篡改电表数据是一种典型的窃电行为。针对此类窃电行为,现有的检测方法需要标记好的数据集或额外的电力系统状态信息,这在现实中很难获得或即使获得也与实际值存在较大误差。因此,利用较低维度的数据来实现对此类窃电行为进行检测的方...篡改电表数据是一种典型的窃电行为。针对此类窃电行为,现有的检测方法需要标记好的数据集或额外的电力系统状态信息,这在现实中很难获得或即使获得也与实际值存在较大误差。因此,利用较低维度的数据来实现对此类窃电行为进行检测的方法亟待深入研究。创新性地结合最大互信息系数(maximum information coefficient,MIC)技术和基于密度峰值的快速聚类算法提出了一种新的融合检测方法。该方法利用最大互信息系数度量管理线损与用户特定行为之间的相关性,采用CFSFDP定位异常用电用户,适用性强,能够检测多种不同类型的窃电行为。最后利用爱尔兰智能电表数据集进行了算法验证,结果证明了该方法的良好性能。展开更多
Short-term load forecasting of regional distribution network is the key to the economic operation of smart distribution systems,which not only requires high accuracy and fast calculation speed,but also has a diversity...Short-term load forecasting of regional distribution network is the key to the economic operation of smart distribution systems,which not only requires high accuracy and fast calculation speed,but also has a diversity of influential factors and strong randomness.This paper proposes a short-term load forecasting model for regional distribution network combining the maximum information coefficient,factor analysis,gray wolf optimization,and generalized regression neural network(MIC-FA-GWO-GRNN).To screen and decrease the dimension of the multiple-input features of the short-term load forecasting model,MIC is first used to quantify the non-linear correlation between the load and input features,and to eliminate the ineffective features,and then FA is used to reduce the dimension of the screened input features on the premise of preserving the main information of input features.After that the high-precision short-term丨oad forecasting based on GWO-GRNN model is realized.GRNN is used to regressively analyze the input features after screening and dimension reduction,and the parameter of GRNN is optimized by using the GWO,which has strong global searching ability and fast convergence.Finally a case study of a regional distribution network in Tianjin,China verifies the accuracy and applicability of the proposed forecasting model.展开更多
文摘篡改电表数据是一种典型的窃电行为。针对此类窃电行为,现有的检测方法需要标记好的数据集或额外的电力系统状态信息,这在现实中很难获得或即使获得也与实际值存在较大误差。因此,利用较低维度的数据来实现对此类窃电行为进行检测的方法亟待深入研究。创新性地结合最大互信息系数(maximum information coefficient,MIC)技术和基于密度峰值的快速聚类算法提出了一种新的融合检测方法。该方法利用最大互信息系数度量管理线损与用户特定行为之间的相关性,采用CFSFDP定位异常用电用户,适用性强,能够检测多种不同类型的窃电行为。最后利用爱尔兰智能电表数据集进行了算法验证,结果证明了该方法的良好性能。
基金supported by the National Key Research and Development Program of China(2017YFB0903300)Research Program of State Grid Corporation of China(SGTYHT/16-JS-198)the National Natural Science Foundation of China(51807134).
文摘Short-term load forecasting of regional distribution network is the key to the economic operation of smart distribution systems,which not only requires high accuracy and fast calculation speed,but also has a diversity of influential factors and strong randomness.This paper proposes a short-term load forecasting model for regional distribution network combining the maximum information coefficient,factor analysis,gray wolf optimization,and generalized regression neural network(MIC-FA-GWO-GRNN).To screen and decrease the dimension of the multiple-input features of the short-term load forecasting model,MIC is first used to quantify the non-linear correlation between the load and input features,and to eliminate the ineffective features,and then FA is used to reduce the dimension of the screened input features on the premise of preserving the main information of input features.After that the high-precision short-term丨oad forecasting based on GWO-GRNN model is realized.GRNN is used to regressively analyze the input features after screening and dimension reduction,and the parameter of GRNN is optimized by using the GWO,which has strong global searching ability and fast convergence.Finally a case study of a regional distribution network in Tianjin,China verifies the accuracy and applicability of the proposed forecasting model.
