Using Spectral Decomposition to Detect Dirty Solar Panels and Minimize Impact on Energy Production
AbstractDirt and dust deposits on the surface of a solar panel array obstruct the amount of light that can reach the photovoltaic cells, reducing the amount of electricity produced. Solar panels are cleaned when the energy drop has already occurred and is detected. This work presents an algorithm designed to detect dirty solar panels. It is based on the spectral decomposition of the scattered light reflected off the panels' surface by analyzing color images of the surface obtained using digital cameras. It applies the statistical classification method of Mahalanobis distance to separate images where it detects the excess reflected light, classifying them as having a high probability of representing dirty solar panels. It aims to minimize the loss of energy by warning solar plants operators to clean panels before the energy drop becomes significant.
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