Research on Fault Diagnosis Method for Photovoltaic Array Based on XGBoost Algorithm

Authors

  • Zongyu Zhang Guizhou Power Grid Co.
  • Bodi Liu Guizhou Power Grid Co.
  • Chun Xie Guizhou Power Grid Co.
  • Ermei Yan Guizhou Power Grid Co.

DOI:

https://doi.org/10.4108/ew.7224

Keywords:

Faults, Detection, PV arrays, Linear discriminant analysis (LDA), World cup optimization (WCO), Extreme gradient boosting (XGBoost)

Abstract

INTRODUCTION: Photovoltaic (PV) energy sources frequently experience issues, including fragmentation, open-circuit, short-circuiting, and other common and hazardous problems. The current focus of PV research is on fault detection within solar arrays. Traditional models encounter challenges in identifying errors due to uncertainties in panel settings and the complex nature of the actual PV structure.

OBJECTIVES: This study aims to introduce a novel Extreme Gradient Boosting (XGBoost) approach for fault diagnosis in PV arrays.

METHODS: The XGBoost algorithm is trained using collected PV array defect data samples. Data preprocessing is performed to manage missing values and remove noisy data. Feature extraction is conducted using Linear Discriminant Analysis (LDA) to improve detection accuracy. To further enhance XGBoost’s performance, the World Cup Optimization (WCO) approach is applied to select optimal features from the extracted data. Fault detection is then conducted using the XGBoost algorithm on the processed data. Various indicators are utilized for performance assessment within the Python environment.

RESULTS: The comparative analysis demonstrates that this research improves fault detection efficiency in PV arrays compared to existing methodologies.

CONCLUSION: The study presents an effective method for enhancing fault detection in PV systems, showcasing the advantages of the XGBoost and WCO-based approach over conventional methods.

Downloads

Download data is not yet available.

References

[1] Sharma, V.K., Singh, R., Gehlot, A., Buddhi, D., Braccio, S., Priyadarshi, N. and Khan, B., 2022. Imperative role of photovoltaic and concentrating solar power technologies towards renewable energy generation. International Journal of Photoenergy, 2022(1), p.3852484.

[2] Dhanraj, J.A., Mostafaeipour, A., Velmurugan, K., Techato, K., Chaurasiya, P.K., Solomon, J.M., Gopalan, A. and Phoungthong, K., 2021. An effective evaluation of fault detection in solar panels. Energies, 14(22), p.7770.

[3] Lazzaretti, A.E., Costa, C.H.D., Rodrigues, M.P., Yamada, G.D., Lexinoski, G., Moritz, G.L., Oroski, E., Goes, R.E.D., Linhares, R.R., Stadzisz, P.C. and Omori, J.S., 2020. A monitoring system for online fault detection and classification in photovoltaic plants. Sensors, 20(17), p.4688.

[4] Fotopoulou, M., Rakopoulos, D., Trigkas, D., Stergiopoulos, F., Blanas, O. and Voutetakis, S., 2021. State-of-the-art low and medium voltage direct current (DC) microgrids. Energies, 14(18), p.5595.

[5] Mustafa, R.J., Gomaa, M.R., Al-Dhaifallah, M. and Rezk, H., 2020. Environmental impacts on the performance of solar photovoltaic systems. Sustainability, 12(2), p.608.

[6] Soomar, A.M., Hakeem, A., Messaoudi, M., Musznicki, P., Iqbal, A. and Czapp, S., 2022. Solar photovoltaic energy optimization and challenges. Frontiers in Energy Research, 10, p.879985.

[7] dos Santos, S.A.A., Torres, J.P.N., Fernandes, C.A. and Lameirinhas, R.A.M., 2021. The impact of aging of solar cells on the performance of photovoltaic panels. Energy Conversion and Management: X, 10, p.100082.

[8] Fairbrother, A., Quest, H., Özkalay, E., Wälchli, P., Friesen, G., Ballif, C. and Virtuani, A., 2022. Long‐Term Performance and Shade Detection in Building Integrated Photovoltaic Systems. Solar Rrl, 6(5), p.2100583.

[9] Lipták, R. and Bodnár, I., 2021. Simulation of fault detection in photovoltaic arrays. Analecta Technica Szegedinensia, 15(2), pp.31-40.

[10] Wang, A. and Xuan, Y., 2021. Close examination of localized hot spots within photovoltaic modules. Energy Conversion and Management, 234, p.113959.

