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计算机系统应用英文版:2016,25(9):137-143
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基于随机森林算法的配网抢修故障量预测方法
(1.国网甘肃省电力公司 兰州供电公司, 兰州 730050;2.国网甘肃省电力公司, 兰州 730030)
Method for Fault Forecasting in Repair of Distribution Network Based on the Random Forest Algorithm
(1.State Grid Lanzhou Branch Electic Power Company of Gansu, Lanzhou 730050, China;2.State Grid Gansu Electic Power Company, Lanzhou 730030, China)
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Received:December 28, 2015    Revised:March 31, 2016
中文摘要: 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.
Abstract: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.
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基金项目:国家自然科学基金(61103175,61300104);教育部科学技术研究重点项目(212086);福建省科技创新平台建设(2009J1007);福建省自然科学基金(2013J01230);福建省高校杰出青年科学基金(JA12016);福建省高等学校新世纪优秀人才支持计划(JA13021)
引用文本:
程淼海,楼俏,王琼,王国军,胡殿刚,李韶瑜.基于随机森林算法的配网抢修故障量预测方法.计算机系统应用,2016,25(9):137-143
CHENG Miao-Hai,LOU Qiao,WANG Qiong,WANG Guo-Jun,HU Dian-Gang,LI Shao-Yu.Method for Fault Forecasting in Repair of Distribution Network Based on the Random Forest Algorithm.COMPUTER SYSTEMS APPLICATIONS,2016,25(9):137-143