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All Journal PROtek : Jurnal Ilmiah Teknik Elektro Andalasian International Journal of Applied Science, Engineering, and Technology Power Elektronik : Jurnal Orang Elektro
Hasanah, Mardini
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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 1 
A Novel Model for Prediction of Flashover 150kV Polluted Insulator Based on Nonlinear Autoregressive External Input Neural Network Hasanah, Mardini; Novizon, Novizon; Qatrunnada, Rusvaira; Warmi, Yusreni; Amalia, Sitti; Yamashika, Herris
PROtek : Jurnal Ilmiah Teknik Elektro Vol 11, No 3 (2024): Protek: Jurnal Ilmiah Teknik Elektro
Publisher : Program Studi Teknik Elektro Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/protk.v11i3.7344

Abstract

This study aims to use an artificial neural network to forecast the flashover voltage of a polluted high-voltage insulator. Practical tests were conducted on a high-voltage insulator to gather data for the neural network. These tests were carried out with varying levels of real contaminants from used insulators, with each level of contamination measured in milliliters. The collected data provides flashover voltage values corresponding to different pollution amounts and their conductivity in each insulator zone. The Nonlinear Autoregressive External Input Neural Network (NarxNet) is employed to predict the flashover voltage and assess the pollution state of the insulator. The results demonstrate that the NarxNet method achieves a 93.74% accuracy rate in predicting the flashover voltage of high-voltage insulators, compared to the results from practical tests.
The Influence of Lightning and Grounding Conditions On Flashover Events in a 150kV Transmission Line Novizon, Novizon; Qatrunnada, Rusvaira; Hasanah, Mardini; Laksono, Heru Dibyo
Andalasian International Journal of Applied Science, Engineering and Technology Vol. 4 No. 2 (2024): July, 2024
Publisher : LPPM Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/aijaset.v4i2.152

Abstract

Effect of lightning parameters and grounding system impedance on transient overvoltage that occurs in 150 kV overhead transmission lines during lightning using ATP-EMTP modeling. The induced overvoltage that jumps across the insulator is calculated for further analysis. Various parameters that directly influence directly influence the backflashover phenomenon,, such as the effect of lightning strike amplitude, time of rise (front) and decay (tail) of lightning impulses, foot grounding system, and ground resistivity,, are discussed in more detail. Surge arresters are used in this study to overcome the overvoltage that can cause backflashover. The performance of the transmission system after using a surge arrester is investigated at the time of a lightning strike. The results indicate that the parameters of the advance time and the lightning current amplitude greatly affect the overvoltage magnitude voltage. Other parameters have little effect on the overvoltage in the transmission line.
Optimalisasi Pemasangan Trafo Sisip untuk Perbaikan Drop Tegangan Pelanggan pada Trafo RTP 033 ULP Peureulak PT PLN (Persero) hasanah, mardini; Puspitasari, Pipit; Oktavia, Yosa; Qatrunnada, Rusvaira; Septiyeni, Tesya Uldira
Power Elektronik : Jurnal Orang Elektro Vol 14, No 1 (2025): POWER ELEKTRONIK
Publisher : Politeknik Harapan Bersama Tegal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/polektro.v14i1.7827

Abstract

This study aims to analyze the installation of insert transformers in overcoming voltage drop problems in customers at the RTP 033 transformer of ULP Peureulak PT PLN (Persero). The research was conducted at transformer RTP 033 located in the Paya Uno area, Rantau Peureulak District, East Aceh, which experienced significant voltage drop problems. Through manual calculations using the formula in the RST phase, the voltage drop values were 22.99%, 20.63%, and 21.05% before the installation of the insert transformer. After the installation of the insert transformer, the voltage drop value was successfully reduced below the permitted standard of less than 5% with the voltage drop value for each phase being 2.18%, 2.63%, and 3.06%. The measurement results in the field show a significant increase in distribution voltage at points that previously experienced a decrease. In addition to improving voltage quality, the installation of the insert transformer also had a positive impact on increasing electrical energy consumption. There was an increase in kWh sales of 13.14% after the installation of the insert transformer. Based on the research results, it can be concluded that the installation of an insert transformer is an effective solution to overcome the voltage drop problem in the distribution network. Thus, the quality of electricity service to customers can be improved, and the risk of damage to electronic equipment due to voltage fluctuations can be minimized.
A Novel Model for Prediction of Flashover 150kV Polluted Insulator Based on Nonlinear Autoregressive External Input Neural Network Hasanah, Mardini; Novizon, Novizon; Qatrunnada, Rusvaira; Warmi, Yusreni; Amalia, Sitti; Yamashika, Herris
PROtek : Jurnal Ilmiah Teknik Elektro Vol 11, No 3 (2024): Protek: Jurnal Ilmiah Teknik Elektro
Publisher : Program Studi Teknik Elektro Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/protk.v11i3.7344

Abstract

This study aims to use an artificial neural network to forecast the flashover voltage of a polluted high-voltage insulator. Practical tests were conducted on a high-voltage insulator to gather data for the neural network. These tests were carried out with varying levels of real contaminants from used insulators, with each level of contamination measured in milliliters. The collected data provides flashover voltage values corresponding to different pollution amounts and their conductivity in each insulator zone. The Nonlinear Autoregressive External Input Neural Network (NarxNet) is employed to predict the flashover voltage and assess the pollution state of the insulator. The results demonstrate that the NarxNet method achieves a 93.74% accuracy rate in predicting the flashover voltage of high-voltage insulators, compared to the results from practical tests.
Advanced in Islanding Detection and Fault Classification for Grid-Connected Distributed Generation using Deep Learning Neural Network Qatrunnada, Rusvaira; Novizon, Novizon; Hasanah, Mardini; Angraini, Tuti; Anton, Anton
PROtek : Jurnal Ilmiah Teknik Elektro Vol 12, No 1 (2025): Protek : Jurnal Ilmiah Teknik Elektro
Publisher : Program Studi Teknik Elektro Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/protk.v12i1.7573

Abstract

Nowadays, the use of renewable energy is increasing, especially distributed power generation (DG) connected to the power grid. There are several problems when DG is connected to the grid. The principal obstacle pertains to the detachment of Distributed Generation (DG) from the grid, a phenomenon well known as islanding. Islanding detection is an important task that should be completed in no more than two seconds. Earlier studies have shown several approaches to islanding detection. The use of an Artificial Neural Network (ANN) based on the learning vector quantization (LVQ) technique is proposed in this paper for fault classification and islanding detection in grid-connected distributed generators. The method consists of discrete wavelet transform (DWT), which extracts some features from the fault signal. Then, LVQ is used to classify the disturbance and detect islanding events. Power, entropy, and total harmonic distortion (THD) are used to obtain the total harmonic value. All features become inputs for LVQ, and system disturbances, lightning, and islanding disturbances are used as LVQ outputs. There are 600 datasets consisting of 200 datasets for each fault as training data. To test the LVQ training results, 120 datasets consisting of 40 datasets for each disturbance are used. The training error is made at 0.1 percent to get good testing results. The test results from 120 datasets showed that the test data achieved 99.10% accuracy. In other words, the test results are very effective because there are only 0.9% errors, and there are 2 test data that do not match the actual situation.
Co-Authors Angraini, Tuti Anton Anton Laksono, Heru Dibyo Novizon, Novizon Oktavia, Yosa Puspitasari, Pipit Qatrunnada, Rusvaira Septiyeni, Tesya Uldira Sitti Amalia Yamashika, Herris Yusreni Warmi