Work place: K J Somaiya School of Engineering (formerly known as K J Somaiya College of Engineering), Somaiya Vidyavihar University, Vidyavihar, Mumbai, 400077, India
E-mail: sonalipatil@somaiya.edu
Website: https://orcid.org/0000-0002-4358-4937
Research Interests:
Biography
Sonali A. Patil is a Professor in the Department of Information Technology at K.J. Somaiya College of Engineering, Somaiya Vidyavihar University, Mumbai, India. She currently also serves as the Associate Dean for Academic Programmes. She earned her Ph.D. in Technology, and holds an M.E. degree in Electronics from Walchand College of Engineering, Sangli. Dr. Patil has over 22 years of teaching experience and one year of industrial experience. She is a recognized postgraduate (PG) teacher at both the University of Mumbai and Somaiya Vidyavihar University. She has supervised numerous M.Tech students in the fields of information security and computer engineering. As a recognized Ph.D. supervisor, she has successfully guided one Ph.D. scholar and is currently supervising five more doctoral students. Her current research interests include Geographic Information Systems (GIS), image processing, and image analysis. She has authored several publications in peer-reviewed international journals, conference proceedings, and edited volumes, particularly in the domains of image processing, information security and geographical information systems. In addition to her academic contributions, she holds one patent and two registered copyrights.
By Sujata P. Pathak Sonali A. Patil
DOI: https://doi.org/10.5815/ijigsp.2025.04.01, Pub. Date: 8 Aug. 2025
Solar power stands as a pivotal renewable energy source for the twenty-first century. However, the optimal functioning of solar panels is often hindered by various faults, necessitating accurate and early defect detection to maximize energy production. Existing solar panel fault identification models encounter challenges such as low precision, difficulty in distinguishing fault types, and poor generalization due to limited and unbalanced data samples. This paper introduces a novel and effective approach, leveraging a Binary Cascaded Convolutional Classifier augmented with visual and thermal image combinations to address these limitations. The proposed model adeptly classifies five distinct types of solar panel faults, including single cell hotspots, diode hotspots, dust/ shadow hotspots, multicell hotspots, and Potential-Induced Degradation (PID) hotspots. Through image augmentation techniques like rotation, shifting, sheering, resizing, jittering, and blurring applied to visual and thermal images, inter-class feature variance is increased. Binary Cascaded Convolutional Neural Network (BCCNN) classifiers are trained using an enriched dataset, each specifically designed to differentiate between dust/ shadow hotspots and other fault categories. The adoption of a binary method significantly enhances precision, allowing for focused fault identification and classification. The proposed model surpasses existing literature in terms of precision (99.8%), accuracy (98.5%) and recall (98.4%), underscoring its effectiveness across all five fault classes. In summary, this research marks a substantial advancement in the realm of solar panel fault identification, presenting a more precise and effective fault detection methodology that has the potential to significantly enhance the maintenance and longevity of solar energy systems.
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