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首页> 外文期刊>Electric power systems research >Determination of optimal supercapacitor-lead-acid battery energy storage capacity for smoothing wind power using empirical mode decomposition and neural network
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Determination of optimal supercapacitor-lead-acid battery energy storage capacity for smoothing wind power using empirical mode decomposition and neural network

机译：基于经验模态分解和神经网络的风力发电最佳超级电容器铅酸蓄电池储能容量确定

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摘要

A new approach to determine the capacity of a supercapacitor-battery hybrid energy storage system (HESS) in a microgrid is presented. The microgrid contains significant wind power generation and the HESS is to smooth out the fluctuations in the delivered power to load. Using empirical mode decomposition (EMD) technique, historical wind power data is firstly analyzed to yield the intrinsic mode functions (IMF) of the wind power. From the instantaneous frequency-time profiles of the IMF, the gap frequency is identified and utilized in the design of filters which decompose the wind power into the high- and low-frequency components. Power smoothing is then achieved by regulating the output powers of the supercapacitors and batteries to negate the high- and low-frequency fluctuating power components, respectively. The degree of smoothness of the resulting power delivered to load is assessed in terms of a newly developed level of smoothness (LOS) criteria. A neural network model is then utilized to determine the storage capacity of the HESS through the minimization of an objective function which contains the costs of the HESS and that associated with the achieved LOS. Example of the design of a supercapacitor-lead acid battery HESS for an existing wind farm demonstrates the efficacy of the proposed approach. (C) 2015 Elsevier B.V. All rights reserved.
机译：提出了一种确定微电网中超级电容器-电池混合储能系统（HESS）容量的新方法。微电网包含大量风力发电，HESS可以消除向负载传递的电力波动。利用经验模态分解（EMD）技术，首先对历史风电数据进行分析，以得出风电的固有模式函数（IMF）。根据IMF的瞬时频率-时间曲线，可以识别出间隙频率并将其用于将风能分解为高频和低频分量的滤波器的设计中。然后，通过调节超级电容器和电池的输出功率以分别抵消高频和低频波动的功率分量来实现功率平滑。根据新开发的平滑度（LOS）标准评估传递给负载的最终功率的平滑度。然后利用神经网络模型通过使目标函数最小化来确定HESS的存储容量，该目标函数包含HESS的成本以及与实现的LOS相关的成本。用于现有风电场的超级电容器铅酸电池HESS的设计示例证明了该方法的有效性。 （C）2015 Elsevier B.V.保留所有权利。

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