Service Lifetime Prediction Of Photovoltaic Modules Based On Life Field

Photovoltaic（PV）modules are one of the core components of a PV power generation system,and accurately understanding their lifetime under actual usage conditions is of great significance for evaluating the investment efficiency and maintenance costs of PV power generation systems.At present,the service life of PV modules is mainly predicted by the method of laboratory accelerated life test and the method of modeling based on field performance degradation data.However,both methods have limitations:the former cannot fully simulate the various influencing factors in actual environments,leading to significant deviations from actual engineering results;while the latter is limited by the geographic variability of data,making its prediction results only applicable to the source data location.Since the geographic area variability of PV module service lifetime is caused by different levels of performance degradation influencing factors in different areas,the service lifetime of PV modules should be consistent if the performance degradation influencing factors are consistent.Furthermore,as the influencing factors are consistent and continuous,the service lifetime of PV modules will be continuously distributed in this geographic region,and the continuous field function can be used to describe its distribution quantitatively.Based on this,this study systematically carried out the following research work on PV modules applied to the Chinese mainland geographic region:Firstly,the physical structure of PV modules was analyzed,and the performance characteristics and failure modes of each component were explained.Based on this,the Failure Mode and Effects Analysis（FMEA）method was applied to determine the eight quantifiable key factors that affect the performance degradation of PV modules:precipitation,wind speed,temperature,relative humidity,sunshine duration,solar radiation,PM10 concentration,and SO₂/NO₂concentration.Then these factors were quantitatively described.Secondly,to solve the issue of missing data on key factors that may exist in some prefecture-level cities,spatial interpolation was used to process the data.And the interpolation accuracy was evaluated through cross-validation.The interpolation result with the highest accuracy was ultimately selected as the value for missing data.Thirdly,considering the time weight of the key factors,the panel data model of its influencing factors was constructed,geographic clustering of the key factors was analyzed with the Fuzzy C-Means algorithm,which the geographic regions with the most consistent eight influencing factors were divided into one category.Finally,for specific geographic region categories,a lifetime field model of PV modules was established based on latitude and longitude coordinates.And the model was solved using the service lifetime data of PV modules at known geographic locations within the region.A lifetime field model of PV modules within the clustering region was constructed for the four provinces of Guangdong,Hainan,Guangxi,and Fujian,which constitute the study area.The relative errors of the predicted results based on the lifespan field model for Guangzhou,Shenzhen,and Zhuhai were 3.7149%,8.1525%,and 6.6753%,respectively,revealing the distribution pattern of PV module lifetime.The paper proposes a lifetime field model based on regional clustering that can predict the service lifetime of PV modules in the same geographical region.It can reveal the distribution service lifetime of photovoltaic modules in this region and provide reference for photovoltaic component installation and maintenance.