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JURNAL NASIONAL TEKNIK ELEKTRO
Published by Universitas Andalas
ISSN : 23022949     EISSN : 24077267     DOI : -
Core Subject : Engineering,
Electrical & Electronics Engineering
Jurnal Nasional Teknik Elektro (JNTE) adalah jurnal ilmiah peer-reviewed yang diterbitkan oleh Jurusan Teknik Elektro Universitas Andalas dengan versi cetak (p-ISSN:2302-2949) dan versi elektronik (e-ISSN:2407-7267). JNTE terbit dua kali dalam setahun untuk naskah hasil/bagian penelitian yang berkaitan dengan elektrik, elektronik, telekomunikasi dan informatika.
Arjuna Subject : -
Articles 6 Documents
 1 
Search results for , issue "Vol 12, No 1: March 2023" : 6 Documents clear
Voltage Stability Analysis of Power System with Photovoltaic Power Plant Adrianti Adrianti; Rada Tamara Putri; Muhammad Nasir
JURNAL NASIONAL TEKNIK ELEKTRO Vol 12, No 1: March 2023
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v12n1.1055.2023

Abstract

Photovoltaic power plants usually do not provide reactive power output; hence the application of large photovoltaics in power systems will decrease the voltage stability level of the power system. Capacitor banks can provide reactive power to compensate the photovoltaic plants; therefore, capacitor banks can overcome the reactive power deficiency of photovoltaic plants. However, the effect of capacitor bank installation on the system’s voltage stability is unknown.  Therefore, the research aims to investigate whether installing a capacitors bank can restore the level of system voltage stability. The study employs the method of Voltage Stability Margin and transient stability simulation to the IEEE 9 bus system. The IEEE 9 bus system is modified where one generator of the system is replaced with a photovoltaic plant, and a capacitor bank is also installed. The study results show that the modified system voltage stability level is lower than the original system. When the capacity of the capacitor bank is increased to the maximum allowable value, the voltage stability level rises. However, it is still unable to be restored to its original value.
Performance Comparison of FBMC-OQAM and CP-OFDM Using AWGN Channel Anggun Fitrian Isnawati; Dhia Fikri Zam Zami; M. Lukman Leksono
JURNAL NASIONAL TEKNIK ELEKTRO Vol 12, No 1: March 2023
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v12n1.1034.2023

Abstract

The 5G NR network planning covers the types of use scenarios and applications that include Enhanced Mobile Broadband (eMBB), Ultra-Reliable and Low Latency Communications (URLLC), and Massive Machine Type Communications (MTC). Regarding multicarrier modulation schemes, Orthogonal Frequency Division Multiplexing (OFDM) has become the most popular choice in previous technology, so OFDM is a strong candidate for its 5G NR technology application. However, OFDM has disadvantages such as higher PAPR and decreased bandwidth efficiency due to the addition of CP. These weaknesses can be overcome by the FBMC modulation scheme with Offset Quadrature Amplitude Modulation (OQAM) as a more efficient CP replacement for its implementation in 5G NR. This study analyzed the development of OQAM in Filter Bank multicarrier (FBMC) and compared it with using Cyclic Prefix (CP) based on OFDM using the AWGN channel. The first step of this research is to present an overview of the modulation scheme used. Next, compare the performance of FBMC-OQAM and CP-OFDM by analyzing several Bit Error Rate (BER) simulation results against the SNR value when both systems use the same simulation parameters. Based on the test results of each BER, both methods have different values, almost 2 dB for the same BER results. It indicates that the FBMC-OQAM system reached the BER value of 10-4 at SNR 15 dB. The CP-OFDM system, meanwhile, was able to achieve a BER value of 10-4 at SNR 17 dB. These results indicate that the FBMC-OQAM system is superior to CP-OFDM based on the BER values.
Speed Control of an Electrical Cable Extrusion Process Using Artificial Intelligence-Based Technique Robert Agyare Ofosu; Erwin Normanyo; N-Yo Abdul-Aziz; Stephen Smart Stickings
JURNAL NASIONAL TEKNIK ELEKTRO Vol 12, No 1: March 2023
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v12n1.1045.2023

