Fault Diagnosis in Overhead Power Line Using Deep Belief Network

The reliable operation of overhead power lines is critical for the uninterrupted supply of electricity and the overall stability of power systems. However, these power lines are susceptible to a wide range of faults, including line breakage, short circuits, insulation degradation, and equipment malfunctions. Detecting and diagnosing these faults in a timely and accurate manner is crucial to minimize downtime, prevent further damage to the power infrastructure, and ensure the safety of both the power system and the general public. Traditional fault diagnosis methods for overhead power lines often rely on manual inspections or rule-based algorithms. These methods have several limitations, such as being time-consuming, labor-intensive, and subject to human errors. Manual inspections require skilled personnel to physically inspect the power lines, which can be challenging and dangerous, particularly in adverse weather conditions or remote locations. The primary objective of this thesis is to explore the potential of Deep Belief Networks (DBNs) for fault diagnosis in overhead power lines. To achieve this overarching objective, this paper provides practical recommendations and guidelines for optimizing the fault diagnosis system using DBNs. These recommendations address various aspects such as data collection, preprocessing, feature extraction, model architecture, training parameters, and system deployment.