Scientist develops machine-learning method to identify faulty solar panels

A scientist in Sweden has developed a new hybrid local features-based method using thermographs to identify faulty solar panels. A researcher from Sweden's Jönköping University has proposed a machine learning-based health monitoring approach for PV systems using infrared thermography. The method is based on a hybrid local features-based approach to monitor panels, designed to resist scaling, noise, rotation, and haze. It achieved 98% training accuracy and 96. 8% testing accuracy. "Existing image processing-based machine learning approaches for health and fault diagnosis are often limited to specific ...

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