Inferring power grid transformer to meter association using inconsistent geospatial data

使用不一致的地理空间数据推断电网变压器与仪表的关联

• 批准号: 543219-2019
• 负责人: Makonin, Stephen
• 金额: $ 1.82万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=680791
• 关键词: Inferring; power; grid; transformer; meter

Having up-to-date and accurate geospatial data of a power utility company is critically important to maintaining the overall health of the power grid. Routine grid maintenance can change the physical topology of the grid; however, the subsequent update to the geospatial information systems may be forgotten or entered incorrectly by personnel. Some meters may have inaccurate or outdated transformer connectivity information (i.e., data identifying what transformer is directly upstream of a meter). Other meters may have an incorrect phase association. Without accurate data, Awesense is unable to provide important reports about energy balancing, theft analysis, load balancing, and transformer health information. The proposed research project brings together researchers from SFU and Awesense with the goal of developing computational methods to optimally find the inaccurate geospatial data in a utility's power grid information system by developing algorithms that quantify the confidence of meter connectivity and/or association information data. Outcomes from this project will be used to enhance Awesense's existing software services platform by adding new feature offerings.

拥有电力公司的最新和准确的地理空间数据对于维护电网的整体健康至关重要。例行的电网维护可能会改变电网的物理拓扑结构;然而，地理空间信息系统的后续更新可能会被人员忘记或输入错误。某些仪表可能具有不准确或过时的Transformer连接信息（即，识别什么Transformer直接位于仪表上游的数据）。其他电表可能具有不正确的相位关联。如果没有准确的数据，Awesense就无法提供有关能源平衡、盗窃分析、负载平衡和Transformer健康信息的重要报告。拟议的研究项目汇集了来自SFU和Awesense的研究人员，目标是开发计算方法，通过开发量化仪表连接性和/或关联信息数据的置信度的算法，以最佳方式找到公用事业电网信息系统中不准确的地理空间数据。该项目的成果将用于通过添加新功能来增强Awesense现有的软件服务平台。