International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
An Algorithm Based On Discrete Wavelet Transform For Faults Detection, Locati...paperpublications3
Abstract: An electric power distribution system is the final stage in the delivery of electric power; it carries electricity from the transmission system to individual consumers. Fault classification and location is very important in power system engineering in order to clear fault quickly and restore power supply as soon as possible with minimum interruption. Hence, ensuring its efficient and reliable operation is an extremely important and challenging task. With availability of inadequate system information, locating faults in a distribution system pose a major challenge to the utility operators. In this paper, a faults detection, location and classification technique using discrete wavelet multi-resolution approach for radial distribution systems are proposed. In this distribution network, the current measurement at the substation have been utilized and is demonstrated on 9-bus distribution system. Also in this work distribution system model was developed and simulated using power system block set of MATLAB to obtain fault current waveforms. The waveforms were analyzed using the Discrete Wavelet Transform (DWT) toolbox by selecting suitable wavelet family. It was estimated and achieved using Daubechies ‘db5’ discrete wavelet transform.
Analysis and Estimation of Harmonics Using Wavelet TechniqueRadita Apriana
The paper develops an approach based on wavelet technique for the evaluation and estimation of
harmonic contents of power system waveform. The proposed algorithm decomposes the signal waveforms
into the uniform frequency sub-bands corresponding to the odd harmonic components of the signal. The
proposed implementation of algorithm determines the frequency bands of harmonics which retain both the
time and frequency relationship of the original waveforms and uses a method to suppress those
harmonics.Thewaveletalgorithm is selected to obtain compatible output bands with the harmonic groups
defined in the standards for power-supply systems. A comparative analysis will be done with the input and
the results obtained from the wavelet transform (WT) for different measuring conditions and Simulation
results are given.
A Utilisation of Improved Gabor Transform for Harmonic Signals Detection and ...Yayah Zakaria
his paper presents a utilization of improved Gabor transform for harmonic signals detection and classification analysis in power distribution system. The Gabor transform is one of time frequency distribution technique with a capability of representing signals in jointly time-frequency domain and known as time frequency representation (TFR). The estimation of spectral
information can be obtained from TFR in order to identify the characteristics of the signals. The detection and classification of harmonic signals for 10 unique signals with numerous characteristic of harmonics with support of rule-based classifier and threshold setting that been referred to IEEE standard 1159 2009. The accuracy of proposed method is determined by using MAPE and the outcome demonstrate that the method gives high accuracy of harmonic signals classification. Additionally, Gabor transform also gives 100 percent correct classification of harmonic signals. It is verified that the proposed method is accurate and cost efficient in detecting and classifying harmonic signals in distribution system.
Power Quality Identification Using Wavelet Transform: A Reviewpaperpublications3
Abstract: In this paper we used wavelet transform which is useful in signal processing. Wavelet transform is used for analyze different power quality events. The power quality events like pure sine voltage wave, voltage sag, swell, harmonics, impulse can obtained using wavelet transform. The wavelet algorithm is a useful tool for signal processing. Fourier transform is also used for denoising but limited to stationary signals .For continuous analysis of non-stationary signals wavelet transform is used. The presence of noise can be detected by wavelet methods and analyze the noisy signal. The noisy signal can be denoised using wavelet transform.
Detection of Transmission Line Faults by Wavelet Based Transient ExtractionIDES Editor
In this paper, a novel technique is applied to detect
fault in the transmission line using wavelet transform. Three
phase currents are monitored at both ends of the transmission
line using global positioning system synchronizing clock.
Wavelet transform, which is very fast and sensitive to noise, is
used to extract transients in the line currents for fault
detection. Fault index is calculated based on the sum of local
and remote end detail coefficients and compared with
threshold value to detect the fault. Proposed technique is
tested for various faults and fault inception angles. Simulation
results are presented showing the selection of proper
threshold value for fault detection.
A Survey on Classification of Power Quality Disturbances in a Power SystemIJERA Editor
Nowerdays, due to the penetration of power electronics based loads and microprocessor based controlled loads. We have to give more importance to power Quality problems. In order to improve the power quality, the sources of power quality disturbances should be recognized and classified earlier. So many techniques are proposed so far in many research papers under feature extraction and classification. This paper gives a survey of the various papers and it will be use full for the researchers to know about the various methods discussed and helps to do the further work in this area.
A Sensitive Wavelet-Based Algorithm for Fault Detection in Power Distribution...IDES Editor
This paper presents a wavelet based technique for
detection and classification of abnormal conditions that occur
on power distribution lines. The transients associated with these
conditions contain a large spectrum of frequencies, which are
analyzed using wavelet transform approach. The proposed
technique depends on a sensitive fault detection parameter
(denoted SFD) calculated from wavelet multi-resolution
decomposition of the three phase currents. The simulation
results of this study clearly indicate that the proposed technique
can be successfully used to detect not only faults that could not
be detected by conventional relays but also abnormal transients
like load switching and inrush currents
An Algorithm Based On Discrete Wavelet Transform For Faults Detection, Locati...paperpublications3
Abstract: An electric power distribution system is the final stage in the delivery of electric power; it carries electricity from the transmission system to individual consumers. Fault classification and location is very important in power system engineering in order to clear fault quickly and restore power supply as soon as possible with minimum interruption. Hence, ensuring its efficient and reliable operation is an extremely important and challenging task. With availability of inadequate system information, locating faults in a distribution system pose a major challenge to the utility operators. In this paper, a faults detection, location and classification technique using discrete wavelet multi-resolution approach for radial distribution systems are proposed. In this distribution network, the current measurement at the substation have been utilized and is demonstrated on 9-bus distribution system. Also in this work distribution system model was developed and simulated using power system block set of MATLAB to obtain fault current waveforms. The waveforms were analyzed using the Discrete Wavelet Transform (DWT) toolbox by selecting suitable wavelet family. It was estimated and achieved using Daubechies ‘db5’ discrete wavelet transform.
