Investigation of thermal and electrical characteristics of crystalline silicon photovoltaic modules under varying operational conditions
- Authors: Vumbugwa, Monphias
- Date: 2022-12
- Subjects: Photovoltaic power generation -- South Africa , Silicon crystals -- South Africa , Solar cells
- Language: English
- Type: Doctoral theses , text
- Identifier: http://hdl.handle.net/10948/60014 , vital:62733
- Description: Solar energy has become an attractive and environmentally mindful method in electrical power generation as it contributes significantly to meeting the high demand for the power needed for socio and economic developments. The rise in deployment of Photovoltaic (PV) facilities with large capacity creates the need for accurate and reliable PV inspection techniques for optimum performance, the longevity of PV modules and quick return on PV investment. The performance of PV modules in the field is often monitored through several inspection methods that require a rapid throughput such as Thermal Infrared (TIR) imaging and current-voltage (I-V) measurements. Unmanned Aerial Vehicle (UAV) based TIR imaging is widely applied in large PV plants since it is cost-effective and is usually conducted in-situ while the plant is operating at irradiance levels above 600 W.m-2 . One of the outcomes of the interpretations of TIR images is an attempt to quantify the energy loss in PV plants associated with the abnormal thermal signatures identified on TIR images. No standard procedure has yet outlined the quantification of energy loss related to TIR images of underperforming modules since the interpretation of TIR images remains a challenge. PV modules operate under dynamic operating conditions which can influence the results and interpretation of thermal and electrical characterisation measurements. Dynamic operation conditions refer to any disorders in the operation of the modules and cells which cause a change in the current and voltage characteristics of the PV source. These dynamic operation conditions include; changesin load conditions, irradiance, soiling and shading levels. The tests were done under steady state conditions. Although measurements are generally done while the operating conditions are as steady as possible, some changes in conditions have a profound effect on thermal and electrical measurements. In this study, these effects and some of the changes in conditions that cause them were studied. , Thesis (PhD) -- Faculty of Science, School of Computer Science, Mathematics, Physics and Statistics, 2022
- Full Text:
- Date Issued: 2022-12
- Authors: Vumbugwa, Monphias
- Date: 2022-12
- Subjects: Photovoltaic power generation -- South Africa , Silicon crystals -- South Africa , Solar cells
- Language: English
- Type: Doctoral theses , text
- Identifier: http://hdl.handle.net/10948/60014 , vital:62733
- Description: Solar energy has become an attractive and environmentally mindful method in electrical power generation as it contributes significantly to meeting the high demand for the power needed for socio and economic developments. The rise in deployment of Photovoltaic (PV) facilities with large capacity creates the need for accurate and reliable PV inspection techniques for optimum performance, the longevity of PV modules and quick return on PV investment. The performance of PV modules in the field is often monitored through several inspection methods that require a rapid throughput such as Thermal Infrared (TIR) imaging and current-voltage (I-V) measurements. Unmanned Aerial Vehicle (UAV) based TIR imaging is widely applied in large PV plants since it is cost-effective and is usually conducted in-situ while the plant is operating at irradiance levels above 600 W.m-2 . One of the outcomes of the interpretations of TIR images is an attempt to quantify the energy loss in PV plants associated with the abnormal thermal signatures identified on TIR images. No standard procedure has yet outlined the quantification of energy loss related to TIR images of underperforming modules since the interpretation of TIR images remains a challenge. PV modules operate under dynamic operating conditions which can influence the results and interpretation of thermal and electrical characterisation measurements. Dynamic operation conditions refer to any disorders in the operation of the modules and cells which cause a change in the current and voltage characteristics of the PV source. These dynamic operation conditions include; changesin load conditions, irradiance, soiling and shading levels. The tests were done under steady state conditions. Although measurements are generally done while the operating conditions are as steady as possible, some changes in conditions have a profound effect on thermal and electrical measurements. In this study, these effects and some of the changes in conditions that cause them were studied. , Thesis (PhD) -- Faculty of Science, School of Computer Science, Mathematics, Physics and Statistics, 2022
- Full Text:
- Date Issued: 2022-12
Defect Classification in Photovoltaic Modules through Thermal Infrared Imaging using Machine Learning
- Dunderdale, Christopher, Clohessy, C M
- Authors: Dunderdale, Christopher , Clohessy, C M
- Date: 2020
- Subjects: Photovoltaic power generation -- South Africa
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10948/48280 , vital:40838
- Description: As the global energy demand continues to soar, solar energy has become an attractive and environmentally conscious method to meet this demand. This study examines the use of machine learning techniques for defect detection and classification in photovoltaic systems using thermal infrared images. A deep learning and feature-based approach is also investigated for the purpose of detecting and classifying defective photovoltaic modules. The VGG-16 and MobileNet deep learning models are shown to provide good performance for the classification of defects. The scale invariant feature transform (SIFT) descriptor, combined with the random forest and support vector machine classifier, is also used to discriminate between defective and non-defective photovoltaic modules in a South African setting. The successful implementation of this approach has significant potential for cost reduction in defect classification over currently available methods.
