Integrating AI - driven Fault Detection and Protection Technique for Electric Power Component and System
Keywords:
Artificial Intelligence, Deep Forest, Support Vector Machines (SVM), Neural Networks (NN), Fault Detection and ProtectionAbstract
The increasing complexity and interconnectivity of modern electric power systems have heightened the need for reliable and intelligent fault detection and protection mechanisms. This research focuses on the integration of Artificial Intelligence (AI)-driven fault detection and protection techniques using the Artificial Neural Network (ANN) method for electric power components and systems. The proposed ANN-based framework is designed to accurately detect, classify, and isolate faults in real time by learning from historical and simulated system data. The model utilizes key electrical parameters such as current, voltage, and power factor variations as input features to identify various fault conditions, including short circuits, open circuits, and transient disturbances. Simulation studies are conducted on standard test systems to evaluate the model’s performance under different loading and fault scenarios. The ANN demonstrates high accuracy and fast response compared to traditional protection methods such as overcurrent relays and differential protection schemes. Additionally, the system exhibits adaptive learning capabilities, enabling continuous improvement in fault diagnosis as grid conditions evolve. The integration of AI-based ANN algorithms significantly enhances system reliability, reduces downtime, and minimizes damage to equipment
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