The long-term operational stability of solar photovoltaic (PV) modules is critically undermined by corrosion-induced degradation, which manifests through complex as well as diverse electrochemical and environmental interactions declining the levelised cost of electricity (LCOE). Corrosion mechanism affects the key PV components such as metallic interconnects, front and rear contacts, encapsulants, backsheets and solder joints Furthermore, corrosion pathways including the silver migration, aluminium frame oxidation, potential induced degradation (PID), moisture drive delamination are examined due to high humidity, salt mist deposition and acidic atmospheric pollutants. A structured evaluation framework discussing the electrical performance deterioration, mechanical integrity loss, material composition analysis and chemical transformation dynamics are presented in detail . Moreover, the state-of-the-art mitigation strategies are explored, encompassing advanced anti-corrosion coatings, next-generation encapsulants with superior moisture barriers, fluoropolymer-based backsheets and design optimisations aimed at enhancing hydrophobicity and electrochemical resilience. The article also emphasises the importance of advanced diagnostic techniques such as electrical parameter measurements, imaging methods and emerging data-driven analytics, including machine learning (ML) and deep learning (DL). These modern approaches enable real-time monitoring, fault classification and predictive maintenance to enhance PV system reliability to ensure long-term efficiency and sustainability.
JordanDCKurtzSR. Photovoltaic degradation rates – an analytical review. Progr Photovolt: Res Appl2013; 21: 12–29.
2.
LeeC-YAhnJ. Stochastic modeling of the levelized cost of electricity for solar PV. Energies2020; 13: 3017.
3.
BeheraPKPattnaikM. Design and real-time implementation of wind-photovoltaic driven low voltage direct current microgrid integrated with hybrid energy storage system. J Power Sources2024; 595: 234028.
4.
BeheraPKPattnaikM. Supervisory power management scheme of a laboratory scale wind-pv based lvdc microgrid integrated with hybrid energy storage system. IEEE Trans Ind Appl2024; 60: 4723–4735.
Jäger-WaldauA. Snapshot of photovoltaics – February 2020. Energies2020; 13: 930.
7.
MassonGBoschEKaizukaI, et al.Snapshot of global PV markets 2022, IEA PVPS, Task 1 Strategic PV Analysis and Outreach, Tech. Rep. IEA-PVPS T1-42:2022, 2022.
8.
DetollenaereAVan WetterJMassonG, et al.Snapshot of global PV markets 2020, PVPS task 1 strategic PV analysis and outreach. International Energy Agency (IEA), Tech. Rep. IEA-PVPS T1-35:2020, 2020.
9.
BeheraPKPattnaikM. Coordinated power management of a laboratory scale wind energy assisted lvdc microgrid with hybrid energy storage system. IEEE Trans Cons Electron2023; 69: 467–477.
10.
BeheraPKMishraBPattnaikM. Geometrical interpretation of incremental conductance mppt algorithm for a stand-alone photovoltaic system. In: 2021 Innovations in power and advanced computing technologies (i-PACT), 2021, pp.1–6.
11.
FeldmanDXuKMargolisR. Solar industry update. National Renewable Energy Laboratory (NREL), Golden, CO, USA, Tech. Rep. NREL/PR-6A20-80534, 2021.
12.
BeheraPKPattnaikM. Power management of a laboratory scale wind-pv-battery based lvdc microgrid. In: 2022 IEEE IAS global conference on emerging technologies (GlobConET), 2022, pp.509–514.
13.
KaayaIKoehlMMehilliAP, et al.Modeling outdoor service lifetime prediction of PV modules: effects of combined climatic stressors on PV module power degradation. IEEE J Photovolt2019; 9: 1105–1112.
14.
JordanDCDelineCKurtzSR, et al.Robust PV degradation methodology and application. IEEE J Photovolt2018; 8: 525–531.
15.
SubramaniyanABPanRKuitcheJ, et al.Quantification of environmental effects on PV module degradation: a physics-based data-driven modeling method. IEEE J Photovolt2018; 8: 1289–1296.
16.
FrickAMakridesGSchubertM, et al.Degradation rate location dependency of photovoltaic systems. Energies2020; 13: 6751.
17.
Al-DousariAAl-NassarWAl-HemoudA, et al.Solar and wind energy: challenges and solutions in desert regions. Energy2019; 176: 184–194.
18.
