ANN distance protection for transmission lines

• Main Authors: Africa, Aaron Don M., Naco, Isaiah Kyle A., Castillo, John Joseph M., Valdes, Victor Antonio R., Wu, Shawn Reece T.
• Format: text
• Published: Animo Repository 2019
• Subjects: Neural networks (Computer science); Fault-tolerant computing; Telecommunication lines; Electrical and Computer Engineering
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/1565; https://animorepository.dlsu.edu.ph/context/faculty_research/article/2564/type/native/viewcontent
• Institution: De La Salle University

When using transmission lines, faults often happen during the flow of signals. One way to protect transmission lines is to use distance protection relays. These types of relays, however, are still affected by fault resistance. Fault conditions often follow a certain pattern. Having a relay that recognizes these patterns can help improve performance. This paper presents a method to implement this using an artificial neural network (ANN). An ANN is one of the many applications of machine learning, which in meaning, is the algorithms that give the ability to computers to obtain knowledge through data and adapt to make certain deductions and choices. This paper will only consider the effect of the fault resistance of a single line-to-ground type of fault. The ANN should be programmed to identify numerous patterns that each correspond to different fault conditions. This will help in identifying and classifying unknown patterns detected in the transmission lines. © 2019, World Academy of Research in Science and Engineering. All rights reserved.