Thermo-Mechanical Assessment And Defect Detection In Photovoltaic Modules

Photovoltaic(PV)has emerged as a promising and phenomenal renewable energy technology in the recent past and the PV market has developed at an exponential rate during the time.However,a large number of early failure and degradation cases are also observed leading to underperformance and unreliability.The understanding of module’s thermo-mechanical behavior,fast and accurate measurement of defects,and impact assessment of defects is important to enhance the PV system performance,lifetime and reliability.On the other hand,conventional visual monitoring and assessment process is commonly used in the field,which is mainly dependent upon human abilities and often involve human error.Moreover,it is only practicable on small-scale and requires long time.With the rising use of PV solar energy and ongoing installation of large-scale PV power plants worldwide,the automation of PV monitoring and assessment methods becomes important.Here,the present study focuses on thermo-mechanics,non-destructive/non-contact defect measurements,and artificial intelligence/deep learning based automatic defect detection in PV modules.This study comprises five main parts as given subsequently.In the first part,thermo-mechanical behavior of smart wire connected(SWCT),and busbar(BB)PV modules during their entire life is studied to identify defects/cracks sensitive regions.The behavior of these modules in response to loads experienced during manufacturing,transportation,and subsequent field loading stages is investigated.In BB modules,the solar cell regions/parts near busbar ends are found as high stressed regions and therefore,are more sensitive to origination of cracks.Moreover,the SWCT and BB interconnection techniques are compared.During manufacturing phase,39.3 MPa and 40.4 MPa stresses are induced in SWCT cells and copper wires respectively;while,60 MPa and 87 MPa stresses are induced in BB cells and BBs respectively.Similar to manufacturing,relatively lower stresses are generated in SWCT modules during subsequent life stages.SWCT is found to be a relatively less stress generating process and less prone to thermal and dynamic load conditions.In the second part of study,an improved approach for detecting defects in PV modules using thermography,electroluminescence(EL)spectroscopy and current-voltage(Ⅰ-Ⅴ)measurements in combination is proposed.The measurements are carried out on normal operating and defective modules.The modules are analyzed qualitatively and quantitatively.The defective area is computed with an image processing scheme and the defects are correlated with deviations in Ⅰ-Ⅴ curve characteristics,power losses and degradation.In the third part of study,an improved outdoor thermography scheme based on temperature modulation in PV modules through altering the electrical behavior of single cell is proposed for detecting defects.The proposed scheme provides more detailed information about defects in outdoor infrared(IR)images.A new performance factor based on translated power output is also developed and evaluated that represents the quantitative impact of defects.In addition,an image processing scheme to acquire precise information about defective regions in IR images is presented.In the fourth part of study,a state-of-the-art deep learning-based defect detection technique is proposed for automatic detection of PV cell defects in EL images.The framework is practical and applied for experimental testing of PV cells.The cracks and other kinds of defects detectable from EL imaging are also discussed.In the fifth part,isolated deep learning and develop-model transfer deep learning based frameworks are proposed for automatic detection of PV module defects in IR images.These frameworks achieved state-of-the-art results and are practical for experimental testing.Furthermore,various kinds of defects shown up by IR imaging are also discussed.