Real-Time Anomaly Detection in Solar Panel Arrays: Integrating Single Shot Multibox Detector (Ssd) With Iot and Edge Computing

Convolutional Neural Networks (CNNs) have revolutionized feature extraction for fault detection in solar panels by using hierarchical spatial extraction using convolutional layers These networks reveal important features such as cracks, hotspots, and internal cell anomalies while reducing redundancy. Using pre-trained algorithms such as ResNet and VGGNet enhances transfer learning and accelerates convergence, strengthens model accuracy error detection Noise filtering techniques including a Gaussian filter, mean filtering, and a Fast Fourier transform (FFT) for error detection. Eliminating image noise when storing information which is important for real-time fault detection, the Single Shot Multibox Detector (SSD) efficiently predicts bounding box-class probabilities with its multi-scale feature detection and anchor box mechanism. This simultaneous detection of faults in large solar panel arrays is possible. IoT sensors support these processes by providing real-time assessment of system integrity and environmental conditions, supported by edge computation for minimal latency fault by adaptive unsupervised learning approaches by separation forest algorithms integrated for anomaly detection. The knowledge is further enhanced by integrating these techniques. A comprehensive framework is provided for solar panel analysis, fault detection, and better performancepage margins and justified.