An Efficient Libed and GBLRU-Based Solar Panel Hotspot Detection System Using Thermal Images

In the Photovoltaic (PV) system, monitoring, assessing, and detecting the occurred faults is essential. Autonomous diagnostic models are required to examine the solar plants and to detect the anomalies within these PV panels since the prevailing hotspot detection models were unable to detect the faults rapidly and consistently. A novel Log Inverse Bilateral Edge Detector (LIBED) and Gated Bernoulli Logmax Recurrent Unit (GBLRU)-centered Solar Panel (SP) hotspot detection scheme is proposed in this research that analyzed the operating PV module’s thermal images. Images are applied for the image processing steps prior to hotspot detection. By utilizing the Contrast Limited Adaptive Histogram Equalization (CLAHE) model, the image’s contrast has been augmented in the image processing step. The alpha (α) Modified Histogram Blending (αMHB) method is utilized to eliminate the outlier data available in the image. Subsequently, an effective LIBED contour detection method was utilized to detect the SP. Several features are extracted by utilizing the detected panels. Then, optimal features are chosen as of the extracted features by utilizing the Barnacles Mating Optimizer (BMO) algorithm. The GBLRU was utilized to predict the defective panels. The defective panels’ hotspots were isolated by utilizing the Haversine Self-Organizing Map (HSOM) model. The experimental evaluation of the proposed system’s performance is analyzed with the prevailing classifiers. The state-of-art methods were outperformed by the proposed GBLRU-based Hotspot detection system. The efficiency 94.34%, accuracy 97.23%, hot-spot detection rate 91.23% had been attained which were improved outcomes compared to existed models.