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

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.