Analysis and Estimation of Harmonics Using Wavelet TechniqueRadita Apriana
The paper develops an approach based on wavelet technique for the evaluation and estimation of
harmonic contents of power system waveform. The proposed algorithm decomposes the signal waveforms
into the uniform frequency sub-bands corresponding to the odd harmonic components of the signal. The
proposed implementation of algorithm determines the frequency bands of harmonics which retain both the
time and frequency relationship of the original waveforms and uses a method to suppress those
harmonics.Thewaveletalgorithm is selected to obtain compatible output bands with the harmonic groups
defined in the standards for power-supply systems. A comparative analysis will be done with the input and
the results obtained from the wavelet transform (WT) for different measuring conditions and Simulation
results are given.
A Utilisation of Improved Gabor Transform for Harmonic Signals Detection and ...Yayah Zakaria
his paper presents a utilization of improved Gabor transform for harmonic signals detection and classification analysis in power distribution system. The Gabor transform is one of time frequency distribution technique with a capability of representing signals in jointly time-frequency domain and known as time frequency representation (TFR). The estimation of spectral
information can be obtained from TFR in order to identify the characteristics of the signals. The detection and classification of harmonic signals for 10 unique signals with numerous characteristic of harmonics with support of rule-based classifier and threshold setting that been referred to IEEE standard 1159 2009. The accuracy of proposed method is determined by using MAPE and the outcome demonstrate that the method gives high accuracy of harmonic signals classification. Additionally, Gabor transform also gives 100 percent correct classification of harmonic signals. It is verified that the proposed method is accurate and cost efficient in detecting and classifying harmonic signals in distribution system.
Power Quality Identification Using Wavelet Transform: A Reviewpaperpublications3
Abstract: In this paper we used wavelet transform which is useful in signal processing. Wavelet transform is used for analyze different power quality events. The power quality events like pure sine voltage wave, voltage sag, swell, harmonics, impulse can obtained using wavelet transform. The wavelet algorithm is a useful tool for signal processing. Fourier transform is also used for denoising but limited to stationary signals .For continuous analysis of non-stationary signals wavelet transform is used. The presence of noise can be detected by wavelet methods and analyze the noisy signal. The noisy signal can be denoised using wavelet transform.
Detection of Transmission Line Faults by Wavelet Based Transient ExtractionIDES Editor
In this paper, a novel technique is applied to detect
fault in the transmission line using wavelet transform. Three
phase currents are monitored at both ends of the transmission
line using global positioning system synchronizing clock.
Wavelet transform, which is very fast and sensitive to noise, is
used to extract transients in the line currents for fault
detection. Fault index is calculated based on the sum of local
and remote end detail coefficients and compared with
threshold value to detect the fault. Proposed technique is
tested for various faults and fault inception angles. Simulation
results are presented showing the selection of proper
threshold value for fault detection.
A Survey on Classification of Power Quality Disturbances in a Power SystemIJERA Editor
Nowerdays, due to the penetration of power electronics based loads and microprocessor based controlled loads. We have to give more importance to power Quality problems. In order to improve the power quality, the sources of power quality disturbances should be recognized and classified earlier. So many techniques are proposed so far in many research papers under feature extraction and classification. This paper gives a survey of the various papers and it will be use full for the researchers to know about the various methods discussed and helps to do the further work in this area.
A Sensitive Wavelet-Based Algorithm for Fault Detection in Power Distribution...IDES Editor
This paper presents a wavelet based technique for
detection and classification of abnormal conditions that occur
on power distribution lines. The transients associated with these
conditions contain a large spectrum of frequencies, which are
analyzed using wavelet transform approach. The proposed
technique depends on a sensitive fault detection parameter
(denoted SFD) calculated from wavelet multi-resolution
decomposition of the three phase currents. The simulation
results of this study clearly indicate that the proposed technique
can be successfully used to detect not only faults that could not
be detected by conventional relays but also abnormal transients
like load switching and inrush currents
Abstract: Power system are designed to operate at a frequency of 50 or 60 Hz. However, certain types of non linear loads produce current and voltages with frequencies that are integer multiple of the fundamental frequency. These frequency components known as harmonic pollution and is having adverse effect on the power system network.
Inverter is the part of day to day life for every individual. Energy adversity is of special attention in recent days. Because of the sine wave output of the inverter, the electronic devices are efficiently operated. Most of the inverters provide square wave output. Electronic devices run by this inverter may be damaged due to harmonic contents. Available sine wave inverters are expensive and their output is not so good. Hence the improvement in the square wave output of the inverter is most essential. .For getting pure sine wave output SPWM technique is used in inverter. The PWM inverter has been the main choice in power electronics because of its simplicity. SPWM is the mostly used method in motor control and inverter applications. Most of the harmonics can be removed by the SPWM technique and hence improves the quality of power converter. By using active or passive filters or both, we can improve the power quality .
Keywords: Filter, Active filter, Passive filter, Low-pass, High-pass, Band-pass, Band-stop, Characteristic impedance, Cut-off frequency.
Title: An Overview of Constant-k Type Filters
Author: Rashmi Vaishya, Ashish Choubey
ISSN 2349-7815
International Journal of Recent Research in Electrical and Electronics Engineering (IJRREEE)
Paper Publications
Accurate Evaluation of Interharmonics of a Six Pulse, Full Wave - Three Phase...idescitation
Interharmonics are the non-integral multiples of
the system’s fundamental frequency. The interharmonic
components can be apprehended as the intermodulation of
the fundamental and harmonic components of the system with
any other frequency components introduced by the load. These
loads include static frequency converters, cyclo-converters,
induction motors, arc furnaces and all the loads not pulsating
synchronously with the fundamental frequency of the system.
The harmonic and interharmonic components inflict common
damage to the system and apart from these damages the
interharmonics also cause light flickering, sideband torques
on motor/generator and adverse effects on transformer and
motor components. To
filter/compensate the interharmonic
components, their accurate evaluation is essential and to
achieve the same the Iterative algorithm has been proposed.
