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Insulations in high voltage engineering are a concern to users in terms of its performance, expected lifetime, and long-time reliability. Insulation failure can allow leakage current (LC) to flow and causes tracking and erosion as well as flashover. Tracking and erosion test complying with BS EN 60857-2007 are conducted to capture the set of leakag...
Citations
... This is further supported by J.Li [7] that this correlation can be used to determine the pollution severity and the flashover voltage occurrence. Other researcher used the 3rd, 5th, and 7th 3 ( 5 ℎ + 7 ℎ ) ⁄ to analyse the LC under different environmental condition where it has been reported that the 7th harmonic has a considerable effect on the performance of the LC under wetting conditions [8][9][10][11]. N.Bashir [12] used the third to fifth harmonic ratio of the LC to determine the effect of pollution severity on the aged glass insulator. He concluded that the aging of the insulator has a significant effect on the LC odd harmonic components. ...
Leakage current has been widely utilized as an effective means to monitor the pollution severity of outdoor insulators. The indices of the odd harmonic components of the leakage current have proven to be reliable indicators of insulator performance, particularly in the third to fifth harmonic orders. However, significant challenges exist in using these indices to monitor and classify the leakage current of aged insulators under varying harmonic and pollution conditions. This paper introduces the harmonic index K((3/(5+7) ) which employs third up to seventh odd harmonic component of the leakage current as a diagnostic tool to determine the pollution severity of aged insulators under light, medium and heavy contamination levels. The experiments were conducted using 12 different sets of 11kV insulators with varying degrees of aging. The results indicate that the harmonic index Ki for new and moderately aged insulators decreases with an increase in contamination and is lower than that of heavily aged insulators.
... The study applied the fast Fourier transform (FFT) to analyse the EMG signals. It is a mathematical technique that is used to convert the data from time domain to frequency domain (Sulaiman et al., 2013), (Abdullah et al., 2012), (Abidin et al., 2013). However, application of FFT in analysing the EMG signals has a limitation to process non-stationary signals due to spectral characteristics change in time ( Abidin et al., 2013). ...
The fast Fourier transforms (FFT) is commonly applied in transformation of electromyography (EMG) signals from the time domain to the frequency domain. However, this technique has a limitation to provide the time-frequency information for EMG signals. This paper presents the analysis of EMG signal for contraction of muscle activity by using spectrogram. Spectrogram is one of the time-frequency representations (TFR) that represents the three-dimensional of the signal with respect to time and frequency in magnitude presentations. The contraction of muscle activity was based on manual lifting of a 5 kg load performed by the right biceps brachii at lifting height of 75 cm and 140 cm. Ten healthy volunteers in fresh condition participated as subjects to acquire raw data of EMG signals. The raw data of EMG signals were then analysed using MATLAB 2011 to obtain the TFR. Based on the TFR, this study obtained the instantaneous RMS Voltage (Vrms(t)) to visualize the trend of the EMG signals performance in window size of 1024. Results of this study evince that the lifting height of 140 cm obtained higher Vrms than 75 cm. It concluded that the application of spectrogram is able to counter the limitation of FFT in providing the time-frequency information for EMG signals.
... Parameters of the signal such as instantaneous RMS voltage, direct current voltage (V DC ) and alternating current voltage (V AC ) are estimated from the time frequency representation (TFR) to identify the signal information. The information is important to detect the battery signals [8]. ...
Battery is an alternative option that can be substituted for future energy demand. Numerous type of battery is used in industries to propel portable power and its makes the task of selecting the right battery type is crucial. These papers discuss the implementation of linear time-frequency distribution (TFD) in analysing lead acid battery signals. The time-frequency analysis technique selected is spectrogram. Based on, the time-frequency representations (TFR) obtain, the signal parameter such as instantaneous root mean square (RMS) voltage, direct current voltage (VDC) and alternating current voltage (VAC) are estimated. The parameter is essential in identifying signal characteristics. This analysis is focussing on lead-acid battery with nominal battery voltage of 6 and 12V and storage capacity from 5 until 50Ah, respectively. The results show that spectrogram technique is capable to estimate and identify the signal characteristics of Lead Acid battery.
... However, this paper focuses on spectrogram to analyze power quality signals in time-frequency representation (TFR). Spectrogram which is squared magnitude of STFT gives the time waveform energy distribution in joint time-frequency domain and has been used in many applications [9] [10]. ...
... Spectrogram is improvement of FFT to cater non-stationary signals whose spectral characteristics change in time [9] [10]. This technique can be defined as: ...
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.
... Spectrogram is the result of calculating the frequency spectrum of windowed frames of a compound signal [8], [9]. This TFD is calculated as follow: ...
