Abdul Rahim Abdullah

Power quality analysis using linear time-frequency distribution

This paper presents the implementation of linear time-frequency distribution (TFD) techniques to analyze power quality signals. The power quality signals are swell, sag, interruption, transient, harmonic, interharmonic, notching and normal voltage based on IEEE Std. 1159-1995. The time-frequency analysis techniques selected are spectrogram and Gabor transform. From the time-frequency representation (TFR) obtained, the signal parameters are estimated to identify the signal characteristics. Identified signal characteristics that are used as input to a classifier includes the average of root mean square (RMS) voltage, total waveform distortion, total harmonic distortion and total nonharmonic distortion and duration of swell, sag, interruption and transient. From the linear time-frequency analysis techniques used, the optimal technique is chosen in terms of accuracy, computation complexity and memory size.

Detection and Classification of Power Quality Disturbances Using Time-Frequency Analysis Technique

This paper presents the detection and classifications of power quality disturbances using time-frequency signal analysis. The method used is based on the pattern recognition approach. It consists of parameter estimation followed classification. Based on the spectrogram time-frequency analysis, a set of signal parameters are estimated as input to a classifier network. The power quality events that are analyzed are swell, sag, interruption, harmonic, interharmonic, transient, notching and normal voltage. The parameter estimation is characterized by voltage signal in rms per unit, waveform distortion, harmonic distortion and interharmonic distortion. A rule based system is developed to detect and classify the various types of power quality disturbances. The system has been tested with 100 data for each power quality event at SNR from OdB to 50dB to verify its performance. The results show that the system gives 100 percent accuracy of power quality signals at 30 dB of SNR.

Power quality analysis using spectrogram and gabor transformation

This paper discusses the implementation of time-frequency analysis techniques to analyze power quality disturbances. The approached methods are spectrogram and Gabor transform algorithms. Signal parameters such as time marginal and frequency marginal are extracted from the time-frequency distributions. The parameters are analyzed in terms of correctness measurement of root mean square (RMS), total harmonic distortion (THD), total waveform distortion (TWD) and total interharmonic distortion (TnHD) values. Power quality events that are analyzed are swell, sag, interruption, harmonic, interharmonic, transient, notching and normal voltage. The results show that Gabor transform provides better performance in terms of correctness of parameters measurement, window length, frequency resolution and memory size.

Power quality analysis using smooth-windowed wigner-ville distribution

Bilinear time-frequency distributions (TFDs) are developed to represent time-varying signal jointly in time and frequency representation (TFR). Since the TFDs offer good time and frequency resolution, they are appropriate to analyze power quality signals that consist of magnitude variation and multiple frequencies. However, the TFD suffer from cross-terms interferences due to their bilinear structures. In this paper, smooth-windowed Wigner-Ville distribution (SWWVD) is used to analyze power quality signals. The power quality signals are swell, sag, interruption, harmonic, interharmonic and transient. To get accurate TFR, the parameters of the separable kernel are estimated from the signal. A set of performance measures is defined and used to compare the TFR for various kernel parameters. The comparison shows that signals with different parameters require different kernel settings to get the optimal TFR.

Power quality signals detection using S-transform

Power quality has become very important issue over the last decade. Poor quality can cause equipment failure, data and economical. An automated monitoring system is needed to ensure signal quality, reduces diagnostic time and rectifies failures. In this paper, S-transform is used to analyze the power quality signals such as swell, sag, interruption, harmonic, interharmonic and transient based on IEEE Std. 1159-2009 to detect, localize and classify the disturbance. The S-transform is used to represent the signals in time-frequency representation (TFR).). To get an accurate TFR, the parameters are estimated to identify the signal characteristics. The signal characteristics are the root means square voltage (Vrms), total harmonic distortion (THD), total non harmonic distortion (TnHD) and total waveform distortion (TWD). To verify the performance of S-transform several sets of data with different time duration are analyzed to determine the accuracy of S-transform. The lowest value of mean absolute percentage error (MAPE) gives the highest accuracy to provide the best performance of TFD.

Leakage current analysis on polymeric surface condition using time-frequency distribution

Leakage current frequency components are frequently used as a tool for surface condition monitoring on polymeric insulation material and their pollution severity. Fast Fourier Transform (FFT) is one of the methods that are applied for the analysis, but it has some limitation in non-stationary signal. This paper presents analysis of leakage current on polymeric insulation material for high voltage application in frequency domain and time-frequency representation. Tracking and erosion test (Inclined Plane Test (IPT)) complying with BS EN 60587-2007 is conducted on polymeric insulation to select a set of different leakage current patterns from capacitive, resistive and discharge. Then, the leakage current patterns are analysed in frequency domain and time-frequency representation using Fast Fourier Transform and spectrogram technique, respectively. It is found that the surface condition of polymeric insulation material state can be classified accurately by using spectrogram compared to Fast Fourier Transform.

