Predicting photovoltaic module degradation using current-voltage

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dc.contributor.advisor Morrison, Alan P. en
dc.contributor.advisor Leahy, Paul en
dc.contributor.author Al Mahdi, Hussain
dc.date.accessioned 2022-06-02T15:47:47Z
dc.date.available 2022-06-02T15:47:47Z
dc.date.issued 2022-02-01
dc.date.submitted 2022-02-01
dc.identifier.citation Al Mahdi, H. 2022. Predicting photovoltaic module degradation using current-voltage. PhD Thesis, University College Cork. en
dc.identifier.endpage 177 en
dc.identifier.uri http://hdl.handle.net/10468/13282
dc.description.abstract With 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.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher University College Cork en
dc.rights © 2022, Hussain Al Mahdi. en
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/ en
dc.subject PV failures detection en
dc.subject Machine learning models en
dc.subject PV degradation en
dc.subject PV simulation en
dc.title Predicting photovoltaic module degradation using current-voltage en
dc.type Doctoral thesis en
dc.type.qualificationlevel Doctoral en
dc.type.qualificationname PhD - Doctor of Philosophy en
dc.internal.availability Full text available en
dc.description.version Accepted Version en
dc.contributor.funder Ministry of Education, Saudi Arabia en
dc.description.status Not peer reviewed en
dc.internal.school Electrical and Electronic Engineering en
dc.internal.conferring Autumn 2022 en
dc.relation.project Ministry of Education, Saudi Arabia (Grant no. IR18112) en
dc.availability.bitstream openaccess


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© 2022, Hussain Al Mahdi. Except where otherwise noted, this item's license is described as © 2022, Hussain Al Mahdi.
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