Data driven battery anomaly detection based on shape based clustering for the data centers class

被引：39

作者：

Haider, Syed Naeem ^{[1
]}

Zhao, Qianchuan ^{[1
]}

Li, Xueliang ^{[1
]}

机构：

[1] Tsinghua Univ, Dept Automat & BNRist, Ctr Intelligent & Networked Syst CFINS, Beijing 100084, Peoples R China

来源：

JOURNAL OF ENERGY STORAGE | 2020年 / 29卷

基金：

中国国家自然科学基金;

关键词：

Data centres; Data driven model; K shape-based clustering; Anomaly detection; Battery health; LEAD-ACID-BATTERIES; LITHIUM-ION BATTERIES; STATE-OF-HEALTH; TEMPERATURE; RESISTANCE; ALGORITHM; SYSTEMS; STORAGE;

D O I：

10.1016/j.est.2020.101479

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

Batteries are a significant part of data centers, which ensure the uninterrupted working of a data center. Using online measurement to find out odd batteries in data centers is challenging due to lack of training samples since there are only a very few full charging-discharging cycles during the lifetime of batteries. In this paper, a new battery anomaly detection method based on time series clustering is proposed. This method uses only battery operating data and does not depend on offline testing data, thus provides a way to improve the maintenance efficiency and lessen batteries operating risks in data centers. Effectiveness of the proposed method is demonstrated and confirmed by a case study for 40 batteries in an existent data center.

引用

页数：10

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