Evaluación de la factibilidad económica de la implementación de actividades de mantenimiento por condición en paneles solares de una granja fotovoltaica de 10 MW, considerando el impacto del indicador %PR en Celsia Colombia.
| dc.contributor.advisor | Santana Viloria, Leonardo Gerardo | |
| dc.creator | Gallo Lopez, Andres Felipe | |
| dc.creator | Salas Reyes, Jaime Humberto | |
| dc.date.accessioned | 2026-01-15T16:37:16Z | |
| dc.date.created | 2025-12-06 | |
| dc.description.abstract | El estudio analiza la factibilidad económica de implementar mantenimiento por condición en paneles solares de una granja fotovoltaica de 10 MW en Celsia Colombia, evaluando su impacto en el indicador %PR (Performance Ratio). Se busca determinar si esta estrategia mejora la eficiencia operativa y reduce costos en comparación con el mantenimiento tradicional. Actualmente, Celsia no tiene claridad sobre el impacto técnico-económico del mantenimiento por condición en sus módulos solares. Datos históricos de la Granja Solar Yumbo muestran variaciones en el %PR, influenciadas por la frecuencia de lavados. Según la literatura, un mantenimiento óptimo puede elevar el %PR hasta un 88%, lo que sugiere un potencial de mejora del 10-15%. El estudio revisa la evolución de estrategias de mantenimiento, desde enfoques reactivos hasta métodos avanzados basados en análisis de datos e inteligencia artificial. Se destacan tecnologías como sensores de suciedad, monitoreo por imagen y modelos predictivos que optimizan las intervenciones y reducen costos operativos. Investigaciones previas sugieren que el mantenimiento predictivo puede reducir tiempos de inactividad en un 25% y aumentar la generación de energía en un 3%. Los resultados esperados incluyen la optimización del %PR, la reducción de costos operativos y la mejora en la confiabilidad de los activos fotovoltaicos. La evaluación permitirá determinar si la implementación del mantenimiento por condición es viable y beneficiosa para Celsia, alineando esta estrategia con la gestión estratégica y los KPI de la empresa. | |
| dc.description.abstractenglish | This study analyzes the economic feasibility of implementing condition-based maintenance on solar panels at a 10 MW photovoltaic farm owned by Celsia Colombia, evaluating its impact on the Performance Ratio (%PR). The aim is to determine if this strategy improves operational efficiency and reduces costs compared to traditional maintenance. Currently, Celsia lacks clarity regarding the technical and economic impact of condition-based maintenance on its solar modules. Historical data from the Yumbo Solar Farm show variations in the %PR, influenced by the frequency of washing. According to the literature, optimal maintenance can increase the %PR by up to 88%, suggesting a potential improvement of 10-15%. The study reviews the evolution of maintenance strategies, from reactive approaches to advanced methods based on data analysis and artificial intelligence. It highlights technologies such as dirt sensors, image monitoring, and predictive models that optimize interventions and reduce operating costs. Previous research suggests that predictive maintenance can reduce downtime by 25% and increase energy generation by 3%. Expected results include optimized uptime percentage (%PR), reduced operating costs, and improved reliability of photovoltaic assets. The evaluation will determine whether implementing condition-based maintenance is viable and beneficial for Celsia, aligning this strategy with the company's strategic management and KPIs. | |
| dc.format.extent | 77 páginas | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12010/38793 | |
| dc.language.iso | es | |
