电池储能系统SOC神经网络融合估计方法  

SOC Neural Network Fusion Estimation Method for Battery Energy Storage System

作　　者：孙玉树 李宏川 王波[3] 贾东强[3] 裴玮[1,2] 唐西胜 SUN Yushu;LI Hongchuan;WANG Bo;JIA Dongqiang;PEI Wei;TANG Xisheng(Institute of Electrical Engineering,Chinese Academy of Sciences,Beijing 100190,China;University of Chinese Academy of Sciences,Beijing 100049,China;State Grid Beijing Electric Power Company,Beijing 100031,China)

机构地区：[1]中国科学院电工研究所,北京100190 [2]中国科学院大学,北京100049 [3]国网北京市电力公司,北京100031

出　　处：《湖南大学学报（自然科学版）》2023年第10期31-40,共10页Journal of Hunan University:Natural Sciences

基　　金：国家重点研发计划项目(2021YFB2402002);中国科学院青年创新促进会项目(2023000018)。

摘　　要：为了更好地获取电池储能系统当前的运行状态,提出了基于神经网络融合的电池储能系统SOC估计方法.首先,对比分析了前馈(BP)、门控循环单元(GRU)和长短时记忆(LSTM)神经网络算法的优劣,BP计算时间较短,LSTM对时序数据估计精度较高;然后,利用KL散度、皮尔逊相关系数和灰色关联度分析了不同输入参量和SOC的相关程度,并和LSTM估计结果相比对,筛选出对电池储能系统SOC影响较大的特征参量;最后,应用经验模态分解算法将SOC数据分解为多个分量,利用样本熵将分量聚合为高低两个频段,进而应用BP、LSTM神经网络算法分频段估计,和单一策略相比,该方法在提高SOC估计精度的同时,减少了计算时间.To better obtain the current operating state of the battery energy storage system,a state of charge(SOC) evaluation method of the battery energy storage system based on neural network fusion is proposed.First,the advantages and disadvantages of back-propagation(BP),gated recurrent unit(GRU),and long and short-term memory(LSTM) neural network algorithms are compared.The calculation time of BP is usually short,while the estimation accuracy of LSTM for temporal data is high.Then the correlation degree between different input parameters and SOC is analyzed by KL divergence,Pearson correlation coefficient,and grey correlation degree.Compared with the LSTM estimation results,the characteristic parameters that have a greater impact on the SOC of the battery energy storage system are selected.Finally,the empirical mode decomposition algorithm is applied to decompose the SOC data into multiple components,and the sample entropy is used to aggregate the components into high and low-frequency bands.BP and LSTM neural network algorithms are used to estimate SOC in different frequency bands.Compared with a single strategy,the proposed method not only improves the estimation accuracy of SOC,but also reduces the calculation time.

关 键 词：电池储能系统 SOC融合估计 相关性分析 经验模态分解 样本熵 

分 类 号：TM614[电气工程—电力系统及自动化]