Fault Classification & Detection in IEEE 34 Bus System Using Convolutional Neural Network

This paper presents a novel approach by means of Convolutional Neural Networks (CNNs) for fault classification and detection in the IEEE 34 bus system, which serves as a representative model for distribution networks. The IEEE 34 bus system is known for its complexity, featuring multiple buses, branches, and loads, making it an ideal testbed for evaluating advanced fault management techniques. The proposed CNN-based methodology leverages the ability of CNNs to automatically learn hierarchical features from system data, such as voltage and current measurements, without the need for manual feature extraction. The key contributions of this paper include the development and implementation of a CNN architecture tailored for fault classification and detection tasks in the IEEE 34 bus system. The CNN model is trained using labeled data sets containing information about different fault scenarios, including 1-phase, 2-phase, and 3-phase faults. The training process involves optimizing the network parameters to improve accuracy and robustness in fault identification. Simulation results demonstrate the effectiveness of the CNN-based approach in accurately classifying and detecting faults in the IEEE 34 bus system. The CNN model shows high accuracy rates in distinguishing between different fault types and provides rapid response times for fault detection, thereby enhancing the overall reliability and resilience of the distribution network. The implications of this research extend to the broader context of intelligent fault management systems in power engineering. By harnessing the capabilities of CNNs, power system operators can benefit from more efficient and automated fault management processes, leading to improved system performance, reduced downtime, and enhanced grid reliability.