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Proposed ANFIS Based Approach for Fault Tracking, Detection, Clearing and Rearrangement for Photovoltaic System

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Department of Electrical Power and Machines Engineering, Faculty of Engineering, Helwan University, Cairo 11795, Egypt
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Faculty of Engineering and Technology, Future University in Egypt, Cairo 11835, Egypt
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Department of Electrical Engineering and Automation, School of Electrical Engineering, Aalto University, FI-00076 Espoo, Finland
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Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan 81542, Egypt
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Department of Electrical Engineering, Shoubra Faculty of Engineering, Benha University, Cairo 11629, Egypt
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio Passaro
Sensors 2021, 21(7), 2269; https://doi.org/10.3390/s21072269
Received: 25 February 2021 / Revised: 21 March 2021 / Accepted: 22 March 2021 / Published: 24 March 2021
(This article belongs to the Section Optical Sensors)
In the last few decades, photovoltaics have contributed deeply to electric power networks due to their economic and technical benefits. Typically, photovoltaic systems are widely used and implemented in many fields like electric vehicles, homes, and satellites. One of the biggest problems that face the relatability and stability of the electrical power system is the loss of one of the photovoltaic modules. In other words, fault detection methods designed for photovoltaic systems are required to not only diagnose but also clear such undesirable faults to improve the reliability and efficiency of solar farms. Accordingly, the loss of any module leads to a decrease in the efficiency of the overall system. To avoid this issue, this paper proposes an optimum solution for fault finding, tracking, and clearing in an effective manner. Specifically, this proposed approach is done by developing one of the most promising techniques of artificial intelligence called the adaptive neuro-fuzzy inference system. The proposed fault detection approach is based on associating the actual measured values of current and voltage with respect to the trained historical values for this parameter while considering the ambient changes in conditions including irradiation and temperature. Two adaptive neuro-fuzzy inference system-based controllers are proposed: (1) the first one is utilized to detect the faulted string and (2) the other one is utilized for detecting the exact faulted group in the photovoltaic array. The utilized model was installed using a configuration of 4 × 4 photovoltaic arrays that are connected through several switches, besides four ammeters and four voltmeters. This study is implemented using MATLAB/Simulink and the simulation results are presented to show the validity of the proposed technique. The simulation results demonstrate the innovation of this study while proving the effective and high performance of the proposed adaptive neuro-fuzzy inference system-based approach in fault tracking, detection, clearing, and rearrangement for practical photovoltaic systems. View Full-Text
Keywords: PV arrays; fault detection; ANFIS; data analysis; module rearrangement PV arrays; fault detection; ANFIS; data analysis; module rearrangement
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MDPI and ACS Style

Bendary, A.F.; Abdelaziz, A.Y.; Ismail, M.M.; Mahmoud, K.; Lehtonen, M.; Darwish, M.M.F. Proposed ANFIS Based Approach for Fault Tracking, Detection, Clearing and Rearrangement for Photovoltaic System. Sensors 2021, 21, 2269. https://doi.org/10.3390/s21072269

AMA Style

Bendary AF, Abdelaziz AY, Ismail MM, Mahmoud K, Lehtonen M, Darwish MMF. Proposed ANFIS Based Approach for Fault Tracking, Detection, Clearing and Rearrangement for Photovoltaic System. Sensors. 2021; 21(7):2269. https://doi.org/10.3390/s21072269

Chicago/Turabian Style

Bendary, Ahmed F., Almoataz Y. Abdelaziz, Mohamed M. Ismail, Karar Mahmoud, Matti Lehtonen, and Mohamed M. F. Darwish. 2021. "Proposed ANFIS Based Approach for Fault Tracking, Detection, Clearing and Rearrangement for Photovoltaic System" Sensors 21, no. 7: 2269. https://doi.org/10.3390/s21072269

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