Power quality is main issue because of the impact to electricity suppliers, equipments, manufacturers and user.To solve the power quality problem, an analysis of power quality disturbances is required to identify and rectify any failures on power system. Most of researchers apply fourier transform in power quality analysis, however the ability of fourier transform is limited to spectral information extraction that can be applied on stationary disturbances. Thus, time-frequency analysis is introduced for analyzing the power quality distubances because of the limitation of fourier transform. This paper presents the analysis of real power quality disturbances using S-transform. This time-frequency distribution (TFD) is presented to analyze power quality disturbances in time-frequency representation (TFR). From the TFR, parameters of the disturbances such as instantaneous of root mean square (RMS), fundamental RMS, total harmonic distortion (THD), total nonharmonic distortion (TnHD) and total waveform distortion (TWD) of the disturbances are estimated. The experimental of three phase voltage inverter and starting motor are conducted in laboratory to record the real power quality disturbances. The disturbances are recorded via data logger system which is mplemented using LabVIEW while the analysis is done using Matlab in offline condition. The results show that S-transform gives good performance in identifying, detecting and analyzing the real power quality disturbances, effectively.
Power quality signals are an important issue to electricity consumers. The signals will affect manufacturing process, malfunction of equipment and economic losses. Thus, an automated monitoring system is required to identify and classify the signals for diagnosis purposes. This paper presents the development of power quality signals classification system using time-frequency analysis technique which is spectrogram. From the time-frequency representation (TFR), parameters of the signal are estimated to identify the characteristics of the signals. The signal parameters are instantaneous of RMS voltage, RMS fundamental voltage, total waveform distortion, total harmonic distortion and total non harmonic distortion. In this paper, major power quality signals are focused based on IEEE Std. 1159-2009 such as swell, sag, interruption, harmonic, interharmonic, and transient. An automated signal classification system using spectrogram is developed to identify, classify as well as provide the information of the signal.
Power quality has become a greater concern nowadays. The increasing number of power electronics equipment contributes to the poor quality of electrical power supply. The power quality signals will affect manufacturing process, malfunction of equipment and economic losses. This paper presents the verification analysis of power quality signals classification system. The developed system is based on linear time-frequency distribution (TFD) which is spectrogram that represents the signals jointly in time-frequency representation (TFR). The TFD is very appropriate to analyze power quality signals that have magnitude and frequency variations. Parameters of the signal such as root mean square (RMS) and fundamental RMS, total waveform distortion (TWD), total harmonic distortion (THD) and total non-harmonic distortion (TnHD) of voltage signal are estimated from the TFR to identify the characteristics of the signal. Then, the signal characteristics are used as input for signal classifier to classify power quality signals. In addition, standard power line measurements are also calculated from voltage and current such as RMS and fundamental RMS voltage and current, real power, apparent power, reactive power, frequency and power factor. The power quality signals focused are swell, sag, interruption, harmonic, interharmonic, and transient based on IEEE Std. 1159-2009. The power quality analysis has been tested using a set of data and the results show that, the spectrogram gives high accuracy measurement of signal characteristics. However, the system offers lower accuracy compare to simulation due to the limitation of the system.
Switches fault in power converter has become compelling issues over the years. To reduce cost and maintenance downtime, a good fault detection technique is an essential. In this paper, the performance of STFT and S transform techniques are analysed and compared for voltage source inverter (VSI) switches faults. The signal from phase current is represented in jointly time-frequency representation (TFR) to estimate signal parameters and characteristics. Then, the degree of accuracy for both STFT and S transform are determined by the lowest value of mean absolute percentage error (MAPE). The results demonstrate that S transform gives better accuracy compare to STFT and is suitable for VSI switches faults detection and identification system.
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