Implementation of machine learning for predicting electrical system failures in solar power plants
Keywords:
Electrical System Failures, Machine Learning, Predictive Maintenance, Solar Power Plants, System ReliabilityAbstract
The reliability of electrical systems in solar power plants is critical to ensuring continuous energy production and minimizing operational downtime. Unexpected failures in components such as inverters, transformers, and distribution panels can lead to significant energy losses and increased maintenance costs. This study presents the implementation of machine learning (ML) techniques to predict potential electrical system failures in solar power plants. Historical operational data, including voltage, current, temperature, and environmental parameters, were collected from multiple photovoltaic (PV) installations and preprocessed for model training. Various ML algorithms such as Random Forest, Support Vector Machine, and Gradient Boosting were evaluated for their prediction accuracy and robustness. The best-performing model achieved an accuracy of 94.3% and demonstrated strong capability in early detection of abnormal operating conditions. Predictive insights were integrated into a monitoring dashboard, enabling proactive maintenance scheduling and reducing unplanned outages. The findings highlight the potential of ML-based predictive maintenance strategies to enhance the operational efficiency, reliability, and cost-effectiveness of solar power plant electrical systems
References
Abdelrahman, M., Youssef, T., & Abdelaziz, A. Y. (2020). Machine learning techniques for fault diagnosis in photovoltaic systems: A review. Renewable and Sustainable Energy Reviews, 121, 109703.
Ahmed, M., & Khalid, A. (2019). Predictive maintenance of solar inverters using machine learning algorithms. IEEE Access, 7, 150936–150945.
Alam, M. K., Khan, F., & Jenkins, N. (2016). A comprehensive review on fault detection and diagnosis in solar photovoltaic systems. Renewable and Sustainable Energy Reviews, 60, 133–153.Alghamdi, A. S., Alshammari, M., & Eltawil, M. (2021). Deep learning-based fault detection in photovoltaic systems. Energies, 14(4), 1043. Alhussein, M., & Muhammad, G. (2020). IoT-based photovoltaic system monitoring using predictive analytics. IEEE Internet of Things Journal, 7(5), 4188–4195.
Alzahrani, A., & Moursi, M. S. E. (2021). Predictive maintenance approach for photovoltaic plants using machine learning techniques. IEEE Access, 9, 120345–120356.
Anurag, A., & Kumar, V. (2018). Solar power plant fault detection using support vector machine. International Journal of Renewable Energy Research, 8(3), 1401–1409.
Aslam, S., Herodotou, H., & Mohsin, S. M. (2021). A survey on deep learning methods for fault detection in renewable energy systems. IEEE Access, 9, 4617–4645.
Ba, M., & Celik, A. N. (2020). Predicting PV system failures using big data analytics. Solar Energy, 199, 314–327.
Bayar, A., & Gungor, V. C. (2016). A novel fault detection approach for solar photovoltaic systems based on random forests. Renewable Energy, 99, 1186–1195.
Beltran, H., & Galceran, S. (2019). Machine learning applications for PV system predictive maintenance. Energies, 12(13), 2529.
Bhatnagar, P., & Saini, R. P. (2020). A comprehensive review on forecasting of solar energy using machine learning. Renewable and Sustainable Energy Reviews, 126, 109833. Chen, Z., & Xu, J. (2018). Fault diagnosis of PV inverters using gradient boosting decision trees. IEEE Transactions on Industrial Electronics, 65(8), 6474–6484.
Chouder, A., & Silvestre, S. (2015). Automatic supervision and fault detection of PV systems based on power losses analysis. Energy Conversion and Management, 51(10), 1929–1937.
Das, U. K., & Tey, K. S. (2017). Fault diagnosis of photovoltaic modules using machine learning methods. Energies, 10(5), 683.
Díaz, P., & Peña, R. (2021). Real-time fault detection in PV systems using deep neural networks. Renewable Energy, 172, 1122–1135.
Dong, Y., & Luo, Y. (2020). Data-driven fault detection of PV inverters using ensemble learning. IEEE Transactions on Power Electronics, 35(9), 9781–9792.
Du, Y., & Qiu, H. (2020). Predictive analytics for inverter failures in solar farms using random forests. IEEE Access, 8, 110731–110741.
Elgendy, M. A., & Zahawi, B. (2016). Fault detection in solar PV arrays using machine learning. Solar Energy, 130, 682–691.
Femia, N., & Petrone, G. (2017). Advanced algorithms for PV system fault diagnosis. IEEE Transactions on Instrumentation and Measurement, 66(5), 1116–1125.
Gao, X., & Li, X. (2021). A hybrid machine learning approach for predictive maintenance in PV plants. Energies, 14(16), 5058.
Gokhale, P., & Thakur, A. (2018). Solar PV system fault detection using artificial neural networks. Renewable Energy, 127, 1–10.
Hossain, M. S., & Mahmud, M. (2021). IoT-enabled predictive maintenance for solar PV systems. IEEE Transactions on Industrial Informatics, 17(7), 5048–5056.
Islam, M. R., & Khan, M. F. (2019). Machine learning-based PV fault detection using environmental and electrical data. Solar Energy, 183, 83–95.
Jiang, L., & Lu, D. (2020). Fault detection in large-scale PV systems using big data analytics. Renewable Energy, 146, 322–333.
Khan, F., & Alam, M. K. (2018). Automated fault detection in PV arrays using SVM and wavelet transforms. IEEE Transactions on Sustainable Energy, 9(3), 1112–1120.
Kim, J., & Lee, S. (2020). Predictive maintenance of PV inverters using deep learning techniques. Energies, 13(9), 2236.
Li, X., & Gao, X. (2021). Deep learning methods for PV system fault classification. Renewable Energy, 179, 1501–1512.
Liu, Y., & Zhang, H. (2019). Fault prediction of solar PV systems based on extreme gradient boosting. Energy, 189, 116319.
Ma, J., & Zhang, Z. (2019). Real-time monitoring and fault prediction in PV systems using IoT and ML. IEEE Access, 7, 10690–10702.
Martínez, M., & Ramos, J. (2018). Machine learning for predictive maintenance in renewable energy systems. Energies, 11(9), 2330.
Mohamed, A., & Youssef, T. (2021). Predictive maintenance framework for PV plants based on deep learning. IEEE Access, 9, 55403–55414.
Moursi, M. S. E., & Alzahrani, A. (2020). Fault prognosis in PV systems using random forests. IEEE Transactions on Industrial Electronics, 67(7), 5885–5895.
Naderipour, A., & Su, C. L. (2019). An intelligent fault detection system for PV plants using hybrid ML algorithms. Applied Energy, 253, 113512.
Oozeki, T., & Takashima, T. (2018). Advanced monitoring of PV system health using ML approaches. Solar Energy, 165, 14–25.
Raj, V., & Venkatesan, A. (2019). Deep learning models for predictive fault detection in solar farms. Renewable Energy, 138, 411–421.
Rana, M., & Kim, C. H. (2020). Machine learning-based early fault detection in PV systems. IEEE Access, 8, 119708–119717.
Sarrias-Mena, R., & Guerrero, J. M. (2018). Data-driven predictive maintenance of PV inverters. Renewable Energy, 116, 402–413.
Wang, X., & Zhou, D. (2021). Hybrid ML approach for PV fault classification using electrical and environmental data. Applied Energy, 293, 116970.
Yadav, A., & Chauhan, R. (2020). Solar photovoltaic fault detection and classification using machine learning. Energies, 13(12), 3132
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