基于数据驱动的电网用户侧故障主动研判技术研究与应用

Research and application of active research and judgment technology of power grid user side fault based on data driven

针对从大量数据中挖掘出对电网安全、经济运行有益信息的技术不够完善,且数据分析准确性低、实用性差的问题,文中基于智能电表、智能断路器、调度系统、营配贯通融合成果数据、故障报修服务请求及历史工单信息等数据,结合电网拓扑关系,利用深度卷积神经网络算法构建出了故障停电预警智能分析模型,其能够主动甄别区域性故障并开展研判、预警及抢修。该系统综合实现了区域故障突发后的快速响应,降低了客服中心话务突增的工作压力,可有效提高用电客户的满意度。

Aiming at the problem that the technology of mining useful information for power grid security and economic operation from a large number of data is not perfect,and the accuracy of data analysis is low,and the practicability is poor.In this paper,based on the smart meter,intelligent circuit breaker,dispatching system,business distribution integration results data,fault repair service request and historical work order information,combined with the power grid topology,using the deep convolution neural network algorithm to build an intelligent analysis model of fault outage early warning,which can actively identify regional faults and carry out research,early warning and emergency repair.The system realizes the rapid response after the regional fault,reduces the work pressure of the customer service center,and effectively improves the customer satisfaction.