Creation of a database of the electrical protection at CERN and how to maintain up to date

This report discusses a multifaceted project that combined database development with advanced machine learning applications. The primary goal was to create comprehensive Excel files dedicated to forming a database of Low Voltage (LV) circuit breaker settings, which is essential for streamlining the management and maintenance of CERN's complex electrical systems. Over time, the project expanded to include the development of an automated recognition system. This system leverages neural networks to enhance the accuracy and efficiency of data entry, using images of circuit breakers settings to automatically update CERN's database with the new tuning state of the protection. Additionally, valuable hands-on experience in transformer testing was gained, another critical component of electrical infrastructure. Under the guidance of experienced professionals, we explored both the theoretical and practical aspects of transformer testing, ensuring safety and precision in low-voltage scenarios. This project allowed us to bridge the gap between data management and electrical engineering, providing a well-rounded experience.