PHOTOVOLTAIC ARRAY SYSTEMS FAULT DETECTION AND CLASSIFICATION USING MACHINE LEARNING APPROACH”

Tushar G. Nakod; Prof. N. N. Mandaogade

doi:10.17605/OSF.IO/HKC9W

Authors

Tushar G. Nakod PG Student M.E.4thSemester,Electronics & Telecommunication Engineering, G H Raisoni college of engineering and management
Prof. N. N. Mandaogade Assistant Professor, Sant Gadge Baba Amravati University, Amravati. Maharashtra, INDIA

DOI:

https://doi.org/10.17605/OSF.IO/HKC9W

Abstract

Over the past few decades, renewable energy has garnered enormous attention which has increased the importance of photovoltaic systems significantly. However, these PV systems are susceptible to a variety of faults resulting in the variability of PV output power. In the absence of timely detection, these faults cause output power to decrease, rendering the PV array system unreliable.Additionally, in some cases, the system falls prey to aging or wear and tear. Therefore, detection of the fault and also the identification of the type of fault are of paramount importance to ensure optimal functioning of the PV array system. In this paper, fault classification and detection in the photovoltaic array systems using machine learning techniques have been attempted. We evaluate the performance of the classifiers based on Support Vector Machine and Random Forest algorithms. Simulation results reveal that the Random Forest classifier has the maximum accuracy (and thus the minimal mean squared error) using MATLAB software.

Downloads

Download data is not yet available.

References

J. Wiles, "Photovoltaic Power Systems And the 2005 National Electrical Code: Suggested Practices," Southwest Technology Development Institute, New Mexico State University November 26, 2008.

S. E. Forman, "Performance of Experimental Terrestrial Photovoltaic Modules," Reliability, IEEE Transactions on, vol. R-31, pp. 235-245, 1982.

E. L. Meyer and E. E. van Dyk, "Assessing the reliability and degradation of photovoltaic module performance parameters," Reliability, IEEE Transactions on, vol. 53, pp. 83-92, 2004.

Y. Zhao, "Master of Science Thesis: Fault Analysis in Solar Photovoltaic Arays," Electrical and Computer Engineering, Northeastern University, Boston, 2010.

Y. Zhao, B. Lehman, J.-F. d. Palma, J. Mosesian, and R. Lyons, "Fault Analysis in Solar PV Arrays under: Low Irradiance Conditions and Reverse Connections," in the 37th IEEE Photovoltaic Specialist Conference, Seattle, WA, 2011.

Y. Zhao, B. Lehman, J.-F. d. Palma, J. Mosesian, and R. Lyons, "Challenges of Overcurrent Protection Devices in Photovoltaic Arrays Brought by Maximum Power Point Tracker," in the 37th IEEE Photovoltaic Specialist Conference, Seattle, WA, 2011.

Y. Zhao, B. Lehman, J.-F. d. Palma, J. Mosesian, and R. Lyons, "Challenges to Overcurrent Protection Devices under Line-line Faults in Solar Photovoltaic Arrays," in the third IEEE Energy Conversion Congress & Exposition (ECCE), Phoenix, AZ, USA, 2011

Il-Song K, "Fault detection algorithm of the photovoltaic system using wavelet transform," in proceedings of the IEEE India international conference on power electronics, New Delhi, pp.1-6, 2010.

Chine, W., Mellit, A.; Lughi, V. Malek, A.; Sulligoi, G.; Pavan, A.M. A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks. Renew. Energy 2016, 90, 501-512.

Mekki, H.; Mellit, A., Salhi, H. Artificial neural network-based modelling and fault detection of partial shaded photovoltaic modules Simul. Model. Pract. Theory 2016, 67, 1-13

Dhimish, M: Holmes, V.; Mehrdadi, B.; Dales, M.; Mather, P. Photovoltaic fault detection algorithm based on theoretical curves modelling and fuzzy classification system. Energy 2017, 140, 276-290

Dhimish, M.; Holmes, V. Fault detection algorithm for grid-connected photovoltaic plants. Sol Energy 2016, 137, 236-245.

Kang, B.K; Kim, S.T.; Bae, S.H.; Park, J.W. Diagnosis of output power lowering in a PV amay by using the kalman-filter algorithm IEEE Trans. Energy Convers. 2012, 27, 885-894

Harrou, F., Sun, Y.; Taghezouit, B.; Saidi, A.; Hamlati, M.E. Reliable fault detection and diagnosis of photovoltaic systems based on statistical monitoring approaches. Renew. Energy 2018, 116, 22-37.

Silvestre, S., da Silva, M.A.; Chouder, A.; Guasch, D., Karatepe, E. New procedure for fault detection in grid connected PV systems based on the evaluation of current and voltage indicators. Energy Convers. Manag. 2014, 86, 241-249.

Yahyaoui, L; Segatto, M.E. A practical technique for on-line monitoring of a photovoltaic plant connected to a single-phase grid. Energy Convers. Manag. 2017, 132, 198-206.

Bonkoungou D, Koalaga Z, Njomo D. Modelling and simulation of photovoltaic module considering single diode equivalent circuit model in MATLAB. Int. J. Emerg. Technol. Adv. Eng. 2013;3:493-502.

W Xiao, W. G. Dunford, and A. Capel, "A novel modeling method for photovoltaic cells," in Proc. IEEE 35th Annu Power Electron. Spec. Conf (PESC), 2004, vol. 3. pp. 1950-1956

K. Ishaque, Z. Salam, H. Taheri, and Syafaruddin, "Modeling and simulation of photovoltaic (PV) system during partial shading based on a two-diode model," Simul. Modelling Pract.

Theory, vol. 19, pp. 1613-1626, 2011

Sunil Rao, Andreas Spanias, Cihan Tepedelenlioglu, "Solar Array Fault Detection using Neural Networks", IEEE International Conference on Industrial CyberPhysical Systems (ICPS), Taipei, May 2019