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

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.

• Tipo de recurso: Article of journal
• Fecha de publicación: 2020
• Institución: Universidad de Bogotá Jorge Tadeo Lozano
• Repositorio: Expeditio: repositorio UTadeo
• Idioma: spa
• OAI Identifier: oai:expeditiorepositorio.utadeo.edu.co:20.500.12010/27482
• Acceso en línea: https://doi.org/10.1016/j.renene.2019.08.064; http://hdl.handle.net/20.500.12010/27482; http://expeditiorepositorio.utadeo.edu.co
• Palabra clave: Diagnosis; Battery energy storage system; Principal component analysis; Energía solar; Generadores de energía fotovoltaica; Células solares
• License: Abierto (Texto Completo)