基于粗糙集的电池储能电站海量数据处理方法

doi:10.11930/j.issn.1004-9649.202102047

中国电力 ›› 2022, Vol. 55 ›› Issue (2): 44-50,208.DOI: 10.11930/j.issn.1004-9649.202102047

• 国家“十三五”智能电网重大专项专栏：（十一）新型储能与能源转化关键技术 • 上一篇下一篇

基于粗糙集的电池储能电站海量数据处理方法

陈娟, 惠东, 范茂松, 胡娟, 褚永金

中国电力科学研究院有限公司, 北京 100085

收稿日期:2021-02-18 修回日期:2021-10-25 出版日期:2022-02-28 发布日期:2022-02-23
作者简介:陈娟（1982—），女，硕士，工程师，从事新能源储能电池管理技术研究，E-mail：wucaichunjuan@aliyun.com
基金资助:
国家电网有限公司科技项目（DG71-19-022）。

Massive Data Processing Method for Battery Energy Storage Power Stations Based on Rough Sets

CHEN Juan, HUI Dong, FAN Maosong, HU Juan, CHU Yongjin

China Electric Power Research Institute, Beijing 100085, China

Received:2021-02-18 Revised:2021-10-25 Online:2022-02-28 Published:2022-02-23
Supported by:
This work is supported by Science and Technology Project of SGCC (No.DG71-19-022).

摘要/Abstract

摘要： 锂离子电池的储能电站在运行过程中上传的数据量庞大，数据采样率高，增大了实时在线评估工作的难度。如何提取并压缩有效数据，同时保证压缩后的数据具有保真性，成为数据挖掘预处理所面对的主要问题。针对上述问题，选取储能电站特定工况下一个电池簇的数据，利用粗糙集方法，对数据的属性进行约简，根据正态分布的$ 2\sigma $原则对属性值进行二元逻辑划分，将单体电池划分为频繁检测对象和普通检测对象，压缩了在线处理的数据量。最后，对同一电池簇20天内不同工况数据进行验证，证明了该处理方法的有效性。

关键词: 储能电站, 数据处理, 粗糙集, 属性约简, 统计学

Abstract: During the operation of energy storage power stations with lithium-ion batteries, huge amounts of uploaded data and high-frequency data sampling increase the difficulty of online real-time evaluation. How to extract and compress effective data and ensure the fidelity of the compressed data has become the main content of preprocessing in data mining. Given the above problems, the attributes of the data from a battery cluster are first reduced by the method using rough set theory. The battery cluster is selected from the energy storage power station under a specific working condition. Then, according to the $ 2\sigma $ principle of normal distribution, the attribute values are divided on the basis of binary logic, and the battery cells are classified into frequent detection objects and infrequent detection objects, which reduces the amount of data processed online. Finally, the data of the same battery cluster under different working conditions in 20 days are taken to verify the effectiveness of the processing method.

Key words: energy storage power station, data processing, rough set, attribute reduction, statistics

陈娟, 惠东, 范茂松, 胡娟, 褚永金. 基于粗糙集的电池储能电站海量数据处理方法[J]. 中国电力, 2022, 55(2): 44-50,208.

CHEN Juan, HUI Dong, FAN Maosong, HU Juan, CHU Yongjin. Massive Data Processing Method for Battery Energy Storage Power Stations Based on Rough Sets[J]. Electric Power, 2022, 55(2): 44-50,208.

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基于粗糙集的电池储能电站海量数据处理方法

Massive Data Processing Method for Battery Energy Storage Power Stations Based on Rough Sets

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Massive Data Processing Method for Battery Energy Storage Power Stations Based on Rough Sets

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