There are several thousand piping components in a nuclear power plant. These components are affected by degradation mechanisms such as FAC (Flow-Accelerated Corrosion), cavitation, flashing, and LDI (Liquid Droplet Im...There are several thousand piping components in a nuclear power plant. These components are affected by degradation mechanisms such as FAC (Flow-Accelerated Corrosion), cavitation, flashing, and LDI (Liquid Droplet Impingement). Therefore, nuclear power plants implement inspection programs to detect and control damages caused by such mechanisms. UT (Ultrasonic Test), one of the non-destructive tests, is the most commonly used method for inspecting the integrity of piping components. According to the management plan, several hundred components, being composed of as many as 100 to 300 inspection data points, are inspected during every RFO (Re-Fueling Outage). To acquire UT data of components, a large amount of expense is incurred. It is, however, difficult to find a proper method capable of verifying the reliability of UT data prior to the wear rate evaluation. This study describes the review of UT evaluation process and the influence of UT measurement error. It is explored that SAM (Square Average Method), which was suggested as a method for reliability analysis in the previous study, is found to be suitable for the determination whether the measured thickness is acceptable or not. And, safety factors are proposed herein through the statistical analysis taking into account the components’ type.展开更多
为应对电力系统碳排放计算中效率和精度不足的问题,文章提出一种基于时空图神经网络(spatiotemporal graph neural network,ST-GNN)的数据驱动方法,旨在高效计算节点碳排放因子以及支路碳流和碳流损耗。文章首先分析电力系统碳排放流计...为应对电力系统碳排放计算中效率和精度不足的问题,文章提出一种基于时空图神经网络(spatiotemporal graph neural network,ST-GNN)的数据驱动方法,旨在高效计算节点碳排放因子以及支路碳流和碳流损耗。文章首先分析电力系统碳排放流计算的复杂性及传统方法的局限性,进而设计以有功-无功(active and reactive power,PQ)节点、有功-电压(active power and voltage,PV)节点和平衡节点特征为输入的ST-GNN模型,实现碳排放因子及支路碳流的直接计算,并确定支路碳流损耗。其中PQ节点的特征有功功率和无功功率,来源于电力系统运行数据,PV节点的发电功率和电压来自发电机的运行特性,平衡节点的输入包括电压和相位角,确保系统的功率平衡。通过IEEE 9节点、IEEE 57节点和IEEE118节点系统的实验,验证了所提方法的有效性。结果表明,ST-GNN模型在碳排放因子、支路碳流和碳损耗的计算精度上显著优于线性回归、决策树、长短期记忆网络和多层感知机,特别在复杂电力网络中表现突出。该研究为电力系统碳排放监测和优化提供了精准高效的技术支持。展开更多
文摘There are several thousand piping components in a nuclear power plant. These components are affected by degradation mechanisms such as FAC (Flow-Accelerated Corrosion), cavitation, flashing, and LDI (Liquid Droplet Impingement). Therefore, nuclear power plants implement inspection programs to detect and control damages caused by such mechanisms. UT (Ultrasonic Test), one of the non-destructive tests, is the most commonly used method for inspecting the integrity of piping components. According to the management plan, several hundred components, being composed of as many as 100 to 300 inspection data points, are inspected during every RFO (Re-Fueling Outage). To acquire UT data of components, a large amount of expense is incurred. It is, however, difficult to find a proper method capable of verifying the reliability of UT data prior to the wear rate evaluation. This study describes the review of UT evaluation process and the influence of UT measurement error. It is explored that SAM (Square Average Method), which was suggested as a method for reliability analysis in the previous study, is found to be suitable for the determination whether the measured thickness is acceptable or not. And, safety factors are proposed herein through the statistical analysis taking into account the components’ type.
文摘为应对电力系统碳排放计算中效率和精度不足的问题,文章提出一种基于时空图神经网络(spatiotemporal graph neural network,ST-GNN)的数据驱动方法,旨在高效计算节点碳排放因子以及支路碳流和碳流损耗。文章首先分析电力系统碳排放流计算的复杂性及传统方法的局限性,进而设计以有功-无功(active and reactive power,PQ)节点、有功-电压(active power and voltage,PV)节点和平衡节点特征为输入的ST-GNN模型,实现碳排放因子及支路碳流的直接计算,并确定支路碳流损耗。其中PQ节点的特征有功功率和无功功率,来源于电力系统运行数据,PV节点的发电功率和电压来自发电机的运行特性,平衡节点的输入包括电压和相位角,确保系统的功率平衡。通过IEEE 9节点、IEEE 57节点和IEEE118节点系统的实验,验证了所提方法的有效性。结果表明,ST-GNN模型在碳排放因子、支路碳流和碳损耗的计算精度上显著优于线性回归、决策树、长短期记忆网络和多层感知机,特别在复杂电力网络中表现突出。该研究为电力系统碳排放监测和优化提供了精准高效的技术支持。