The main cause of spectral leakage errors is the truncation of
the time-domain signal. The proposed adaptive approach
calculates the immaculate window width, eliminating the
spectral leakage errors in the frequency domain and thereby
the interharmonics/harmonics can be calculated accurately.
The algorithm does not require any inputs regarding the
system frequency and interharmonic constituents of the
system. The only parameter required is the signal sequence
obtained by sampling the analog signal at equidistant sampling
interval.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Wavelet energy moment and neural networks based particle swarm optimisation f...journalBEEI
In this study, a combined approach of discrete wavelet transform analysis and a feed forward neural networks algorithm to detect and classify transmission line faults. The proposed algorithm uses a multi -resolution analysis decoposition of three-phasecurrents only to calculate the wavelet energy moment of detailed coefficients. In comparison with the energy spectrum, the energy moment could reveal the energy distribution features better, which is beneficial when extracting signal features. Theapproach use particle swarm optimization algorithm to train a feed forward neural network. The goal is the enhancement of the convergence rate, learning process and fill up the gap of local minimum point.The purposed scheme consists of two FNNs, one for detecting and another for classifying all the ten types of faults using Matlab/Simulink. The proposed algorithm have been extensively tested on a system 400 kV, 3 phases, 100 km line consideringvarious fault parameter variations.
Analysis of harmonics using wavelet techniqueIJECEIAES
This paper develops an approach based on wavelet technique for the estimation of harmonic presents in power system signals. The proposed technique divides the power system signals into different frequency sub-bands corresponding to the odd harmonic components of the signal. The algorithm helps to determine both the time and frequency information from the harmonic frequency bands. The comparative study will be done with the input and the results attained from the wavelet transform (WT) for different conditions and Simulation results are given.
Comparative analysis on an exponential form of pulse with an integer and non-...IJERA Editor
The theme of this paper is to make a fundamental comparative analysis on time-bandwidth product of a short
duration pulse, whose amplitude is varied with an exponent as an integer and non-integer. The time-bandwidth
product is the most significant factor in pulse compression techniques which is used very often in radar systems
for better detection of the target and resolving the ambiguities in the both range and velocity with the help of
ambiguity function. In this paper, different exponents have been used and it is observed that the non-integer
exponents are giving slightly better quantitative parameters like time-bandwidth product, relative sidelobe level
and main lobe widths. To improve these quantitative parameters, phase variations have been incorporated with
the differentiated pulses of the original exponential signal. Finally, the modulation has also been applied on the
pulses to observe the results in real practical applications. From all these analysis, it is concluded that the
differentiated non-integer exponential pulse with bi-polar variations is giving better pulse compression
requirements (time-bandwidth product, peak sidelobe level, 3-dB beamwidth and main lobe widths) compared
to all other pulse forms. The outputs of the matched filter are also observed for each pulse shape.
Microwave Planar Sensor for Determination of the Permittivity of Dielectric M...journalBEEI
This paper proposed a single port rectangular microwave resonator sensor. This sensor operates at the resonance frequency of 4GHz. The sensor consists of micro-strip transmission line and applied the enhancement method. The enhancement method is able to improve the return loss of the sensor, respectively. Plus, the proposed sensor is designed and fabricated on Roger 5880 substrate. Based on the results, the percentage of error for the proposed rectangular sensor is 0.2% to 8%. The Q-factor of the sensor is 174.
A Review of Methods Employed to Identify Flicker Producing SourcesTELKOMNIKA JOURNAL
Because of increasing requirements of the present consumers and industrial units utilizing
sensitive loads, there is need of good power quality in order to retain the power quality standards.
Nowadays the study of the voltage flicker is becoming essential part of power quality studies. The flicker is
typically the effect of a rapidly changing load which is large with respect to the short circuit ability of an
electrical supply system. The inferior effects of voltage flicker include malfunctioning of power electronic
equipment. Also it causes annoying effects to human. Hence detection of the flicker source is an essential
step in the power quality assessment process. This paper delivers a review about methods used to identify
flicker producing loads in accordance wi th IEC 61000-4-15. Once the report related to the disturbance
place is known, an investigation and corrective action can be accordingly carried out. Also a method based
upon Discrete Wavelet Transform and Artificial Neural Network is proposed to detect initial instance of
occurrence of flicker.
Abstract: Power system are designed to operate at a frequency of 50 or 60 Hz. However, certain types of non linear loads produce current and voltages with frequencies that are integer multiple of the fundamental frequency. These frequency components known as harmonic pollution and is having adverse effect on the power system network.
Inverter is the part of day to day life for every individual. Energy adversity is of special attention in recent days. Because of the sine wave output of the inverter, the electronic devices are efficiently operated. Most of the inverters provide square wave output. Electronic devices run by this inverter may be damaged due to harmonic contents. Available sine wave inverters are expensive and their output is not so good. Hence the improvement in the square wave output of the inverter is most essential. .For getting pure sine wave output SPWM technique is used in inverter. The PWM inverter has been the main choice in power electronics because of its simplicity. SPWM is the mostly used method in motor control and inverter applications. Most of the harmonics can be removed by the SPWM technique and hence improves the quality of power converter. By using active or passive filters or both, we can improve the power quality .
Keywords: Filter, Active filter, Passive filter, Low-pass, High-pass, Band-pass, Band-stop, Characteristic impedance, Cut-off frequency.
Title: An Overview of Constant-k Type Filters
Author: Rashmi Vaishya, Ashish Choubey
ISSN 2349-7815
International Journal of Recent Research in Electrical and Electronics Engineering (IJRREEE)
Paper Publications
Accurate Evaluation of Interharmonics of a Six Pulse, Full Wave - Three Phase...idescitation
Interharmonics are the non-integral multiples of
the system’s fundamental frequency. The interharmonic
components can be apprehended as the intermodulation of
the fundamental and harmonic components of the system with
any other frequency components introduced by the load. These
loads include static frequency converters, cyclo-converters,
induction motors, arc furnaces and all the loads not pulsating
synchronously with the fundamental frequency of the system.