- Full Text:
- Date Issued: 2020
- Authors: Dunderdale, Christopher , Clohessy, C M
- Date: 2020
- Subjects: Photovoltaic power generation -- South Africa
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10948/48280 , vital:40838
- Description: As the global energy demand continues to soar, solar energy has become an attractive and environmentally conscious method to meet this demand. This study examines the use of machine learning techniques for defect detection and classification in photovoltaic systems using thermal infrared images. A deep learning and feature-based approach is also investigated for the purpose of detecting and classifying defective photovoltaic modules. The VGG-16 and MobileNet deep learning models are shown to provide good performance for the classification of defects. The scale invariant feature transform (SIFT) descriptor, combined with the random forest and support vector machine classifier, is also used to discriminate between defective and non-defective photovoltaic modules in a South African setting. The successful implementation of this approach has significant potential for cost reduction in defect classification over currently available methods.
- Full Text:
- Date Issued: 2020
Investigation of the performance of photovoltaic systems
- Authors: Alistoun, Warren James
- Date: 2012
- Subjects: Photovoltaic power systems -- South Africa , Photovoltaic power generation -- South Africa
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:10517 , http://hdl.handle.net/10948/d1008396 , Photovoltaic power systems -- South Africa , Photovoltaic power generation -- South Africa
- Description: The main objective of this study was to investigate the performance of grid integrated PV systems. A data acquisition (DAQ) system was developed to monitor the performance of an existing grid integrated PV system with battery storage. This system is referred to as a grid assisted PV system. A data logger was used together with the inverters built in data logger to monitor environmental and electrical data on a grid tie PV system which was deployed during this study. To investigate the performance of these grid integrated PV systems PV and BOS device characterization was performed. This was achieved by using current voltage curve tracers and the DAQ system developed. Energy yield estimations were calculated referring to the literature review and a meteorological reference for comparison with measured energy yields from the grid tie PV system.
- Full Text:
- Date Issued: 2012
- Authors: Alistoun, Warren James
- Date: 2012
- Subjects: Photovoltaic power systems -- South Africa , Photovoltaic power generation -- South Africa
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:10517 , http://hdl.handle.net/10948/d1008396 , Photovoltaic power systems -- South Africa , Photovoltaic power generation -- South Africa
- Description: The main objective of this study was to investigate the performance of grid integrated PV systems. A data acquisition (DAQ) system was developed to monitor the performance of an existing grid integrated PV system with battery storage. This system is referred to as a grid assisted PV system. A data logger was used together with the inverters built in data logger to monitor environmental and electrical data on a grid tie PV system which was deployed during this study. To investigate the performance of these grid integrated PV systems PV and BOS device characterization was performed. This was achieved by using current voltage curve tracers and the DAQ system developed. Energy yield estimations were calculated referring to the literature review and a meteorological reference for comparison with measured energy yields from the grid tie PV system.
- Full Text:
- Date Issued: 2012
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