Data

Desert Knowledge Australia Solar Centre Fault Data

Central Queensland University
Shaben Kayamboo (Aggregated by)
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.25946/25330279.v1&rft.title=Desert Knowledge Australia Solar Centre Fault Data&rft.identifier=10.25946/25330279.v1&rft.publisher=Central Queensland University&rft.description=This thesis explores the enhancement of photovoltaic (PV) system reliability through the development and optimisation of machine learning algorithms for proactive fault detection. Utilising data from the Desert Knowledge Australia Solar Centre, the study focuses on inverter failures and intermittent issues, employing advanced machine learning techniques for predictive analysis. The research introduces novel adaptive methods for fault prediction and differentiation, significantly contributing to predictive maintenance strategies. These findings pave the way for future investigations into robust predictive maintenance frameworks, aiming to improve PV system efficiency and support the transition towards sustainable energy solutions.&rft.creator=Shaben Kayamboo&rft.date=2024&rft_rights= https://creativecommons.org/licenses/by-nc-sa/4.0/&rft_subject=Machine learning not elsewhere classified&rft_subject=Photovoltaic power systems&rft_subject=Solar Photovoltaic (PV) System&rft_subject=Machine Learning&rft_subject=Predictive Maintenance&rft_subject=Historical Data Analysis&rft_subject=Fault Detection&rft.type=dataset&rft.language=English Access the data
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This thesis explores the enhancement of photovoltaic (PV) system reliability through the development and optimisation of machine learning algorithms for proactive fault detection. Utilising data from the Desert Knowledge Australia Solar Centre, the study focuses on inverter failures and intermittent issues, employing advanced machine learning techniques for predictive analysis. The research introduces novel adaptive methods for fault prediction and differentiation, significantly contributing to predictive maintenance strategies. These findings pave the way for future investigations into robust predictive maintenance frameworks, aiming to improve PV system efficiency and support the transition towards sustainable energy solutions.

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Subjects
Fault Detection | Historical Data Analysis | Machine Learning | Machine learning not elsewhere classified | Photovoltaic power systems | Predictive Maintenance | Solar Photovoltaic (PV) System |

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ACN 633 798 857