基于大数据和机器学习的用电异常行为分析系统总体架构

用户用电异常行为不仅对接入设备和用户本身产生影响，更会危及电网的正常运行，因此对用电异常行为的分析至关重要。基于大数据和机器学习技术，设计了一种用电异常行为分析系统，并提出了系统设计的总体框架和相关配置。所设计系统对用户用电的用电量、电压质量、负载及三相不平衡率、无功及功率因数等方面可以进行异常分析，并以可视化的方式向管理员和用户展示。同时，对高风险用户进行预警和跟踪处理，对窃电行为展开调查分析。本系统可以有效分析用户用电异常行为及进行窃电预警，对电网稳定运行起到关键作用。

Overall architecture analysis system of abnormal electricity behavior based on big data and machine learning

The abnormal behavior of users'' electricity consumption does not know how to affect the access equipment and users, and may even endanger the healthy operation of the power grid. Therefore, the analysis of the abnormal behavior of electricity consumption is very important. Based on big data and machine learning technology, a power consumption abnormal behavior analysis system is designed, and proposes the overall framework and related configuration of the system design. The design system can perform abnormal analysis on the electricity consumption, voltage quality, load and three-phase unbalance rate, reactive power and power factor of the user''s electricity, and display it to the administrator and users in a visual way. At the same time, it conducts early warning and tracking treatment for high-risk users, and investigates and analyzes the behavior of electricity theft. This system effectively analyzes the user''s abnormal electricity consumption behavior and can carry out early warning of electricity theft, which plays a certain role in the good operation of the power grid.