The harmonic and interharmonic components inflict common
damage to the system and apart from these damages the
interharmonics also cause light flickering, sideband torques
on motor/generator and adverse effects on transformer and
motor components. To
filter/compensate the interharmonic
components, their accurate evaluation is essential and to
achieve the same the Iterative algorithm has been proposed.
The main cause of spectral leakage errors is the truncation of
the time-domain signal. The proposed adaptive approach
calculates the immaculate window width, eliminating the
spectral leakage errors in the frequency domain and thereby
the interharmonics/harmonics can be calculated accurately.
The algorithm does not require any inputs regarding the
system frequency and interharmonic constituents of the
system. The only parameter required is the signal sequence
obtained by sampling the analog signal at equidistant sampling
interval.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Wavelet energy moment and neural networks based particle swarm optimisation f...journalBEEI
In this study, a combined approach of discrete wavelet transform analysis and a feed forward neural networks algorithm to detect and classify transmission line faults. The proposed algorithm uses a multi -resolution analysis decoposition of three-phasecurrents only to calculate the wavelet energy moment of detailed coefficients. In comparison with the energy spectrum, the energy moment could reveal the energy distribution features better, which is beneficial when extracting signal features. Theapproach use particle swarm optimization algorithm to train a feed forward neural network. The goal is the enhancement of the convergence rate, learning process and fill up the gap of local minimum point.The purposed scheme consists of two FNNs, one for detecting and another for classifying all the ten types of faults using Matlab/Simulink. The proposed algorithm have been extensively tested on a system 400 kV, 3 phases, 100 km line consideringvarious fault parameter variations.
Analysis of harmonics using wavelet techniqueIJECEIAES
This paper develops an approach based on wavelet technique for the estimation of harmonic presents in power system signals. The proposed technique divides the power system signals into different frequency sub-bands corresponding to the odd harmonic components of the signal. The algorithm helps to determine both the time and frequency information from the harmonic frequency bands. The comparative study will be done with the input and the results attained from the wavelet transform (WT) for different conditions and Simulation results are given.
Comparative analysis on an exponential form of pulse with an integer and non-...IJERA Editor
The theme of this paper is to make a fundamental comparative analysis on time-bandwidth product of a short
duration pulse, whose amplitude is varied with an exponent as an integer and non-integer. The time-bandwidth
product is the most significant factor in pulse compression techniques which is used very often in radar systems
for better detection of the target and resolving the ambiguities in the both range and velocity with the help of
ambiguity function. In this paper, different exponents have been used and it is observed that the non-integer
exponents are giving slightly better quantitative parameters like time-bandwidth product, relative sidelobe level
and main lobe widths. To improve these quantitative parameters, phase variations have been incorporated with
the differentiated pulses of the original exponential signal. Finally, the modulation has also been applied on the
pulses to observe the results in real practical applications. From all these analysis, it is concluded that the
differentiated non-integer exponential pulse with bi-polar variations is giving better pulse compression
requirements (time-bandwidth product, peak sidelobe level, 3-dB beamwidth and main lobe widths) compared
to all other pulse forms. The outputs of the matched filter are also observed for each pulse shape.
Microwave Planar Sensor for Determination of the Permittivity of Dielectric M...journalBEEI
This paper proposed a single port rectangular microwave resonator sensor. This sensor operates at the resonance frequency of 4GHz. The sensor consists of micro-strip transmission line and applied the enhancement method. The enhancement method is able to improve the return loss of the sensor, respectively. Plus, the proposed sensor is designed and fabricated on Roger 5880 substrate. Based on the results, the percentage of error for the proposed rectangular sensor is 0.2% to 8%. The Q-factor of the sensor is 174.
A Review of Methods Employed to Identify Flicker Producing SourcesTELKOMNIKA JOURNAL
Because of increasing requirements of the present consumers and industrial units utilizing
sensitive loads, there is need of good power quality in order to retain the power quality standards.
Nowadays the study of the voltage flicker is becoming essential part of power quality studies. The flicker is
typically the effect of a rapidly changing load which is large with respect to the short circuit ability of an
electrical supply system. The inferior effects of voltage flicker include malfunctioning of power electronic
equipment. Also it causes annoying effects to human. Hence detection of the flicker source is an essential
step in the power quality assessment process. This paper delivers a review about methods used to identify
flicker producing loads in accordance wi th IEC 61000-4-15. Once the report related to the disturbance
place is known, an investigation and corrective action can be accordingly carried out. Also a method based
upon Discrete Wavelet Transform and Artificial Neural Network is proposed to detect initial instance of
occurrence of flicker.
The Six Highest Performing B2B Blog Post FormatsBarry Feldman
If your B2B blogging goals include earning social media shares and backlinks to boost your search rankings, this infographic lists the size best approaches.
Shunt Faults Detection on Transmission Line by Waveletpaperpublications3
Abstract: Transmission line fault detection is a very important task because major portion of power system fault occurring in transmission system. This paper represents a fast and reliable method of transmission line shunt fault detection. MATLAB Simulink use for modeled an IEEE 9-bus test power system for case study of various faults. In proposed work Daubechies wavelet is applied for decomposition of fault transients. The application of wavelet analysis helps in accurate classification of the various fault patterns. Wavelet entropy measure based on wavelet analysis is able to observe the unsteady signals and complexity of the system at time-frequency plane.
The result shows that the proposed method is capable to detect all the shunt faults.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
An Improved Detection and Classification Technique of Harmonic Signals in Pow...Yayah Zakaria
This paper introduces an improved detection and classification technique of harmonic signals in power distribution using time-frequency distribution (TFD) analysis which is spectrogram. The spectrogram is an appropriate approach to signify signals in jointly time-frequency domain and known as time frequency representation (TFR). The spectral information of signals can
be observed and estimated plainly from TFR due to identify the
characteristics of the signals. Based on rule-based classifier and the threshold settings that referred to IEEE Standard 1159 2009, the detection and classification of harmonic signals for 100 unique signals consist of various characteristic of harmonics are carried out successfully. The accuracy of proposed method is examined by using MAPE and the result show that the technique provides high accuracy. In addition, spectrogram also gives 100 percent correct classification of harmonic signals. It is proven that the proposed method is accurate, fast and cost efficient for detecting and classifying harmonic signals in distribution system.