[11] Karimi, M., Samet, H., Ghanbari, T. and Moshksar, E., 2020. A current-based approach for hotspot detection in photovoltaic strings. International Transactions on Electrical Energy Systems, 30(9), p.e12517.

[12] Chen, S.Q., Yang, G.J., Gao, W. and Guo, M.F., 2020. Photovoltaic fault diagnosis via semisupervised ladder network with string voltage and current measures. IEEE Journal of Photovoltaics, 11(1), pp.219-231.

[13] Zhao, J., Sun, Q., Zhou, N., Liu, H. and Wang, H., 2020. A photovoltaic array fault diagnosis method considering the photovoltaic output deviation characteristics. International Journal of Photoenergy, 2020(1), p.2176971.

[14] Liu, Y., Ding, K., Zhang, J., Li, Y., Yang, Z., Zheng, W. and Chen, X., 2021. Fault diagnosis approach for photovoltaic array based on the stacked auto-encoder and clustering with IV curves. Energy Conversion and Management, 245, p.114603.

[15] Li, C., Yang, Y., Zhang, K., Zhu, C. and Wei, H., 2021. A fast MPPT-based anomaly detection and accurate fault diagnosis technique for PV arrays. Energy Conversion and Management, 234, p.113950.

[16] Abbas, M. and Zhang, D., 2021. A smart fault detection approach for PV modules using Adaptive Neuro-Fuzzy Inference framework. Energy Reports, 7, pp.2962-2975.

[17] Eskandari, A., Aghaei, M., Milimonfared, J. and Nedaei, A., 2023. A weighted ensemble learning-based autonomous fault diagnosis method for photovoltaic systems using genetic algorithm. International Journal of Electrical Power & Energy Systems, 144, p.108591.

[18] Fan, J., Rao, S., Muniraju, G., Tepedelenlioglu, C. and Spanias, A., 2020, June. Fault classification in photovoltaic arrays using graph signal processing. In 2020 IEEE Conference on Industrial Cyberphysical Systems (ICPS) (Vol. 1, pp. 315-319). IEEE.

[19] Tao, C., Wang, X., Gao, F. and Wang, M., 2020. Fault diagnosis of photovoltaic array based on deep belief network optimized by genetic algorithm. Chinese Journal of Electrical Engineering, 6(3), pp.106-114.

[20] Hojabri, M., Kellerhals, S., Upadhyay, G. and Bowler, B., 2022. IoT-based PV array fault detection and classification using embedded supervised learning methods. Energies, 15(6), p.2097.

[21] Badr, M.M., Hamad, M.S., Abdel-Khalik, A.S., Hamdy, R.A., Ahmed, S. and Hamdan, E., 2021. Fault identification of photovoltaic array based on machine learning classifiers. IEEE Access, 9, pp.159113-159132.

[22] Li, B., Delpha, C., Migan-Dubois, A. and Diallo, D., 2021. Fault diagnosis of photovoltaic panels using full I–V characteristics and machine learning techniques. Energy Conversion and Management, 248, p.114785.

[23] Basnet, B., Chun, H. and Bang, J., 2020. An intelligent fault detection model for fault detection in photovoltaic systems. Journal of Sensors, 2020(1), p.6960328.

[24] Hajji, M., Harkat, M.F., Kouadri, A., Abodayeh, K., Mansouri, M., Nounou, H. and Nounou, M., 2021. Multivariate feature extraction-based supervised machine learning for fault detection and diagnosis in photovoltaic systems. European Journal of Control, 59, pp.313-321.

[25] Kapucu, C. and Cubukcu, M., 2021. A supervised ensemble learning method for fault diagnosis in photovoltaic strings. Energy, 227, p.120463.

[26] Wang, J., Gao, D., Zhu, S., Wang, S., and Liu, H., 2023. Fault diagnosis method of photovoltaic array based on support vector machine. Energy sources, part a: recovery, utilization, and environmental effects, 45(2), pp.5380-5395.

Downloads

Published

19-11-2024

How to Cite

1.
Zhang Z, Liu B, Xie C, Yan E. Research on Fault Diagnosis Method for Photovoltaic Array Based on XGBoost Algorithm. EAI Endorsed Trans Energy Web [Internet]. 2024 Nov. 19 [cited 2024 Dec. 9];12. Available from: https://publications.eai.eu/index.php/ew/article/view/7224

Issue

Section

Advanced Wireless Power Transmission Technology