Fault Detection in Electrical Power Transmission Systems Using Artificial Neural Network Algorithms

Abstract

The reliability and stability of electrical power transmission systems are critical for ensuring uninterrupted power supply. Fault detection in these systems is a significant challenge due to the complexity and dynamic nature of the electrical grid. This thesis presents a novel approach for fault detection in electrical power transmission systems using an Artificial Neural Network (ANN) algorithms. The proposed method leverages the pattern recognition capabilities of ANNs to accurately identify and classify various types of faults, including Voltage and Frequency anomalies. By analyzing these anomalies at one end of the transmission line, the ANN model is trained to detect anomalies and predict fault conditions with high precision. The performance of the ANN-based fault detection system is evaluated and validated through extensive simulations in the MATLAB environment, demonstrating its effectiveness in real-time fault detection and classification. The results indicate that the ANN algorithm not only enhances the speed and accuracy of fault detection but also reduces the risk of power outages and equipment damage. This research contributes to the development of more resilient and intelligent Power Transmission Systems, paving the way for future advancements in Smart Grid technologies.