基于极限学习机与格兰杰检验的风电功率缺失数据补齐  

The Complement of The Missing Data Based on the Extreme Learning Machine and Granger Test in Wind Power

作　　者：杨茂[1] 王金鑫 都键 Yang Mao;Wang Jinxin;Du Jian(Jilin Provincial Key Laboratory of Modern Power System Simulation and Control,Northeast Electric Power University,Jilin Jilin 132012;Institute for Economics and Technology of Chifeng Power Supply Company,Chifeng Inner Mongolia 024000)

机构地区：[1]东北电力大学现代电力系统仿真控制与绿色电能新技术吉林省重点实验室,吉林吉林132012 [2]国网赤峰供电公司经济技术研究所,内蒙古赤峰024000

出　　处：《东北电力大学学报》2019年第4期27-34,共8页Journal of Northeast Electric Power University

基　　金：国家自然科学基金(51307017);吉林省产业技术与专项开发项目(2014Y124)

摘　　要：风电场输出功率的完整性对于研究人员后续的数据分析工作具有重要的意义.分别从时间和空间入手,提出一种基于极限学习机与格兰杰检验的风电功率缺失数据补齐方法.首先,提出一种输出权重优化的极限学习机,对缺失数据段前后的数据建立目标约束,进行时间上的补齐;然后,将格兰杰因果检验运用在数据补齐模型中,找到空间上与功率缺失风机对应的因果风机,进行空间上数据的补齐;最后,运用多重插补法,将两组数据补齐结果进行组合.就单机与多机的数据缺失情况进行补齐,通过与自适应神经模糊推理模型进行效果比较,补齐效果有明显的提高.The integrity of the data of wind power has the vital significance of data analysis for the researchers later work.According to the perspective of the time and space,we put forward a kind of missing data filling model based on spatial and temporal distribution.First,we put forward a kind of output weight optimization extreme learning machine,establish a goal through the points after missing data,consider the data before and after the missing data,comleting the missing data in the temporal.Then,we use the granger causality test in this model,find the corresponding wind turbines,comleting the missing data in the spatial.Finally we use multiple imputation method,combination the two groups of data.With the situation single or more wind turbines losing data,we compare the results with ANFIS model,the filling effect is significantly improved.

关 键 词：数据补齐 时空分布 极限学习机 格兰杰因果检验 多重插补 

分 类 号：TP29[自动化与计算机技术—检测技术与自动化装置]