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                <text>Conference Papers</text>
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              <text>A Intelligent Approach for Fault Detection in Solar Photovoltaic Systems based on BERT-BiGRU Network</text>
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              <text>Gated Recurrent Unit (GRU); Solar photovoltaic (PV); Transform Invariant Low-rank Textures (TILT)</text>
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              <text>Large-scale photovoltaic (PV) plant problem identification and diagnosis is expected to grow more difficult in the future as more and more plants of increasing capacity enter into existence. To keep large-scale PV installations safe, reliable, and productive, automatic identification and localization of any mal-operation among thousands of PV modules is necessary. In order to identify problems in PV plants, the suggested method compares the 'residuals' (fault indicator signals) generated by each string to a predetermined threshold. The suggested method relies on three distinct processes: data preparation, feature extraction, and model training. Preprocessing employs the method of Transform Invariant Low-rank Textures (TILT). The most useful and efficient measurements from feature extraction are kept while less important ones are discarded in favor of the Reduced Kernel PCA technique. Let's move on to model training with BERT-BiGRU. The proposed method is clearly superior compared to the two leading options, BERT and GRU. The proposed method had a 97.36% success rate.  2023 IEEE.</text>
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              <text>Hafeezuddin M.; Garapati B.; Vijayakumar T.; Priyanka G.; Chauhan A.; Vidya A.</text>
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              <text>International Conference on Sustainable Communication Networks and Application, ICSCNA 2023 - Proceedings, pp. 685-690.</text>
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              <text>Institute of Electrical and Electronics Engineers Inc.</text>
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              <text>2023-01-01</text>
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              <text>&lt;a href="https://doi.org/10.1109/ICSCNA58489.2023.10370188" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ICSCNA58489.2023.10370188&lt;/a&gt;
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              <text>Restricted Access</text>
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              <text>ISBN: 979-835031398-7</text>
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              <text>Online</text>
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              <text>Conference paper</text>
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              <text>Hafeezuddin M., Geethanjali College of Engineering and Technology, Department of Eee, Hyderabad, India; Garapati B., Geethanjali College of Engineering and Technology, Department of Eee, Telangana, Cheeryal, India; Vijayakumar T., Dr.Mahalingam College of Engineering and Technology, Department of Artificial Intelligence and Data Science, Coimbatore, India; Priyanka G., Sri Eshwar College of Engineering, Department of Computer Science and Business Systems, Coimbatore, India; Chauhan A., School of Sciences, Christ (Deemed to Be University), Department of Life Sciences, Karnataka, Bengaluru, India; Vidya A., K.Ramakrishnan College of Engineering, Department of Electronics and Communication Engineering, Trichy, Samayapuram, India</text>
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