A DWT AND PATTERN RECOGNITION APPROACH FOR FAULT DETECTION AND CLASSIFICATION IN TRANSMISSION NETWORKS

Abstract

The reliable and efficient operation of high-voltage transmission lines is essential for ensuring the stability and quality of electrical power distribution. To address this concern, this research paper presents an indepth study on the application of wavelet transform for detecting and classifying faults in high-voltage transmission lines. Fault detection and classification are crucial tasks in the maintenance and operation of power systems to minimize downtime and ensure the safety of personnel and equipment. The wavelet transform has proven to be a powerful tool for analyzing transient signals in electrical systems, making it a valuable technique for fault detection and classification. This paper provides a comprehensive review of wavelet transform theory, its application to fault detection, and classification algorithms. Additionally, it discusses various case studies and practical implementations, highlighting the advantages and limitations of wavelet-based techniques. The results demonstrate the effectiveness of wavelet transform in enhancing the reliability and efficiency of high-voltage transmission line monitoring and maintenance.