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Abstract
Introduction. The paper proposes a geo-adaptive methodology for monitoring and improving the reliability of grid-connected photovoltaic inverters under the climatic conditions of Uzbekistan. It considers the combined impact of climatic, electrical, and operational factors on inverter performance and substantiates an approach based on a composite “reliability–resilience” indicator. The study shows that degradation and failures develop more intensively in hot and dusty regions, while predictive monitoring can reduce the risk of downtime.
Methods and materials. The study is based on an analytical literature review, operational observations, and inverter failure statistics. Classical reliability assessment methods, Weibull-based modeling, SCADA/IoT telemetry, digital twins, and machine learning algorithms were used as the methodological framework. The proposed geo-adaptive methodology integrates climatic, grid, and operational parameters into a unified risk-assessment system.
Results. The analysis shows that inverter efficiency degrades by about 0.8-1.5% per year in semi-arid climates, while the annual failure rate reaches 6-7% in hot desert regions. About 65% of failures are associated with thermal overstress of power electronic components. It is demonstrated that the geo-adaptive methodology makes it possible to build a digital risk profile for each plant, improve failure prediction accuracy, and optimize preventive maintenance.
Conclusion. The reliability of grid-connected PV systems in Uzbekistan is strongly influenced by climate, power quality, and the thermal operating conditions of power electronics. The proposed geo-adaptive methodology provides a scientific and practical basis for more accurate risk assessment, reduction of emergency shutdowns, and improvement of maintenance efficiency. Pilot implementation at real facilities is the logical next step for further validation of the approach.
References
[1]. International Energy Agency (IEA). Solar Energy Policy in Uzbekistan: A Roadmap. - Paris: IEA, 2022. - 124 p.
[2]. European Bank for Reconstruction and Development (EBRD). EBRD approves new country strategy for Uzbekistan // Press Release. - 2024.- January 26.
[3]. IRENA. Renewable Power Generation Costs in 2023. - Abu Dhabi: International Renewable Energy Agency, 2024. - 186 p.
[4]. World Bank. Growth and Job Creation in Uzbekistan: An In-Depth Diagnostic. - Washington DC: World Bank, 2018. - 214 p.
[5]. World Bank. Uzbekistan to Invest in Modernizing Electricity Distribution Networks with World Bank Support // Press Release. - 2025. - May 15.
[6]. Ministry of Energy of the Republic of Uzbekistan. On Causes of Power Outages. - Tashkent, 2020.
[7]. IRENA. Uzbekistan: Renewables Readiness Assessment. - Abu Dhabi: International Renewable Energy Agency, 2024. - 98 p.
[8]. Jalolova S. World Bank approves $100 million to modernize Uzbekistan’s power grid // The Times of Central Asia. - 2025. - May 19.
[9]. Masdar celebrates inauguration of landmark 100 MW Nur Navoi Solar Project in Uzbekistan // PR Newswire. - 2021. - August 28.
[10]. Government of Uzbekistan. Updated Nationally Determined Contribution of the Republic of Uzbekistan. - Tashkent, 2021.
[11]. National Renewable Energy Laboratory (NREL). Photovoltaic Inverter Reliability Assessment. - Golden (CO): NREL, 2019. - Report NREL/TP-5D00-74462.
[12]. Авезова Н.Р., Шогучкаров С.К., Кудратов А.Р. Критерии устойчивости и надежности сетевых фотоэлектрических систем: методология оценки и практическое применение // Проблемы информатики и энергетики. - 2024. - №4. - С. 56-70.
[13]. International Energy Agency (IEA). Uzbekistan 2022: Energy Policy Review. - Paris: OECD/IEA, 2022.
[14]. Strategy for the Transition of the Republic of Uzbekistan to a Green Economy for 2019–2030. Resolution of the Cabinet of Ministers of the Republic of Uzbekistan No. 841. - Tashkent, 2019.
[15]. Alavi O., Kaaya I., De Ceuninck W., Van Mierlo J. Assessing the impact of PV panel climate-based degradation rates on inverter reliability in grid-connected solar energy systems // Heliyon. -2024. - Vol. 10. - No. 3. - e25839.
[16]. Idbouhouch O., Taqi S., Ajerrar A. et al. Assessing PV inverter efficiency degradation under semi-arid climate scenarios // Heliyon. -2024. - Vol. 10. - No. 23. - e40308.
[17]. Bošnjaković M., Stojkov M., Katinić M., Lacković I. Effects of extreme weather conditions on PV systems // Sustainability. - 2023. -Vol. 15. - No. 22. - Art. 16044.
[18]. Sarita K., Saket R.K., Khan B. Reliability, availability, and condition monitoring of inverters of grid-connected solar photovoltaic systems // IET Renewable Power Generation. - 2023. - Vol. 17. - No. 2. - P. 202-213.
[19]. Li T., Wang P., Sun Y., Li Y. Reliability evaluation of photovoltaic system considering thermal characteristics of components // Electronics. - 2021. - Vol. 10. - No. 21. - Art. 2661.
[20]. Kumar A.S., Arul S., Sudhakar B., Kottursamy M. Reliability analysis of PV inverters considering locational impact // E3S Web of Conferences. -2024. -Vol. 540. - Art. 10011.
[21]. Singh S., Saket R.K., Khan B. A comprehensive review of reliability assessment methodologies for grid-connected photovoltaic systems // IET Renewable Power Generation. - 2023. - Vol. 17. - No. 7. - P. 1859-1880.
[22]. Kull K., Asad B., Khan M.A., Naseer M.U., Kallaste A., Vaimann T. Faults, failures, reliability, and predictive maintenance of grid-connected solar systems: A comprehensive review // Applied Sciences. - 2023. -Vol. 15. - No. 21. - Art. 11461.
[23]. Francisti J., Fodor K., Balogh Z., Magdin M. Predictive modeling and anomaly detection in solar PV inverters using machine learning // Results in Engineering. - 2025. - Vol. 28. - Art. 108348.
[24]. Ramos E., Vale Z. Integrating machine learning and digital twins for smart grid asset management: A review of hybrid predictive maintenance approaches // Energies. -2023. - Vol. 16. - No. 15. -Art. 5712.
[25]. Kumar R., Mishra S.K., Sahoo A.K. et al. Data-driven prediction-based reliability assessment of solar energy systems incorporating uncertainties for generation planning // Scientific Reports. -2025. - Vol. 15. - Art. 9335.
[26]. Vita V., Fotis G., Chobanov V., Pavlatos C., Mladenov V. Predictive maintenance for distribution system operators in increasing transformers’ reliability // Electronics. - 2023. -Vol. 12. - No. 6. - Art. 1356.
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Copyright (c) 2026 Avezova, N.R., Mirzabaev, A.M., Kulmatov, X.X., Asadov, A.E. (Muallif)