Abstract
Battery storage is usually applied in the renewable energy (RE) plant for improving RE utilisation and integration ability to the power grid. Battery health status detection is essential for plant reliable, safe and efficient operation. This study presents a battery anomaly and degradation diagnosis method based on data mining technology. First, battery cell characteristic vectors are set and classified under charging, discharging and standing states, respectively. Synthetic characteristic vectors are formed for abnormal battery cell identification by K-means algorithm. Second, battery degradation degree is estimated by searching and comparing with ideal performance curve under the same running status in the historical database. Finally, taking an actual renewable energy plant with battery storage, for example, the results verified the correctness and validity of the proposed method.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
