Early Detection of Anomalies in Photovoltaic Module Strings Using Decision Trees for MPPT Solar Charger Systems

  • Authors

    • D.Ramesh Reddy Department of Electronics and Communication Engineering, Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering and ‎Technology, Hyderabad, Telangana 500090, India
    • T. S. Saravanan Department of Electrical and Electronics Engineering, Rajalakshmi Engineering College, Chennai, Tamil Nadu 602105, India
    • P . Subhashini Department of Information Technology, Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai, Tamil Nadu ‎‎600062, India
    • Saravana Selvan School of Professional Engineering, Manukau Institute of Technology, Tech Park Campus, Auckland 2104, New Zealand
    • Sonia Maria D'Souza Department of Artificial Intelligence and Machine Learning, New Horizon College of Engineering, Bengaluru, Karnataka 560103, India
    • Thiyagesan M Department of Electrical and Electronics Engineering, R.M.K Engineering College, Kavaraipettai, Tamil Nadu 601206, India
    • M . DuraiRaj Department of Computer Science Engineering, Bharathidasan University, Tiruchirappalli, Tamil Nadu 620024, India
    • Raja-‎sekhara Babu L Department of Computer Science Engineering, Bharathidasan University, Tiruchirappalli, Tamil Nadu 620024, India
    https://doi.org/10.14419/xxh9h294

    Received date: April 18, 2025

    Accepted date: June 18, 2025

    Published date: June 30, 2025

  • Machine Learning; Photovoltaic Arrays; Decision Tree Models; MPPT Solar Chargers.

  • Abstract

    This study presents a decision tree (DT) based machine learning approach to detect early anomalies and faults in solar maximum power point ‎tracking (MPPT) integrated photovoltaic (PV) module circuits. A four-panel array, built using a single diode model, is simulated to generate ‎a synthetic, balanced dataset for training and evaluation. Among the different models tested, including neural networks (NN) and support ‎vector classifiers (SVC), the DT model showed the best performance in precision and recall across all anomaly labels while maintaining ‎simplicity and low computational cost. Currently, the model is limited to four module configurations, and the use of synthetic data may lead ‎to overfitting when applied to real-world scenarios. Nevertheless, the methodology can be adapted to grid configurations with other known ‎parameters. This work provides a practical basis for integrating early anomaly detection systems into PV installations, increasing operational ‎efficiency and reducing maintenance costs.

