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All Journal International Journal of Electrical and Computer Engineering International Journal of Power Electronics and Drive Systems (IJPEDS) TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Mustafa Manap
Universiti Teknikal Malaysia Melaka
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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An improved smooth-windowed Wigner-Ville distribution analysis for voltage variation signal Mustafa Manap; Abdul Rahim Abdullah; Srete Nikolovski; Tole Sutikno; Mohd Hatta Jopri
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 5: October 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (713.933 KB) | DOI: 10.11591/ijece.v10i5.pp4982-4991

Abstract

This paper outlines research conducted using bilinear time-frequency distribution (TFD), a smooth-windowed wigner-ville distribution (SWWVD) used to represent time-varying signals in time-frequency representation (TFR). Good time and frequency resolutions offer superiority in SWWVD to analyze voltage variation signals that consist of variations in magnitude. The separable kernel parameters are estimated from the signal in order to get an accurate TFR. The TFR for various kernel parameters is compared by a set of performance measures. The evaluation shows that different kernel settings are required for different signal parameters. Verification of the TFD that operated at optimal kernel parameters is then conducted. SWWVD exhibits a good performance of TFR which gives high peak-to-side lobe ratio (PSLR) and signal-to-cross-terms ratio (SCR) accompanied by low main-lobe width (MLW) and absolute percentage error (APE). This proved that the technique is appropriate for voltage variation signal analysis and it essential for development in an advanced embedded system.
Identification of harmonic source location in power distribution network Mohd Hatta Jopri; Aleksandr Skamyin; Mustafa Manap; Tole Sutikno; Mohd Riduan Mohd Shariff; Aleksey Belsky
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 13, No 2: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v13.i2.pp938-949

Abstract

This paper presents the experimental set-up of identification of harmonic source location in the power distribution network using time-frequency analysis, known as S-transform (ST) at the point of common coupling (PCC). S-transform offers high frequency resolution in analyzing the low frequency component and able to represent signal parameters in time-frequency representation (TFR) such as TFR impedance (ZTFR). The proposed method is based on IEEE Std. 1459-2010, ST, and the significant relationship of spectral impedances components (ZS) that been extracted from the ZTFR, consist of the fundamental impedance (Z1) and harmonic impedance (Zh). This experiment was conducted out on an IEEE 4-bus test feeder with a harmonic producing load in numerous different scenarios. The experimental was tested and verified for three consecutive months. The findings of this study reveal that the proposed method provides 100 percent correct identification of harmonic source location.
An analysis of voltage source inverter switches fault classification using short time Fourier transform Mustafa Manap; Srete Nikolovski; Aleksandr Skamyin; Rony Karim; Tole Sutikno; Mohd Hatta Jopri
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 12, No 4: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v12.i4.pp2209-2220

Abstract

The dependability of power electronics systems, such as three-phase inverters, is critical in a variety of applications. Different types of failures that occur in an inverter circuit might affect system operation and raise the entire cost of the manufacturing process. As a result, detecting and identifying inverter problems for such devices is critical in industry. This study presents the short-time Fourier transform (STFT) for fault classification and identification in three-phase type, voltage source inverter (VSI) switches. TFR represents the signal analysis of STFT, which includes total harmonic distortion, instantaneous RMS current, RMS fundamental current, total non harmonic distortion, total waveform distortion and average current. The features of the faults are used with a rule-based classifier based on the signal parameters to categorise and detect the switch faults. The suggested method's performance is evaluated using 60 signals containing short and open circuit faults with varying characteristics for each switch in VSI. The classification results demonstrate the proposed technique is good to be implemented for VSI switches faults classification, with an accuracy classification rate of 98.3 percent.
Accurate harmonic source identification using S-transform Mohd Hatta Jopri; Abdul Rahim Abdullah; Rony Karim; Srete Nikolovski; Tole Sutikno; Mustafa Manap
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 5: October 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i5.5632

