Abnormal Working Conditions Judgment Based on Data Sharing System for Power Distribution Station Area

Abstract

As important components of the Ubiquitous Power Internet of Things, cloud and edge nodes play key technical support roles for its construction. However, data resources stored in them have the characteristics of diverse structure and huge scale. There is an urgent need to improve the efficiency of data sharing services and decision-making responses. This paper builds a data sharing system among cloud and edge nodes to realize information & business collaboration which can promote rapidly responding to advanced services at edge nodes. Based on this system, a method of judging abnormal working conditions in the power distribution station area is proposed. The cloud will use machine learning methods to train the characteristic data and obtain the judgment model. Meanwhile, the edge nodes will judge the abnormal working condition on-site according to the model. It can provide support for the improvement of the regional autonomy and intelligence level of the power distribution station area.