摘要
针对当前国网信息系统中电能表故障预测模型比较简单、不够全面和没有具体电能表月故障数预测模型的问题,基于时间序列建立综合时间序列预测模型,实现对批次电能表月故障数较准确的预测。首先计算电能表月故障数的移动平均序列,去除微小波动;然后根据序列是否有明显长期趋势,选用ARIMA模型或指数平滑模型对移动平均序列进行预测;最后采用反向移动平均,实现对整个批次电能表月故障数准确的短期预测。通过与BP神经网络模型的预测进行对比,验证了综合时间序列模型的实用性和准确性。在此基础上,建立电能表月故障总数预测模型。计量资产管理部门可以根据所提方法对故障电能表数进行预测,根据预测结果进行备货,提高管理部门的资源配置合理性和工作效率。
The existing watt-hour meter fault prediction models in the State Grid information system are relatively simple and insufficient,and there is no specific model for predicting the monthly fault number of watt-hour meters.Based on time series,an integrated time series prediction model is established for an accurate prediction of the monthly fault number of batch watt-hour meters.Firstly,the moving average sequence is calculated for the monthly fault number of watt-hour meters to remove small fluctuations.And then,the ARIMA model or exponential smoothing model is selected to predict the moving average sequence according to the long-term trend of the sequence.Finally,the reverse moving average is used to realize the accurate short-term prediction of the monthly fault number of the whole batch of watt-hour meters.By comparison with the BP neural network model,the practicability and accuracy of the proposed time series model is verified.On this basis,a monthly fault prediction model is established.The measurement asset management departments can use the proposed method to predict the number of faulted watthour meters,and prepare the stock according to the prediction results,consequently improving the rationality of resource allocation and work efficiency.
作者
李媛
郑安刚
谭煌
陈昊
程淑亚
蔡慧
王黎欣
LI Yuan;ZHENG Angang;TAN Huang;CHEN Hao;CHENG Shuya;CAI Hui;WANG Lixin(China Electric Power Research Institute Co.,Ltd.,Beijing 100192,China;College of Mechanical and Electrical Engineering,China Jiliang University,Hangzhou 310018,China;State Grid Zhejiang Electric Power Research Institute,Hangzhou 310014,China)
出处
《中国电力》
CSCD
北大核心
2020年第6期72-80,共9页
Electric Power
基金
国家电网公司科技项目(配用电设备健康状态在线监测、高效运维及智能评价关键技术研究及应用,JL71-18-019)。
关键词
电能表
月故障数
时间序列
BP神经网络
电能表合理分配
watt-hour meter
monthly fault number
time series
BP neural network
reasonable distribution of watt-hour meters
