- -

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

RiuNet: Institutional repository of the Polithecnic University of Valencia

Share/Send to

Cited by

Statistics

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

Show full item record

Banguero-Palacios, E.; Correcher Salvador, A.; Pérez-Navarro Gómez, Á.; García Moreno, E.; Aristizabal, A. (2020). Diagnosis of a battery energy storage system based on principal component analysis. Renewable Energy. 146:2438-2449. https://doi.org/10.1016/j.renene.2019.08.064

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/168596

Files in this item

Item Metadata

Title: Diagnosis of a battery energy storage system based on principal component analysis
Author: Banguero-Palacios, Edison Correcher Salvador, Antonio Pérez-Navarro Gómez, Ángel García Moreno, Emilio Aristizabal, Andrés
UPV Unit: Universitat Politècnica de València. Departamento de Ingeniería de Sistemas y Automática - Departament d'Enginyeria de Sistemes i Automàtica
Universitat Politècnica de València. Instituto de Ingeniería Energética - Institut d'Enginyeria Energètica
Issued date:
Abstract:
[EN] 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, ...[+]
Subjects: Diagnosis , Battery energy storage system , Principal component analysis , State of health
Copyrigths: Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
Source:
Renewable Energy. (issn: 0960-1481 )
DOI: 10.1016/j.renene.2019.08.064
Publisher:
Elsevier
Publisher version: https://doi.org/10.1016/j.renene.2019.08.064
Project ID:
Universidad Tecnológica del Chocó/BPIN 2013000100285
Thanks:
The authors would like to acknowledge the research project "Implementacion de un programa de desarrollo e investigacion de energias renovables en el departamento del Choco, BPIN 2013000100285 (in Spanish)" and the Universidad ...[+]
Type: Artículo

References

Perera, A. T. D., Attalage, R. A., Perera, K. K. C. K., & Dassanayake, V. P. C. (2013). Designing standalone hybrid energy systems minimizing initial investment, life cycle cost and pollutant emission. Energy, 54, 220-230. doi:10.1016/j.energy.2013.03.028

Krieger, E. M., Cannarella, J., & Arnold, C. B. (2013). A comparison of lead-acid and lithium-based battery behavior and capacity fade in off-grid renewable charging applications. Energy, 60, 492-500. doi:10.1016/j.energy.2013.08.029

Aksakal, C., & Sisman, A. (2018). On the Compatibility of Electric Equivalent Circuit Models for Enhanced Flooded Lead Acid Batteries Based on Electrochemical Impedance Spectroscopy. Energies, 11(1), 118. doi:10.3390/en11010118 [+]
Perera, A. T. D., Attalage, R. A., Perera, K. K. C. K., & Dassanayake, V. P. C. (2013). Designing standalone hybrid energy systems minimizing initial investment, life cycle cost and pollutant emission. Energy, 54, 220-230. doi:10.1016/j.energy.2013.03.028

Krieger, E. M., Cannarella, J., & Arnold, C. B. (2013). A comparison of lead-acid and lithium-based battery behavior and capacity fade in off-grid renewable charging applications. Energy, 60, 492-500. doi:10.1016/j.energy.2013.08.029

Aksakal, C., & Sisman, A. (2018). On the Compatibility of Electric Equivalent Circuit Models for Enhanced Flooded Lead Acid Batteries Based on Electrochemical Impedance Spectroscopy. Energies, 11(1), 118. doi:10.3390/en11010118

Dhundhara, S., Verma, Y. P., & Williams, A. (2018). Techno-economic analysis of the lithium-ion and lead-acid battery in microgrid systems. Energy Conversion and Management, 177, 122-142. doi:10.1016/j.enconman.2018.09.030

Li, X., Shu, X., Shen, J., Xiao, R., Yan, W., & Chen, Z. (2017). An On-Board Remaining Useful Life Estimation Algorithm for Lithium-Ion Batteries of Electric Vehicles. Energies, 10(5), 691. doi:10.3390/en10050691

Ariza Chacón, H., Banguero, E., Correcher, A., Pérez-Navarro, Á., & Morant, F. (2018). Modelling, Parameter Identification, and Experimental Validation of a Lead Acid Battery Bank Using Evolutionary Algorithms. Energies, 11(9), 2361. doi:10.3390/en11092361

Copetti, J. B., Lorenzo, E., & Chenlo, F. (1993). A general battery model for PV system simulation. Progress in Photovoltaics: Research and Applications, 1(4), 283-292. doi:10.1002/pip.4670010405

Guasch, D., & Silvestre, S. (2003). Dynamic battery model for photovoltaic applications. Progress in Photovoltaics: Research and Applications, 11(3), 193-206. doi:10.1002/pip.480

Blaifi, S., Moulahoum, S., Colak, I., & Merrouche, W. (2016). An enhanced dynamic model of battery using genetic algorithm suitable for photovoltaic applications. Applied Energy, 169, 888-898. doi:10.1016/j.apenergy.2016.02.062

Blaifi, S., Moulahoum, S., Colak, I., & Merrouche, W. (2017). Monitoring and enhanced dynamic modeling of battery by genetic algorithm using LabVIEW applied in photovoltaic system. Electrical Engineering, 100(2), 1021-1038. doi:10.1007/s00202-017-0567-6

Gao, Z., Cecati, C., & Ding, S. X. (2015). A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches. IEEE Transactions on Industrial Electronics, 62(6), 3757-3767. doi:10.1109/tie.2015.2417501

