基于贝叶斯模型的输电线路运行参量关联规则挖掘及预测方法

发布时间：2018-01-04 13:07

本文关键词：基于贝叶斯模型的输电线路运行参量关联规则挖掘及预测方法　出处：《电网技术》2017年11期 　论文类型：期刊论文

【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。
[Abstract]:The accurate evaluation of the operating state of the transmission line, diagnosis and prediction for power system security, provide technical support to the economic and efficient operation. Analysis and prediction model using single parameter of traditional transmission line, and the running state of transmission line is also affected by meteorological conditions, operating conditions and other factors. So the measurement data of low quality, environment stochastic large limitations, traditional methods have limitations in accuracy and timeliness. This paper presents association rules mining method based on Bayesian network for mining association rules between transmission line road running parameters, can be more intuitive to reflect the relevance between data, and effectively improves the calculation efficiency; state parameters obtained mining association rules are applied to predict the line, can improve the accuracy of predicted results. Finally a 500kV transmission line as an example, provided Association rules are used to predict line load and line temperature. The results show that this method can improve the prediction accuracy, and verify the effectiveness and feasibility of the association rule mining method.

【作者单位】：上海交通大学电气工程系;国网山东省电力公司电力科学研究院;
【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)~~
【分类号】：TM75
【正文快照】： 0引言随着国民经济的高速发展,社会用电需求日益增大,电力短缺已经成为一个经济发展的限制因素[1]。目前,电网输电能力的不足日显突出,在加快电网规划建设之外,通过动态增容等技术的改造和升级以提升输送能力、提高输电效率成为研究方向[2-3]。现广泛研究的输电线路热稳态模型

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