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All Journal Andalas Journal of Electrical and Electronic Engineering Technology
Nurfi Syahri
Universitas Andalas
Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering

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Fault Type Classification of 150 kV Transmission Line using Wavelet Multi-Resolution Analysis Method Novizon Novizon; Nurfi Syahri; Silvia Wulandari; Tesya Uldira Septiyeni; Rahadian Asneli Putri
Andalas Journal of Electrical and Electronic Engineering Technology Vol. 1 No. 2 (2021): November 2021
Publisher : Electrical Engineering Dept, Engineering Faculty, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (341.451 KB) | DOI: 10.25077/ajeeet.v1i2.15

Abstract

To identify the fault, wavelet method is used in solving complex protection problems. This study uses a new approach, namely the wavelet multi resolution analysis method with its application where multi resolution analysis works to analyze signals at different frequencies with the same resolution. In this study, the classification of fault types that occur in the 150 kV transmission line quickly and accurately is carried out using the wavelet multi resolution analysis method. This research is included in applied research and was designed using computer simulation software, namely ATP and MATLAB. The data transmission system used is the Maninjau Hydroelectric Power Plant transmission line to Pauh Limo Substation. The modeled transmission system is given 1-phase to ground, 2-phase to ground, 2- phase, 3-phase and lightning faults. To determine the accuracy of this classification, the fault is varied according to the distance and impedance of the disturbance. From the analysis of the simulation results and calculations, based on the wavelet multi resolution analysis method used in fault classifying, the average value of the approximation coefficient used to classify the type of fault is obtained. Based on the results of the study, it can be said that all types of fault analyzed in this study have met the classification requirements using the wavelet multi resolution analysis method
Co-Authors Novizon, Novizon Rahadian Asneli Putri Silvia Wulandari Tesya Uldira Septiyeni