| dc.relation.references | Abdulla, H., Sleptchenko, A., & Nayfeh, A. (2024). Photovoltaic systems operation and maintenance: A review and future directions. Renewable and Sustainable Energy Reviews, 185, 113456. https://www.sciencedirect.com/science/article/pii/S1364032124000650 | |
| dc.relation.references | Abuín, J. R. (2007). Regresión lineal múltiple. IdEyGdM-Ld Estadística. | |
| dc.relation.references | Ahmed, M., Seraj, R., & Islam, S. M. S. (2020). The k-means algorithm: A comprehensive survey and performance evaluation. Electronics, 9(8), 1295. https://www.mdpi.com/2079-9292/9/8/1295 | |
| dc.relation.references | Alshareef, M. J. (2023). A comprehensive review of the soiling effects on PV module performance. IEEE Access, 11, 134623–134651. https://www.researchgate.net/publication/375968387_A_comprehensive_review_of_the_soiling_ effects_on_PV_Module_Performance | |
| dc.relation.references | Box, G. E. P., & Tiao, G. C. (1975). Intervention analysis with applications to economic and environmental problems. Journal of the American Statistical Association, 70(349), 70–79. https://alnap.cdn.ngo/media/documents/box-tiao1975.pdf | |
| dc.relation.references | Celsia. (2018). Paneles solares: ¿Cómo funcionan y qué son? https://www.celsia.com/es/blog-celsia/paneles-solares-como-funcionan-y-que-son/ | |
| dc.relation.references | Chiteka, K., Arora, R., Sridhara, S. N., & Enweremadu, C. C. (2020). A novel approach to solar PV cleaning frequency optimisation for soiling mitigation. Scientific African, 8, e00459. https://www.sciencedirect.com/science/article/pii/S2468227620301976 | |
| dc.relation.references | Costa, S. C., Diniz, A. S. A., & Kazmerski, L. L. (2018). Solar energy dust and soiling R&D progress: Literature review update for 2016. Renewable and Sustainable Energy Reviews, 82, 2504–2536. https://www.sciencedirect.com/science/article/abs/pii/S1364032117312406 | |
| dc.relation.references | Fthenakis, V. M., & Kim, H. C. (2011). Photovoltaics: Life-cycle analyses. Solar Energy, 80(4), 443–456. https://www.sciencedirect.com/science/article/abs/pii/S0038092X09002345 | |
| dc.relation.references | Gackowiec, P. (2019). General overview of maintenance strategies: Concepts and approaches. Multidisciplinary Aspects of Production Engineering, 2, 126–139. https://www.semanticscholar.org/paper/General-overview-of-maintenance-strategies–-andGackowiec/9dfbda19ad80c2e237e68f5c45ea213cdf3c2960 | |
| dc.relation.references | Gøran, S. (2022). Optimising maintenance operations in photovoltaic solar plants using data analysis for predictive maintenance [Master’s thesis]. Norwegian University of Life Sciences. https://nmbu.brage.unit.no/nmbu-xmlui/handle/11250/3027040 | |
| dc.relation.references | Guney, I., Onat, N., & Ng, A. (2010). Cost calculation algorithm for photovoltaic systems. In Paths to sustainable energy (pp. 211–236). https://www.intechopen.com/chapters/9530 | |
| dc.relation.references | Høiaas, I., Grujic, K., Imenes, A. G., Burud, I., Olsen, E., & Belbachir, N. (2022). Inspection and condition monitoring of large-scale photovoltaic power plants: A review of imaging technologies. Renewable and Sustainable Energy Reviews, 161, 112353. https://www.sciencedirect.com/science/article/pii/S1364032122002647 | |
| dc.relation.references | Hyndman, R. J., & Athanasopoulos, G. (2021). Forecasting: Principles and practice (3rd ed.). OTexts. https://otexts.com/fpp3 | |
| dc.relation.references | IDCOL. (2021). Large-scale solar photovoltaic systems: Principles of operation and maintenance. https://idcol.org/social/0257eaaf439a8f6aa721a1f060499a4e.pdf | |
