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Photovoltaic power generation is an important part of the current new energy sources. With the development of photovoltaic power generation technology, the corresponding security operation technology has become a new research topic. In this paper, a multi-level fire detection platform based on multi-source heterogeneous information fusion is proposed. The platform includes two levels of detection, level 1 is smoke detection through video monitoring data. Level 2 is the fire detection of data collected by meteorological monitoring. Through the two-level detection, the fire hazard situation and fire hazard location can be accurately located.

Solar cell efficiency is one of the most concerned issues during the photovoltaic power generation. The existed micro-crack detection approaches mainly rely on manual work with the detection efficiency is low. Besides, there lacks exploration in algorithms for separating the region of damage without much workforce. In this paper, we propose a two-stage deep scheme, especially for the polycrystalline micro-crack detection method. A region of interest (ROI) proposal method based on the canny feature and the wavelet feature for micro-crack is raised. The computational efficiency and detection accuracy are greatly improved. Firstly, we split the original electroluminescent image (EL image) by its grid line and cut them into fixed-size squares. Then the ROI proposal method is applied to extract candidate boxes as the inputs of the second stage. At the second stage, a modified convolution neural network based on the candidate boxes is supervised by binary labels. Our experimental results demonstrate that our polycrystalline micro-crack detection scheme outperforms other traditional frameworks. This work is the first attempt to solve engineering problems about micro-crack by deep learning techniques in photovoltaic systems, without many high-quality labels and computing power.