Detection, classification, and rating of electrical faults and power quality

The reliable operation of power systems is essential to prevent over-voltages, excessive currents, outages, and equipment damage, which can pose risks to users and lead to interruptions in the power supply. To address these challenges, a robust power safety system is required to swiftly identify, categorize, and pinpoint faults, ultimately reducing their impact. This paper presents a comprehensive approach for locating, classifying, and detecting faults within power systems. The primary objectives of this paper are to accurately identify and categorize different fault types occurring at various resistance levels and locations, gain insights into the causes of interruptions, expedite power restoration, and minimize recurrence. Additionally, this research aims to enhance understanding of the components of the protection system, enabling the implementation of preventive measures to reduce the likelihood of service interruptions and equipment damage. To achieve precise problem identification, classification, and fault localization, the proposed approach employs simple neural networks and efficient machine learning models. Leveraging the capabilities of neural networks, this technology yields precise results in power system fault analysis.