Real-Time Fault Detection and Isolation in Power Systems for Improved Digital Grid Stability Using an Intelligent Neuro-Fuzzy Logic

This research aims to address the challenges of fault detection and isolation (FDI) in digital grids, focusing on improving the reliability and stability of power systems. Traditional fault detection techniques, such as rule-based fuzzy systems and conventional FDI methods, often struggle with the dynamic nature of modern grids, resulting in delays and inaccuracies in fault classification. To overcome these limitations, this study introduces a Hybrid Neuro-Fuzzy Fault Detection Model that combines the adaptive learning capabilities of neural networks with the reasoning strength of fuzzy logic. The model’s performance was evaluated through extensive simulations on the IEEE 33-bus test system, considering various fault scenarios, including line-to-ground faults (LGF), three-phase short circuits (3PSC), and harmonic distortions (HD). The quantitative results show that the model achieves 97.2% accuracy, a false negative rate (FNR) of 1.9%, and a false positive rate (FPR) of 2.3%, demonstrating its high precision in fault diagnosis. The qualitative analysis further highlights the model’s adaptability and its potential for seamless integration into smart grids, micro grids, and renewable energy systems. By dynamically refining fuzzy inference rules, the model enhances fault detection efficiency without compromising computational feasibility. These findings contribute to the development of more resilient and adaptive fault management systems, paving the way for advanced smart grid technologies.