Department: Mechatronics

The workshop in brief:

presents an AI-based approach for fault detection and allocation in photovoltaic (PV) systems to improve reliability, efficiency, and operational safety. Machine learning and deep learning models analyze electrical and environmental data to detect, classify, and localize various PV faults in real time. By leveraging advanced architectures such as neural networks, convolutional models, and transformers, the proposed system enhances fault recognition accuracy under diverse operating conditions and supports efficient maintenance and energy management.

Participants:  25 Participants

Place: Tishk International University, Education Building 302

Session topics: 

2 Session

The workshop demonstrated that AI-driven fault detection and allocation significantly outperform conventional rule-based methods in accuracy, adaptability, and fault localization. Participants recognized the potential of the proposed framework for real-time monitoring, predictive maintenance, and reducing system downtime. The outcome highlighted the importance of deploying intelligent diagnostic tools to support reliable PV system operation and the integration of renewable energy into smart grid infrastructures.