Solar Panel Faults? VGG19 & Jellyfish to the Rescue!

"Solar Panel Faults? VGG19 & Jellyfish to the Rescue!" explores how the VGG19 deep learning model, combined with Jellyfish optimization, accurately detects and classifies solar panel defects. This AI-powered approach enhances fault detection efficiency, boosts energy output, and ensures faster, smarter maintenance in photovoltaic systems for sustainable energy solutions.

Solar panels can suffer from various faults like cracks, hotspots, and delamination that reduce energy efficiency and pose safety risks. Traditional inspection methods are often manual, time-consuming, and error-prone. Enter VGG19—a powerful convolutional neural network (CNN)—and the Jellyfish Search Optimizer, a nature-inspired metaheuristic algorithm.

VGG19, a deep learning model known for its strong feature extraction capabilities, is used to analyze thermal and visual images of solar panels. It automatically learns to detect subtle patterns and abnormalities associated with faults. However, training deep learning models like VGG19 can be computationally intensive and sensitive to hyperparameters.

That’s where the Jellyfish Search Optimizer comes in. Inspired by the swarm behavior of jellyfish in ocean currents, this algorithm fine-tunes VGG19’s parameters to enhance accuracy and speed. It ensures that the model doesn’t just detect faults—but does so with high precision and minimal false alarms.

Together, VGG19 and Jellyfish create a smart, automated inspection system that enables real-time, scalable fault detection in solar farms. This innovation reduces maintenance costs, minimizes energy loss, and accelerates the shift toward reliable, AI-driven renewable energy solutions.

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