Computational intelligence for solar photovoltaic power plant monitoring and fault diagnosis: a machine learning approach
Main Article Content
Abstract
Solar photovoltaic (PV) power plants are increasingly deployed in tropical regions such as Indonesia, yet their performance is often degraded by undetected faults including partial shading, dust accumulation, and module mismatch. This study presents a computational intelligence framework for real-time monitoring and fault diagnosis of grid-connected PV systems from a computer science perspective. The framework consists of three main components: (1) a data acquisition module that simulates 12 months of PV system operation (25 kWp capacity) using meteorological data from Medan, Indonesia, generating 8,760 hourly samples of voltage, current, power, irradiance, and temperature; (2) a machine learning-based fault classifier using Random Forest (RF) and Support Vector Machine (SVM) algorithms to distinguish between four fault types (normal operation, partial shading, dust accumulation, and module mismatch) and one healthy state; and (3) a web-based dashboard built with PHP and MySQL for real-time visualization and alerting. Experimental results show that the Random Forest classifier achieves 97.3% accuracy, 95.8% precision, and 96.2% recall, outperforming SVM (91.6% accuracy). The algorithm detects faults within 1.8 seconds of occurrence, enabling rapid operator response. The proposed system is implemented as an open-source prototype and can be deployed on low-cost hardware (Raspberry Pi 4) with an average response time of 1.8 seconds. The framework is validated using tropical climate data from Medan, Indonesia, addressing a gap in existing PV fault diagnosis research. This research contributes a practical, software-based fault diagnosis tool for PV system operators in tropical environments
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Sirait, R. ., Pakpahan, A. ., Junaidi, J., Pakpahan, R. ., & Matondang, A. A. . (2026). Computational intelligence for solar photovoltaic power plant monitoring and fault diagnosis: a machine learning approach. Journal of Intelligent Decision Support System (IDSS), 9(2), 110-127. https://doi.org/10.35335/idss.v9i2.369
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Z Zumhari, Hutajulu, E., Sibarani, B., Sirait, R., Anggriani, T., & Matondang, A. A. (2025). Advancing Environmental and Health Pollution Monitoring in Medan, Indonesia: A Mechatronics-Based Meta-Analysis. Journal of Geoscience, Engineering, Environment, and Technology, 10(3), 402–407. https://doi.org/10.25299/jgeet.2025.10.3.24694
Alam, M. S., Al-Ismail, F. S., Hossain, M. S., & Rahman, S. M. (2023). Ensemble Machine-Learning Models for Accurate Prediction of Solar Irradiation in Bangladesh. Processes, 11(3), 908. https://doi.org/10.3390/pr11030908
Attouri, K., Hajji, M., Mansouri, M., Harkat, M. F., Kouadri, A., Nounou, H., & Nounou, M. (2020). Fault detection in photovoltaic systems using machine learning technique. Proceedings of the 17th International Multi-Conference on Systems, Signals and Devices, SSD 2020, 3138, 207–212. https://doi.org/10.1109/SSD49366.2020.9364094
Bortolini, R., Rodrigues, R., Alavi, H., Vecchia, L. F. D., & Forcada, N. (2022). Digital Twins’ Applications for Building Energy Efficiency: A Review. Energies, 15(19), 7002. https://doi.org/10.3390/en15197002
De Silva, F. B. Y. R., Bandara, P., Attalage, R., De, F., 1#, S., Bandara, R., & Attalage, R. A. (2021). Performance Analysis of a Photovoltaic System under DifferentConfigurations. (December), 44–49. https://www.researchgate.net/publication/357154639
Hardi, S., Matondang, A. A., & Rambe, A. H. (2021). Modeling of transient caused by lightning strike at Nias high voltage substation using ATP-EMTP case study. Journal of Physics: Conference Series, 1811(1), 012049. https://doi.org/10.1088/1742-6596/1811/1/012049
Hasan, S., Suherman, & Muchlishiin, M. (2021). Voltage - Current Waveforms Of Single-phase Power Transformer Due To DC Bias. Journal of Physics: Conference Series, 1811(1), 012080. https://doi.org/10.1088/1742-6596/1811/1/012080
Hong, Y.-Y., Josh Domingo Lopez, D., & Wang, Y.-Y. (2024). Solar Irradiance Forecasting Using a Hybrid Quantum Neural Network: A Comparison on GPU-Based Workflow Development Platforms. IEEE Access, 12, 145079–145094. https://doi.org/10.1109/ACCESS.2024.3472053
Jayasiri, G., Herat, S., & Kaparaju, P. (2024). Repair and Reuse or Recycle: What Is Best for Small WEEE in Australia? Sustainability, 16(7), 3035. https://doi.org/10.3390/su16073035
Kannan, N., & Vakeesan, D. (2016). Solar energy for future world: - A review. Renewable and Sustainable Energy Reviews, 62, 1092–1105. https://doi.org/10.1016/j.rser.2016.05.022
Khandeparkar, V., Shreshtha, & Ramu, S. K. (2025). Effectiveness of supervised machine learning models for electrical fault detection in solar PV systems. Scientific Reports, 15(1), 34919. https://doi.org/10.1038/s41598-025-18802-4
Liu, X., Li, W., Guo, X., Su, B., Guo, S., Jing, Y., & Zhang, X. (2025). Advancements in Energy-Storage Technologies: A Review of Current Developments and Applications. Sustainability, 17(18), 8316. https://doi.org/10.3390/su17188316
Lodhi, E., Liu, X., Xiong, G., Khan, M. A., Lodhi, Z., Nawaz, T., Dilawar, A., Tarkoma, S., & Wang, F. (2025). SmartPV-AIoT: an AIoT-integrated framework for fault diagnosis and remote monitoring in photovoltaic systems. Energy Conversion and Management: X, 27, 101117. https://doi.org/10.1016/j.ecmx.2025.101117
Lund, P. D., Lindgren, J., Mikkola, J., & Salpakari, J. (2015). Review of energy system flexibility measures to enable high levels of variable renewable electricity. Renewable and Sustainable Energy Reviews, 45, 785–807. https://doi.org/10.1016/j.rser.2015.01.057
Matondang, A. A., Simanjuntak, M., & ... (2024). Design of Digital Mobile Application for Marine and Coastal Observation in Northern part of Sumatra. Journal of Applied …, 8(2).