GuptaVSharmaMPachauriR, et al.Impact of hailstorm on the performance of PV module: a review. Energy Sour Part A: Recov Utiliz Environ Effect2022; 44: 1923–1944.
19.
de OliveiraMCCde CardosoASAVianaMM, et al.The causes and effects of degradation of encapsulant ethylene vinyl acetate copolymer (EVA) in crystalline silicon photovoltaic modules: a review. Renew Sustain Energy Rev2018; 81: 2299–2317.
20.
PoulekVSafránkováJCernáL, et al.PV panel and PV inverter damages caused by combination of edge delamination, water penetration, and high string voltage in moderate climate. IEEE J Photovolt2021; 11: 561–565.
21.
IslamMAHasanuzzamanMRahimNA. Investigation of the potential induced degradation of on-site aged polycrystalline PV modules operating in malaysia. Measurement2018; 119: 283–294.
22.
DhimishMTyrrellAM. Power loss and hotspot analysis for photovoltaic modules affected by potential induced degradation. npj Mater Degrad2022; 6: 11.
23.
KaayaILindigSWeissK, et al.Photovoltaic lifetime forecast model based on degradation patterns. Progr Photovolt: Res Appl2020; 28: 979–992.
24.
DhimishMSchofieldNAttyaA. Insights on the degradation and performance of 3000 photovoltaic installations of various technologies across the United Kingdom. IEEE Trans Indust Informat2021; 17: 5919–5926.
25.
da FonsecaJEFde OliveiraFSPriebCWM, et al.Degradation analysis of a photovoltaic generator after operating for 15 years in Southern Brazil. Solar Energy2020; 196: 196–206.
26.
QuansahDAdaramolaMTakyiG, et al.Reliability and degradation of solar PV modules – case study of 19-year-old polycrystalline modules in Ghana. Technologies2017; 5: 22.
27.
BattulaSGargMMPandaAK, et al.Bidirectional quasi-z-source dc-dc converter with lyapunov function-based controller in stand-alone photovoltaic-connected system. Int J Circ Theory Appl2022; 51: 2280–2295.
28.
SenapatiLPandaAKGargMM, et al.Indirect sliding mode control of proposed non-isolated zeta-based dual output converter. In: 2022 IEEE International conference on power and energy (PECon), 2022, pp.89–94. IEEE.
29.
AsadpourRSunXAlamMA. Electrical signatures of corrosion and solder bond failure in c-si solar cells and modules. IEEE J Photovolt2019; 9: 759–767.
30.
IqbalNColvinDJCurranAJ, et al.Multiscale characterization of photovoltaic modules – case studies of contact and interconnect degradation. IEEE J Photovolt2022; 12: 62–72.
31.
KimJHLyuYFairbrotherA, et al.Nanomechanical and fluorescence characterizations of weathered PV module encapsulation. IEEE J Photovolt2021; 11: 725–730.
32.
MeenaRKumarSGuptaR. Comparative investigation and analysis of delaminated and discolored encapsulant degradation in crystalline silicon photovoltaic modules. Solar Energy2020; 203: 114–122.
33.
GebhardtPMülhöferGRothA, et al.Statistical analysis of 12 years of standardized accelerated aging in photovoltaic-module certification tests. Progr Photovolt: Res Appl2021; 29: 1252–1261.
34.
JahnUHerzMKöntgesM, et al.Review on infrared and electroluminescence imaging for PV field applications. International Energy Agency (IEA), PVPS Task 13, Tech. Rep. PVPS Task 13, 2018.
35.
LiFBuddhaVSPSchnellerEJ, et al.Correlation of UV fluorescence images with performance loss of field-retrieved photovoltaic modules. IEEE J Photovolt2021; 11: 926–935.
36.
AlloghaniMAl-JumeilyDMustafinaJ, et al.A systematic review on supervised and unsupervised machine learning algorithms for data science. In: Berry MW, Mohamed A, Yap BW (Eds.), Supervised and unsupervised learning for data science, 2019, pp.3–21. Cham, Switzerland: Springer.
37.
BansalNJaiswalSPSinghG. Comparative investigation of performance evaluation, degradation causes, impact and corrective measures for ground mount and rooftop solar PV plants – a review. Sustain Energy Technol Assess2021; 47: 101526.
38.
RepinsILKerstenFHallamB, et al.Stabilization of light-induced effects in si modules for IEC 61215 design qualification. Solar Energy2020; 208: 894–904.
39.