  • References

    1. Srivastava, S. (2019). Benchmarking Facebook’s Prophet, PELT and Twitter’s Anomaly Detection and Automated Deployment to Cloud (Master’s thesis, University of Twente, Enschede, The Netherlands).
    2. Taylor, S. J., & Letham, B. (2018). Forecasting at scale. American Statistician, 72, 37–45. https://doi.org/10.1080/00031305.2017.1380080.
    3. Que, Z., Liu, Y., Guo, C., Niu, X., Zhu, Y., & Luk, W. (2019). Real-time anomaly detection for flight testing using autoencoder and LSTM. In Proceedings of the 2019 IEEE International Conference on Field-Programmable Technology (ICFPT) (pp. 379–382). Tianjin, China. https://doi.org/10.1109/ICFPT47387.2019.00072.
    4. Hu, D., Zhang, C., Yang, T., & Chen, G. (2020). Anomaly detection of power plant equipment using long short-term memory based autoencoder neural network. Sensors, 20, 6164. https://doi.org/10.3390/s20216164.
    5. Toshniwal, A., Mahesh, K., & Jayashree, R. (2020). Overview of anomaly detection techniques in machine learning. In Proceedings of the 2020 IEEE Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) (pp. 808–815). Palladam, India. https://doi.org/10.1109/I-SMAC49090.2020.9243329.
    6. Pereira, J., & Silveira, M. (2018). Unsupervised anomaly detection in energy time series data using variational recurrent autoencoders with attention. In Proceedings of the 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 1275–1282). Orlando, FL, USA. https://doi.org/10.1109/ICMLA.2018.00207.
    7. Tsai, C. W., Yang, C. W., Hsu, F. L., Tang, H. M., Fan, N. C., & Lin, C. Y. (2020). Anomaly detection mechanism for solar generation using semi-supervision learning model. In Proceedings of the 2020 IEEE Indo–Taiwan 2nd International Conference on Computing, Analytics and Networks (Indo-Taiwan ICAN) (pp. 9–13). Rajpura, India. https://doi.org/10.1109/Indo-TaiwanICAN48429.2020.9181310.
    8. Iyengar, S., Lee, S., Sheldon, D., & Shenoy, P. (2018). SolarClique: Detecting anomalies in residential solar arrays. In Proceedings of the 1st ACM SIGCAS Conference on Computing and Sustainable Societies (pp. 1–10). Menlo Park and San Jose, CA, USA. https://doi.org/10.1145/3209811.3209860.
    9. Hempelmann, S., Feng, L., Basoglu, C., Behrens, G., Diehl, M., Friedrich, W., Brandt, S., & Pfeil, T. (2020). Evaluation of unsupervised anomaly detection approaches on photovoltaic monitoring data. In Proceedings of the 2020 47th IEEE Photovoltaic Specialists Conference (PVSC) (pp. 2671–2674). Calgary, AB, Canada. https://doi.org/10.1109/PVSC45281.2020.9300481.
    10. Hariri, S., Kind, M. C., & Brunner, R. J. (2019). Extended isolation forest. IEEE Transactions on Knowledge and Data Engineering, 33, 1479–1489. https://doi.org/10.1109/TKDE.2019.2947676.
    11. Wang, Q., Paynabar, K., & Pacella, M. (2021). Online automatic anomaly detection for photovoltaic systems using thermography imaging and low rank matrix decomposition. Journal of Quality Technology, 1–14. https://doi.org/10.1080/00224065.2021.1948372.
    12. Balzategui, J., Eciolaza, L., & Maestro-Watson, D. (2021). Anomaly detection and automatic labeling for solar cell quality inspection based on gen-erative adversarial network. Sensors, 21, 4361. https://doi.org/10.3390/s21134361.
    13. Benninger, M., Hofmann, M., & Liebschner, M. (2020). Anomaly detection by comparing photovoltaic systems with machine learning methods. In Proceedings of the NEIS 2020, Conference on Sustainable Energy Supply and Energy Storage Systems (pp. 1–6). Hamburg, Germany.
    14. Mulongo, J., Atemkeng, M., Ansah-Narh, T., Rockefeller, R., Nguegnang, G. M., & Garuti, M. A. (2020). Anomaly detection in power generation plants using machine learning and neural networks. Applied Artificial Intelligence, 34, 64–79. https://doi.org/10.1080/08839514.2019.1691839.
    15. Awaysheh, F. M., Alazab, M., Gupta, M., Pena, T. F., & Cabaleiro, J. C. (2020). Next-generation big data federation access control: A reference model. Future Generation Computer Systems, 108, 726–741. https://doi.org/10.1016/j.future.2020.02.052.
    16. Kebande, V. R., Awaysheh, F. M., Ikuesan, R. A., Alawadi, S. A., & Alshehri, M. D. (2021). A blockchain-based multi-factor authentication mod-el for a cloud-enabled Internet of Vehicles. Sensors, 21, 6018. https://doi.org/10.3390/s21186018.
    17. Son, J., Jeong, S., Park, H., & Park, C.-E. (2020). The effect of particulate matter on solar photovoltaic power generation over the Republic of Ko-rea. Environmental Research Letters, 15, 9. https://doi.org/10.1088/1748-9326/ab905b.
    18. Dajuma, A., Yahaya, S., Touré, S., Diedhiou, A., Adamou, R., Konaré, A., Sido, M., & Golba, M. (2016). Sensitivity of solar photovoltaic panel efficiency to weather and dust over West Africa: Comparative experimental study between Niamey (Niger) and Abidjan (Côte d’Ivoire). Computa-tional Water, Energy, and Environmental Engineering, 5, 123–147. https://doi.org/10.4236/cweee.2016.54012.
    19. Padmanathan, K., Govindarajan, U., Ramachandaramurthy, V. K., Sudar Oli Selvi, T., & Jeevarathinam, B. (2018). Integrating solar photovoltaic energy conversion systems into industrial and commercial electrical energy utilization—A survey. Journal of Industrial Information Integration, 10, 39–54. https://doi.org/10.1016/j.jii.2018.01.003.

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  • How to Cite

    Reddy, D. ., Saravanan , T. S. ., Subhashini, P. ., Selvan, S. ., D'Souza, S. M. ., M, T., DuraiRaj, M. ., & L , R.- ‎sekhara B. (2025). Early Detection of Anomalies in Photovoltaic Module Strings Using Decision Trees for MPPT Solar Charger Systems. International Journal of Basic and Applied Sciences, 14(2), 462-469. https://doi.org/10.14419/xxh9h294