电网结构识别就是通过快速遍历电网拓扑结构获取电网结构类型(环网/辐射网)、节点平均度、内部联络线数量等结构信息。为满足调度权下放这种新形势下的快速、精确识别"组团式"电网结构的需求,对IEC 61970电力系统公共信息模型...电网结构识别就是通过快速遍历电网拓扑结构获取电网结构类型(环网/辐射网)、节点平均度、内部联络线数量等结构信息。为满足调度权下放这种新形势下的快速、精确识别"组团式"电网结构的需求,对IEC 61970电力系统公共信息模型(Common Information Model,缩写为CIM)进行了适当的扩展,并运用节点融合技术的思想构建了节点/支路模型。在该模型的基础上,运用深度优先搜索(DFS)和广度优先搜索(BFS)算法实现了"组团式"电网结构的自动识别。之后,以XML文件为底层载体,采用面向对象的程序设计方法开发了基于CIM的"组团式"电网结构在线自动识别软件系统。最后,用广东省实际电力系统的测试结果验证了所发展的软件系统的有效性。展开更多
To enhance the accuracy and efficiency of bridge damage identification,a novel data-driven damage identification method was proposed.First,convolutional autoencoder(CAE)was used to extract key features from the accele...To enhance the accuracy and efficiency of bridge damage identification,a novel data-driven damage identification method was proposed.First,convolutional autoencoder(CAE)was used to extract key features from the acceleration signal of the bridge structure through data reconstruction.The extreme gradient boosting tree(XGBoost)was then used to perform analysis on the feature data to achieve damage detection with high accuracy and high performance.The proposed method was applied in a numerical simulation study on a three-span continuous girder and further validated experimentally on a scaled model of a cable-stayed bridge.The numerical simulation results show that the identification errors remain within 2.9%for six single-damage cases and within 3.1%for four double-damage cases.The experimental validation results demonstrate that when the tension in a single cable of the cable-stayed bridge decreases by 20%,the method accurately identifies damage at different cable locations using only sensors installed on the main girder,achieving identification accuracies above 95.8%in all cases.The proposed method shows high identification accuracy and generalization ability across various damage scenarios.展开更多
文摘电网结构识别就是通过快速遍历电网拓扑结构获取电网结构类型(环网/辐射网)、节点平均度、内部联络线数量等结构信息。为满足调度权下放这种新形势下的快速、精确识别"组团式"电网结构的需求,对IEC 61970电力系统公共信息模型(Common Information Model,缩写为CIM)进行了适当的扩展,并运用节点融合技术的思想构建了节点/支路模型。在该模型的基础上,运用深度优先搜索(DFS)和广度优先搜索(BFS)算法实现了"组团式"电网结构的自动识别。之后,以XML文件为底层载体,采用面向对象的程序设计方法开发了基于CIM的"组团式"电网结构在线自动识别软件系统。最后,用广东省实际电力系统的测试结果验证了所发展的软件系统的有效性。
基金The National Natural Science Foundation of China(No.52361165658,52378318,52078459).
文摘To enhance the accuracy and efficiency of bridge damage identification,a novel data-driven damage identification method was proposed.First,convolutional autoencoder(CAE)was used to extract key features from the acceleration signal of the bridge structure through data reconstruction.The extreme gradient boosting tree(XGBoost)was then used to perform analysis on the feature data to achieve damage detection with high accuracy and high performance.The proposed method was applied in a numerical simulation study on a three-span continuous girder and further validated experimentally on a scaled model of a cable-stayed bridge.The numerical simulation results show that the identification errors remain within 2.9%for six single-damage cases and within 3.1%for four double-damage cases.The experimental validation results demonstrate that when the tension in a single cable of the cable-stayed bridge decreases by 20%,the method accurately identifies damage at different cable locations using only sensors installed on the main girder,achieving identification accuracies above 95.8%in all cases.The proposed method shows high identification accuracy and generalization ability across various damage scenarios.
