Deep neural network based approach for detection of defective solar cell

Abstract

Abstract In the current scenario, renewable energies play a crucial factor to fulfil the increasing power demand in accordance with environment protection. With the advancement of solar energy, this reduces the gap of providing the power supply with the protection to the environmental parameter. However, it is also analyzed that the efficiency of power supply degrades due to the defects of solar cell. Electroluminescence (EL) image can be found to be the source for visually identifying the various kinds of defects. Although manual approach for classifying the EL imaging for identifying the defect solar is possible but those methods suffers from the time and cost factors. In order to overcome these constraints, a simple and elegant deep learning based approach is proposed in this paper to classify the different kinds of defects present in the EL image of solar cell. In this work, we first pre-process the EL image for removing the noise and distortions, then used Deep Siamese convolution neural network (CNN) for classifying the different kinds of defects present in the solar cell. Proposed model is tested on the standard EL image dataset. Simulation results show the proposed model provides better classification accuracy towards finding the defective solar cell.