基于负荷特征传导的配电台区全状态精准感知与数字化应用

Full-state accurate perception and digital application of distribution area based on load characteristic transmission

中文关键词: 配电台区  负荷特征传导  全状态精准感知  数字化应用

英文关键词: distribution area  load characteristic conduction  full state accurate perception  digital application

中文摘要:

      针对低压配电网存在的状态感知水平较低、数据共享挖掘不足等典型问题，结合能源互联网示范台区项目建设内容，以能源互联网的建设理念进行“信息支撑”和“价值创造”2 方面的技术突破和应用。首先利用用户负荷特征和故障特征在低压电网中的纵、横向传导特性，构建台区的变、线、户、相位、电气阻抗完整拓扑信息，极大简化采集架构，完成台区全状态精准感知，实现宽带高速电力线载波在信息感知上的高效利用；同时提出数字化业务支撑能力框架，以数据为驱动，探索能源互联网示范台区在电网数字化管理、用户数字化服务和政府数字化治理 3 个方面的典型业务场景；通过南京的 11 个小区完成示范应用，拓扑识别准确率可达 100%，阻抗计算准确率大于 90%，同时试点的 603 户完成了数据挖掘共享。

英文摘要:

      Aiming at the typical problems of low voltage distribution network, such as low status perception level and insufficient data sharing and mining, the project construction content of energy Internet demonstration platform area is combined, and technological breakthroughs are maked through applications in two aspects of“information support”and“value creation”with the construction concept of energy Internet. Firstly, the complete topological information of transformer, line, household, phase, electrical impedance of the station area is constructed based on the longitudinal and transverse conduction characteristics of user load characteristics and fault characteristics in the low-voltage power network, which greatly simplifies the acquisition framework and completes the full state accurate perception of the low -voltage area, so as to realize the efficient utilization of the information perception by high-speed power line carrier. At the same time, the framework of digital business support capability is proposed to explore the typical business scenarios of the energy Internet demonstration area in three aspects of power grid digital management, user digital service and government digital governance driven by data. Through the demonstration application of11 communities in Nanjing, the accuracy of topology identification can reach 100%, and the accuracy of impedance calculation is greater than 90%. At the same time, 603 households in the pilot have completed data mining and sharing.