PV module fault diagnosis uses convolutional neural network

Researchers in China have created a dataset of various PV faults and normalized it to accommodate different array sizes and typologies. After testing the new approach in combination with the 1D-CNN machine learning technique under different conditions, they found an accuracy of more than 99%. Researchers from China's Shandong University have developed a novel method for fault diagnosis in PV arrays, using feature engineering and one-dimensional convolutional neural networks (1D-CNN). The novelty of the technique lies in the feature engineering step, which normalizes raw inputs and makes them ...

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