Predicting photovoltaic module degradation using current-voltage

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dc.availability.bitstreamopenaccess
dc.contributor.advisorMorrison, Alan P.en
dc.contributor.advisorLeahy, Paulen
dc.contributor.authorAl Mahdi, Hussain
dc.contributor.funderMinistry of Education, Saudi Arabiaen
dc.date.accessioned2022-06-02T15:47:47Z
dc.date.available2022-06-02T15:47:47Z
dc.date.issued2022-02-01
dc.date.submitted2022-02-01
dc.description.abstractWith the global increase of photovoltaic (PV) modules deployment in recent years, monitoring techniques to ensure a safe and healthy operation have become crucial. Despite PV modules being considered reliable devices, failures and extreme degradations often occur. They have been frequently classified into two categories: optical and electrical. Of optical failures, degradation of the ethylene-vinyl acetate (EVA) encapsulant due to prolonged ultra-violet (UV) exposure and other environmental stress factors, such as temperature and humidity, is the most common failure. Conversely, electrical failures are linked to low shunt resistance (Rsh). Unidentified degradation mechanisms in these categories lead to catastrophic failure. The objective of this dissertation is to find comprehensive techniques to predict and detect these degradations at the initial stages. This can be achieved by continuously monitoring the current-voltage (I-V) curve parameters of the solar cell, from healthy-state (no degradation) to failure-state (critical degradation). The EVA degradation research was accomplished using an electrical circuit simulator (SPICE) to simulate an experimental result. In addition to simulations, experiments were performed to artificially lower the solar cell shunt resistance. The effect of EVA and shunt resistance degradations on solar cells’ major parameters; maximum power output (Pmax), short-circuit current (ISC), open-circuit voltage (VOC), and fill factor were analysed. As a result, novel linear models were developed and proposed as strong predictors and observers, and also are suitable for implementation in online monitoring systems for operational PV modules. Training a machine learning model to classify degradation mechanisms was also shown effective. Corrections should be made if critical degradation is detected. Aside from the potential risks to the PV system, the thesis showed that replacing a degraded PV module is cost-effective before the critical degradation state.en
dc.description.statusNot peer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationAl Mahdi, H. 2022. Predicting photovoltaic module degradation using current-voltage. PhD Thesis, University College Cork.en
dc.identifier.endpage177en
dc.identifier.urihttps://hdl.handle.net/10468/13282
dc.language.isoenen
dc.publisherUniversity College Corken
dc.relation.projectMinistry of Education, Saudi Arabia (Grant no. IR18112)en
dc.rights© 2022, Hussain Al Mahdi.en
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectPV failures detectionen
dc.subjectMachine learning modelsen
dc.subjectPV degradationen
dc.subjectPV simulationen
dc.titlePredicting photovoltaic module degradation using current-voltageen
dc.typeDoctoral thesisen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhD - Doctor of Philosophyen
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