Detection of Heart Blocks in ECG Signals by Spectrum and Time-Frequency Analysis

The electrocardiogram (ECG) is a non-invasive test that records the electrical activity of the heart and is important in the investigation of cardiac abnormalities. Each portion of the ECG waveform carries various types of information for the cardiologists analyzing patients heart condition. ECG interpretation at the present time remains dependent manually in time domain. It is difficult for the cardiologists to make a correct diagnosis of cardiac disorder. A computerized interpretation of ECG is needed in order to make the diagnosis more efficient. This paper discusses the use of digital signal processing approach for the detection of heart blocks in ECG signals. Signal analysis techniques such as the periodogram power spectrum and spectrogram time-frequency analysis are employed to analyze ECG variations. Seven subjects are identified: normal, first degree heart block, second degree heart block type I, second degree heart block type II, Third degree heart block, right bundle branch block and left bundle branch block. Analysis results revealed that normal ECG subject is able to maintain higher peak frequency range (8 Hz), while heart block subjects revealed a significant low peak frequency range (< 4 Hz). The results revealed that the periodogram power spectrum can be used to differentiate between normal and heart block subjects, while the spectogram time-frequency analysis is used to give better characterization of ECG parameters. These analyses can be used to construct ECG monitoring and analyzing system for heart blocks detection.

A new vector draft method for harmonic source detection at point of common coupling

In modern power networks, the issue of power quality (PQ) is becoming very important because of the increasing of load which sensitive to current disturbances. This is mainly due to the increasing use of non-linear power electronic devices draws non-sinusoidal current and creating a current distortion. As a result there is increasing need for PQ to be monitored to establish the type, sources and locations of PQ disturbances, allowing remedial measures to be taken. Consequently, harmonic is one of the most concerned power quality disturbances. The detection of harmonic source is necessary for power quality strategy development. This paper introduces a new single-point measurement method to estimate the harmonic source by using phase spectrogram (PS) and frequency spectrogram (FS) based on a vector draft method. A measurement at the point of common coupling (PCC) with harmonic distortion is done by simulation via PSCAD. Then PSCADs data are analyzed by using spectrogram in MATLAB. To be precise, voltage and current waveforms are normalized with fundamental magnitude respectively. Next, the normalized voltage and current are plotted on the vector draft to estimate the perpendicular point between the vectors. The center point of the normalized voltage is the boundary between downstream and upstream. The harmonic source can be detected base on the perpendicular points location that fall on the particular region. The comparison between actual and power direction result have been conducted. Finally, the proposed method is similar with the actual result and more truthful than power direction method.

Open switch faults analysis in voltage source inverter using spectrogram

The performance and effects is critical factor in industry, especially usage power inverter such as motor, switching and control circuit. Until now, the statistic of effects still increase in the application. In order to overcome this, the spectrogram technique is used to represent the signals in time frequency representation (TFR). This paper introduces time-frequency distribution (TFD) technique for detecting and identifying the open circuit fault in application of inverter. The condition monitoring is based on time-frequency distribution. Since Fast Fourier Transform (FFT) is one of the techniques to analyze the signal, but it has some limitations in non-stationary signal. From TFR the parameters such as root means square voltage (Vrms), total waveform distortion (TWD), total harmonic distortion (THD) and total non-harmonics (TnHD) for voltage source inverter (VSI) are used to identify the characteristic of the signals. The result shows that spectrogram technique capable to identify and evaluate the information of voltage source inverter (VSI). The proposed technique is verified as simulation results.

Power Quality Analysis Using Bilinear Time-Frequency Distributions

Bilinear time-frequency distributions (TFDs) are powerful techniques that offer good time and frequency resolution of time-frequency representation (TFR). It is very appropriate to analyze power quality signals which consist of nonstationary and multi-frequency components. However, the TFDs suffer from interference because of cross-terms. Many TFDs have been implemented, and there is no fixed window or kernel that can remove the cross-terms for all types of signals. In this paper, the bilinear TFDs are implemented to analyze power quality signals such as smooth-windowed Wigner-Ville distribution (SWWVD), Choi-Williams distribution (CWD), B-distribution (BD), and modified B-distribution (MBD). The power quality signals focused are swell, sag, interruption, harmonic, interharmonic, and transient based on IEEE Std, 1159-1995. A set of performance measures is defined and used to compare the TFRs. It shows that SWWVD presents the best performance and is selected for power quality signal analysis. Thus, an adaptive optimal kernel SWWVD is designed to determine the separable kernel automatically from the input signal.