本論文提出以遞歸模糊類神經網路為架構之智慧型控制器應用於電池儲能系統之中來實現風場功率平滑化之控制。電池儲能系統為兩級式架構，由雙向直流至直流轉換器和三階層變流器組成。風場功率平滑化控制的目的是減緩風場功率的波動性，解決風場輸出功率因變化劇烈而不適合直接導入市電的問題，以維持電力系統之供電品質與穩定性。另外，本論文所提出的風場功率平滑化控制方法也能減少所需電池儲能系統所需容量之大小，進而節省所需成本。在不同風速變化之情況下，本論文所提出之風場功率平滑化控制方法能達成以減緩風場功率的波動性以及減少所需電池儲能系統所需容量之大小。本論文將詳細推導遞歸模糊類神經網路控制器之網路架構與線上學習法則，另一方面也利用PSIM軟體進行電池儲能系統之相關模擬，以證明其在實作時之可行性，最後本論文透過實作結果以驗證所提出控制方法之有效性。;This thesis presents an intelligent controller based on the recurrent fuzzy neural network (RFNN) algorithm for the battery energy storage system (BESS) using in the wind farm power smoothing application. A two-stage BESS is composed of a bidirectional DC/DC converter and a three-level inverter. The purpose of wind farm power smoothing control is to mitigate the wind farm power fluctuation problem when it is fed directly to the grid. The proposed wind farm power smoothing control method can maintain the quality and stability of the power system and reduce the required BESS capacity and the investment cost. Moreover, the network structure and on-line learning algorithm of the RFNN are introduced in detail. Additionally, some simulation results are given to verify the design of the BESS via PSIM. Finally, the feasibility of the proposed control scheme is verified using some experiment results.
