Proposed ANFIS Based Approach for Fault Tracking, Detection, Clearing and Rearrangement for Photovoltaic System.
Ahmed F. Bendary,Almoataz Y. Abdelaziz,Mohamed Ismail,Karar Mahmoud,Karar Mahmoud,Matti Lehtonen,Mohamed M. F. Darwish,Mohamed M. F. Darwish +7 more
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In this paper, an adaptive neuro-fuzzy inference system-based fault detection approach is proposed for photovoltaic systems, which 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.Abstract:
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.read more
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References
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Journal ArticleDOI
ANFIS: adaptive-network-based fuzzy inference system
TL;DR: The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference System implemented in the framework of adaptive networks.
Journal ArticleDOI
Artificial intelligence techniques for photovoltaic applications: A review
Adel Mellit,Soteris A. Kalogirou +1 more
TL;DR: The paper outlines an understanding of how AI systems operate by way of presenting a number of problems in photovoltaic systems application, mainly because of their symbolic reasoning, flexibility and explanation capabilities.
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Successive identification of a fuzzy model and its applications to prediction of a complex system
Michio Sugeno,Kazuo Tanaka +1 more
TL;DR: It is shown from two examples that the successive identification method of a fuzzy model is very useful for modeling complex systems.
Journal ArticleDOI
Automatic supervision and fault detection of PV systems based on power losses analysis
Aissa Chouder,Santiago Silvestre +1 more
TL;DR: In this paper, an automatic supervision and fault detection procedure for PV systems, based on the power losses analysis, has been presented, which includes parameter extraction techniques to calculate main PV system parameters from monitoring data, taking into account the environmental irradiance and module temperature evolution.
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