| dc.relation.references | International Energy Agency. (2020). Renewables 2020: Analysis and forecast to 2025. https://www.iea.org/reports/renewables-2020 | |
| dc.relation.references | Jordan, D. C., & Kurtz, S. R. (2013). Photovoltaic degradation rates: An analytical review. Progress in Photovoltaics: Research and Applications, 21(1), 12–29. https://www.nrel.gov/docs/fy12osti/51664.pdf | |
| dc.relation.references | Keisang, K., Bader, T., & Samikannu, R. (2021). Review of operation and maintenance methodologies for solar photovoltaic microgrids. Frontiers in Energy Research, 9. https://www.frontiersin.org/articles/10.3389/fenrg.2021.730230/full | |
| dc.relation.references | Kontopoulou, V. I., Panagopoulos, A. D., Kakkos, I., & Matsopoulos, G. K. (2023). A review of ARIMA vs. machine learning approaches for time series forecasting in data-driven networks. Future Internet, 15(8), 255. https://www.mdpi.com/1999-5903/15/8/255 | |
| dc.relation.references | Livera, A., Theristis, M., Micheli, L., Fernández, E. F., Stein, J. S., & Georghiou, G. E. (2022). Operation and maintenance decision support system for photovoltaic systems. IEEE Access, 10, 42481–42496. https://ieeexplore.ieee.org/document/9758804 | |
| dc.relation.references | López, F. G. (2021). Operational expenditures model for renewable plant [Master’s thesis]. Universidade de Santiago de Compostela | |
| dc.relation.references | Mellit, A., & Kalogirou, S. A. (2008). Artificial intelligence techniques for photovoltaic applications: A review. Progress in Energy and Combustion Science, 34(5), 574–632. https://www.sciencedirect.com/science/article/pii/S0360128508000034 | |
| dc.relation.references | Messenger, R. A., & Abtahi, A. (2017). Photovoltaic systems engineering (4th ed.). CRC Press. | |
| dc.relation.references | Mobley, R. K. (2002). An introduction to predictive maintenance. ButterworthHeinemann. | |
| dc.relation.references | Moubray, J. (2004). Reliability-centered maintenance (2nd ed.). Industrial Press | |
| dc.relation.references | National Renewable Energy Laboratory. (2013). Ground-mounted photovoltaic systems: Fixed-tilt versus tracking. https://www.nrel.gov/docs/fy13osti/58760.pdf | |
| dc.relation.references | Olorunfemi, B. O., Ogbolumani, O. A., & Nwulu, N. (2022). Solar panels dirt monitoring and cleaning for performance improvement: A systematic review on smart systems. Sustainability, 14(17), 10920. https://www.mdpi.com/2071-1050/14/17/10920 | |
| dc.relation.references | Oprea, S.-V., Carcadea, E., Raboaca, M. S., Badea, G., Eftene, M., & Pana, C. (2019). Photovoltaic power plants (PV-PP) reliability indicators for improving operation and maintenance activities: A case study of PV-PP Agigea located in Romania. IEEE Transactions on Industry Applications, 55(3), 3068–3076. https://ieeexplore.ieee.org/document/8672887 | |
| dc.relation.references | Orosz, T., Rassõlkin, A., Arsénio, P., Poór, P., Valme, D., & Sleisz, Á. (2024). Current challenges in operation, performance, and maintenance of photovoltaic panels. Energies, 17(6), 1306. https://www.mdpi.com/1996-1073/17/6/1306 | |
| dc.relation.references | Pakkiraiah, B., & Sukumar, G. D. (2016). Research survey on various MPPT performance issues to improve the solar PV system efficiency. Journal of Solar Energy, 8012432. https://www.hindawi.com/journals/jen/2016/8012432 | |
| dc.relation.references | Pascual, D., Pla, F., & Sánchez, S. (2007). Algoritmos de agrupamiento. Métodos Informáticos Avanzados, 164–174. | |
| dc.relation.references | Patel, M., & Smith, J. (2023). Performance indicators for photovoltaic systems maintenance: An analytical approach. Solar Energy Materials and Solar Cells, 230, 111272. https://www.sciencedirect.com/science/article/pii/S0927024821005584 | |
