19 январ 2026
2ИНТЕЛЛЕКТУАЛЬНАЯ IoT-ОРИЕНТИРОВАННАЯ ПЛАТФОРМА ДЛЯ ОЦЕНКИ ТЕХНИЧЕСКОГО СОСТОЯНИЯ СИЛОВЫХ ТРАНСФОРМАТОРОВ В РЕЖИМЕ РЕАЛЬНОГО ВРЕМЕНИ
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[1] Zhou, H., Zhang, Y., & Li, P. (2021). Smart IoT Applications in Transformer Condition Monitoring. IEEE Access, 9, 124557–124568.
[2] Kumar, V., & Singh, R. (2022). Artificial Intelligence for Predictive Transformer Maintenance. Electric Power Systems Research, 208, 107864.
[3] Gupta, A. (2023). Statistical Modelling of Transformer Health in IoT-Integrated Networks. Energies, 16(4), 1442.
[4] Patel, R., Desai, M., & Khan, S. (2021). IoT-Enabled SCADA Systems for Smart Substations. IEEE Transactions on Industrial Informatics, 17(9), 6045–6057.
[5] Aliyev, F., & Yusupov, D. (2024). Real-Time Transformer Monitoring Using Edge Computing. Sensors, 24(1), 52–66.
[6] Li, J., Wang, X., & Zhang, Y. (2023). Intelligent condition monitoring of power transformers using IoT-enabled sensors and machine learning models. IEEE Transactions on Industrial Informatics, 19(8), 6543–6555. https://doi.org/10.1109/TII.2023.3248761
[7] Sharma, P., & Singh, R. (2022). Integration of IoT and SCADA for real-time monitoring and fault diagnosis in electrical substations. Electric Power Systems Research, 212, 108389. https://doi.org/10.1016/j.epsr.2022.108389
[8] Kumar, S., & Khan, M. A. (2021). A hybrid AI model for transformer health prediction based on ANN and SVM classifiers. Energy Reports, 7, 876–884. https://doi.org/10.1016/j.egyr.2021.01.039
[9] Bakar, A., Mohamad, H., & Yusuf, M. (2023). Internet of Things-based monitoring architecture for oil-immersed transformers. Sensors, 23(5), 2541. https://doi.org/10.3390/s23052541
[10] Tabrizi, M., & Gholami, M. (2022). Condition assessment of power transformers using health index and fuzzy inference systems. IEEE Access, 10, 100234–100245. https://doi.org/10.1109/ACCESS.2022.3204523
[11] Rahman, M. A., & Islam, S. (2021). Machine learning and deep learning approaches for transformer fault diagnosis: A comparative study. Energies, 14(17), 5329. https://doi.org/10.3390/en14175329
[12] Ahmed, M. F., & Ali, Z. (2023). Thermal modeling and lifetime prediction of power transformers under IoT-based monitoring systems. Applied Energy, 337, 120929. https://doi.org/10.1016/j.apenergy.2023.120929
[13] Zhou, H., & Lin, J. (2022). Development of cloud-based predictive maintenance systems for smart grids using IoT data analytics. Renewable and Sustainable Energy Reviews, 169, 113013. https://doi.org/10.1016/j.rser.2022.113013
[14] Raj, A., & Mehta, R. (2023). Smart transformer monitoring through wireless sensor networks and SCADA integration. Measurement, 215, 112889. https://doi.org/10.1016/j.measurement.2023.112889
[15] Chen, D., & Zhao, L. (2024). Reliability estimation of power transformers using real-time IoT data and Bayesian networks. Electric Power Components and Systems, 52(4–5), 312–326. https://doi.org/10.1080/15325008.2024.2331123.
[1] Zhou, H., Zhang, Y., & Li, P. (2021). Smart IoT Applications in Transformer Condition Monitoring. IEEE Access, 9, 124557–124568.
[2] Kumar, V., & Singh, R. (2022). Artificial Intelligence for Predictive Transformer Maintenance. Electric Power Systems Research, 208, 107864.
[3] Gupta, A. (2023). Statistical Modelling of Transformer Health in IoT-Integrated Networks. Energies, 16(4), 1442.
[4] Patel, R., Desai, M., & Khan, S. (2021). IoT-Enabled SCADA Systems for Smart Substations. IEEE Transactions on Industrial Informatics, 17(9), 6045–6057.
[5] Aliyev, F., & Yusupov, D. (2024). Real-Time Transformer Monitoring Using Edge Computing. Sensors, 24(1), 52–66.
[6] Li, J., Wang, X., & Zhang, Y. (2023). Intelligent condition monitoring of power transformers using IoT-enabled sensors and machine learning models. IEEE Transactions on Industrial Informatics, 19(8), 6543–6555. https://doi.org/10.1109/TII.2023.3248761
[7] Sharma, P., & Singh, R. (2022). Integration of IoT and SCADA for real-time monitoring and fault diagnosis in electrical substations. Electric Power Systems Research, 212, 108389. https://doi.org/10.1016/j.epsr.2022.108389
[8] Kumar, S., & Khan, M. A. (2021). A hybrid AI model for transformer health prediction based on ANN and SVM classifiers. Energy Reports, 7, 876–884. https://doi.org/10.1016/j.egyr.2021.01.039
[9] Bakar, A., Mohamad, H., & Yusuf, M. (2023). Internet of Things-based monitoring architecture for oil-immersed transformers. Sensors, 23(5), 2541. https://doi.org/10.3390/s23052541
[10] Tabrizi, M., & Gholami, M. (2022). Condition assessment of power transformers using health index and fuzzy inference systems. IEEE Access, 10, 100234–100245. https://doi.org/10.1109/ACCESS.2022.3204523
[11] Rahman, M. A., & Islam, S. (2021). Machine learning and deep learning approaches for transformer fault diagnosis: A comparative study. Energies, 14(17), 5329. https://doi.org/10.3390/en14175329
[12] Ahmed, M. F., & Ali, Z. (2023). Thermal modeling and lifetime prediction of power transformers under IoT-based monitoring systems. Applied Energy, 337, 120929. https://doi.org/10.1016/j.apenergy.2023.120929
[13] Zhou, H., & Lin, J. (2022). Development of cloud-based predictive maintenance systems for smart grids using IoT data analytics. Renewable and Sustainable Energy Reviews, 169, 113013. https://doi.org/10.1016/j.rser.2022.113013
[14] Raj, A., & Mehta, R. (2023). Smart transformer monitoring through wireless sensor networks and SCADA integration. Measurement, 215, 112889. https://doi.org/10.1016/j.measurement.2023.112889
[15] Chen, D., & Zhao, L. (2024). Reliability estimation of power transformers using real-time IoT data and Bayesian networks. Electric Power Components and Systems, 52(4–5), 312–326. https://doi.org/10.1080/15325008.2024.2331123.