Abstract

Most cable manufacturing companies use Programmable Logic Controllers with conventional controllers to control line speed during cable extrusion. These traditional controllers have difficulties keeping the line speed constant, causing surface defects on the extruded cables and affecting the quality of the manufactured cables. To overcome these challenges, data on the causes of defects during cable manufacturing were collected from a cable manufacturing company in Ghana to ascertain the possible causes during cable manufacturing. Adaptive Neuro-Fuzzy Inference System (ANFIS) controller was designed to provide a constant line speed during the cable extrusion process. To ascertain its robustness, the ANFIS controller was compared to a conventional Proportional Integral Derivative controller and a Fuzzy Logic controller. The controllers were designed and simulated using MATLAB/Simulink software. The analysis of the collected data indicated that a break in insulation/ sheath was a frequently occurring defect during the cable manufacturing process due to improper line speed control of the machines used in the cable manufacturing process. Based on the results obtained from the various controllers, it was concluded that the ANFIS controller was robust in achieving stability regarding line speed variations.
Fault Detection and Diagnosis of a 3-Phase Induction Motor Using Kohonen Self-Organising Map Robert Agyare Ofosu; Benjamin Odoi; Daniel Fosu Boateng; Asaph Mbugua Muhia
JURNAL NASIONAL TEKNIK ELEKTRO Vol 12, No 1: March 2023
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v12n1.1047.2023

Abstract

This paper uses the Kohonen Self-Organising Map (KSOM) to detect, diagnose, and classify induction motor faults. A series of simulations using models of the 3-phase induction motor based on real industrial motor parameters were performed using MATLAB/Simulink under fault conditions such as inter-turn, power frequency variation, over-voltage and unbalance in supply voltage. The model was trained using the input signals of the various fault conditions. Various faults from an unseen induction motor were fed to the model to test the model’s ability to detect and classify induction motor faults. The KSOM adapted to the conditions of the unseen motor, detected, diagnosed and classified these faults with an accuracy of 94.12%.
Path Loss Prediction Accuracy Based On Random Forest Algorithm in Palembang City Area Sukemi Sukemi; Ahmad Fali Oklilas; Muhammad Wahyu Fadli; Bengawan Alfaresi
JURNAL NASIONAL TEKNIK ELEKTRO Vol 12, No 1: March 2023
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v12n1.1052.2023

Abstract

Path loss is a mechanism where the signal from the transmitting antenna to the receiver in a wireless network is attenuated during transmission across a medium due to external field conditions. In the telecommunication design, precise and efficient calculations are required. Random forest, as a machine learning-based path loss prediction model, is used in this study. Machine learning-based path loss prediction, random forest, has a low level of complexity and a high level of predictability. The data was collected using the drive test method at the Trans Musi busway area on the 4G network in Palembang, South Sumatra, Indonesia. The data ratio comprised 20% of the testing set and the rest of the training set. As a result, it was obtained that the prediction accuracy of 9.24% of mean absolute percentage error (MAPE) and root mean square error (RMSE) was 13.6 decibels (dB).  Using hyperparameter tuning for random forest results in optimizing the model used, resulting in accuracy prediction for 8.00% of MAPE and RMSE was 11.8 dB, which is better than the previous results.
ANN-Based Electricity Theft Classification Technique for Limited Data Distribution Systems Monister Yaw Kwarteng; Francis Boafo Effah; Daniel Kwegyir; Emmanuel Asuming Frimpong
JURNAL NASIONAL TEKNIK ELEKTRO Vol 12, No 1: March 2023
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Electricity theft has been a challenge for distribution systems over the years. Theft presents a massive cost to the system operators and other issues such as transformer overloading, line loading, etc. It has become crucial for measures to be implemented to combat illegal electricity consumption. This work sought to develop an artificial neural network-based electricity theft classifier for distribution systems with limited data, i.e., systems that can only provide consumption data alone and no auxiliary data. First, a novel data pre-processing method was proposed for the systems with consumption data only. Again, synthetic minority oversampling is employed to deal with the unbalance problem in the theft detection dataset. Afterwards, an artificial neural network (ANN)-based classifier was proposed to classify customers as normal or fraudulent. The proposed method was tested on actual electricity theft data from the Electricity Company of Ghana (ECG) and its performance compared to random forest (RF) and logistic regression (LR) classifiers. The proposed ANN-based classifier performed exceptionally by producing the best results over RF and LR regarding precision, recall, F1-score, and accuracy of 99.49%, 100%, 99.75%, and 99.74%, respectively.

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