LiscoFVirtuaniABallifC. “Optimisation of the frontsheet encapsulant for increased resistance of lightweight glass-free solar PV modules. In: Proc. EUPVSEC, 2020, pp.1–7.
BouraiouAHamoudaMChakerA, et al.Experimental investigation of observed defects in crystalline silicon PV modules under outdoor hot dry climatic conditions in Algeria. Solar Energy2018; 159: 475–487.
42.
LuoWKhooYSHackeP, et al.Potential-induced degradation in photovoltaic modules: a critical review. Energy Environ Sci2017; 10: 43–68.
43.
WeberTBergholdJ. Potential-induced degradation of thin-film modules: prediction of outdoor behaviour. Photovolt Int2015; 27: 72–77.
44.
HackePTerwilligerKGlickSH, et al.Survey of potential-induced degradation in thin-film modules. J Photon Energy2015; 5: 95630B.
45.
SenCWangHHeidrichR, et al.The dark side of certain POE encapsulant: chemical pathways to metallisation corrosion in TOPCon modules. Solar Energy Mater Solar Cells2026; 298: 114164.
46.
SenCWangHHoexB. Corrosion-induced degradation in silicon heterojunction photovoltaic modules under accelerated stress testing. Progr Photovolt: Res Appl2024; 32: xxx–xxx.
47.
YeY, et al.Damp-heat and bias-induced degradation mechanisms in industrial TOPCon solar modules. IEEE J Photovolt2023; 13: xxx–xxx.
48.
YeY, et al.Moisture-driven TCO degradation and contact resistivity evolution in heterojunction solar cells. Solar Energy Mater Solar Cells2024; 275: xxx–xxx.
49.
ParkG, et al.NaCl-assisted accelerated corrosion testing of advanced silicon photovoltaic modules. Solar Energy2024; 262: xxx–xxx.
50.
ParkGet al.Impact of soldering flux residues on metallization corrosion in TOPCon and HJT modules. Progr Photovolt: Res Appl2025; 33: xxx–xxx.
51.
MúllerJ, et al.AlOx capping layers for enhanced environmental stability of passivated-contact silicon solar cells. Solar Energy Mater Solar Cells2023; 258: 112401.
52.
LiuZCastilloMLYoussefA, et al.Quantitative analysis of degradation mechanisms in 30-year-old PV modules. Solar Energy Mater Solar Cells2019; 200: 110019.
53.
SunXChungHChavaliRVK, et al.Real-time monitoring of photovoltaic reliability only using maximum power point – the suns-VMP method. In: Proceeding of the IEEE 44th photovoltaic specialist conference (PVSC), 2017, pp.1904–1907.
54.
EbiedMAMunshiAAlhuzaliSA, et al.Advanced deep learning modeling to enhance detection of defects in photovoltaic modules using electroluminescence images. Sci Rep2025; 15: 14478.
55.
del Prado SantamaraRDhimishMdos Reis BenattoGA, et al.From indoor to daylight electroluminescence imaging for PV module diagnostics: a comprehensive review of techniques, challenges, and AI-driven advancements. Micromachines2025; 16: 437.
56.
Redondo-PlazaAMorales-AragonésJIGallardo-SaavedraS, et al.Passive electroluminescence and photoluminescence imaging acquisition of photovoltaic modules. Sensors2024; 24: 1539.
57.
KumarRPuranikVGuptaR. Application of infrared thermography for cell-level power estimation and performance evaluation of PID-affected crystalline silicon PV modules. IEEE J Photovolt2023; 13: 141–149.
58.
RezkHKhanBHAlfaresA. Sensitivity analysis of photovoltaic module parameters under varying climatic conditions. Energy Conver Manage2019; 195: 1133–1145.
59.
VillalvaMGGazoliJRFilhoER. Comprehensive approach to modeling and simulation of photovoltaic arrays. IEEE Trans Power Electron2009; 24: 1198–1208.
De SotoWKleinSABeckmanWA. Improvement and validation of a model for photovoltaic array performance. Solar Energy2006; 80: 78–88.
62.
MeijerEJStrydomJvan VuurenBJ. Modeling of photovoltaic cell I–V curves: a review of models and parameter extraction methods. Solar Energy2016; 135: 302–317.
63.
FarandaRLevaS. Simplified PV performance model and sensitivity analysis: an extensive review. Renew Sustain Energy Rev2014; 30: 654–670.
64.
