Intelligent Protection And Fault Management In Electrical Power Systems Using Ai Techniques
DOI:
https://doi.org/10.63665/IJMEC.1103.02Keywords:
Artificial Intelligence, Power System Protection, Fault Detection, Machine Learning, Neural NetworksAbstract
Electrical power systems face increasing complexity with renewable energy integration and expanding transmission networks. Traditional protection schemes struggle with dynamic fault scenarios, necessitating advanced intelligent solutions. This research investigates artificial intelligence techniques for enhancing power system protection and fault management. The study examines machine learning algorithms including Neural Networks, Support Vector Machines, and Deep Learning models for fault detection, classification, and localization. Through systematic analysis of AI-based protection systems, this research evaluates performance metrics across various fault scenarios. The hypothesis proposes that AI techniques significantly improve fault detection accuracy and response time compared to conventional methods. Results demonstrate that ensemble machine learning models achieve 99.96% accuracy in fault detection, with neural networks showing 99.52% classification accuracy. Deep learning approaches reduce false alarm rates while maintaining specificity above 98%. Implementation of reinforcement learning for adaptive protection demonstrates substantial improvements in grid resilience. The findings establish AI techniques as transformative tools for modern power system protection, offering enhanced reliability, faster fault isolation, and improved system stability essential for smart grid infrastructure.
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