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 GrangerTest in Wind Power

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.文中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,对缺失点后的数据建立目标约束,综合考虑缺失数据段前、后的数据,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.