基于经验模态分解和神经网络的微网混合储能容量优化配置 = Capacity optimization of hybrid energy storage systems in microgrid using empirical mode decomposition and neural network - 孙承晨 Sun Chengchen (2018)
提出一种针对独立微网的超级电容／蓄电池混合储能系统(HESS)的容量优化方法。运用经验模态分解技术,将一段记录完全的非平稳风功率分解成为若干固有模态函数(IMF)。在各固有模态函数的瞬时频率-时间曲线的基础上,通过“分频频率”将原始风功率分解成高频与低频2部分,并分别采用HESS中的超级电容和蓄电池来平抑风功率的高频、低频波动分量。平抑后输入负荷侧的功率平滑度可通过平滑度指标量化。采用神经网络模型优化HESS的容量,通过成本和平滑度指标之间的折中实现HESS的容量优化配置。基于某风电场实测数据的仿真实验验证了所提方法的有效性。 A new approach to determine the capacity of a supercapacitor-battery hybrid energy storage system (HESS) in an independent microgrid is presented. Using empirical mode decomposition technique, the historical non-stationary wind power is firstly analyzed to yield some intrinsic mode functions (IMFs) of wind power. From the instantaneous frequency-time profiles of the IMF, the so-called gap frequency is identified and allows wind power to be decomposed into high and low frequency components. Power smoothing is then achieved by regulating the output power of the supercapacitor and battery to mitigate the high and lower frequency fluctuating components of power 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 utilized to determine the capacity of the HESS through finding a compromise between the cost of the system and the LOS of the power. Simulation results, based on a set of data obtained from a real wind farm, demonstrate the efficiency of the proposed approach.