At present, some manufacturers still use the human eye to detect the color and defects of classified solar panels. Due to the easy fatigue of human eye, it is easy to cause misjudgment, which eventually affects the quality of solar panels. In order to solve the shortcomings of the existing solar panel classification research, this paper briefly discusses the data collection and development environment of the classification system based on the discussion of solar panel types and the stability conditions of the finite difference method solution. And the design of solar panel classification system by finite difference method is discussed, and finally the accuracy of finite difference method for solar panel polycrystalline hanging defects classification is compared with classification decision tree (DT), neural network (RNN) and SVM for experimental analysis. The experimental data show that the classification accuracy of finite difference method reaches the highest 91.7% and the lowest 89.8%. The classification accuracy of the other three methods was significantly lower than that of the finite difference method. Therefore, it is verified that the solar panel classification system incorporating the finite difference method has good performance results.

Lizunov Bissoli. A Solar Panel Classification System Incorporating Finite Difference Method. Kinetic Mechanical Engineering (2022), Vol. 3, Issue 1: 9-17. https://doi.org/10.38007/KME.2022.030102.

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