基于电力物联网的数据智能检测模型研究

Research on Intelligent Data Detection Model Based on Power Internet of Things

为支撑电力物联网数据共享,发挥电网数据价值,文章针对当前电力物联网数据平台中存在的技术组件多样、应用难度大、检索数据困难、数据应用门槛高和数据模型管控机制不完善等问题,优化电力物联网数据平台整体框架,提出基于孤立森林的量测类实时数据质量异常检测及改进算法,通过抢修故障研判和停电故障研判2个场景仿真验证数据质量检测算法的可行性,仿真试验结果表明该模型能够有效提升数据质量检测准确率。

In order to support the sharing of power IoT data and give full play to the value of power grid data,aiming at the problems of diverse technical components,difficulty in application,difficulty in finding data,high data application threshold,imperfect data model management and control mechanisms in the current power IoT data platform,this paper optimizes the overall framework of the power IoT data platform and proposes an isolated forest-based measurement real-time data quality anomaly detection and improved algorithm.The feasibility of the data quality detection algorithm is verified through two scenarios of experiment and emergency repair failure judgment and power failure judgement.It can provide a certain technical support for the construction of the power IoT data platform.