测时间过长以及故障选极不完善的问题,提出基于长短时记忆(long short term memory,LSTM)循环神经网络(recurrent neural network,RNN)的特高压直流输电线路继电保护故障检测方法。首先,基于快速傅里叶变换分析特高压直流输电系统暂态故障特征,使用相模变换和小波变换提取出故障特征量作为输入数据。其次,将输入数据输入到LSTM-RNN中进行前向传播,对系统故障特征进行深度学习,同时使用反向传播方式更新网络参数,将深层的特征量输入到Softmax分类器中进行分类,把故障识别分成区外故障、母线故障和线路故障,故障分类为正极故障、负极故障和双极故障,并输出识别结果。最后,在PSCAD/EMTDC仿真条件下,搭建特高压直流输电模型。验证结果表明:所提的方法在特高压直流输电线路继电保护的故障检测、故障选极上具有更好的效果,相比于人工神经网络、卷积神经网络、支持向量机,故障识别准确率分别提升4.71%、6.57%、9.32%。
