Detection and Classification of Physical and Electrical Fault in PV Array System by Random Forest-Based Approach

Sikandar Shah SYED; Bin  Li; Anqi  Zheng

doi:10.31258/ijeepse.7.2.67-84

Sikandar Shah SYED Yangzhou University, Yangzhou, China
Bin Li Yangzhou University, Yangzhou, China
Anqi Zheng Yangzhou University, Yangzhou, China

DOI: https://doi.org/10.31258/ijeepse.7.2.67-84

Abstract viewed: 243 times

pdf downloaded: 93 times

Keywords: AC Protection, Electrical Fault, Machine Learning, Physical Fault, PV Database, Solar Photovoltaic.

Abstract

The importance of solar photovoltaic (PV) systems has increased over the past ten years due to the solar PV industry's explosive growth. To ensure the reliable, safe, and efficient operation of residential PV systems, fault detection is crucial. Early classification of faults can improve PV system performance and reduce damage and energy loss. Many recent studies have focused on classifying and detecting PV faults but most of them are limited to specific reasons like Real-world data can be restricted, unbalanced, or include noise, all of which may decrease the effectiveness of ML models. This paper proposes a method for identifying and classifying both physical and electrical faults in the PV array system applying a machine learning (Random Forest) model to that is trained using a synthetic photovoltaic training database. Make use of a synthetic PV database opens the door to a more precise, effective, and scalable PV system by addressing the limitations of real-world data. MATLAB is used to create a synthetic database while scikit-learn tool in Jupyter Notebook is used to train an ML model are the two main steps in this paper. The performance of the proposed model is compared with the existing ML model and achieves the most effective algorithm offering higher accuracy in detection of 98.6% and classification accuracy is 94.2% for both physical and electrical faults after being successfully tested on real-world datasets and trained on historical data from the PV array system (PV Database).

References

H. Haes Alhelou, M. E. Hamedani-Golshan, T. C. Njenda and P. Siano, "A Survey on Power System Blackout and Cascading Events: Research Motivations and Challenges," Energies, vol. 12, 2019.

Y. Zhang, Y. Xu and Z. Y. Dong, "Robust Ensemble Data Analytics for Incomplete PMU Measurements-Based Power System Stability Assessment," IEEE Transactions on Power Systems, vol. 33, pp. 1124-1126, 2018.

L. Lawton, M. Sullivan, K. Van Liere, A. Katz and J. Eto, "A framework and review of customer outage costs: Integration and analysis of electric utility outage cost surveys," 2003.

A. Jaech, B. Zhang, M. Ostendorf and D. S. Kirschen, "Real-Time Prediction of the Duration of Distribution System Outages," IEEE Transactions on Power Systems, vol. 34, pp. 773-781, 2019.

S. S. Gururajapathy, H. Mokhlis and H. A. Illias, "Fault location and detection techniques in power distribution systems with distributed generation: A review," Renewable and sustainable energy reviews, vol. 74, p. 949–958, 2017.

A. Et-taleby, Y. Chaibi, M. Benslimane and M. Boussetta, "Applications of machine learning algorithms for photovoltaic fault detection: a review," Statistics, Optimization & Information Computing, vol. 11, p. 168–177, 2023.

R. Nijman, "Automatically and real-time identifying malfunctioning PV systems using massive on-line PV yield data," 2018.

O. Tsafarakis, P. Moraitis, B. B. Kausika, H. Van Der Velde, S. ’t Hart, A. de Vries, P. de Rijk, M. M. De Jong, H.-P. van Leeuwen and W. Van Sark, "Three years experience in a Dutch public awareness campaign on photovoltaic system performance," IET Renewable Power Generation, vol. 11, p. 1229–1233, 2017.

N. A. Engerer and J. Hansard, "Real-time simulations of 15,000+ distributed PV arrays at sub-grid level using the regional PV simulation system (RPSS)," in Proceesings of the Solar World Congress, 2015.

