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A Critical Review of Time-frequency Distribution Analysis for Detection and Classification of Harmonic Signal in Power Distribution System M. H. Jopri; A. R. Abdullah; T. Sutikno; M. Manap; M. R. Ab Ghani; M. R. Yusoff
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 6: December 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1219.077 KB) | DOI: 10.11591/ijece.v8i6.pp4603-4618

Abstract

This paper presents a critical review of time-frequency distributions (TFDs) analysis for detection and classification of harmonic signal. 100 unique harmonic signals comprise of numerous characteristic are detected and classified by using spectrogram, Gabor transform and S-transform. The rulebased classifier and the threshold settings of the analysis are according to the IEEE Standard 1159 2009. The best TFD for harmonic signals detection and classification is selected through performance analysis with regards to the accuracy, computational complexity and memory size that been used during the analysis.
A Diagnostic Analytics of Harmonic Source Signature Recognition by Using Periodogram M. H. Jopri; A. R. Abdullah; T. Sutikno; M. Manap; M. R. Ab Ghani; A. S. Hussin
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 6: December 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (875.81 KB) | DOI: 10.11591/ijece.v8i6.pp5399-5408

Abstract

This paper presents a diagnostic analytics of harmonic source signature recognition of rectifier and inverter-based load in the distribution system with single-point measurement at the point of common coupling by utilizing Periodogram. Signature recognition pattern is used to distinguish the harmonic sources accurately by obtaining the distribution of harmonic and interharmonic components and the harmonic contribution changes.  This is achieved by using the significant signature recognition of harmonic producing load obtained from analysing the harmonic contribution changes. Based on voltage and current signature analysis, the distribution of harmonic components can be divided into three zones. To distinguish between the harmonic producing loads, the harmonic components are observed at these zones to get the signature recognition pattern. The result demonstrate that periodogram technique accurately diagnose and distinguish the type of harmonic sources in the distribution system.
An Improved of Multiple Harmonic Sources Identification in Distribution System with Inverter Loads by Using Spectrogram M.H. Jopri; A.R. Abdullah; M. Manap; M.R. Yusoff; T. Sutikno; M.F. Habban
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 7, No 4: December 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v7.i4.pp1355-1365

Abstract

This paper introduces an improved of multiple harmonic sources identification that been produced by inverter loads in power system using time-frequency distribution (TFD) analysis which is spectrogram.  The spectrogram is a very applicable method to represent signals in time-frequency representation (TFR) and the main advantages of spectrogram are the accuracy, speed of the algorithm and use low memory size such that it can be computed rapidly. The identification of multiple harmonic sources is based on the significant relationship of spectral impedances which are the fundamental impedance (Z1) and harmonic impedance (Zh) that extracted from TFR. To verify the accuracy of the proposed method, MATLAB simulations carried out several unique cases with different harmonic producing loads on IEEE 4-bus test feeder cases. It is proven that the proposed method is superior with 100% correct identification of multiple harmonic sources. It is envisioned that the method is very accurate, fast and cost efficient to localize harmonic sources in distribution system.
An Evaluation of Linear Time Frequency Distribution Analysis for VSI Switch Faults Identification M.F. Habban; M. Manap; A.R. Abdullah; M.H. Jopri; T. Sutikno
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 8, No 1: March 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v8.i1.pp1-9

Abstract

This paper present an evaluation of linear time frequency distribution analysis for voltage source inverter system (VSI). Power electronic now are highly demand in industrial such as manufacturing, industrial process and semiconductor because of the reliability and sustainability. However, the phenomenon that happened in switch fault has become a critical issue in the development of advanced. This causes problems that occur study on fault switch at voltage source inverter (VSI) must be identified more closely so that problems like this can be prevented. The TFD which is STFT  and S-transform method are analyzed the switch fault of VSI.  To identify the VSI switches fault, the parameter of fault signal such as instantaneous of average current, RMS current, RMS fundamental current, total waveform distortion, total harmonic distortion and total non-harmonic distortion can be estimated from TFD. The analysis information are useful especially for industrial application in the process for identify the switch fault detection. Then the accuracy of both method, which mean STFT and S-transform are identified by the lowest value of mean absolute percentage error (MAPE). In addition, the S-transform gives a better accuracy compare with STFT and it can be implement for fault detection system.
A verification of periodogram technique for harmonic source diagnostic analytic by using logistic regression M. Manap; M. H. Jopri; A. R. Abdullah; R. Karim; M. R. Yusoff; AH Azahar
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 1: February 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i1.10390

Abstract

A harmonic source diagnostic analytic is vital to identify the root causes and type of harmonic source in power system. This paper introduces a verification of periodogram technique to diagnose harmonic sources by using logistic regression classifier. A periodogram gives a correct and accurate classification of harmonic signals. Signature recognition pattern is used to distinguish the harmonic sources accurately by obtaining the distribution of harmonic and interharmonic components and the harmonic contribution changes. This is achieved by using the significant signature recognition of harmonic producing load obtained from the harmonic contribution changes. To verify the performance of the propose method, a logistic regression classifier will analyse the result and give the accuracy and positive rate percentage of the propose method. The adequacy of the proposed methodology is tested and verified on distribution system for several rectifier and inverter-based loads.