Research On Hot Spot Fault Detection Of Photovoltaic Cells

Photovoltaic power generation is developing rapidly as a clean and renewable energy.However,photovoltaic hot spots have seriously affected the power generation performance and life of photovoltaic modules.This article aims to understand the formation mechanism of hot spot failures,simulate the formation of hot spots,analyze the hot spot effect,and construct The hot spot feature value database improves the efficiency of photovoltaic cell hot spot fault detection.In this paper,the theoretical research on the hot spot of photovoltaic cells is carried out.Based on the engineering model of traditional photovoltaic panels,the hot spot model of photovoltaic cells under shadow shading is derived,and the engineering mathematical model of the surface heat and output current of photovoltaic panels under partial shadow conditions is constructed,and obtained The surface heat of the photovoltaic panel has a quadratic function relationship with the output current.Secondly,Matlab/Simulink simulation is used to analyze the influence of parameters such as light intensity and temperature on the electrical characteristics of photovoltaic cells.The conclusions include that light intensity is positively correlated with the short-circuit current of photovoltaic modules,and temperature is inversely proportional to the open circuit voltage of photovoltaic modules.The shadow shielding method and the injection current method are used to simulate the occurrence of hot spots in the photovoltaic cell array.Analyze the changes of module parameters by changing the photovoltaic cell connection mode,shading conditions and current value.The results show that: the greater the shadow shading,the lower the total output power of the photovoltaic module;the higher the injection current value,the higher the power loss of the monomer,and the trend of temperature rise satisfies the quadratic function.In order to simulate the actual UAV thermal imaging detection method,the laboratory built a hot spot simulation UAV detection experimental platform,and changed the input current,temperature and thermal imaging sensor detection height,ambient temperature and wind intensity to obtain the photovoltaic panel surface temperature change curve and Thermal imaging maps,establish a segment function that characterizes the statistical values of pixels at different temperatures,and use K-means clustering algorithm to build a feature value database to locate faulty photovoltaic panels.The research results show that the input current has controllability on the surface temperature of solar photovoltaic panels.The increase in input current will increase the surface temperature of photovoltaic panels.For every 0.2A increase,the surface temperature of photovoltaic panels will increase by 2℃ on average.When the temperature sensor is used to detect the hot spot of the photovoltaic panel,the data obtained is more significant when the sensor height is 45 mm.When the simulated UAV platform is tilted at 15° and 20°,the detection data is more obvious,and the detection results of platform vertical and tilt are roughly the same.Changing the local environmental temperature and wind has little effect on the temperature detection results.When the thermal imaging sensor is used to detect the hot spots of photovoltaic panels,the infrared thermal imaging feature library constructed by the proposed segmentation function and K-means clustering hybrid algorithm can accurately locate the hot spots.