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

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

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.

Suggested Citation

  • Banguero, Edison & Correcher, Antonio & Pérez-Navarro, Ángel & García, Emilio & Aristizabal, Andrés, 2020. "Diagnosis of a battery energy storage system based on principal component analysis," Renewable Energy, Elsevier, vol. 146(C), pages 2438-2449.
    • Handle: RePEc:eee:renene:v:146:y:2020:i:c:p:2438-2449
    DOI: 10.1016/j.renene.2019.08.064
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    3. Ma, Mina & Li, Xiaoyu & Gao, Wei & Sun, Jinhua & Wang, Qingsong & Mi, Chris, 2022. "Multi-fault diagnosis for series-connected lithium-ion battery pack with reconstruction-based contribution based on parallel PCA-KPCA," Applied Energy, Elsevier, vol. 324(C).
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