| dc.relation.references | Poór, P., Ženíšek, D., & Basl, J. (2019). Historical overview of maintenance management strategies: Development from breakdown maintenance to predictive maintenance. In Proceedings of the International Conference on Industrial Engineering and Operations Management. https://www.researchgate.net/publication/335444202 | |
| dc.relation.references | Porter, M. E. (1985). Competitive advantage: Creating and sustaining superior performance. https://resource.1st.ir/PortalImageDb/ScientificContent/Competitive%20Advantage.pdf | |
| dc.relation.references | Qureshi, M. S., Umar, S., & Nawaz, M. U. (2024). Machine learning for predictive maintenance in solar farms. International Journal of Advances in Engineering and Technology Innovation, 6(1), 32–40. https://ijaeti.com/index.php/Journal/article/view/228 | |
| dc.relation.references | Rönnlund, M. (2025). Optimizing photovoltaic systems: KPI analysis and SCADA recommendations [Master’s thesis, Lund University]. https://lup.lub.lu.se/luur/download?func=downloadFile&recordOId=9183854&fileOId=9183855 | |
| dc.relation.references | Santhoshi, B. K., Sundaram, K. M., Padmanaban, S., Holm-Nielsen, J. B., & K. K., P. (2019). Critical review of PV grid-tied inverters. Energies, 12(10), 1921. https://www.mdpi.com/1996-1073/12/10/1921 | |
| dc.relation.references | Sethiya, S. K. (2006). Condition based maintenance (CBM). https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf | |
| dc.relation.references | Suárez, R., Catota, P., Quishpe, C., Jácome, F., & Valencia, R. (2024). Una revisión de las plataformas IoT utilizadas para el monitoreo en tiempo real de sistemas fotovoltaicos. Nexos Científicos, 8(2), 29–41. https://nexoscientificos.vidanueva.edu.ec/index.php/ojs/article/view/100/329 | |
| dc.relation.references | The Editors of Encyclopaedia Britannica. (2025, October 18). Solar panel. In Encyclopaedia Britannica. https://www.britannica.com/technology/solar-panel | |
| dc.relation.references | Walker, H., Lockhart, E., Desai, J., Ardani, K., Klise, G., Lavrova, O., … Pochiraju, A. (2020). Model of operation-and-maintenance costs for photovoltaic systems (NREL/TP-5C00-74840). https://www.nrel.gov/docs/fy20osti/74840.pdf | |
| dc.subject | Mantenimiento por condición | |
| dc.subject | Performance Ratio (PR) | |
| dc.subject | Panel solar | |
| dc.subject | Eficiencia | |
| dc.subject | Costo | |
| dc.subject.keyword | Condition-based maintenance | |
| dc.subject.keyword | Performance Ratio (PR) | |
| dc.subject.keyword | Solar panel | |
| dc.subject.keyword | Efficiency | |
| dc.subject.keyword | Cost | |
| dc.subject.lemb | Energía solar - Instalaciones fotovoltaicas | |
| dc.subject.lemb | Centrales de energía solar - Mantenimiento predictivo | |
| dc.subject.lemb | Generación de energía eléctrica | |
| dc.title | Evaluación de la factibilidad económica de la implementación de actividades de mantenimiento por condición en paneles solares de una granja fotovoltaica de 10 MW, considerando el impacto del indicador %PR en Celsia Colombia. | |
| dc.type.coar | http://purl.org/coar/resource_type/c_bdcc |
Archivos
Bloque original
1 - 1 de 1
Cargando...
- Nombre:
- Proyecto final MBA - Andres Gallo Jaime Salas.pdf
- Tamaño:
- 1.42 MB
- Formato:
- Adobe Portable Document Format
- Descripción:
- Tesis
Bloque de licencias
1 - 2 de 2
Cargando...
- Nombre:
- license.txt
- Tamaño:
- 3.28 KB
- Formato:
- Item-specific license agreed upon to submission
- Descripción:
Cargando...
- Nombre:
- FOR-EFE-GDB-007_AUTORIZACION_DE_PUBLICACION_DE_TESIS_O_TRABAJO_DE_GRADO (1) (2).pdf
- Tamaño:
- 248.49 KB
- Formato:
- Adobe Portable Document Format
- Descripción:
- Carta de autorización
