Integrating ANN and ANFIS for effective fault detection and location in modern power grid

The increasing complexity and demand for reliability in modern power systems necessitate advanced techniques for fault detection, classification, and location. This work presents a comprehensive study on the application of Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) for fault management in power systems. ANFIS, combining the benefits of neural networks and fuzzy logic, offers a robust framework for handling the non-linearities and uncertainties inherent in power system faults. The proposed method leverages historical fault data to train the ANFIS model, enabling it to accurately detect, classify, and locate various types of faults, including line-to-ground, line-to-line, and three-phase faults. The model’s performance is evaluated using a simulated power system environment, and its effectiveness is validated through extensive testing under different fault scenarios. Results demonstrate that the ANFIS-based approach achieves high accuracy in fault detection and classification, significantly reducing the response time. Additionally, the system exhibits a strong capability in pinpointing fault locations with minimal error margins. This research underscores the potential of ANFIS as a powerful tool for improving the consistency and competence of fault management in power systems. The findings suggest that integrating ANFIS into existing protection schemes can lead to improved operational efficiency (97–99%), whereas in case of ANN, the efficiency is (92–95%) resilience and reduced downtime. Future work will focus on real-time implementation and the incorporation of ANFIS with other smart grid technologies to further augment fault management capabilities.