The increasing of sensitive electrical equipment in
our technologies is the biggest issues to power line system. The
quality of electrical power supply becomes more concern to
customers or electric users. Power quality signals will affect
manufacturing process, failure of equipment and economic
losses. This paper presents the development of power quality
signals monitoring system using linear time-frequency
distribution (TFD) which is spectrogram. The TFD is used to
represent the signals in time-frequency representation (TFR) and
is very appropriate to analyze power quality signals that have
magnitude and frequency variations. Parameters of signal such
as instantaneous of root means square (RMS) voltage,
fundamental RMS voltage, total waveform distortion (TWD),
total harmonic distortion (THD) and total non-harmonic
distortion (TnHD) are estimated from the TFR to identify the
signal characteristics. Standard power line measurements are
also calculated from voltage and current such as RMS voltage
and current, real power, apparent power, reactive power,
frequency and power factor. The developed system also shows
high accuracy monitoring system that presents very low absolute
percentage error (APE) measurements and is suitable for power
quality monitoring purpose.
In manufacturing industries, manual lifting is commonly practiced by workers in their routine
to move or transport the objects to a desired place. Manual lifting with high repetition and
loading on the arm will contribute the effects of soft tissues and muscle fatigue that will affect
the performance of the worker to work with efficient. This paper presents the analysis of EMG
signal from muscle activity to see the performance of muscle fatigue. Various researchers have
proposed fast Fourier transforms (FFT) in analysing the EMG signal. However, this technique
only gives spectral information but does not provide temporal information. Thus, the technique
is not suitable for EMG analysis that consists of magnitude and frequency variation. To
overcome the limitation, spectrogram is proposed to analyse the signal because it can represent
the signal in jointly time-frequency representation (TFR). In fatigue muscle activities, ten
volunteers in fresh condition and no previous of history injury are used as the subjects. Data is
taken from right Biceps Branchii with lifting height of 140 cm and load mass of 5 kg. This
research shows that the repeatability of manual lifting will contribute to the muscle fatigue for
all the phases stated in this paper. This study concludes that phase 2 contribute highest effort by
doing manual lifting task, compared to phase 1, 3 and 4, but all phases experienced the muscle
fatigue.
Nowadays, energy storage improves the reliability and efficiency of electric utility system. The most common device used for storing electrical energy is battery. Obtaining an accurate data of battery parameter is important because it will be avoid unexpected system interruption and prevent permanent damage to the internal structure of the batteries. The objective of this study is to apply time-frequency distribution (TFD) which is spectrogram technique in analysis of voltage charging and discharging signal for lithium-ion battery. Spectrogram represents the battery signal in time frequency representation (TFR) which is appropriate to analyze the signal before displaying the instantaneous RMS voltage (Vrms), direct current voltage (VDC) and alternating current voltage (VAC) parameter value. This paper focuses on lithium-ion (Li-ion) battery with nominal voltage of 6 and 12V and various storage capacities from 5 to 50Ah. The battery model is implemented in MATLAB/SIMULINK. From the results, the Li-ion battery parameter could be identified using spectrogram. © 2015 AENSI Publisher All rights reserved. To Cite This Article: Rizanaliah Kasim, Abdul Rahim Abdullah, Nur Asmiza Selamat, Nur Hazilina Bahari and Mohd Zulkifli Ramli, Lithium-ion Battery Parameter Analysis Using Spectrogram. Aust. J. Basic & Appl. Sci., 9(12): 76-80, 2015
Power quality disturbances present noteworthy ramifications on electricity consumers, which can
affect manufacturing process, causing malfunction of equipment and inducing economic losses. Thus, an
automated system is required to identify and classify the signals for diagnosis purposes. The development of
power quality disturbances detection and classification system using linear time-frequency distribution (TFD)
technique which is spectrogram is presented in this paper. The TFD is used to represent the signals in
time-frequency representation (TFR), hence it is handy for analyzing power quality disturbances. The signal
parameters such as instantaneous of RMS voltage, RMS fundamental voltage, total waveform distortion
(TWD), total harmonic distortion (THD) and total non-harmonic distortion (TnHD) are estimated from the TFR
to identify the characteristic of the signals. The signal characteristics are then served as the input for signal
classifier to classify power quality disturbances. Referring to IEEE Std. 1159-2009, the power quality
disturbances such as swell, sag, interruption, harmonic and interharmonic are discussed. Standard power line measurements, like voltage and current in RMS, active power, reactive power, apparent power, power factor and frequency are also calculated. To verify the performance of the system, power quality disturbances with various
characteristics will be generated and tested. The system has been classified with 100 data at SNR from 0dB to
40dB and the outcomes imply that the system gives 100 percent accuracy of power quality disturbances
classification at 34dB of SNR. Since the low absolute percentage error present, the system achieves highly
accurate system and suitable for power quality detection and classification purpose.
