Desert Knowledge Australia Solar Centre Fault Data

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.本论文通过开发并优化面向主动故障检测的机器学习算法，探究提升光伏（Photovoltaic，PV）系统可靠性的可行路径。本研究依托澳大利亚沙漠知识太阳能中心（Desert Knowledge Australia Solar Centre）的实测数据集，聚焦逆变器故障与间歇性运行异常问题，采用先进机器学习技术开展预测性分析。本研究提出了用于故障预测与故障辨识的新型自适应方法，为预测性维护策略的优化提供了重要支撑。本研究成果为后续构建稳健的预测性维护框架开辟了研究方向，旨在提升光伏系统运行效率，助力向可持续能源解决方案转型。