The identification of important nodes in a power grid has considerable benefits for safety. Power networks vary in many aspects, such as scale and structure. An index system can hardly cover all the information in var...The identification of important nodes in a power grid has considerable benefits for safety. Power networks vary in many aspects, such as scale and structure. An index system can hardly cover all the information in various situations. Therefore, the efficiency of traditional methods using an index system is case-dependent and not universal. To solve this problem, an artificial intelligence based method is proposed for evaluating power grid node importance. First, using a network embedding approach, a feature extraction method is designed for power grid nodes, considering their structural and electrical information. Then, for a specific power network, steady-state and node fault transient simulations under various operation modes are performed to establish the sample set. The sample set can reflect the relationship between the node features and the corresponding importance. Finally, a support vector regression model is trained based on the optimized sample set for the later online use of importance evaluation. A case study demonstrates that the proposed method can effectively evaluate node importance for a power grid based on the information learned from the samples. Compared with traditional methods using an index system, the proposed method can avoid some possible bias. In addition, a particular sample set for each specific power network can be established under this artificial intelligence based framework, meeting the demand of universality.展开更多
针对智能电网设备运维检修阶段多源数据动态性较高的特点,研究面向智能电网设备运维检修阶段的多源建筑信息模型(building information modeling,BIM)数据存储方法。利用数据接口适配器连接电力系统各运维检修相关系统,采集电网设备运...针对智能电网设备运维检修阶段多源数据动态性较高的特点,研究面向智能电网设备运维检修阶段的多源建筑信息模型(building information modeling,BIM)数据存储方法。利用数据接口适配器连接电力系统各运维检修相关系统,采集电网设备运维检修多源数据,所采集数据利用数据转换引擎转换为多源BIM数据,传送至MySQL数据库。从MySQL数据库中调取整合后的运维检修多源数据,构建电网设备多源BIM数据模型。利用边缘节点采集多源BIM数据信息,选取压缩感知算法重构多源BIM数据,形成重构数据与稀疏字典原子并上传至云端服务器,构建完备稀疏字典,实现多源BIM数据存储。仿真结果表明:该方法存储面向智能电网设备运维检修的多源BIM数据,上传内容与读取内容一致,存储性能优越。展开更多
文摘The identification of important nodes in a power grid has considerable benefits for safety. Power networks vary in many aspects, such as scale and structure. An index system can hardly cover all the information in various situations. Therefore, the efficiency of traditional methods using an index system is case-dependent and not universal. To solve this problem, an artificial intelligence based method is proposed for evaluating power grid node importance. First, using a network embedding approach, a feature extraction method is designed for power grid nodes, considering their structural and electrical information. Then, for a specific power network, steady-state and node fault transient simulations under various operation modes are performed to establish the sample set. The sample set can reflect the relationship between the node features and the corresponding importance. Finally, a support vector regression model is trained based on the optimized sample set for the later online use of importance evaluation. A case study demonstrates that the proposed method can effectively evaluate node importance for a power grid based on the information learned from the samples. Compared with traditional methods using an index system, the proposed method can avoid some possible bias. In addition, a particular sample set for each specific power network can be established under this artificial intelligence based framework, meeting the demand of universality.
文摘针对智能电网设备运维检修阶段多源数据动态性较高的特点,研究面向智能电网设备运维检修阶段的多源建筑信息模型(building information modeling,BIM)数据存储方法。利用数据接口适配器连接电力系统各运维检修相关系统,采集电网设备运维检修多源数据,所采集数据利用数据转换引擎转换为多源BIM数据,传送至MySQL数据库。从MySQL数据库中调取整合后的运维检修多源数据,构建电网设备多源BIM数据模型。利用边缘节点采集多源BIM数据信息,选取压缩感知算法重构多源BIM数据,形成重构数据与稀疏字典原子并上传至云端服务器,构建完备稀疏字典,实现多源BIM数据存储。仿真结果表明:该方法存储面向智能电网设备运维检修的多源BIM数据,上传内容与读取内容一致,存储性能优越。
