Đang chuẩn bị liên kết để tải về tài liệu:
Wind Farm Impact in Power System and Alternatives to Improve the Integration P10

Công nghệ cơ khí thường tạo ra các giả lập mô phỏng hoạt động của các đối tượng, như quy trình chế tạo thực tế theo trình tự tối ưu hóa sự thực hiện, hiệu quả kinh tế và chi phí năng lượng trước khi quyết định lựa chọn một thiết kế cụ thể. | 214 Wind Farm - Impact in Power System and Alternatives to Improve the Integration any time but in the Electricity Market the hourly average is the required to RSE agents. The proposed reference model for wind power forecasting by Madsen Madsen 2004 is applied for hourly average power in nowcasting as the required in the Spanish regulation as Ph 2 Aq Ph 1 - Aq P 3 where Aq and P are parameters computed from large-term training information. This reference model which we can call as improved persistence or Wiener persistence is harder to beat because is based in the shortest-term information Ph and in the longest-term information P. The basic theory for using ANN in prediction its architectures and algorithms are in the area of adaptive and predictive linear filterMandic Chambers 2001 . The use of ANN has generated generalizations that has introduced improvements in the original linear models by allowing the construction of nonlinear predictive systems. The relationship between ANN in special recurrent architectures with linear predictive systems as ARMA allows nonlinear generalizations of previous statistical linear approaches. A generalization of recurrent ANN is the multilayer recurrentLi 2003 Mandic Chambers 2001 . In the wind power forecasting the problem can be formulated by using Feed Forward FNN without feedback or Recurrent RNN ones Ph 2 F Vh . . Vh-n 1 Ph . . Ph-m 1 4 The used training procedure was the Bayesian regularization Foresee Hagan 1997 MacKay 1992 which updates the weight and bias values according to the Levenberg-Marquardt Levenberg 1944 Marquardt 1963 optimization procedure. It uses as goal function a combination of squared errors and weights and then determines the correct combination so as to produce a network that generalizes well. The Bayesian regularization implementation that has been used is the implemented in the training function trainbr of the Neural Networks Toolbox of MATLABDemuth et al. 2008 . The NARX architecture have been used .