In order to improve the accuracy of wind power prediction (WPP), we propose a WPP based on multivariate phase space reconstruction (MPSR) and multivariate linear regression (MLR). Firstly, the multivariate time series (TS) are constructed through reasonable selection of wind power and weather factors, which are closely associated with wind power. Secondly, the phase space of the multivariate time series is reconstructed based on the chaos theory and C-C method. Thirdly, an auto regression model for multivariate phase space is created by regarding phase variables as state variables, and the very-short-term wind power is predicted by using a multi-linear regression algorithm. Finally, a parallel algorithm based on map/reduce is presented to improve computing speed. A cloud computing platform, Hadoop consisting of five nodes, is established as a matter of convenience, followed by the prediction of wind power of a wind farm in the Hunan province of China. The experimental results show that the model based on MPSR and MLR is more accurate than both the continuous method and the simple approximation method, and the parallel algorithm based on map/reduce effectively accelerates the computing speed.

With the rapid development of wind generation, the box substation is widely applied in the wind farm. However the box substation is more prone to break down than the conventional power transformer, due to the uncertainty of wind power. It is difficult for the existing transformer condition assessment to fulfill the requirement of ''few people on duty'' operation model, which is promoted by both the domestic and foreign wind farms. To improve the safety and reliability of the wind generation, an on-line condition assessment of the box substation in wind farm based on hypothesis testing is proposed. Firstly, an ensemble learning based on least squares support vector regression (EL-LSSVR) is proposed. Secondly, EL-LSSVR is applied to the historical data of the normal box substation, to establish a normal hot spot temperature model. Then the normal hot spot temperature model learns from the real-time data of the evaluated box substation on-line, to establish a real-time hot spot temperature model. Thirdly, the hypothesis testing is employed to analyze the significance between the real-time hot spot temperature and the normal hot spot temperature, which are generated by the real-time hot spot temperature model and the normal hot spot temperature model respectively. The condition assessment of the box substation is implemented according to the significance. Finally, the proposed method is validated by the condition assessment experiment of the box substation, which is installed at a wind farm in South China.