Diagnosis of a battery energy storage system based on principal component analysis

Edison Banguero, Antonio Correcher, Ángel Pérez-Navarro, Emilio García and Andrés Aristizabal

Renewable Energy, 2020, vol. 146, issue C, 2438-2449

Abstract: This paper proposes the use of principal component analysis (PCA) for the state of health (SOH) diagnosis of a battery energy storage system (BESS) that is operating in a renewable energy laboratory located in Chocó, Colombia. The presented methodology allows the detection of false alarms during the operation of the BESS. The principal component analysis model is applied to a parameter set associated to the capacity, internal resistance and open circuit voltage of a battery energy storage system. The parameters are identified from experimental data collected daily. The PCA model retains the first 5 components that collect 80.25% of the total variability. During the test under real operation contidions, PCA diagnosed a degradation of state of health fastest than the comercial battery controller. A change in the charging modes lead to a battery recovery that was also monitored by the proposed algortihm, and control actions are proposed that lead the BESS to work in normal conditions.

Keywords: Diagnosis; Battery energy storage system; Principal component analysis; State of health (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:146:y:2020:i:c:p:2438-2449

DOI: 10.1016/j.renene.2019.08.064

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