Y. Zhao, L. Yang, B. Lehman, J.-F. de Palma, J. Mosesian and R. Lyons, "Decision tree-based fault detection and classification in solar photovoltaic arrays," in 2012 Twenty-Seventh Annual IEEE Applied Power Electronics Conference and Exposition (APEC), 2012.

A. Charki, P.-O. Logerais, D. Bigaud, C. M. F. Kébé and A. Ndiaye, "Lifetime assessment of a photovoltaic system using stochastic Petri nets," International Journal of Modelling and Simulation, vol. 37, p. 149–155, 2017.

M. Bressan, Y. El-Basri and C. Alonso, "A new method for fault detection and identification of shadows based on electrical signature of faults," in 2015 17th European Conference on Power Electronics and Applications (EPE'15 ECCE-Europe), 2015.

A. Sayed, M. El-Shimy, M. El-Metwally and M. Elshahed, "Reliability, availability and maintainability analysis for grid-connected solar photovoltaic systems," Energies, vol. 12, p. 1213, 2019.

T. Berghout, L.-H. Mouss, T. Bentrcia, E. Elbouchikhi and M. Benbouzid, "A deep supervised learning approach for condition-based maintenance of naval propulsion systems," Ocean Engineering, vol. 221, p. 108525, 2021.

L. Breiman, "Random forests," Machine learning, vol. 45, p. 5–32, 2001.

G. T. Klise, O. Lavrova and R. L. Gooding, "PV System Component Fault and Failure Compilation and Analysis.," 2018.

S. A. Hicks, I. Strümke, V. Thambawita, M. Hammou, M. A. Riegler, P. Halvorsen and S. Parasa, "On evaluation metrics for medical applications of artificial intelligence," Scientific reports, vol. 12, p. 5979, 2022.

C. Del Cañizo, A. B. Cristóbal, L. Barbosa, G. Revuelta, S. Haas, M. Victoria and M. Brocklehurst, "Promoting citizen science in the energy sector: Generation Solar, an open database of small-scale solar photovoltaic installations," Open Research Europe, vol. 1, 2021.

S. A. M. Javadian, A. M. Nasrabadi, M.-R. Haghifam and J. Rezvantalab, "Determining fault's type and accurate location in distribution systems with DG using MLP neural networks," in 2009 International conference on clean electrical power, 2009.

Y. Aslan, "An alternative approach to fault location on power distribution feeders with embedded remote-end power generation using artificial neural networks," Electrical Engineering, vol. 94, p. 125–134, 2012.

F. Dehghani and H. Nezami, "A new fault location technique on radial distribution systems using artificial neural network," 2013.

W. Li, D. Deka, M. Chertkov and M. Wang, "Real-time faulted line localization and PMU placement in power systems through convolutional neural networks," IEEE Transactions on Power Systems, vol. 34, p. 4640–4651, 2019.

A. Zainab, S. S. Refaat, D. Syed, A. Ghrayeb and H. Abu-Rub, "Faulted line identification and localization in power system using machine learning techniques," in 2019 IEEE International Conference on Big Data (Big Data), 2019.

H. Okumus and F. M. Nuroglu, "A random forest-based approach for fault location detection in distribution systems," Electrical Engineering, vol. 103, p. 257–264, 2021.

S. R. Madeti and S. N. Singh, "Modeling of PV system based on experimental data for fault detection using kNN method," Solar Energy, vol. 173, p. 139–151, 2018.

N. Dahal, O. Abuomar, R. King and V. Madani, "Event stream processing for improved situational awareness in the smart grid," Expert Systems with Applications, vol. 42, p. 6853–6863, 2015.

U. Adhikari, T. H. Morris and S. Pan, "Applying hoeffding adaptive trees for real-time cyber-power event and intrusion classification," IEEE Transactions on Smart Grid, vol. 9, p. 4049–4060, 2017.