If there are local defects in the solar cell module, the output efficiency of the module will decrease. This article introduces the detection of local defect of solar cell module based on the infrared image technology. It finally establishes an image library which contains some infrared images of seven kinds of defects and a basic standard which can judge the quality of the module. The reasons of some defects are listed in the article. The unqualified rate will greatly decrease for the technique. The research is helpful to eliminate the module that contains recessive defects. The life of the modules also will be extended.

Automatic identification of defects on solar cell modules is the key of improving components efficiency of power generation and achieving the maximum utilization of energy[1]. This paper took the debris defects as an example. Considered the fact that scale factor would have an impact on moment invariants in discrete state, this paper adopted an improved moment invariant for the image feature extraction of debris defects. The scale factor of the new moment invariants is eliminated by transformation. Therefore they have the properties of translation, rotation and scaling simultaneously. Meanwhile, SVM (Support Vector Machine) classifier was used to identify the debris defects. The experimental results show that, compared with the traditional moment invariants, the identification rate of using the improved moment invariants for feature extraction is 96%.

Firstly the paper introduces solar modules' internal structure and its equivalent circuit. According to different shading situation output characteristic of solar modules, the problem was analyzed by equivalent circuit. Through the actual shading test, the same type of solar modules in different shading mode solar module I-V characteristics and the influence of power were measured. Finally, the shading of effects the different types of solar modules was comparative analyzed. Research results can be used as the reference of solar modules installation location.