Noura, H. N., Allal, Z., Salman, O., & Chahine, K. (2025). Explainable artificial intelligence of tree-based algorithms for fault detection and diagnosis in grid-connected photovoltaic systems. Engineering Applications of Artificial Intelligence, 139, 109503. https://doi.org/10.1016/j.engappai.2024.109503
Parulian Siagian, Aprima A. Matondang, Andreas V. H. Simanjuntak, Budhi S. Kusuma, Joel Panjaitan, & Andrean V. H. Simanjuntak. (2025). Experimental of Environmental Development Using Continuous Solar Dryer With Solid Dehumidification For Coffee Drying. Journal of Geoscience, Engineering, Environment, and Technology, 10(02), 162–169. https://doi.org/10.25299/jgeet.2025.10.02.21492
Prakash, J., Shandilya, A., & Shinde, A. V. (2023). Performance analysis of Solar PV under varying operating conditions. https://www.researchgate.net/publication/369591945
Purba, A. M., Napitu, D. H. S., Lestari, M. W., Simangunsong, J., Hutajulu, E., Hutabarat, N. F., & Matondang, A. A. (2026). Energy Consumption and Charging Infrastructure Analysis for Electric Bus Deployment in Urban Transportation Systems. Jurnal Penelitian Pendidikan IPA, 12(4), 639–648. https://doi.org/10.29303/jppipa.v12i4.11221
S Suparmono, C Cholish, A Abdullah, Sinambela, H., Alda, T., Matondang, A. A., & Simanjuntak, A. V. (2025). Quantitative Assessment of Passive Load Balancing for a Designed HVAC Installation: Case study in Magnificent Hall, Deli Park. Journal of Geoscience, Engineering, Environment, and Technology, 10(3), 397–401. https://doi.org/10.25299/jgeet.2025.10.3.24696
Sani, A., Warman, E., Depari, A. P. S., Simanjuntak, F., & Suherman, S. (2018). Comparing Solar Panel Performances under Certain Environment Circumstances by using Sensor Circuits. Proceedings of the International Conference of Science, Technology, Engineering, Environmental and Ramification Researches, 192–195. https://doi.org/10.5220/0010075301920195
Sitorus, N., Ginting, B. B., Panjaitan, B. P., Simanjuntak, B. E., Naibaho, P. R. P., Matondang, A. A., & Simanjuntak, A. V. H. (2025). Design and Performance Evaluation of a 200 Wp Off-Grid Solar Photovoltaic Module for Renewable Energy in Indonesia. Journal of Geoscience, Engineering, Environment, and Technology, 10(3), 408–412. https://doi.org/10.25299/jgeet.2025.10.3.24981
Subrata, A. C., Sutikno, T., Sunardi, S., Pamungkas, A., Arsadiando, W., & Cahya Baswara, A. R. (2022). A laboratory scale IoT-based measuring of the solar photovoltaic parameters. International Journal of Reconfigurable and Embedded Systems (IJRES), 11(2), 135. https://doi.org/10.11591/ijres.v11.i2.pp135-145
Valdec, D., Hajdek, K., Majnarić, I., & Čerepinko, D. (2021). Influence of Printing Substrate on Quality of Line and Text Reproduction in Flexography. Applied Sciences, 11(17), 7827. https://doi.org/10.3390/app11177827
Z Zumhari, Hutajulu, E., Sibarani, B., Sirait, R., Anggriani, T., & Matondang, A. A. (2025). Advancing Environmental and Health Pollution Monitoring in Medan, Indonesia: A Mechatronics-Based Meta-Analysis. Journal of Geoscience, Engineering, Environment, and Technology, 10(3), 402–407. https://doi.org/10.25299/jgeet.2025.10.3.24694
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