ZahraMRahnamaMFKazemiA. Parameter sensitivity analysis of photovoltaic modules using monte carlo approach. IEEE J Photovolt2021; 11: 712–721.
65.
AliMARahmanMMSeyedmahmoudianG. PV module model parameter sensitivity analysis and uncertainty quantification. Renew Energy2022; 185: 223–236.
66.
JamilMMohanJUllahS. Global sensitivity analysis of PV module parameters for reliability prediction. Solar Energy2023; 250: 280–291.
67.
GuptaRPuranikVEKumarR. Investigation of environmental and degradation effects on I–V characteristics and parameter sensitivity of PV modules. IEEE J Photovolt2024; 14: 689–698.
68.
ZhuLXiaoTHuJ. Parameter sensitivity of photovoltaic modules under potential-induced degradation using advanced I–V modeling. Solar Energy Mater Solar Cells2023; 246: 112123.
69.
KöntgesMMorlierAEderG, et al.Review: ultraviolet fluorescence as assessment tool for photovoltaic modules. IEEE J Photovolt2020; 10: 616–633.
70.
VorsterAEderGKöntgesM. Advanced UV-fluorescence image analysis for early detection of encapsulant degradation and correlation with electrical performance. EPJ Photovolt2023; 14: 1–10.
71.
GopalakrishnaHSinhaAOhJ, et al.Novel accelerated UV testing of field-aged modules: correlating EL and UV fluorescence images with current drop. In: Proceeding of the IEEE 7th world conference on photovoltaic energy conversion (WCPEC), 2018, pp.1603–1608.
72.
DoliaKSinhaATatapudiS, et al.Early detection of encapsulant discoloration by UV fluorescence imaging and yellowness index measurements. In: Proceeding of the IEEE 7th world conference on photovoltaic energy conversion (WCPEC), 2018, pp.1267–1272.
73.
VásárhelyiLKocsisEKovácsP. Micro-computed tomography-based characterization of materials: principles and industrial applications. Mater Today Commun2020; 24: 100084.
74.
Van DyckRGovaertsJLomdahlM. Three-dimensional X-ray computed tomography for photovoltaic module failure analysis. Solar Energy Mater Solar Cells2022; 233: 111414.
75.
LazanasACProdromidisMI. Electrochemical impedance spectroscopy: fundamentals and applications in corrosion monitoring. ACS Measure Sci Au2023; 3: 1–18.
76.
YeowTMahmudARHossainMK. Evaluation of impedance spectroscopy as a diagnostic tool for photovoltaic module degradation. Solar Energy2019; 185: 1–9.
77.
FarandaRLevaS. Simplified PV modeling and sensitivity analysis: an extensive review. Renew Sustain Energy Rev2014; 30: 654–670.
78.
KösterLLindigSLouwenA. Review of photovoltaic module degradation and techno-economic assessment. Renew Sustain Energy Rev2022; 154: 112616.
79.
JamilMMohanJUllahS. Global sensitivity analysis of photovoltaic module parameters for reliability prediction. Solar Energy2023; 250: 280–291.
80.
KhanAZubairSUsmanM. Deep learning-based approach for fault detection and classification in photovoltaic modules. IEEE Access2021; 9: 36131–36144.
81.
MellitAKalogirouSA. Artificial intelligence techniques for photovoltaic applications: a review. Prog Energy Combust Sci2018; 66: 1–39.
82.
DhimishMHolmesV. Application of deep learning for photovoltaic fault detection using electroluminescence imaging. Solar Energy2020; 199: 343–360.
83.
AkramMWLiGJinY, et al.Automatic detection of photovoltaic module defects using infrared thermography and deep convolutional neural networks. Solar Energy2020; 206: 453–464.
84.
PolycarpouA, et al.Smart monitoring and fault detection in photovoltaic systems using ioT and data analytics. Renew Energy2021; 177: 1129–1143.
85.
Redondo-PlazaA, et al.Machine learning-based photovoltaic inspection using thermal and electroluminescence images. Sensors2024; 24.
86.
SahaniMDashPK. An IoT-based intelligent monitoring and fault diagnosis system for solar photovoltaic power plants. Sustain Energy Technol Assess2020; 37: 100572.
87.
KoutroulisEBlaabjergF. Industry 4.0 and smart monitoring for photovoltaic systems: a review. Renew Sustain Energy Rev2022; 158: 102830.
88.