An Improved Detection and Classification Technique of Harmonic Signals in Pow...IJECEIAES
This paper introduces an improved detection and classification technique of harmonic signals in power distribution using time-frequency distribution (TFD) analysis which is spectrogram. The spectrogram is an appropriate approach to signify signals in jointly time-frequency domain and known as time frequency representation (TFR). The spectral information of signals can be observed and estimated plainly from TFR due to identify the characteristics of the signals. Based on rule-based classifier and the threshold settings that referred to IEEE Standard 1159 2009, the detection and classification of harmonic signals for 100 unique signals consist of various characteristic of harmonics are carried out successfully. The accuracy of proposed method is examined by using MAPE and the result show that the technique provides high accuracy. In addition, spectrogram also gives 100 percent correct classification of harmonic signals. It is proven that the proposed method is accurate, fast and cost efficient for detecting and classifying harmonic signals in distribution system.
An appropriate fault detection and classification of power system transmission line using discrete wavelet transform and artificial neural networks is performed in this paper. The analysis is carried out by applying discrete wavelet transform for obtained fault phase currents. The work represented in this paper are mainly concentrated on classification of fault and this classification is done based on the obtained energy values after applying discrete wavelet transform by taking this values as an input for the neural network. The proposed system and analysis is carried out in Matlab Simulink.
WAVELET- FUZZY BASED MULTI TERMINAL TRANSMISSION SYSTEM PROTECTION SCHEME IN ...Wireilla
In This Paper, A New Protection Scheme In The Areas Of Accurate Fault Detection, Classification And Location Estimation For Multi Terminal Transmission System Compensated With Statcom Is Proposed. The Fault Indices Of All The Phases At All The Terminals Are Obtained By Analyzing The Detail Coefficients Of Current Signals Through Bior 1.5 Mother Wavelet. The Complete Digital Simulation Of A Transmission System With Statcom Is Performed Using Matlab /Simulink For Fault Detection, Classification, And Faulty Terminal Identification With Variations In Fault Distance And Fault Inception Angle For All Types Of Faults And Fuzzy Inference System Is Used To Estimate The Fault Location. The Protection Scheme Yielded Accurate Results Within Half Cycle And Show That The Above Scheme Is Suitable For Multi Terminal Transmission System With And Without Statcom Compensation.
WAVELET- FUZZY BASED MULTI TERMINAL TRANSMISSION SYSTEM PROTECTION SCHEME IN ...ijfls
In This Paper, A New Protection Scheme In The Areas Of Accurate Fault Detection, Classification And
Location Estimation For Multi Terminal Transmission System Compensated With Statcom Is Proposed.
The Fault Indices Of All The Phases At All The Terminals Are Obtained By Analyzing The Detail
Coefficients Of Current Signals Through Bior 1.5 Mother Wavelet. The Complete Digital Simulation Of A
Transmission System With Statcom Is Performed Using Matlab /Simulink For Fault Detection,
Classification, And Faulty Terminal Identification With Variations In Fault Distance And Fault Inception
Angle For All Types Of Faults And Fuzzy Inference System Is Used To Estimate The Fault Location. The
Protection Scheme Yielded Accurate Results Within Half Cycle And Show That The Above Scheme Is
Suitable For Multi Terminal Transmission System With And Without Statcom Compensation.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
A Utilisation of Improved Gabor Transform for Harmonic Signals Detection and ...IJECEIAES
This paper presents a utilization of improved Gabor transform for harmonic signals detection and classification analysis in power distribution system. The Gabor transform is one of time frequency distribution technique with a capability of representing signals in jointly time-frequency domain and known as time frequency representation (TFR). The estimation of spectral information can be obtained from TFR in order to identify the characteristics of the signals. The detection and classification of harmonic signals for 100 unique signals with numerous characteristic of harmonics with support of rule-based classifier and threshold setting that been referred to IEEE standard 1159 2009. The accuracy of proposed method is determined by using MAPE and the outcome demonstrate that the method gives high accuracy of harmonic signals classification. Additionally, Gabor transform also gives 100 percent correct classification of harmonic signals. It is verified that the proposed method is accurate and cost efficient in detecting and classifying harmonic signals in distribution system.
International Journal of Engineering Research and Applications (IJERA) aims to cover the latest outstanding developments in the field of all Engineering Technologies & science.
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
Application of Hilbert-Huang Transform in the Field of Power Quality Events A...idescitation
This paper deals with the analysis of PQ
abnormalities using Hilbert–Huang Transform (HHT). HHT
can be applied to both non-stationary as well as non-linear
signals and it provides the energy-frequency-time
representation of the signal. HHT is a time–frequency analysis
method having low order of complexity and does not include
the frequency resolution and time resolution fundamentals.
So, it has the potential to outperform the frequency resolution
and time resolution based methods. Several cases have been
considered to present the efficiency of HHT. For the case
study, various PQ abnormalities like voltage sag, swell and
harmonics with sag are considered. These PQ abnormalities
are subjected to HHT and the results are shown in the form of
IMFs, instantaneous frequency, absolute value, phase and
Hilbert Huang Spectrum. The results shows that the HHT
performs better than the any other time resolution and
frequency resolution based methods.
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Ih3514441454
1. Haroon Ashfaq et al Int. Journal of Engineering Research and Application
ISSN : 2248-9622, Vol. 3, Issue 5, Sep-Oct 2013, pp.1444-1454
RESEARCH ARTICLE
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OPEN ACCESS
Fault Current Detection of Three Phase Power Transformer
Using Wavelet Transform
Haroon Ashfaq1 and Mohammad. Noorullah Quadri2
Assistant Professor, Department of Electrical Engineering, Jamia Millia Islamia , New Delhi, India1
Design Engineer, Electrical Department, N_ Arc Consulting, New Delhi, India2
Abstract
The disturbances of power systems are nonperiodic, nonstationary, short duration and impulse super-imposed
nature.The wavelet transform is one of the most suitable tool for the analysis of power system disturbances. In
this paper, the application of wavelet transforms to determine the type of fault and accurate classification for the
change in the wave shape due to fault occurrence is investigated. The maximum detail coefficient and energy
level of each type of fault are characteristic in nature and are used for distinguishing the fault types.
