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All Journal International Journal of Electrical and Computer Engineering International Journal of Power Electronics and Drive Systems (IJPEDS) Perfecting a Video Game with Game Metrics Indonesian Journal of Electrical Engineering and Computer Science

Srete Nikolovski

University of Osijek

Author-ID : 2372717

Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

Published : 6 Documents Claim Missing Document

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Articles

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

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.

ANFIS Used as a Maximum Power Point Tracking Algorithm for a Photovoltaic System Dragan Mlakić; Ljubomir Majdandžić; Srete Nikolovski
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 2: April 2018
Publisher : Institute of Advanced Engineering and Science

Photovoltaic (PV) modules play an important role in modern distribution networks; however, from the beginning, PV modules have mostly been used in order to produce clean, green energy and to make a profit. Working effectively during the day, PV systems tend to achieve a maximum power point accomplished by inverters with built-in Maximum Power Point Tracking (MPPT) algorithms. This paper presents an Adaptive Neuro-Fuzzy Inference System (ANFIS), as a method for predicting an MPP based on data on solar exposure and the surrounding temperature. The advantages of the proposed method are a fast response, non-invasive sampling, total harmonic distortion reduction, more efficient usage of PV modules and a simple training of the ANFIS algorithm. To demonstrate the effectiveness and accuracy of the ANFIS in relation to the MPPT algorithm, a practical sample case of 10 kW PV system and its measurements are used as a model for simulation. Modelling and simulations are performed using all available components provided by technical data. The results obtained from the simulations point to the more efficient usage of the ANFIS model proposed as an MPPT algorithm for PV modules in comparison to other existing methods.

Comparative Analysis of Power Quality Indices for Different Lighting Techniogies in Public Lightning Jurica Perko; Srete Nikolovski; Ljubomir Majdandžić
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 2: April 2018
Publisher : Institute of Advanced Engineering and Science

As with any other product, having electricity of a certain quality is very important for consumers. Its quality has become the key feature that can be disturbed by loads based on power electronic devices. Electricity producers are striving to provide reliable and quality electricity supply to consumers. Public lighting systems that are based on LED lighting technology can potentially positively or negatively affect the power quality because they contain power electronics components in LED drivers. Because of that, two 7-day measurements were conducted on the same public lighting branch but with different lighting technologies (high pressure sodium, known as HPS and LED technology). Results of the measurements were analysed and compared. By analysing the results, other problems were also identified by using LED lighting technology. These problems are addressed in the conclusion of the paper.

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

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

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.

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

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 Abdul Rahim Abdullah Aleksandr Skamyin Dragan Mlakić Jingwei Too Jurica Perko Ljubomir Majdandzic Ljubomir Majdandžić Mohd Hatta Jopri Mustafa Manap Rony Karim Rony Karim Tole Sutikno

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