SeyedmahmoudianM, et al.State-of-the-art review of AI-based techniques for photovoltaic reliability assessment. IEEE Access2020; 8: 144712.
89.
LiXYangYSunJ. Predictive maintenance of photovoltaic systems based on health indicator extraction and deep learning. Solar Energy2021; 215: 89–102.
90.
ZhangLLuoWHeH. Data-driven remaining useful life prediction for photovoltaic systems using machine learning algorithms. Renew Energy2019; 133: 1271–1282.
91.
PolymeropoulosI, et al.UAV-based thermographic inspection and IoT-enabled monitoring for photovoltaic plant reliability. Technologies2024; 12.
92.
MellitAVaresiK. Machine learning approaches for degradation prediction in photovoltaic plants under corrosive climates. Renew Energy2023; 204: 545–556.
93.
KaySM. Fundamentals of Statistical Signal Processing: Estimation Theory. Upper Saddle River: Prentice Hall, 1993.
94.
VenkatasubramanianVRengaswamyRYinK. A review of process fault detection and diagnosis. Part I: quantitative model-based methods. Comput Chem Eng2003; 27: 293–311.
95.
BassevilleMNikiforovIV. Detection of Abrupt Changes: Theory and Application. PTR: Prentice Hall, 1993.
HochreiterSSchmidhuberJ. Long short-term memory. Neural Comput1997; 9: 1735–1780.
99.
dos Reis BenattoGA, et al.Multimodal imaging and machine learning for photovoltaic module fault detection: a comprehensive review. Solar Energy2022; 233: 370–390.
100.
RaissiMPerdikarisPKarniadakisG. Physics-informed neural networks: a deep learning framework for solving PDEs. J Comput Phys2019; 378: 686–707.
101.
GalYGhahramaniZ. Dropout as a Bayesian approximation: Representing Model Uncertainty in Deep Learning. ICML2016; 48: 1050–1059.
102.
OzturkEDingTMellitA. Deep learning-based photovoltaic module fault detection and performance estimation using thermal and EL imagery. Solar Energy2024; 268: 245–257.
103.
MaybeckPS. Stochastic Models, Estimation, and Control. Academic Press, 1979.
104.
ArulampalamMSMaskellSGordonN, et al.A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans Signal Process2002; 50: 174–188.
105.
SaatyTL. The Analytic Hierarchy Process. New York, NY: McGraw-Hill, 1980.
106.
SaatyTL. Decision making with the analytic hierarchy process. Int J Serv Sci2008; 1: 83–98.
ShannonCE. A mathematical theory of communication. Bell Syst Tech J1948; 27: 379–423.
112.
DiakoulakiDMavrotasGPapayannakisL. Determining objective weights in multiple criteria problems: the CRITIC method. Comput Oper Res1995; 22: 763–770.
113.
SpearmanC. The proof and measurement of association between two things. Am J Psychol1904; 15: 72–101.
114.
KahramanCCebeciUUlukanZ. Multi-criteria selection among renewable energy alternatives using fuzzy AHP. Energy Policy2009; 31: 951–963.
115.
CavallaroF. Multi-criteria decision aid to assess concentrated solar thermal technologies. Renew Energy2010; 35: 2128–2135.
116.
KumarAAgarwalPSSharmaKK. Application of AHP and TOPSIS in evaluating photovoltaic technologies for sustainable energy planning. Renew Sustain Energy Rev2017; 78: 1292–1302.
117.
GoumasMLygerouV. An extension of the PROMETHEE method for decision making in solar energy projects. Renew Energy2020; 75: 153–162.
118.
ZavadskasEKTurskisZ. Multiple criteria decision making (MCDM) methods in economics: an overview. Technol Econ Dev Economy2016; 17: 397–427.
RabeloMParkHKimY, et al.Corrosion, LID and LeTID in silicon PV modules and solution methods to improve reliability. Trans Electr Elect Mater2021; 22: 575–583.
121.
LiX, et al.Predictive maintenance of photovoltaic systems based on deep learning. Solar Energy2021; 215: 89–102.
122.
DhimishMHolmesV. Deep learning for electroluminescence-based photovoltaic fault detection. Solar Energy2020; 199: 343–360.
123.
AkramM, et al.CNN-based automated detection of photovoltaic defects using infrared thermography. Solar Energy2020; 206: 453–464.
124.
ZhangL, et al.Data-driven remaining useful life prediction for photovoltaic systems. Renew Energy2019; 133: 1271–1282.
