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: sujatapathak@somaiya.edu
Website: https://orcid.org/ 0000-0002-7596-8425
Research Interests:
Biography
Sujata P. Pathak is working as an Assistant Professor in the Department of Information Technology at K.J. Somaiya College of Engineering, Somaiya Vidyavihar University, Mumbai (India). She received M.E. degree in Computer Engineering from Mumbai University in 2010.
She is a recognized PG teacher at the University of Mumbai. She has supervised many M. Tech. students in information security. She is PhD Research Scholar in Computer Engineering department and working as an Assistant Professor in Information technology department of K J Somaiya College of Engineering, SVU, Mumbai, INDIA. She has rich experience of 22 years in Teaching. She is life member of Indian Society of Technical education (ISTE) and student member of IEEE. She has more than 15 papers in National/International conferences and journals to her credit. Her areas of interest are Image Processing, Soft computing, and deep learning. In addition to academic performance, she has one patent and one copyright registered.
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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