基于GBDT的电力计量设备故障预测

Fault Prediction of Power Metering Equipment Based on GBDT

电力计量设备的故障风险预测可以减少国家电网因为故障风险带来的损失。文中首先进行了数据的预处理和特征选取;其次,设计了基于GBDT的故障大类、故障小类以及设备寿命周期的预测;最后,对设计的模型进行了有效性和先进性的验证。实验在中国电力科研研究院提供的数据上进行。由实验结果可知,所提算法对6种故障类型的预测准确率为90.56%,查全率为92.95%,F1值为91.71%。相比回归、BP神经网络、Adaboost、决策树算法,梯度提升决策树算法在参数调优条件下的性能最优。

The fault risk prediction of power metering equipment can reduce the loss caused by the fault risk of the national grid.Firstly,the data preprocessing and feature selection are carried out.Secondly,the GBDT-based fault categories,fault subclasses and equipment life cycle prediction are designed.Finally,the validity and advancement of the designed model are verified.Data used in the experiment are provided by China Electric Power Research Institute.The experimental results show that the prediction accuracy of the six fault types by using the proposed algorithm is 90.56%,the recall rate is 92.95%,and the F1 value is 91.71%.Compared with regression,BP neural network,Adaboost and decision tree algorithm,the gradient lifting decision tree algorithm has the best performance under parameter tuning conditions.