储能科学与技术 ›› 2023, Vol. 12 ›› Issue (9): 2937-2945.doi: 10.19799/j.cnki.2095-4239.2023.0332

• 储能测试与评价 • 上一篇    下一篇

基于运行数据的储能电站电池组一致性评估方法

高欣1(), 王若谷1, 高文菁2, 邓泽军1, 梁睿祺3, 杨騉3   

  1. 1.国网陕西省电力科学研究院,陕西 西安  710054
    2.中国矿业大学,江苏 徐州 221116
    3.西安交通大学电气工程学院,陕西 西安 710049
  • 收稿日期:2023-05-09 修回日期:2023-06-20 出版日期:2023-09-05 发布日期:2023-09-16
  • 通讯作者: 高欣 E-mail:18209183315@163.com
  • 作者简介:高欣(1993—),女,硕士,从事储能系统研发,E-mail:18209183315@163.com

Consistency evaluation method of battery pack in energy storage power station based on running data

Xin GAO1(), Ruogu WANG1, Wenjing GAO2, Zejun DENG1, Ruiqi LIANG3, Kun YANG3   

  1. 1.Shanxi Electric Power Research Institute of State Electricity Network, Xi'an 710054, Shaanxi, China
    2.China University of Mining and Technology, Xuzhou 221116, Jiangsu, China
    3.Department of Electrical Engineering, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China
  • Received:2023-05-09 Revised:2023-06-20 Online:2023-09-05 Published:2023-09-16
  • Contact: Xin GAO E-mail:18209183315@163.com

摘要:

本工作以大容量磷酸铁锂电池储能电站为研究对象,立足于储能系统中处于工程场景的电池组日常运行数据。首先,根据电池运行数据分析表征锂离子电池电压、温度的一致性关键参量;其次,提取能够有效反映电池组一致性的评估特征;最后,基于此类特征,将储能电站的一致性分析分为两个层次,提出了针对站内大规模电池组的一致性分析算法以及基于DBSCAN (density-based spatial clustering of applications with noise) 聚类的电池组内异常单体电池筛选算法。结果表明,所提算法能够高效地获取储能电站运行数据中与电池组一致性相关的关键电气特征量,准确判断储能系统内电池组的一致性情况以及定位可能出现故障的单体电池。本研究有助于判断在工程场景中大规模电池组的一致性状态,并能及时准确地筛选出异常单体电池,保障储能电站电池组的安全性。

关键词: 储能电站, 锂离子电池, DBSCAN聚类算法, 一致性评估

Abstract:

This study takes a large-capacity power station of lithium iron phosphate battery energy storage as the research object, based on the daily operation data of battery packs in the engineering scene of energy storage systems. First, the key parameters characterizing the voltage and temperature consistency of Li-ion batteries were analyzed according to the operating data of the battery. Second, the evaluation features that can effectively reflect the battery pack consistency were extracted. Finally, based on such characteristics, the consistency analysis of the energy storage power station was divided into two levels, and the consistency analysis algorithm was proposed for large-scale battery packs in the station. Furthermore, a screening algorithm was proposed for abnormal cells in battery packs based on density-based spatial clustering of applications with noise (DBSCAN) clustering. The results showed that the proposed algorithm could efficiently obtain the key electrical characteristics related to the battery pack consistency in the operation data of the energy storage power station. Moreover, it could accurately judge the battery pack consistency in the energy storage system and locate the single battery that may fail. This study is helpful in judging the consistent state of large-scale battery packs in engineering scenarios. It can also timely and accurately screen out abnormal single batteries to ensure the battery packs' safety in energy storage power stations.

Key words: energy storage power station, lithium-ion batteries, DBSCAN clustering algorithm, consistency evaluation

中图分类号: