机柜设备作为核电厂集散控制系统(Distributed Control System,DCS)的关键组成部分,其剩余使用寿命(Remaining Useful Life,RUL)预测对保障系统安全可靠运行、人民的生命财产安全具有重大意义。随着大数据时代的来临和人工智能的发展,...机柜设备作为核电厂集散控制系统(Distributed Control System,DCS)的关键组成部分,其剩余使用寿命(Remaining Useful Life,RUL)预测对保障系统安全可靠运行、人民的生命财产安全具有重大意义。随着大数据时代的来临和人工智能的发展,神经网络的运用展开了新的领域,RUL预测方法也变得更加丰富。针对核电设备系统复杂工况应用场景,在故障预测与健康管理(Prognosis and Health Management,PHM)的框架下,提出一种基于深度神经网络的RUL预测方法,挖掘设备故障信息,提取影响设备使用寿命的关键特征,并对该模型进行了训练。试验结果表明,该预测模型能够较为准确地预测RUL,为设备维护的决策提供有意义的信息,从而避免系统的严重故障,对核电机柜设备剩余寿命研究的发展有重要意义。展开更多
This is an extended version of the same titled paper presented at the 21st CIRED. It discusses a new technique for identification and location of defective insulator strings in power lines based on the analysis of hig...This is an extended version of the same titled paper presented at the 21st CIRED. It discusses a new technique for identification and location of defective insulator strings in power lines based on the analysis of high frequency signals generated by corona effect. Damaged insulator strings may lead to loss of insulation and hence to the corona effect, in other words, to partial discharges. These partial discharges can be detected by a system composed of a capacitive coupling device （region between the phase and the metal body of a current transformer）, a data acquisition board and a computer. Analyzing the waveform of these partial discharges through a neural network based software, it is possible to identify and locate the defective insulator string. This paper discusses how this software analysis works and why its technique is suitable for this application. Hence the results of key tests performed along the development are discussed, pointing out the main factors that affect their performance.展开更多
文摘机柜设备作为核电厂集散控制系统(Distributed Control System,DCS)的关键组成部分,其剩余使用寿命(Remaining Useful Life,RUL)预测对保障系统安全可靠运行、人民的生命财产安全具有重大意义。随着大数据时代的来临和人工智能的发展,神经网络的运用展开了新的领域,RUL预测方法也变得更加丰富。针对核电设备系统复杂工况应用场景,在故障预测与健康管理(Prognosis and Health Management,PHM)的框架下,提出一种基于深度神经网络的RUL预测方法,挖掘设备故障信息,提取影响设备使用寿命的关键特征,并对该模型进行了训练。试验结果表明,该预测模型能够较为准确地预测RUL,为设备维护的决策提供有意义的信息,从而避免系统的严重故障,对核电机柜设备剩余寿命研究的发展有重要意义。
文摘This is an extended version of the same titled paper presented at the 21st CIRED. It discusses a new technique for identification and location of defective insulator strings in power lines based on the analysis of high frequency signals generated by corona effect. Damaged insulator strings may lead to loss of insulation and hence to the corona effect, in other words, to partial discharges. These partial discharges can be detected by a system composed of a capacitive coupling device （region between the phase and the metal body of a current transformer）, a data acquisition board and a computer. Analyzing the waveform of these partial discharges through a neural network based software, it is possible to identify and locate the defective insulator string. This paper discusses how this software analysis works and why its technique is suitable for this application. Hence the results of key tests performed along the development are discussed, pointing out the main factors that affect their performance.