Ferrer, A. (2007). Multivariate Statistical Process Control Based on Principal Component Analysis (MSPC-PCA): Some Reflections and a Case Study in an Autobody Assembly Process. Quality Engineering, 19(4), 311-325. doi:10.1080/08982110701621304

Jiang, Q., Yan, X., & Zhao, W. (2013). Fault Detection and Diagnosis in Chemical Processes Using Sensitive Principal Component Analysis. Industrial & Engineering Chemistry Research, 52(4), 1635-1644. doi:10.1021/ie3017016

Fan, J., & Wang, Y. (2014). Fault detection and diagnosis of non-linear non-Gaussian dynamic processes using kernel dynamic independent component analysis. Information Sciences, 259, 369-379. doi:10.1016/j.ins.2013.06.021

Garcia-Alvarez, D., Fuente, M. J., & Sainz, G. I. (2012). Fault detection and isolation in transient states using principal component analysis. Journal of Process Control, 22(3), 551-563. doi:10.1016/j.jprocont.2012.01.007

Banguero, E., Aristizábal, A. J., & Murillo, W. (2017). A Verification Study for Grid-Connected 20 kW Solar PV System Operating in Chocó, Colombia. Energy Procedia, 141, 96-101. doi:10.1016/j.egypro.2017.11.019

Rahman, M. A., Anwar, S., & Izadian, A. (2016). Electrochemical model parameter identification of a lithium-ion battery using particle swarm optimization method. Journal of Power Sources, 307, 86-97. doi:10.1016/j.jpowsour.2015.12.083

Yang, X., Chen, L., Xu, X., Wang, W., Xu, Q., Lin, Y., & Zhou, Z. (2017). Parameter Identification of Electrochemical Model for Vehicular Lithium-Ion Battery Based on Particle Swarm Optimization. Energies, 10(11), 1811. doi:10.3390/en10111811

Kai, H., Yong-Fang, G., Zhi-Gang, L., Hsiung-Cheng, L., & Ling-Ling, L. (2018). Development of Accurate Lithium-Ion Battery Model Based on Adaptive Random Disturbance PSO Algorithm. Mathematical Problems in Engineering, 2018, 1-13. doi:10.1155/2018/3793492

Venter, G., & Sobieszczanski-Sobieski, J. (2003). Particle Swarm Optimization. AIAA Journal, 41(8), 1583-1589. doi:10.2514/2.2111

Layadi, T. M., Champenois, G., Mostefai, M., & Abbes, D. (2015). Lifetime estimation tool of lead–acid batteries for hybrid power sources design. Simulation Modelling Practice and Theory, 54, 36-48. doi:10.1016/j.simpat.2015.03.001

Rahmani, M., & Atia, G. K. (2017). Coherence Pursuit: Fast, Simple, and Robust Principal Component Analysis. IEEE Transactions on Signal Processing, 65(23), 6260-6275. doi:10.1109/tsp.2017.2749215

Bro, R., & Smilde, A. K. (2014). Principal component analysis. Anal. Methods, 6(9), 2812-2831. doi:10.1039/c3ay41907j

Granato, D., Santos, J. S., Escher, G. B., Ferreira, B. L., & Maggio, R. M. (2018). Use of principal component analysis (PCA) and hierarchical cluster analysis (HCA) for multivariate association between bioactive compounds and functional properties in foods: A critical perspective. Trends in Food Science & Technology, 72, 83-90. doi:10.1016/j.tifs.2017.12.006

Soh, W., Kim, H., & Yum, B.-J. (2015). Application of kernel principal component analysis to multi-characteristic parameter design problems. Annals of Operations Research, 263(1-2), 69-91. doi:10.1007/s10479-015-1889-2

Deng, X., Tian, X., Chen, S., & Harris, C. J. (2018). Nonlinear Process Fault Diagnosis Based on Serial Principal Component Analysis. IEEE Transactions on Neural Networks and Learning Systems, 29(3), 560-572. doi:10.1109/tnnls.2016.2635111

De Ketelaere, B., Hubert, M., & Schmitt, E. (2015). Overview of PCA-Based Statistical Process-Monitoring Methods for Time-Dependent, High-Dimensional Data. Journal of Quality Technology, 47(4), 318-335. doi:10.1080/00224065.2015.11918137

Vanhatalo, E., Kulahci, M., & Bergquist, B. (2017). On the structure of dynamic principal component analysis used in statistical process monitoring. Chemometrics and Intelligent Laboratory Systems, 167, 1-11. doi:10.1016/j.chemolab.2017.05.016

Zhao, C., Wang, F., Gao, F., Lu, N., & Jia, M. (2007). Adaptive Monitoring Method for Batch Processes Based on Phase Dissimilarity Updating with Limited Modeling Data. Industrial & Engineering Chemistry Research, 46(14), 4943-4953. doi:10.1021/ie061320f

Nomikos, P., & MacGregor, J. F. (1995). Multivariate SPC Charts for Monitoring Batch Processes. Technometrics, 37(1), 41-59. doi:10.1080/00401706.1995.10485888

Design of Off-Grid Systems with Sunny Island 4.4M/6.0H/8.0H Devices. Version 2.3. SMA. 1-42. http://files.sma.de/dl/1353/Designing-OffGridSystem-PL-en-23.pdf [Accessed on 15.07.2018].

Ungurean, L., Cârstoiu, G., Micea, M. V., & Groza, V. (2016). Battery state of health estimation: a structured review of models, methods and commercial devices. International Journal of Energy Research, 41(2), 151-181. doi:10.1002/er.3598

[-]

recommendations

 

This item appears in the following Collection(s)

Show full item record