Keywords-Transformer faults, differential protection, frequency analysis, wavelet transforms, Matlab simpower
power system disturbance.
I.
INTRODUCTION
Power transformer protection has always been
a challenging problem for protection engineers. The
main concern in protecting this particular element of
power systems lies in the accurate and rapid
discrimination between magnetizing inrush and
different internal faults currents [1] and [2]. Since the
magnetizing branch appears as the shunt element in the
transformer equivalent circuit, the magnetizing current
upsets the balance between the currents at the
transformer terminal and is therefore experienced by
differential relay as fault current. Magnetizing inrush
currents sometimes have high magnitudes that cause
traditional differential relays to initiate trip actions
disconnecting the protected transformer from the
system. Such mal-operation of differential relays can
affect both the reliability and stability of the whole
power system. In addition, magnetizing inrush current
contains a high amount of the second and sometimes
the fifth harmonics [1]-[2] Most of the conventional
transformer protection relays employ the harmonic
analysis approach to identify the type of the current
that flows in the protected transformer. The main idea
of the harmonic restraint differential relays is to extract
the fundamental (1st), the second (2nd) and sometimes
the fifth (5th) harmonics and to compare the ratios of
the 2nd and 5th harmonics with 1st to a predefined
threshold value. There have been different algorithms
to carry out the harmonic analysis. Among these
algorithms, sine cosine correlations, rectangular
transform, discrete Fourier transform (DFT), Leastsquare method, Walsh functions, Haar functions and
Kalman filtering technique, etc. are significant [1] and
[3]. The main drawbacks of the 2nd harmonic restraint
approach that the 2nd harmonic may also exist in some
internal faults of the transformer windings. In addition,
the new low-loss amorphous core materials in modern
power transformers are capable of producing
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magnetizing inrush currents with low 2nd and 5th
harmonic contents [1]-[3]. These traditional signal
processing tools used for frequency analysis are based
on the conditions of stationarity and periodicity.
However, disturbance power systems are of a
nonperiodic, nonstationary, short duration and impulse
super-imposed natures [4]. Efficient frequency analysis
should be able to overcome the limitations of the
traditional signal processing tools. The wavelet
analysis is one of the newly applied frequency tools for
processing
signal
with
complex
characteristics.Transients in power systems result from
a variety of disturbances of power transformer, such
faults currents, are extremely important. Faults
currents are classified as external and internal faults
currents. Wavelet theory is the mathematics, which
deals with building a model for non- stationary signals,
using a set of components that look like small waves,
called wavelets. It has become a well- known useful
tool since its introduction, especially in signal and
image processing [5].
II.
WAVELET TRANSFORM
The wavelet analysis and wavelet transforms
have emerged recently as a powerful tool for signal
processing in different applications, particular for
power
system
applications.
The
transient
characteristics of wavelets can be employed to carry
out exact and effective analysis of signals with
complex frequency-time plane. Moreover, the wavelet
analysis can accommodate non uniform bandwidths as
the bandwidth is higher at higher frequencies, which
make it possible to implement the wavelet analysis
through different levels of decimation in a filter bank
[1]. The applications of wavelet analysis in power
systems include analysis and detection of
electromagnetic transients, power quality assessment,
data compression, and fault detection [4]. The wavelet
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analysis has been recently used in current differential
pilot relay, where current diagnosis is based on
comparing the first level approximation with a
predefined threshold value [4]. Frequency analysis is
not suited for transient analysis, because Fourier based
analysis is based on the sine and cosine functions,
which are not transients. These results in very wide
frequency spectrum in the analysis of transients
Fourier techniques cannot simultaneously achieve
good location in both time and frequency for a
transient signal [5]. The main advantage of wavelet
transform over Fourier is that the size of analysis
technique varies in proportion to the frequency. Hence
offer a better compromise in terms of localization [5].
Level 1
x
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Level 2
HP
2
LP
2
Level 3
d1
HP
2
LP
2
d2
HP
2
d3
LP
2
dn
Fig.2. DWT multi-filter bank framework.
Fig. 1. Analyses of signal using wavelet transform
The wavelet transform decomposes transients
into a series of wavelet components, each of which
corresponds to a time domain signal that covers a
specific octave frequency band containing more
detailed information. Such wavelet components used to
detect, localize, and classify the sources of transients.
Hence, the wavelet transform is feasible and practical
for analyzing power system transients [5-9]. The
discrete wavelet transform (DWT) is normally
implemented by Mallat’s algorithm its formulation is
related to filter bank theory.
Wavelet transform is largely due to this
technique, which can be efficiently implemented by
using only two filters, one high pass (HP) and one low
pass (LP) at level (k). The results are down-sampled by
a factor two and the same two filters are applied to the
output of the low pass filter from the previous stage.
The high pass filter is derived from the wavelet
function (mother wavelet) and measures the details in a
certain input. The low pass filter on the other hand
delivers a smoothed version of the input signal and is
derived from a scaling function, associated to the
mother wavelet. The idea is illustrated in Figure 2
which mathematically is expressed as [5].
Y High [k] =
Low [k] =
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[n]. H [2k − n]
[n]. L [2k − n]
The wavelet analysis is one of the newly
applied frequency analysis tools for processing signals
with complex characteristics. There are different
wavelet families, and they are classified according to
the characteristics of the generated basis functions.
