Identify and Locating the Faults in the Photovoltaic Array Using Neural Network
In making the PV array system work optimally without a hitch, it is important to recognize and know where the fault occurs. The current and voltage represent the conditions of a PV array, so that, in this paper, the proposed method is based on the current and voltage values for each string, four identified conditions, namely free fault conditions, partial shading, short circuit and open circuit. Neural network is used as a tool for predicting the type and location of faults, fault samples are obtained from simulations through PSIM and the learning process is carried out through MATLAB/Simulink, the algorithms used in the learning process are also compared to see which are the best. As a result, neural network was able to identify the type and location of faults on the PV array. This proves that the condition of a PV array can be explained through its voltage and current values.
Keyword: PV array, partial shading, short circuit, open circuit, neural network
F. Zhang, J. Li, C. Feng, and Y. Wu, “In-depth investigation of effects of partial shading on PV array characteristics,” in 2012 Power Engineering and Automation Conference, Wuhan, Hubei, China, Sep. 2012, pp. 1–4. doi: 10.1109/PEAM.2012.6612539.
T. Pei and X. Hao, “A Fault Detection Method for Photovoltaic Systems Based on Voltage and Current Observation and Evaluation,” Energies, vol. 12, no. 9, p. 1712, May 2019, doi: 10.3390/en12091712.
J. Teo, R. Tan, V. Mok, V. Ramachandaramurthy, and C. Tan, “Impact of Partial Shading on the P-V Characteristics and the Maximum Power of a Photovoltaic String,” Energies, vol. 11, no. 7, p. 1860, Jul. 2018, doi: 10.3390/en11071860.
E. N. Sholikhah, M. N. Habibi, N. A. Windarko and D. O. Anggriawan, "Abnormal Detection in Photovoltaic Array Based on Artificial Neural Network," 2020 10th Electrical Power, Electronics, Communications, Controls and Informatics Seminar (EECCIS), 2020, pp. 59-64, doi: 10.1109/EECCIS49483.2020.9263457.
M. Karakose, M. Baygin, and K. S. Parlak, “A new real-time reconfiguration approach based on neural network in partial shading for PV arrays,” in 2014 International Conference on Renewable Energy Research and Application (ICRERA), Milwaukee, WI, USA, Oct. 2014, pp. 633–637. doi: 10.1109/ICRERA.2014.7016462.
S. Laamami, M. Benhamed, and L. Sbita, “Artificial neural network-based fault detection and classification for photovoltaic system,” in 2017 International Conference on Green Energy Conversion Systems (GECS), Hammamet, Tunisia, Mar. 2017, pp. 1–7. doi: 10.1109/GECS.2017.8066211.
S. S. Kumar and A. I. Selvakumar, “Detection of the faults in the photovoltaic array under normal and partial shading conditions,” in 2017 Innovations in Power and Advanced Computing Technologies (i-PACT), Vellore, Apr. 2017, pp. 1–5. doi: 10.1109/IPACT.2017.8244890.
G. M. Masters, “Renewable and Efficient Electric Power Systems,” p. 676.
T. Kumar, B. Kumar, and S. K. Jha, “MATLAB/Simulink model to study solar cell characteristics under partial shading,” in 2017 International conference of Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, Apr. 2017, pp. 642–646. doi: 10.1109/ICECA.2017.8203618.
A. Djalab, N. Bessous, M. M. Rezaoui, and I. Merzouk, “Study of the Effects of Partial Shading on PV Array,” in 2018 International Conference on Communications and Electrical Engineering (ICCEE), El Oued, Algeria, Dec. 2018, pp. 1–5. doi: 10.1109/CCEE.2018.8634512.
S. Motahhir, A. El Ghzizal, S. Sebti, and A. Derouich, “Modeling of Photovoltaic System with Modified Incremental Conductance Algorithm for Fast Changes of Irradiance,” International Journal of Photoenergy, vol. 2018, pp. 1–13, 2018, doi: 10.1155/2018/3286479.
D. Ji, C. Zhang, M. Lv, Y. Ma, and N. Guan, “Photovoltaic Array Fault Detection by Automatic Reconfiguration,” Energies, vol. 10, no. 5, p. 699, May 2017, doi: 10.3390/en10050699.
A. Y. Appiah, X. Zhang, B. B. K. Ayawli, and F. Kyeremeh, “Review and Performance Evaluation of Photovoltaic Array Fault Detection and Diagnosis Techniques,” International Journal of Photoenergy, vol. 2019, pp. 1–19, Feb. 2019, doi: 10.1155/2019/6953530.
K. AbdulMawjood, S. S. Refaat, and W. G. Morsi, “Detection and prediction of faults in photovoltaic arrays: A review,” in 2018 IEEE 12th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG 2018), Doha, Apr. 2018, pp. 1–8. doi: 10.1109/CPE.2018.8372609.
M. K. Alam, F. H. Khan, J. Johnson, and J. Flicker, “PV faults: Overview, modeling, prevention and detection techniques,” in 2013 IEEE 14th Workshop on Control and Modeling for Power Electronics (COMPEL), Salt Lake City, UT, USA, Jun. 2013, pp. 1–7. doi: 10.1109/COMPEL.2013.6626400.
M. Sabbaghpur Arani and M. A. Hejazi, “The Comprehensive Study of Electrical Faults in PV Arrays,” Journal of Electrical and Computer Engineering, vol. 2016, pp. 1–10, 2016, doi: 10.1155/2016/8712960.
Y. H. Chen, R. Liang, Y. Tian, and F. Wang, “A novel fault diagnosis method of PV based-on power loss and I-V characteristics,” IOP Conf. Ser.: Earth Environ. Sci., vol. 40, p. 012022, Aug. 2016, doi: 10.1088/1755-1315/40/1/012022.
H. Patel and V. Agarwal, “MATLAB-Based Modeling to Study the Effects of Partial Shading on PV Array Characteristics,” IEEE Trans. On Energy Conversion, vol. 23, no. 1, pp. 302–310, Mar. 2008, doi: 10.1109/TEC.2007.914308.
K. J and F. Sy, “Modeling of a Photovoltaic Array in MATLAB Simulink and Maximum Power Point Tracking Using Neural Network,” J Electr Electron Syst, vol. 07, no. 03, 2018, doi: 10.4172/2332-0796.1000263.
H. Mekki, A. Mellit, and H. Salhi, “Artificial neural network-based modelling and fault detection of partial shaded photovoltaic modules,” Simulation Modelling Practice and Theory, vol. 67, pp. 1–13, Sep. 2016, doi: 10.1016/j.simpat.2016.05.005.
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