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All Journal International Journal of Electrical and Computer Engineering International Journal of Power Electronics and Drive Systems (IJPEDS) IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Mohd Hatta Jopri
Universiti Teknikal Malaysia Melaka
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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K-nearest neighbor and naïve Bayes based diagnostic analytic of harmonic source identification Mohd Hatta Jopri; Mohd Ruddin Ab Ghani; Abdul Rahim Abdullah; Mustafa Manap; Tole Sutikno; Jingwei Too
Bulletin of Electrical Engineering and Informatics Vol 9, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v9i6.2685

Abstract

This paper proposes a comparison of machine learning (ML) algorithm known as the k-nearest neighbor (KNN) and naïve Bayes (NB) in identifying and diagnosing the harmonic sources in the power system. A single-point measurement is applied in this proposed method, and using the S-transform the measurement signals are analyzed and extracted into voltage and current parameters. The voltage and current features that estimated from time-frequency representation (TFR) of S-transform analysis are used as the input for MLs. Four significant cases of harmonic source location are considered, whereas harmonic voltage (HV) and harmonic current (HC) source type-load are used in the diagnosing process. To identify the best ML, the performance measurement of the proposed method including the accuracy, precision, specificity, sensitivity, and F-measure are calculated. The sufficiency of the proposed methodology is tested and verified on IEEE 4-bust test feeder and each ML algorithm is executed for 10 times due to prevent any overfitting result.
Linear discriminate analysis and k-nearest neighbor based diagnostic analytic of harmonic source identification Mohd Hatta Jopri; Abdul Rahim Abdullah; Mustafa Manap; M. Badril Nor Shah; Tole Sutikno; Jingwei Too
Bulletin of Electrical Engineering and Informatics Vol 10, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i1.2686

Abstract

The diagnostic analytic of harmonic source is crucial research due to identify and diagnose the harmonic source in the power system. This paper presents a comparison of machine learning (ML) algorithm known as linear discriminate analysis (LDA) and k-nearest neighbor (KNN) in identifying and diagnosing the harmonic sources. Voltage and current features that estimated from time-frequency representation (TFR) of S-transform analysis are used as the input for ML. Several unique cases of harmonic source location are considered, whereas harmonic voltage (HV) and harmonic current (HC) source type-load are used in the diagnosing process. To identify the best ML, each ML algorithm is executed 10 times due to prevent any overfitting result and the performance criteria are measured consist of the accuracy, precision, geometric mean, specificity, sensitivity, and F measure are calculated.
Milestone of the most used maximum power point tracking in solar harvesting system Tole Sutikno; Arsyad Cahya Subrata; Mohd Hatta Jopri
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 13, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v13.i3.pp1277-1284

Abstract

Solar harvesting system with photovoltaic (PV) is one of the most desirable renewable energy sources because of its prominent advantages. However, low efficiency due to fluctuating output power is a major problem for PV systems. A technique used to maximize power extraction known as maximum power point tracking (MPPT) has been proposed by various literature to deal with this problem. One of the most widely developed MPPT methods due to its ease of implementation is perturb and observe (P&O). Since the initial discovery of the principle, the P&O method has been extensively modified including the fixed step-size: step-size variables, partial shading, threshold module current, three-point-comparison, maximization of dynamic performance, minimization of dynamic performance, bandwidth of ???? − ???? curve, decoupling, observation of ????????, ????????, and ????????, datasheet parameters, curve fitting, voltage hold P&O, and observation of ???????? and ????????. This paper presents the development of the P&O method from the initial principle to the end as a reference source for readers. The hope is that a new easy and robust P&O method as a complement to the implementation of the MPPT technique is developed in the solar harvesting system.
Support-vector machine and naïve bayes based diagnostic analytic of harmonic source identification Mohd Hatta Jopri; Abdul Rahim Abdullah; Jingwei Too; Tole Sutikno; Srete Nikolovski; Mustafa Manap
Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 1: October 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v20.i1.pp1-8

