Post-cloud solutions in the high-definition diagnostics of solar farms

• Autorzy: Tomecki Ireneusz

Identyfikatory

DOI

10.29119/1641-3466.2024.198.32

• Języki publikacji: EN

Abstrakty

EN

Purpose: The aim of this article is to demonstrate the benefits of using post-cloud solutions in the monitoring and servicing of large photovoltaic farms. Design/methodology/approach: This article analyzes current problems with maintaining high generation of large photovoltaic farms, discusses the limitations of current service methods and indicates the direction of development and possible applications of new IT tools in the field of the Internet of Things to solve these problems and limitations. Findings: This article presents a revolutionary fog computing method and as a tool for building IT infrastructure for monitoring large photovoltaic farms. Originality/value: The information contained in the article concerns the operation of large photovoltaic farms and the next step in the development of monitoring and service tools in terms of maintaining production efficiency at a high level thanks to the use of the latest IT technologies. The author indicates the possible direction of development of IT architecture based on the latest revolutionary methods of collecting and processing data as a solution to the limitations of currently used methods.

Słowa kluczowe

EN

high resolution monitoring; cloud computing; fog computing; resource management; solar power plants; large-scale photovoltaic; private blockchain

PL

monitoring wysokiej rozdzielczości; przetwarzanie w chmurze; przetwarzanie mgłowe; zarządzanie zasobami; elektrownia słoneczna; fotowoltaika na dużą skalę; prywatny blockchain

• Wydawca: Wydawnictwo Politechniki Śląskiej
• Czasopismo: Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska
• Rocznik: 2024
• Tom: z. 198
• Strony: 569--579
• Opis fizyczny: Bibliogr. 17 poz.

Twórcy

autor

Tomecki Ireneusz

• Silesian University of Technology, Faculty of Organization and Management

• https://orcid.org/0000-0003-1816-767X

Bibliografia

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• 2. Alba, A., Maitheli, M.N., Yitzi, S., Olindo, I., Hesan, Z. (2022). Photovoltaic system monitoring and fault detection using peer systems. Prog. Photovolt. Res. Appl., 237. Article 117806.
• 3. Al-Noman, A., Anmin Fu, P., Battula, K.S., Naha, R.K., Garg, S., Mahanti, A. (2020). FogAuthChain: A secure location-based authentication scheme in fog computing environments using Blockchain. Computer Communications, 162, 212-224.
• 4. Bendale, H., Aswar, H., Bamb, H., Desai, P., Aher, C.N. (2023). Deep Learning for Solar Panel Maintenance: Detecting Faults and Improving Performance. 14th International Conference on Computing Communication and Networking Technologies (ICCCNT).
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• 6. Bilen, K., Erdogan, I. (2023). Effects of cooling on performance of photovoltaic/thermal (PV/T) solar panels: A comprehensive review. Solar Energy, Vol. 262, 111829.
• 7. Evans, D. (2011). The Internet of Things How the Next Evolution of the Internet Is Changing Everything. CISCO.
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• 11. García, M., Marroyo, L., Lorenzo, E., Marcos, J., Pérez, M. (2014). Observed degradation in photovoltaic plants affected by hot-spots. Prog. Photovolt. Res. Appl., 22, 1292-1301.
• 12. Guevara, J.C., Torres, R., Fonseca, N. (2020). On the classification of fog computing applications: A machine learning perspective. Journal of Network and Computer Applications, 159, 102596.
• 13. Iorga, M., Feldman, L., Barton, R., Martin, M.J., Goren, N., Mahmoud, C. (2020). Fog Computing Conceptual Model. NIST Special Publication, 500-325.
• 14. Losavio, M. (2020). Fog Computing, Edge Computing and a return to privacy and personal autonomy. Procedia Computer Science, 171, 1750-1759.
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• 16. Tigo Energy Inc. (may 2012). Sources of mismatch in unshaded photovoltaic commercial arrays.
• 17. Wang, T., Zhang, Z., Bhuiyan, M.D.Z.A., Liu, A., Jia, W., Xie, M. (2020). A novel trust mechanism based on Fog Computing in Sensor-Cloud System. Future Generation Computer Systems, 109, 573-582.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).