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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.
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.
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.
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.
Internet of things-based photovoltaics parameter monitoring system using NodeMCU ESP8266 Tole Sutikno; Hendril Satrian Purnama; Anggit Pamungkas; Abdul Fadlil; Ibrahim Mohd Alsofyani; Mohd Hatta Jopri
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i6.pp5578-5587

Abstract

The use of the internet of things (IoT) in solar photovoltaic (PV) systems is a critical feature for remote monitoring, supervising, and performance evaluation. Furthermore, it improves the long-term viability, consistency, efficiency, and system maintenance of energy production. However, previous researchers' proposed PV monitoring systems are relatively complex and expensive. Furthermore, the existing systems do not have any backup data, which means that the acquired data could be lost if the network connection fails. This paper presents a simple and low-cost IoT-based PV parameter monitoring system, with additional backup data stored on a microSD card. A NodeMCU ESP8266 development board is chosen as the main controller because it is a system-on-chip (SOC) microcontroller with integrated Wi-Fi and low-power support, all in one chip to reduce the cost of the proposed system. The solar irradiance, ambient temperature, PV output voltage and PV output current, are measured with photo-diodes, DHT22, impedance dividers and ACS712. While, the PV output power is a product of the PV voltage and PV current. ThingSpeak, an open-source software, is used as a cloud database and data monitoring tool in the form of interactive graphics. The results showed that the system was designed to be highly accurate, reliable, simple to use, and low-cost.
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.
FPGA Based Optimized Discontinuous SVPWM Algorithm for Three Phase VSI in AC Drives Tole Sutikno; Nik Rumzi Nik Idris; Auzani Jidin; Mohd Hatta Jopri
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 3, No 2: June 2013
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1027.866 KB)

Abstract

The discontinuous space vector pulse width modulation (SVPWM) has well-known that can reduce switching losses. By simplifying the thermal management issues, the discontinuous SVPWM can potentially reduce the inverter size and cost. However, using the modulation due to different time interval equations for each sector can introduce glitches at the points when the sector is changed. The more main problem, it can increase unwanted harmonic content and current ripple. Consider the decrease in switching losses associated with discontinuous modulation allows the system to utilize a higher switching frequency, this paper present high frequency switching of optimized discontinuous SVPWM based on FPGA to overcome the problems above. The proposed SVPWM has been successfully implemented by using APEX20KE Altera FPGA to drive on a three phase inverter system with 1.5 kW induction machine as load. The results have proved that the method can reduce harmonic content and current ripple without glitches.DOI : http://dx.doi.org/10.11591/ijpeds.v3i1.735
An assessment of the share contributions of distortion sources for various load parameters Aleksandr Skamyin; Yaroslav Shklyarskiy; Iuliia Dobush; Vasiliy Dobush; Tole Sutikno; Mohd Hatta Jopri
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.pp950-959

Abstract

The method for assessing the contributions of distortion sources based on measuring consumer currents and calculating their projections onto the supply current vector is considered in the paper. Determination of contributions is carried out on the basis of the developed model of an industrial enterprise in the food industry in MATLAB Simulink software. This study presents various cases of simulation, including variable parameters of linear and non-linear consumer load, changes in parameters of external distortion sources and passive harmonic filters. It is shown that the considered method gives correct results in the absence of external distortions in the electrical grid. The considered criteria for the share contributions make it possible to estimate the most efficient place for installing a passive filter in the absence of external distortions. An indicator for evaluating external distortions has also been developed based on calculating the projection of the harmonic system current onto the harmonic current of the shunt filter at the considered frequency.
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.