Abstract

A harmonic source diagnostic analytic is a vital to identify the location and type of harmonic source in the power system. This paper introduces a comparison of machine learning (ML) algorithm which are support vector machine (SVM) and naïve bayes (NB). Voltage and current features are used as the input for ML are extracted from time-frequency representation (TFR) of S-transform. Several unique cases of harmonic source location are considered, whereas harmonic voltage and harmonic current source type-load are used in the diagnosing process. To identify the best ML, the performance measurement of the propose method including accuracy, specificity, sensitivity, and F-measure are calculated. The adequacy of the proposed methodology is tested and verified on IEEE 4-bust test feeder and each ML algorithm is executed for 10 times due to different partitions and to prevent any overfitting result.
A two-element planar multiple input multiple output array for ultra-wideband applications Abdul Kayum Mohammad Zakir Hossain; Muhammad Ibn Ibrahimy; Tole Sutikno; Mohd Hatta Jopri; Jamil Abedalrahim Jamil Alsayaydeh; Mustafa Manap
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp6847-6858

Abstract

In this article, a planar monopole two-element multiple input multiple output (MIMO) array has been designed and characterized with the intention of ultra-wideband (UWB) applications. The array has a voltage standing wave ratio (VSWR) working bandwidth (BW) of 13.258 GHz between 3.394-16.652 GHz, with a fractional BW (FBW) of 132.28% with respect to a center frequency of 10.023 GHz. The two elements of the MIMO array are 900 polarizations mismatched for better isolation. Consequently, less than 20 dB of isolation has been achieved throughout the entire BW. Also observed was a good combined realized peak gain of up to 5.85 dBi and total efficiency of greater than 85%. For MIMO performance key parameters, the array exhibits the envelope correlation coefficient (ECC) <0.0033, diversity gain (DG) >9.983, total active reflection coefficient (TARC) <0.445, mean effective gain difference (MEG12) ≈0 dB, and the channel capacity loss (CCL) <0.4 bps/Hz. This design would encourage designers to create high-performance MIMO antennas for UWB frequency-related applications.
In-line measurement of multiphase flow viscosity Taisiia Ushkova; Alexandra Kopteva; Vadim Shpenst; Tole Sutikno; Mohd Hatta Jopri
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.4856

Abstract

The transportation modes depend entirely on the viscosity of the oil. To date, none of the viscometric methods are able to provide measurements to meet all the requirements of oil flow, features of main oil pipelines, and trends in the oil industry, such as decarbonization and digitalization. The method of inline viscosity measurement of multiphase flow through a metal pipeline can be based on direct gamma ray measurement. It is stipulated by the ability of gamma-radiation to penetrate through the pipeline material without destroying it, as well as by the ability to work with flows containing free gas and the high capability to be introduced into automatic control systems. The authors consider the physical forces acting on the gas inclusions in the oil flow. They determine the physical dependence between the parameters determined by the radioisotope method and the viscosity of oil in the three-phase flow. These studies show good agreement with the work of other scientists. The prospect of further research will be to clarify the mathematical model of gas-oil flow, to increase the accuracy by reducing the number of assumptions made.
Co-Authors Abdul Fadlil Abdul Kayum Mohammad Zakir Hossain Abdul Qaiyum Mohd Shariff Abdul Rahim Abdullah Ahmad Sobri Muda Aleksandr Skamyin Aleksandr Skamyin Aleksey Belsky Alexandra Kopteva Anggit Pamungkas Auzani Jidin Azma Putra Hendril Satrian Purnama Ibrahim Mohd Alsofyani Isa S. Qamber Iuliia Dobush Jamil Abedalrahim Jamil Alsayaydeh Jingwei Too M. Badril Nor Shah Mohd Riduan Mohd Shariff Mohd Ruddin Ab Ghani Mohd Zaki Nuawi Muhammad Ibn Ibrahimy Mustafa Manap Nik Rumzi Nik Idris Nor Azazi Ngatiman Norhashimah Mohd Saad Nur Hasanah Ali Rony Karim Rony Karim Srete Nikolovski Subrata, Arsyad Cahya Suparje Wardiyono Taisiia Ushkova Tole Sutikno Vadim Shpenst Vasiliy Dobush Yaroslav Shklyarskiy