Association Rule Mining and Prediction Method for Transmission Line Operation Parameters Based on Bayesian Model

作者：Yang, Yuewen^*; Hou, Huijuan; Yang, Yi; Chen, Yufeng; Sheng, Gehao; Jiang, Xiuchen

来源：Power System Technology, 2017, 41(11): 3648-3654.

DOI：10.13335/j.1000-3673.pst.2017.0094

• 500 kV transmission lines
• Association rule mining methods
• Bayesian model
• Environmental conditions
• Meteorological condition
• Operation of power system
• State prediction
• Transmission line analysis

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.

• 出版日期2017
• 单位上海交通大学