<![CDATA[Total harmonic distortion (THD) in photovoltaic (PV) systems is a significant challenge that can degrade power quality and disrupt grid stability. This research proposes an innovative approach utilizing artificial neural networks (ANNs) to detect and mitigate THD in real-time. The uniqueness of this study lies in the application of transfer learning to train AI models with limited data, thereby reducing computational time and costs. Additionally, this research adopts a physics-based approach to enhance the interpretability of AI models, allowing us to understand the mechanisms behind model predictions. The results of this study are expected to contribute to the development of more efficient and reliable PV systems, as well as open up new avenues for renewable energy utilization. This research also has intriguing epistemological implications, as it demonstrates the potential of AI to assist us in understanding complex systems like energy systems in novel and profound ways.]]>
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