Abstract. This paper aims to develop and apply a hybrid model of two data analytical methods, multiple linear regressions and least square (MLR&LS), for ultra-short-term wind power prediction (WPP), for example taking, Northeast China electricity demand. The data was obtained from the historical records of wind power from an offshore region, and from a wind farm of the wind power plant in the areas. The WPP achieved in two stages: first, the ratios of wind power were forecasted using the proposed hybrid method, and then the transformation of these ratios of wind power to obtain forecasted values. The hybrid model combines the persistence methods, MLR and LS. The proposed method included two prediction types, multipoint prediction and single-point prediction. WPP is tested by applying different models such as autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA) and artificial neural network (ANN). By comparing results of the above models, the validity of the proposed hybrid model is confirmed in terms of error and correlation coefficient.Comparison of results confirmed that the proposed method works effectively. Additional, forecasting errors were also computed and compared, to improve understanding of how to depict highly variable WPP and the correlations between actual and predicted wind power.

Abstract. Power flow analysis (PFA) is one of the most important issues of power system analysis and design. This paper aims at developing and applying a high-speed sampling method to produce large-capacity sample set based on pattern feature vector with layer structure (PFV with LS) of IEEE 14-bus system data analytical methods. In this study, sampling based pattern vectors are constructed in three layers. The first and second layer vectors are very low dimensional, and the sampling of the third layer is in parallel for each local area. Samples generated by this sampling method can be applied to the calculation of probabilistic load flow (PLF) and probabilistic static security assessment. Simulation results show that the proposed method can improve efficiency of PLF analysis and probabilistic static security assessment. The advantages of using PLF based on PFV with LS as the power flow limit can minimize the complicated mathematical equations. Furthermore, the algorithm is very simple and accurate, especially when the system connected to the wind power. IntroductionIn recent years, power system operation faces a new challenge due to deregulation and restructuring of electricity markets. The PFA is necessary for planning, operation, economic scheduling and exchange of power between utilities. This paper extends the application of the security assessment index. It aims to show the detailed mathematical model of the uncertainties for the grid connected wind farms. Power grid contains other power generation and wind power uncertainties. However, the effects of the uncertainty can be reduced by accurate calculation.Power flow (PF) is the very important tool for the analysis power systems, and it is used in operational and planning. The objective of PF is calculating unspecified bus voltage angles and magnitudes, active and reactive powers, as well as line loadings and their associated real and reactive losses for certain generation and load conditions. Different mathematical techniques have been used to examine PF analysis. These techniques are Newton-Raphson (NR), Fast-Decoupled (FD), and GaussSeidel (GS) methods.One of the best PF methods is NR method. The NR process usually converges faster than other methods, but it takes longer computational time per iteration. However, the proposed method in this paper is a concern in accounting for any kind of limit generation, not only line and curve but also the combination of line and curve with discontinuities.The load flow (LF) studies are performed for power system planning, operation, and control. LF studies data is also used for contingency analysis, outage security assessment, as well as for optimal dispatching and stability. The LF problem has received more attention than all the other power system