Real-time power quality detection and classification system

The increasing number of power electronics equipment contributes to the poor quality of electrical power supply and has become a vital concern to electricity users at all levels of usage. The power quality signals can affect manufacturing process, malfunction of equipment and economic losses. Thus, it is necessary to detect and classify different kind of power quality signals for rectify failures and ensure quality of power line signal. This research presents the analysis power quality signals using time-frequency distributions (TFDs) which are spectrogram, Gabor transform and S-transform for signals detection and classification. Since the signals consist of multi-frequency components and magnitude variation, the TFDs are very appropriate to be used that represent the signals, jointly, in time-frequency representation (TFR). From the TFR, parameters of the signals are estimated and then are used to identify the characteristics of the signals. Referring to IEEE Std. 1159-2009, the signal characteristics are obtained and then served as the input for signal classifier to classify power quality signals. Based on the analysis, the best TFD is identified in terms of accuracy of the signal characteristics, memory size and computation complexity of data processing and chosen for power quality signals detection and classification system. By simulating in MATLAB, the performance of the classification system is verified by generating and classifying 100 signals with various characteristics for each type of power quality signals. In addition, the system is also tested using 100 real signals which were recorded from a power line. The results show that, S-transform is the best TFD and the classification system gives 100 percent correct classification for all power quality signals. For the real signals, the system also presents 100 percent correct classification. Thus, the outcome of this research shows that the system is very appropriate to be implemented for power quality monitoring system

A New Two Points Method for Identify Dominant Harmonic Disturbance Using Frequency and Phase Spectrogram

This paper is focused on a practical new method for dominant harmonic disturbance detection implemented using phase and frequency spectrogram based on two-point method. The first measurement point is measured at the incoming of the point of common coupling while the second measurement point at the incoming of the load. After that, the data is processed with phase and frequency spectrogram. By comparing the data, the dominant harmonic disturbance can be identified clearly. The proposed method is compared with power direction method which is the earliest method normally used in commercial product. Then, simulation and experiment are conducted to verify the accuracy of the proposed method. Finally, the results show the proposed method is more accurate than power direction method. Further work is needed to investigate the performance of the proposed method by field measurement

Voltage Source Inverter Fault Detection System using Time Frequency Distribution

Open-switch and short-switch in a three-phase voltage source inverter (VSI) have a possibility to fault due to problems of switching devices.Any failure of the system in these applications may incur a cost and risk human live. Therefore, knowledgeon the fault mode behaviour of an inverter is extremely important from the standpoint of system design improvement, protection and fault detection. This paper presents detailed simulation results on condition monitoring and fault behaviour of VSI. The results obtained from the developed monitoring system allows user to identify the fault current. The developed system showed the capability in detecting the performance of VSI as well as identifying the characteristics of type of faults. This system provides a precaution and early detection of fault, thus reduces high maintenance cost and prevent critical fault from happening

Power Quality Signals Classification System Using Time-Frequency Distribution

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

Lead Acid Battery Analysis using Spectrogram

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 timefrequency 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

Analysis of power quality disturbances using spectrogram and S-Transform

The performance analysis of spectrogram and S-transform for power quality disturbances such as swell, sag, interruption, harmonic, inter-harmonic, and transient based on IEEE Std 1159-2009 are presented. These analyses are performed to identify the best performance for detection of power quality disturbances. This is important to provide the improvement of power quality which capable to accurately measured, detect the power quality phenomena. Therefore the accurate detection of power quality disturbances can be developed based on the best techniques. By using both techniques, the temporal and spectral information are obtained. From the time frequency representation (TFR) the signal parameters are estimated such as instantaneous root means square voltage (RMS), total waveform distortion (TWD), total harmonic distortion (THD) and total non harmonic distortion (TnHD).The signal characteristics are calculated from signal parameters to verify the performances of both techniques, the APE results are used to identify the accuracy of these techniques. By perform the analysis; the result show the S-transform is a better tool to analyze the transient disturbances whereas for voltage variation and harmonic disturbances the spectrogram gives higher accuracy result. As a conclusion both techniques are capable to analyzed power quality disturbances, and it clearly shows that, the S-transform has an advantages in term of time-frequency resolution which capable to detect and localized various kind of power quality disturbances and it essential for the development of advanced real-time monitoring

Performance Comparison of VSI Switches Faults Analysis Using STFT and S transform

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 timefrequency 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

Performance Evaluation of Real Power Quality Disturbances Analysis using S-transform

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

Performance Verification of Power Quality Signals Classification System

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

A new technique for the reconfiguration of radial distribution network for loss minimization

Over 50 years Malaysia is using the same power transmission channel from the colonial of the British. It is very old and needs some improvement especially in distribution network system. An increment of load demand and losses occurrences in distribution network system have worsen the existing condition. Pertaining to that, a reconfiguration of the distribution network is introduced to resolve the problem. In this paper, a new technique called as Improved Genetic Algorithm (IGA) for reconfiguring distribution network simultaneously implemented with the placement of small scale power generation or Distributed Generation (DG) is presented. Both conventional and improved genetic algorithms are employed within parameter constraint to be significantly compared in response to power losses and voltage profile performances. The algorithm process is initially started with the search solution for the best switching combinations throughout 33 IEEE distribution bus systems. The results convey a better improvement in performance of the improved method compared with the genetic algorithm (GA)