摘要
配网变压器对电力系统的可靠、经济运行发挥着重要作用。过去,维护人员需要花费大量时间逐一检查配网变压器。而如今,随着电力用户电能数据采集系统的广泛部署,可提供有用的量测数据,并将其用于进一步的状态监视。因此,文中提出了一种用于配网变压器的基于数据驱动的异常状态监测数据采集算法,该算法可以及时向操作人员和维护人员发送异常状态警报。在所提的算法中,利用Spearman秩相关系数显示相电流间的相关度,其t统计量用于决定基于假设检验的确定数据采集是否存在异常情况。最后,利用浙江电网的实际采集数据来验证所提算法的有效性,并分别分析正常和异常情况的特征。对不同的显著水平和采样率进行敏感度分析,以考虑其对监测结果的影响;还给出了在实际电力系统中的应用,以证明该算法的实用性。
Distribution network transformers play an important role in the reliable and economical operation of the power system.In the past,maintenance personnel spent a lot of time checking the distribution transformers one by one.Nowadays,with the widespread deployment of electrical energy data collection systems for power users,useful measurement data can be provided and used for further condition monitoring.Therefore,this paper proposes a datadriven abnormal state monitoring data collection algorithm for distribution network transformers,which can send abnormal state alarms to operators and maintenance staff in a timely manner.In the proposed algorithm,the Spearman rank correlation coefficient is used to display the correlation between the phase currents,and its t-statistic is used to determine whether there is an abnormality in the data collection based on the hypothesis test.Finally,the actual collected data of Zhejiang Power Grid is used to verify the effectiveness of the proposed algorithm,and the characteristics of normal and abnormal situations are analyzed separately.Sensitivity analysis is performed for different significance levels and sampling rates to consider its impact on the monitoring results;the application in actual power systems is also given to prove the practicability of the algorithm.
作者
刘林青
葛云龙
李梦宇
赵佩
李飞
LIU Linqing;GE Yunlong;LI Mengyu;ZHAO Pei;LI Fei(Electric Power Research Institute of State Grid Hebei Electric Power Co.,Ltd.,Shijiazhuang 050000 China;State Grid Hebei Electric Power Co.,Ltd.,Shijiazhuang 050000,China)
出处
《高压电器》
CAS
CSCD
北大核心
2020年第9期11-19,共9页
High Voltage Apparatus
基金
国网河北省电力有限公司科技项目(kj2018-019)。
关键词
异常数据采集
数据驱动
配网变压器
t统计
假设检验
abnormal data collection
data-driven
distribution network transformer
t-statistics
hypothesis test
