The active power output of a wind power system needs regulation due to the stochastic nature of wind speed. A flywheel energy storage system (FESS) is a viable option for active power regulation in a wind power plant. An efficient energy management system (EMS) for FESS is required for healthy operation of the overall connected system. A wind speed forecasting based EMS has been proposed in this paper. It utilizes the repeated wavelet transform based ARIMA model for very short-term wind speed forecasting, which has been proven to be better than the methods that exist in the literature. An artificial neural network based model is used to translate the forecasted wind speed to instantaneous wind power output. Considering the initial energy of FESS, an optimization technique has been used to calculate the speed command to be given to FESS for a given energy exchanged with the grid. The proposed control approach extends FESS usability as an energy exchange system for a period of large change in wind speed where the normal control approach saturates the FESS speed. The feasibility of the proposed EMS algorithm has been tested in MATLAB/Simulink.

中文翻译：

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基于预测的与风力发电厂相连的飞轮储能系统的能量管理

由于风速的随机性，风电系统的有功功率输出需要调节。飞轮储能系统 (FESS) 是风力发电厂有功功率调节的可行选择。整个连接系统的健康运行需要一个高效的 FESS 能源管理系统 (EMS)。本文提出了一种基于风速预测的EMS。它利用基于重复小波变换的 ARIMA 模型进行极短期风速预测，已被证明优于文献中存在的方法。基于人工神经网络的模型用于将预测的风速转换为瞬时风功率输出。考虑到 FESS 的初始能量，对于与电网交换的给定能量，已使用优化技术计算要提供给 FESS 的速度命令。所提出的控制方法扩展了 FESS 作为能量交换系统的可用性，用于风速发生较大变化的时期，此时正常控制方法使 FESS 速度饱和。所提出的 EMS 算法的可行性已经在 MATLAB/Simulink 中进行了测试。