Proactive Fault Detection using Machine Learning to Aid Predictive Maintenance in Photovoltaic Systems

In recent history, photovoltaic (PV) systems as a means of energy generation have risen in popularity due to the world’s decreasing reliance on fossil fuels and a stronger focus on combating the adverse effects of climate change. While PV systems have immense potential, their vulnerability to faults substantially threatens their efficiency and reliability, potentially reducing their positive impact on the environment and the world economy. Current PV system maintenance strategies are either reactive or preventive, with a limited focus on predictive methods that leverage advanced machine learning models for fault detection. This thesis addresses this research gap, focusing on the development and optimisation of machine learning algorithms for proactive PV system fault detection. This is accomplished through the analysis of various PV system data parameters such as voltage, current, power, energy delivered or received, performance ratio, and meteorological data, among others. This research investigation started with a data collection process from the Desert Knowledge Australia Solar Centre (DKASC), a facility dedicated to solar energy research. After collecting data from 10 of the most fault-prone sites, rigorous pre-processing steps, including cleaning, transforming, and balancing, were employed. Particular attention was given to inverter failures and inverter intermittent issues, as they were identified as the most common faults, significantly influencing PV system performance and reliability. A variety of machine learning algorithms were employed, including deep learning methods such as Artificial Neural Networks and Recurrent Neutral Networks. However, Kernel SVM and K Nearest Neighbours were found to be most effective in predicting the specific individual faults, inverter failures and inverter intermittent issues, respectively. Subsequent parameter optimisation efforts, including adjusting fault occurrence window sizes, running summary days, classifier hyperparameters, and validation methods, enabled differentiation between those two fault types in a combined faults dataset using the K Nearest Neighbours model. This research project makes two novel contributions to the field. First, it developed an adaptive method for predicting specific faults in PV systems. Second, through a parameter optimisation process, this research created an adaptive method for differentiation between two specific faults. Through these adaptive fault prediction methods, the most effective machine learning model can be selected to predict any particular fault or differentiate between any specific faults, enhancing their real-world utility and impact. The findings from this research have considerable implications for future work in this domain. They serve as a guide for further research and development efforts to inform predictive maintenance strategies for PV systems. Future directions include the investigation of other types of faults, expanding the dataset to include more diverse fault scenarios, exploring advanced feature engineering and selection methods, integrating the developed fault prediction models with practical maintenance scheduling systems, and assessing the economic impacts of these models on the efficiency and cost-effectiveness of PV systems. In summary, while this research does contribute to improving the reliability and efficiency of PV systems through enhanced fault prediction, it also provides direction for further research into developing robust predictive maintenance strategies. The findings of this research support the broader goal of making renewable energy more reliable, efficient, and cost-effective in pursuit of a more sustainable energy-driven future.