针对当前智能电表现场检定效率低、人力成本高、实时性差、无法全量监测等问题,提出一种基于动态线损和渐消记忆递推最小二乘法(dynamic line loss and fading memory recursive least square,DLL-FMRLS)的智能电表误差在线估计算法。首...针对当前智能电表现场检定效率低、人力成本高、实时性差、无法全量监测等问题,提出一种基于动态线损和渐消记忆递推最小二乘法(dynamic line loss and fading memory recursive least square,DLL-FMRLS)的智能电表误差在线估计算法。首先,通过分析台区线损与供电量之间的关系,对传统模型进行改进,提出动态线损误差模型,该模型中线损可随实际供电量变化,使得模型获得的误差估计值更接近实际值;然后,利用FMRLS算法求解动态线损误差模型,以获得智能电表运行误差;最后,根据某省电网公司的实际数据对算法现场验证。结果结果表明,与列文伯格–马夸尔特(Levenberg-Marquardt, LM)算法和限定记忆最小二乘(limited memory recursive least squares,LMRLS)算法相比,所提算法可以有效提高智能电表的误差估计的准确度。展开更多
This paper addresses a problem of flood forecasting with the self-memory function. Considering flood forecasting’s uncertainty and updating demand, a hybrid hydrological model, namely Differential Hy- drological Grey...This paper addresses a problem of flood forecasting with the self-memory function. Considering flood forecasting’s uncertainty and updating demand, a hybrid hydrological model, namely Differential Hy- drological Grey Model with self-memory function (DHGM-SM), is developed. The model has two fold features. One is to establish a self-memorization equation linked with DHGM, that could extract useful information from past data series and realize updating of hydrological dynamic process. The other is that this model has higher efficiency relative to original hydrological model without self-memory func- tion. This approach was applied to river flow forecasting of two representative basins in Tunxi of South China and Daqinggou of North China. It is shown that this hybrid method has satisfactory forecasting accuracy by examination of both calibration and validation.展开更多
文摘针对当前智能电表现场检定效率低、人力成本高、实时性差、无法全量监测等问题,提出一种基于动态线损和渐消记忆递推最小二乘法(dynamic line loss and fading memory recursive least square,DLL-FMRLS)的智能电表误差在线估计算法。首先,通过分析台区线损与供电量之间的关系,对传统模型进行改进,提出动态线损误差模型,该模型中线损可随实际供电量变化,使得模型获得的误差估计值更接近实际值;然后,利用FMRLS算法求解动态线损误差模型,以获得智能电表运行误差;最后,根据某省电网公司的实际数据对算法现场验证。结果结果表明,与列文伯格–马夸尔特(Levenberg-Marquardt, LM)算法和限定记忆最小二乘(limited memory recursive least squares,LMRLS)算法相比,所提算法可以有效提高智能电表的误差估计的准确度。
基金Supported by the National Natural Science Foundation of China (Grant No. 40671035)the Special Fund of Ministry of Science & Technology of China (Grant No. 2006DFA21890)
文摘This paper addresses a problem of flood forecasting with the self-memory function. Considering flood forecasting’s uncertainty and updating demand, a hybrid hydrological model, namely Differential Hy- drological Grey Model with self-memory function (DHGM-SM), is developed. The model has two fold features. One is to establish a self-memorization equation linked with DHGM, that could extract useful information from past data series and realize updating of hydrological dynamic process. The other is that this model has higher efficiency relative to original hydrological model without self-memory func- tion. This approach was applied to river flow forecasting of two representative basins in Tunxi of South China and Daqinggou of North China. It is shown that this hybrid method has satisfactory forecasting accuracy by examination of both calibration and validation.