Wavelet families are classified as orthogonal, biorthogonal and non-orthogonal [10]. Daubieches,
Coiflet, Symlet and Meyer are examples of orthogonal
wavelet families, while B-Spline is an example of biorthogonal wavelet families. Morlet, Gaussian and
Mexican Hat are examples of the non-orthogonal
wavelet families [2], [9]. Appropriate selection of the
mother wavelet for signal representation can maximize
the advantages of this technique. Moreover, the
wavelet analysis will be simplified in terms of the
required number of levels of analysis [10]-[12].One of
the new methods for optimal wavelet analysis selection
is the Minimum Description Length (MDL) data
criteria. This method is based on the optimal number
of wavelet coefficients to be retained for the signal
reconstruction [7].In this work, results are carried out
by using the db6 as mother wavelet for signal analysis
.The wavelet energy is the sum of square of detailed
wavelet transform coefficients. The energy of wavelet
coefficient is varying over different scales depending
on the input signals. The energy of signal is contained
mostly in the approximation part and little in the detail
part. The approximation coefficient at the first level
contains much more energy than the other coefficients
at the same level of the decomposition tree. The faulty
signals have high frequency components; it is more
distinctive to use energy of detail coefficients. These
feature of the current waveform used to distinguish
between fault and healthy condition.
II.A. THE DISCRETE WAVELET TRANSFORM
For a signal x (t ) , the set of analysis and
synthesis coefficients are:
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x(t )
Analysis c j ,k
j ,k
(t )dt
c
Synthesis : x(t )
j
(1)
j , k (t )
j,k
k
(2)
Assuming the existence of a scaling function, (t ) we
can modify the above definition as follows.
V j are getting larger and larger as j
Since the spaces
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As before, we call the signals A1 (t ) and D1 (t ) the
approximation and detail at level 1.We call the
coefficients cA1 (k ) and cD1 (k ) the approximation
coefficients and the detail coefficients at level 1. We
can further decompose A1 (t ) to get [12]:
x(t ) A1 (t ) D1 (t )
cA2 (k )J 2, k (t ) cD2 (k ) J 2, k (t ) cD1 (k ) J 1, k (k )
k
k
k
A2 (t ) D2 (t ) D1 (t )
(6)
goes to we can approximate any signal x (t ) ,
closely by choosing a large enough value of
jJ
and projecting the signal into V J using the basis
J , m (t ) , (all values of m ).
cA0 (m)
x(t )
J ,m
(t )dt
(3)
From these we can approximately recover the signal
as:
x (t )
cA
0
( m ) J , m (t ) (4)
III.
MODELING OF THREE PHASE
POWER TRANSFORMER
USING MATLAB SIMULINK:
The single line diagram of system under
consideration is shown in Fig. 4. The specifications
and connection of the system are as follow: - Rating of
Generator is 132 kV, 30MVA and three-phase power
transformer is 25MVA, 132/66 kV. Transformer is
connected in delta on primary side and it is connected
in star on secondary side with grounded. For load of
10MVA.
m
In effect, we replace the signal x (t ) , by the
approximate signal given by the projection
coefficients, cA0 ( m ) . After this approximation the
signal is now in the space V J and we can be
decomposed it using the subspaces VJ n and WJ n
with their bases J n , k (t ) and J n , k (t ) . It is to
be noted that the scale is VJ WJ 1 VJ 1 getting
larger and larger as the index J n gets more
negative [23].If we take n 1 we get: Using the basis
J 1, k (t ) in W J 1 and J 1, k (t ) in VJ 1
we
have:
x (t ) cA0 ( m) J ,m (t )
m
cA1 ( k ) j 1,k (t ) cD1 ( k ) J 1, k (t )
k
k
Fig.3: SLD of the System under consideration.
For the above system the protection scheme
for three phase transformer is developed based on
differential protection. The system is analyzed when
the system is energized at different internal and
external fault with the load connected in the circuit at
all times.
Figure 4 is modeled figure 5 in MATLAB simulink
with differential protection scheme applied on
transformer as shown in fig.5
A1 (t ) D1 (t )
(5)
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Fig.5 Implementation of Power system model under investigation in MATLAB
IV.
FAULT IDENTIFICATION
In this proposed method, wavelet transform is
first applied to decompose the differential current
signals of power transformer system into a series of
wavelet components each of which covers a specific
frequency band. Thus the time and frequency domain
features of the transients’ signals are extracted for
normal current, magnetizing inrush current, over
excitation current, internal fault current. The sample
of the differential current for 0.4 sec. is taken and is
proceed in MATLAB Wavelet Tool box. One of the
most popular mother wavelets suitable for a wide
range of applications used is Daubichies’s wavelet. In
this work Db6 wavelet is used. The implementation
procedure of Wavelet Transform, in which x[n] is the
original signal obtain from workspace X1, X2 and X3 .
At the first stage, an original signal X1, X2 and X3 is
divided into two halves of the frequency bandwidth,
and sent to both high-pass filter and low-pass filter.
Then the output of low pass filter is further cut in half
of the frequency bandwidth, and sent to the second
stage; this procedure is repeated until the signal is
decomposed to a pre-defined certain level 6. The set of
signals thus represent the same original signal, but all
corresponding to different frequency bands. It is
pointing out that the frequency band of each detail of
the wavelet transform is directly related to the
sampling rate of the original signal. If the original
signal is being sampled Fs Hz, the highest frequency
that the signal could contain, from Nyquist’s theorem,
would be Fs/2 Hz. This frequency would be seen at the
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output of the high frequency filter, which is the first
detail. Thus the band of frequencies between and
would be captured in detail 1; similarly, the band of
frequencies between and would be captured in detail 2,
and so on. The WT is applied with four types of
waveform. These are normal condition, magnetizing
inrush condition, over excitation condition and internal
fault condition. WT coefficients for each condition
obtained, For instance the average value, maximum
value and normalization value can be calculated for
these wavelet transform coefficients. The total number
of the wavelet transform coefficients stays the same
due to the nature of the discrete transform process. The
mean values of d1 (first level), a1 (first level), the
average value of d1 (first level), a1 (first level), and the
normalization of d1 (first level), a1 (first level) are
calculated and stored. Each of the value of every single
coefficient is also a feature of the data. The signal data
generated by SimPowerSystems in MATLAB.
