基于大数据的低压台区线损异常判定方法

Big Data-based Method of Determining Abnormal Line Loss in Low-voltage Station Area

针对低压台区数量众多、线损管理难度大,对其进行精益化管理能够有效提高电网的经济效益。基于此,提出一种基于大数据技术的低压台区线损异常判定模型。首先,介绍了台区线损异常的影响因素,并分成12类;其次,利用大数据技术采集数据,结合三相混合法进行线损潮流计算,并形成线损图;再次,对台区线损图进行聚类分析挖掘,得到典型配电网线损异常图,便于快速检测异常线损类型;最后,基于指南针布局构建配电网异常线损显示,并结合实际算例验证了诊断模型的有效性。

Effective management against line loss of the large number of low-voltage station areas is of great importance to cost-effectiveness improvement of power grids,and also of high difficulty.In view of this,a model for determining abnormal line loss in low-voltage station areas based on big data technology is proposed.First the influencing factors of abnormal line loss are outlined and divided into 12 categories.Second the line loss diagram is obtained by collecting data using big data technology and performing current calculation using three-phase method.The line loss diagram is then clustered and analyzed to obtain a typical distribution network line loss anomaly diagram,which facilitates rapid detection of anomaly type of line loss,and the compass layout is employed to realize anomaly display.The proposed model has been preliminarily indicated effective via a case verification.