Research On Simulation And Detection Of Temperature Field Of Solar Cell Leakage State

As the main material of my country’s solar energy industry,crystalline silicon solar cells have developed into indispensable components in solar photovoltaic systems.However,crystalline silicon solar cells are prone to leakage defects due to the special reasons of their manufacturing process.In the photovoltaic array system,the power generation capacity of the shielded leakage solar cells will be greatly reduced,which is equivalent to being reversed to become a load in the system,consuming the energy of the system,causing local overheating in the system and causing the hot spot effect.May cause a safety accident.Therefore,it is very necessary to conduct real-time detection and evaluation of solar cells during the production process.The thesis researches from two aspects: the temperature field simulation of the leakage state of the solar cell and the detection of the leakage defect.In solar cells,the carrier state is closely related to temperature.Therefore,this paper proposes to analyze the electric heating effect of leakage solar cells under reverse bias voltage from the perspective of microscopic carriers,and derives the expression of heat generation of leakage solar cells under microscopic characterization.Then,using heat transfer theory to analyze the heat conduction process of the leakage solar cell,establish its heat transfer model,and through the finite element simulation analysis,obtain the temperature distribution changes on the surface of the leakage solar cell,analyze the different reverse bias,The influence of different defect concentrations on the surface temperature of the solar cell,and the influence of leakage solar cells on the output characteristics of the solar cell.The simulation results show that the shielded leakage solar cell is prone to breakdown at high temperatures,and the solar cell leakage defect will significantly reduce the performance of the solar cell.In terms of data collection,the defect detection of the leakage solar cell is carried out through the infrared thermal imaging detection system,the infrared image sequence of the leakage solar cell is obtained,and the sequence characteristics are analyzed.Finally,perform image processing on the collected leakage solar cell image sequence to segment and locate defects: propose a dynamic median filter combined with Gaussian smoothing method to denoise the image sequence,and then use homomorphic filtering to enhance the image sequence;propose the Canny algorithm The method of sub-combined curve fitting eliminates the reflection of the image;finally,the independent principal component analysis algorithm and the spectral clustering algorithm are combined to extract and locate the leakage defect.Among them,aiming at the scale parameters of the Gaussian kernel function in the spectral clustering algorithm need to be manually adjusted,it does not have the shortcomings of self-adaptation,and proposes an adaptive spectral clustering algorithm that replaces fixed-scale parameters with local standard deviations.Experiments show that,compared with traditional image segmentation algorithms,the adaptive spectral clustering algorithm can completely retain the edge information of defects while reducing the error rate of solar cell leakage defect detection to 15.4%.