Signals are sampled at the sampling rate of 40 samples
per cycle (over a data window of half cycle).
V.
RESULTS AND DISCUSSION.
Simulation of Power System using Matlab
Simulink: Different types of faults have been
considered for the purpose of analysis these faults are
detected based on recognizing their wave shapes, more
precisely, by differentiating their wave shapes from the
fault current wave shapes using wavelet transform.
These are as follows
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Normal current Condition
Fig.5 (a) WT of differential current for at phase A.
The differential current for this case is obtain
when star connected balance RLC load is connected to
the transformer at line voltage. After it has been
energized, the above figure shows the differential
current Ia, Ib, and Ic signals represent and its wavelet
response. Which indicate that when there are no faults,
the detail coefficient of these signals has very small
threshold (approx.zero) and energy of each signals are
also small, which are present in table 1.
TABLE 1
Maximum, Minimum detail coefficient and energy level of three phases at Normal current condition
Phases
Max. Current
(A)
Dev.
Min. current Dev.
(A)
Energy Level
A
-0.0003597
6.19 KJ
B
0.0004207
-0.0003599
6.17 KJ
C
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0.0004186
0.0004199
-0.0003606
6.14 KJ
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Magnetizing Inrush condition.
Fig.6 (a) WT of Diff. current for Magnetizing Inrush current at phase A.
Fig.6 (b) WT of Diff. current for Magnetizing Inrush current at phase B.
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Fig.6 (c) WT of Difference current at phase C.
TABLE 2
Maximum, Minimum detail coefficient and energy level of three phases at Magnetizing Inrush current condition
Phases
Max. Current Dev.
(A)
Min. Current Dev. (A)
Energy Level
A
0.3097
– 0.05197
4.2×104 KJ
B
0.05197
– 0.3097
4.2×104 KJ
C
0.0004199
– 0.0003606
6.14 KJ
For the case of magnetizing inrush current,
the loaded transformer is energized at rated line supply
line voltage. Three phase current signals at
magnetizing inrush fault condition and detail
coefficient are shown in above figure 6(a), 6(b) & 6(c)
which indicated that there is an abnormal response in
phase A & phase B. The detail coefficients of these
signals have more current threshold than normal
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condition. We take normal condition as reference and
compare this signal to abnormal one. Also energy level
of Phase A and phase B are more than phase C, which
is presented in table 2. On other hand, the
perpendicular line at time axes in figure 6.(a) & 6.(b)
indicate, when the fault inception began in phase A &
phase B although phase C had no such fault indication
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Over Excitation condition in phase C
Fig.7 (a) WT of Differential current at healthy phase.
Fig.7 (b) WT of difference current for over excitation at faulty phase C.
After transformer has been energized, the above figure
7(a) and 7(b) show the three phase differential currents
Ib, and Ic signals and their wavelet responses. After
wavelet decomposition at the first level, maximum
detail of wavelet coefficients were calculated for each
signal waveform with and without fault condition as
shown in above figure, which indicate that there is a
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fault in phase C. The detail coefficient of the faulty
phase has more current deviation than phase, which
represent in table 3. In over- excitated condition and
perpendicular line at time axes in faulty phase
indicated when the fault inception began although the
healthy phase had no fault indication.
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TABLE 3
Maximum, minimum detail coefficient and energy level of three phases at over excitation condition.
Phases
Max. Current Dev. (A)
Min. Current Dev. (A)
Energy Level
A
0.0004186
– 0.0003597
6.19 KJ
B
0.0004207
– 0.0003599
6.17 KJ
C
0.08523
– 0.2704
7.9×104 KJ
Internal fault condition (L – L fault between phase A and B)
Fig.8. (a) WT of Differential current for internal fault at phase A.
Fig.8. (b) WT of Diff. current for internal fault at phase B.
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Fig. 8(c) WT of Differential current at healthy phase C.
TABLE 4
Maximum, Minimum detail coefficient and energy level of three phases at internal fault condition.
Phases
Max. Current Dev. (A)
Min. Current Dev. (A)
Energy Level
A
B
C
3.73
3.745
0.0004199
– 3.734
– 3.736
– 0.0003606
11.9×108 KJ
11.9×108 KJ
6.14 KJ
The occurrence of an internal fault creates a
high frequency distortion in the current waveform.
Figure 8.(a), 8.(b) & 8.(c) represents the differential
current signal of three phases for internal fault
corresponding to internally short circuited on A & B
phases. A high frequency distortion in the current
waveform is observed. The detail coefficients of these
signal has current deviation which is extremely high as
compared to phase C. The deviation energy level is
also very high as compared to all above cases.
VI.
CONCLUSION
The wavelet transform is a powerful tool for
the analysis of current transient phenomena, due to its
ability to extract information from transient signals
simultaneously in the time and frequency domain.
Different types of faults have been considered for the
purpose of analysis. These faults are detected based on
recognizing their wave shapes, more precisely from
the fault current wave shapes using wavelet transform.
The simulated results show that the fault current
detection and classification scheme of three phase
power transformer using wavelet transform is found to
be precise and reliable.
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REFERENCES
[1]
[2]
[3]
[4]
[5]
S. A. Saleh, M. A. Rahman,” Modeling and
Protection of a Three-Phase Power
Transformer
Using
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BIOGRAPHY
Haroon Ashfaq was born in in the Aligarh,
India, on January 17, 1978. He received the B.Tech.
M.Tech. and Ph.D. degrees in Electrical Engineering
from AMU, Aligarh, India. He is currently working as
Assistant Professor in the Department of Electrical
Engineering, Jamia Millia Islamia, New Delhi. His
research interests include renewable energy, hybrid
systems, electric drives, switch gear and protection.
Mohammad Noorullah Quadri, has completed
his Master of Technology in Electrical Power System
Management from Jamia Millia Islamia, New Delhi,
India in 2012 and currently he is Design Engineer in
N_ Arc Consulting, New Delhi, India.
www.ijera.com
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