Choice of supervised machine learning based approaches to predict PV panel faults

The increasing deployment of photovoltaic (PV) systems necessitates efficient fault detection methods to ensure optimal performance and reliability. The performance of the PV panels can be significantly affected by faults and anomalies. Various machine learning techniques can be used for this purpose, each with its strengths and weaknesses. Developing a proper fault diagnosis strategy to identify PV faults for the smooth and uninterrupted operation of PV panels plays a vital role. This paper presents a comparative assessment of different machine learning techniques for photovoltaic (PV) panel fault diagnosis. This study aims to evaluate and compare the performance of various machine learning algorithms including support vector machine (SVM), k-nearest neighbour (KNN), decision tree (DT), discriminant analysis (DA) and ensemble classifier (EC) in identifying and diagnosing faults in PV panels. Through extensive experimentation and analysis, the effectiveness of these machine learning techniques in fault detection and classification is assessed in terms of accuracy, precision and efficiency. Additionally, the robustness and scalability of the models are examined under different operating conditions and fault scenarios. This study used simulated data to train and test machine learning models and evaluated their performance based on accuracy and precision. The findings of this study provide valuable insights into the applicability and performance of data-driven machine learning techniques for fault identification in PV panels, thereby contributing to the development of more efficient and reliable PV systems for the renewable energy industry.