Abstract

This paper introduces the accurate identification of harmonic sources in the power distribution system using time-frequency distribution (TFD) analysis, which is S-transform. The S-transform is a very applicable method to represent signals parameters in time-frequency representation (TFR) such as TFR impedance (ZTFR) and the main advantages of S-transform it can provide better frequency resolution for low frequency components and also offers better time resolution for high-frequency components. The identification of multiple harmonic sources are based on the significant relationship of spectral impedances (ZS) that extracted from the ZTFR, consist of the fundamental impedance (Z1) and harmonic impedance (Zh). To verify the accuracy of the proposed method, MATLAB simulations carried out several unique cases on IEEE 4-bus test feeder cases. It is proven that the proposed method is superior, with 100% correct identification of harmonic source location. It is proven that the method is accurate, fast and cost-efficient to localize harmonic sources in the power distribution system.
An Accurate Classification Method of Harmonic Signals in Power Distribution System by Utilising S-Transform M. Hatta Jopri; A. Rahim Abdullah; Mustafa Manap; M. Faiz Habban; Tole Sutikno
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 15, No 1: March 2017
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v15i1.3119

Abstract

This paper presents an accurate classification method of harmonic signal in power distribution system by using S-transform (ST).  ST has a capability of representing signals in jointly time-frequency domain and known as time frequency representation (TFR). The spectral parameters are estimated from TFR in order to identify the characteristics and to classify the harmonic signals. The classification of harmonic signals with the utilization of pattern recognition approach which is rule-based classifier of 100 unique signals is according to the IEEE standard 519:2014. The accuracy of the proposed method is determined by using MAPE and the results proved that the method provides high accuracy of harmonic signal classification. Additionally, S-transform also gives 100 percent correct classification of harmonic signals. It is proven that the proposed method is accurate in detecting and classifying harmonic signals in the distribution system. 
A Fast Localization of Multiple Harmonic Sources for Rectifier Loads by Utilizing Periodogram M. Hatta Jopri; A. Rahim Abdullah; Mustafa Manap; M. Rahimi Yusoff; Tole Sutikno
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 15, No 1: March 2017
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v15i1.3120

Abstract

This paper introduces a fast method to localize the multiple harmonic sources (MHS) for rectifier based loads in power distribution system by utilizing periodogram technique with a single-point measurement approach at the point of common coupling (PCC). The periodogram is a fast and accurate technique for analyzing and distinguishing MHS location in power system. Matlab simulation is carried out several unique cases on IEEE test feeder cases due to validate the proposed method. The identification of MHS location is based on the significant relationship of spectral impedance which are fundamental impedance (Z1) and harmonic impedance (Zh) that's extracted from an impedance power spectrum. It is verified that the proposed method is fast, accurate, and cost efficient in localizing MHS. In addition, this method also contributes 100% correct identification of MHS location.
Personal air-conditioning system using evapolar as heat waste management Nor Azazi Ngatiman; Abdul Qaiyum Mohd Shariff; Tole Sutikno; Suparje Wardiyono; Mustafa Manap; Mohd Hatta Jopri
Bulletin of Electrical Engineering and Informatics Vol 10, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i6.3283

Abstract

Air-conditioning system that uses compressor-based initiate more energy and affects bill rate. As a result, an application of the Peltier impact module, a portable air-conditioning system is introduced to compensate user convenience by lowering sensible and latent heat inside the office area. Thermoelectric Peltier module is a thermoelectric semiconductor that offers cooling and hot plate once the plate is supplied by electric. The result reduces the cost, power consumption, and give thermal comfort in a dedicated space. The advantage of the study is the ability to cost deduction due to low power consumption and green technology devices factor because without refrigerant that harms the environment. Redesign the product with Evapolar as heat waste management affect the performance and need to be validated. The development stage of this product is better compared to a previous product which offers small scale, light, and portable. This product focuses on the office room, which gives a good feeling to users. This product uses air to remove the heat waste and the result indicates Evapolar is fit enough in dissipating heat. Finally, the performance of this system developed demonstrated that it can attain thermal comfort level.
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.
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.
Co-Authors A. Rahim Abdullah Abdul Kayum Mohammad Zakir Hossain Abdul Qaiyum Mohd Shariff Abdul Rahim Abdullah Aleksandr Skamyin Aleksandr Skamyin Aleksey Belsky Jamil Abedalrahim Jamil Alsayaydeh Jingwei Too M. Badril Nor Shah M. Faiz Habban M. Hatta Jopri M. Rahimi Yusoff Mohd Hatta Jopri Mohd Riduan Mohd Shariff Mohd Ruddin Ab Ghani Muhammad Ibn Ibrahimy Nor Azazi Ngatiman Rony Karim Rony Karim Srete Nikolovski Suparje Wardiyono Tole Sutikno