Advanced technology detects dust on solar panels for efficiency. #SolarPanelMaintenance

Researchers from China and the UK have developed a new dust detection method for photovoltaic (PV) systems using an improved version of the Adam optimization algorithm. The Adam algorithm is commonly used to train networks, but the researchers integrated Warmup and cosine annealing strategies to overcome issues such as falling into local optima and high computational complexity. They obtained a dataset of PV module images and tested the algorithm with a special neural network architecture known as ResNet-18, achieving competitive results with an accuracy of 72.45% and a loss of 0.5301. The improved algorithm also showed better performance in terms of training time and efficiency of dust detection on the surface of PV panels compared to the non-optimized Adam algorithm. The researchers concluded that the improved algorithm is suitable for dust detection on the surface of PV panels in various large photovoltaic power plants. The results of their study were published in Energy and AI and included academics from China’s Shenyang University and Shenyang University of Technology, as well as the University of Surrey in the UK. The researchers also tested the improved Adam algorithm in different neural network architectures, namely ResNet-18, VGG-16, and MobileNetV2, and found that it achieved significantly improved efficiency of dust detection on the surface of photovoltaic panels compared to the Adam algorithm.

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Source link: https://www.pv-magazine.com/2024/02/16/deep-learning-tech-for-dust-detection-in-solar-panels/