Authors - Kalyani Ghuge, Dhruv Battawar, Om Bhoye, Suhani Buche, Adithiya Anantharaman, Anvay Bavdhankar Abstract - For the integration of solar systems within the power grid, there is the requirement for smarter systems that are capable of not only detecting faults but also optimizing their performance. The current paper introduces an innovative hybrid method that focuses on the detection of solar thermal faults and adaptive grid control, where the challenge had existed in the separation of the two aspects. This is achieved through the use of a deep learning U-Net model, where different kinds of solar panel fault types, such as single and multi hotspots, are detected from grayscale thermal images. The different kinds of fault types identified are used as a reinforcement learning approach (PPO), where decisions regarding safe and efficient use of the grid are made while considering fault awareness. Higher priority is granted to critical fault types through rewards that use penalties. It also comes with an immediate safety function to isolate faulty panels with zero delay for smooth and efficient function of the solar energy grid.
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