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In-Field Solar Panel Assessment and Fault Diagnosis
Photovoltaic energy is a green energy that suit from small houses to high-power stations spanning large areas. In such large areas, monitoring individual panels can be a tedious task, especially if it was required to identify operational faults of these panels. Photovoltaic 4.0 technology depend on...
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| Format: | Thesis |
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AUC Knowledge Fountain
2023
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| Summary: | Photovoltaic energy is a green energy that suit from small houses to high-power stations spanning large areas. In such large areas, monitoring individual panels can be a tedious task, especially if it was required to identify operational faults of these panels. Photovoltaic 4.0 technology depend on collecting data from each station and feeding them to a central processing system that can analyze operation data and hopefully locate when a fault happens. In such method, it is crucial to be accurate as much as possible and for measuring device to be accurate as well to have a clear judgement. In this work, we build an analysis module at the center of a photovoltaic 4.0 station implemented in the American University in Cairo. The model is comprehensive in nature and is capable of modelling from individual cell level to the whole panel level as well as dealing with measurement issues to have a good judgement at the end. The used model is based on single-diode model of a solar panel and is capable of modelling solar panels in different environmental conditions and is validated against datasheet and actual measurement. Source code for the analysis module and the dataset are provided. It was shown that Laudani’s method of parameter extraction is more successful compared to Stonelli’s method and translating circuit parameters at different environmental conditions proved to be successful and matching to datasheets. Besides, it provided sufficient predictions without need to an actual weather station. The proposed analysis module provided insights about dusty conditions and irregularities that may exist in solar panel characterizers |
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