Jurnal Rekayasa elektrika
Vol 19, No 1 (2023)

Identification of Power Quality Disturbances Based on Fast Fourier Transform and Artificial Neural Network

Dimas Okky Anggriawan (politeknik elektronika negeri surabaya)
Endro Wahjono (politeknik elektronika negeri surabaya)
Indhana Sudiharto (politeknik elektronika negeri surabaya)
Anang Budikarso (politeknik elektronika negeri surabaya)

Article Info

Publish Date
27 Mar 2023

Abstract

This paper presents the proposed algorithms for the identification of Short Duration RMS Variations and Long Duration RMS Variations combined with harmonic. The proposed algorithms are Fast Fourier Transform (FFT) and Artificial Neural Network (ANN). The Algorithms identify nine types of Power Quality (PQ) disturbances such as normal signal, voltage sag, voltage swell, under voltage, over voltage, voltage sag combined harmonic, voltage swell combined harmonic, undervoltage combined harmonic, and over voltage combined harmonic. FFT is used to obtain the frequency spectrum of each PQ disturbance with frequency sampling of 1000 Hz, data length of 200. Output FFT is used to input data for ANN. Output ANN is a type of nine PQ disturbances. The result shows that proposed algorithms (FFT combined ANN) are effective for identification, which ANN with 20 neurons in the hidden layer has an accuracy of approximately 99.95 %

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Journal Info
Jurnal Rekayasa elektrika
Website

Abbrev

JRE

Publisher

Universitas Syiah Kuala

Subject

Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

Description

The journal publishes original papers in the field of electrical, computer and informatics engineering which covers, but not limited to, the following scope: Electronics: Electronic Materials, Microelectronic System, Design and Implementation of Application Specific Integrated Circuits (ASIC), VLSI ...