摘要
对输电线路运行状态的准确评价、诊断和预测可以为电力系统安全、经济和高效的运行提供技术支持。传统输电线路分析预测模型多使用单一参量,而输电线路运行状态还受到气象条件、运行条件等诸多因素影响。因此受测量数据质量低、环境条件随机性大等限制,传统方法在预测准确性和时效性上具有较大的局限性。该文提出了基于贝叶斯网络的关联规则挖掘方法,用于挖掘输变电线路运行参量之间的关联规则,可更加直观反映出数据间的关联性,并有效提升了计算效率;将挖掘得到的关联规则应用于预测线路的状态参量,可提高预测结果的准确性。最后以某500kV输电线路为例,提取关联规则,并用于预测线路负荷与线路温度,结果表明该方法可提高预测精度,从而验证了该关联规则挖掘方法的有效性和可行性。
Accurate evaluation,diagnosis and prediction of operation state of transmission line can provide technical support for safe,economic and efficient operation of power system.Traditional transmission line analysis and prediction model typically uses a single parameter,but transmission line operating state is also affected by meteorological conditions,operating conditions and other factors.Therefore,due to low quality of the measured data and random nature of environmental conditions,the traditional methods have great limitations in forecasting accuracy and timeliness.An association rule mining method based on Bayesian network is proposed in this paper,to mine association rules between parameters,more directly reflect correlation between the data and effectively improve computational efficiency.The mined association rules applied to forecast circuit state parameters can improve accuracy of predicted results.Finally,taking a 500 kV transmission line as an example,association rules are extracted,and the rules are used to predict load and temperature.The results show that the method can improve prediction accuracy,thus verifying validity and feasibility of the association rule mining methods.
出处
《电网技术》
EI
CSCD
北大核心
2017年第11期3648-3654,共7页
Power System Technology
基金
国家自然科学基金项目(51477100)
国家863高新技术基金项目(2015AA050204)
中国博士后科学基金面上资助项目(2015M581614)~~
关键词
关联规则
输电线路
贝叶斯模型
状态预测
association rules
transmission line
Bayesianmodel
state prediction
