基于随机森林算法的配网抢修故障量预测方法

配网抢修是电力系统运行环节中十分重要的一环，精益化的配网抢修管理不仅能提高电力系统的供电服务质量，也能减少电力公司的经济损失. 本文提出一种新的配网抢修故障数量预测的方法. 首先，基于历史数据，以气温、风力、前一天的故障量、最大最小负荷等作为因变量，对数据做了特征映射等预处理. 然后，应用随机森林算法建立配网抢修故障量预测模型，并预测不同区域、不同电网故障及非电网故障、不同电压维度下未来一天故障量. 在真实电力数据上进行了对比验证，实验结果表明提出的方法具有较好的预测效率和准确性.

Method for Fault Forecasting in Repair of Distribution Network Based on the Random Forest Algorithm

Repair in the distribution network is a very important part of the power system running, the lean of the distribution network emergency management can not only improve the quality of power supply service, but also reduce the economic losses of power companies. In this paper, a new fault forecasting method of repair in the distribution network is proposed. Firstly, based on historical data, the temperature, wind, the fault of the previous day, the maximum and minimum loads, etc are regarded as dependent variables. and feature mapping and preprocessing are performed on the variables. Then, the Random Forest algorithm is applied to establish the fault prediction model of the repair of distribution network, and to forecast the future failure rate in different regions, different power grids and non-grid faults and different voltage dimensions. The experimental results on the real power data show that the proposed method has better prediction efficiency and accuracy.