基于电力大数据变电站设备状态检修技术研究

Research on Condition Maintenance Technology of Substation Equipment Based on Power Big Data

针对传统变电站设备状态检修存在信息采集速度慢、数据不准确、数据分类耗时耗力等问题,结合电力大数据分析方法,设计新型的智能巡检系统,该系统通过设置无人巡检车系统实现变电站设备状态信息的采集、处理和传递,检修人员根据变电站设备的巡检结果判断变电站设备运行状态,针对变电站数据种类的多样化,又采用具有定位功能的多分类融合模型,将不同类别的变电站设备运行数据分类并实现故障定位,使得检修人员能够快速从不同类别的数据信息中寻找目标数据。通过在某变电站试运行的结果表明,智能巡检系统的信息采集速度比以往的基站式采集方式速度提高了近5倍、准确率提高了近20%、多分类融合的分类准确性提高了2%左右。

Aiming at the problems of slow information collection speed,inaccurate data,time-consuming and labor-intensive data classification in the traditional substation equipment status maintenance,combining with the power big data analysis method,a new type of intelligent inspection system was designed.The system is equipped with an unmanned inspection vehicle.The system realizes the collection,processing and transmission of the status information of the substation equipment.The maintenance personnel judge the operation status of the substation equipment according to the inspection results of the substation equipment.In view of the diversification of the data types of the substation,it adopts a multi-category fusion model with positioning function.The classification of the operation data of substation equipment of different categories and the realization of fault location enable maintenance personnel to quickly find target data from different categories of data information.Through trial operation in a substation,the results show that the information collection speed of the intelligent inspection system is nearly 5 times faster than the previous base station collection method,the accuracy rate is increased by nearly 20%,and the classification accuracy of multi-class fusion is improved about 2%.