A New Algorithm of Phase to Ground Fault Location for Electric Power Feeder

2013 ◽  
Vol 313-314 ◽  
pp. 1304-1310
Author(s):  
Yan Song Wang ◽  
Xue Min Liu
Keyword(s):  
Instantaneous Frequency ◽  
Traveling Wave ◽  
Fault Location ◽  
Zero Mode ◽  
Time Frequency ◽  
Ground Fault ◽  
Multi Stage ◽  
Transition Resistance ◽  
Frequency Components ◽  
The Moment

t is a great significance for power supply reliability and smart grid to locate feeder fault quickly and accurately for distribution network. A new ground fault traveling-wave signal time-frequency analysis method is proposed, which decomposes the aerial mode component and zero mode component of fault current based on EMD to get multi-stage stable intrinsic mode function (IMF). The first IMF contains high frequency components which can reflect the mutation of signal. Hilbert-Huang transformation is done for the first IMF to get the instantaneous frequency of signal. And then check the moment of traveling wave arrival by the instantaneous frequency in the figure of IMF time-frequency. The fault distance is computed based on the time difference between the moments of wave arrived of aerial mode component and zero mode components. Simulation shows that this method is able to accurately give the distance of fault without the influence of the random fault condition such as fault transition resistance and the moment of fault.

scholarly journals Traveling-Wave Based Fault Location for Phase-to-Ground Fault in Non-Effectively Earthed Distribution Networks

Energies ◽  
2020 ◽  
Vol 13 (19) ◽  
pp. 5028
Author(s):  
Yani Wang ◽  
Tao Zheng ◽  
Chang Yang ◽  
Li Yu
Keyword(s):  
Traveling Waves ◽  
Traveling Wave ◽  
Wavelet Decomposition ◽  
Distribution Systems ◽  
Fault Location ◽  
Distribution Networks ◽  
Frequency Component ◽  
Time Frequency ◽  
Ground Fault ◽  
Measurement Units

This paper presents a multi-terminal traveling-wave-based fault location method for phase-to-ground fault in non-effectively earthed distribution systems. To improve the accuracy of fault location, a two-terminal approach is used to identify the faulty branch and a single-ended approach is followed to determine the fault distance based on the arrival time of reflected traveling waves. Wavelet decomposition is employed to extract the time-frequency component of the aerial-mode traveling waves. Magnitude and polarity of the wavelet coefficients are used to estimate the fault distance starting from the propagation fault point to the branch terminal. In addition, the network is divided into several sub-networks in order to reduce the number of measurement units. The effectiveness of this approach is demonstrated by simulations considering the phase-to-ground fault that happens at different positions in the distribution network.

A Fault Location Method for HVDC Transmission Lines

2014 ◽  
Vol 556-562 ◽  
pp. 2723-2727 ◽  
Author(s):  
Lu Hua Xing ◽  
Qing Chen ◽  
Bing Lei Xue
Keyword(s):  
Transmission Lines ◽  
Traveling Wave ◽  
Fault Location ◽  
Propagation Characteristic ◽  
Location Method ◽  
Hvdc Transmission ◽  
High Voltage Direct Current ◽  
Current Transmission ◽  
Hvdc Transmission Lines ◽  
Transition Resistance

A fault location method for HVDC (High Voltage Direct Current) transmission lines is proposed in this paper, using voltages and currents measured at two terminals of dc lines in time domain. Fault traveling waves propagate from the fault point to both terminals along the faulted line. The position that the traveling wave head arrives at some moment after the fault can be used to calculate the fault location. To determine the arrival positions of traveling wave head at each time indirectly, propagation characteristic curves of traveling wave heads at local and the remote terminals are calculated with distribution currents using the stationary wavelet transform. The accuracy of fault location will not be affected by transition resistance and fault position. Simulation results show that the presented fault location method can achieve quick and accurate fault location on the whole line under probable operation modes of a bipolar HVDC transmission system.

PCA and ICA Based Prognostic Health Monitoring of Electronic Assemblies Subjected to Simultaneous Temperature-Vibration Loads

2017 ◽  
Author(s):  
Pradeep Lall ◽  
Tony Thomas
Keyword(s):  
Frequency Analysis ◽  
Health Monitoring ◽  
Instantaneous Frequency ◽  
Principal Component ◽  
Component Analysis ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Strain Components ◽  
Frequency Components ◽  
Electronic Assemblies

This paper focusses on health monitoring of electronic assemblies under vibration load of 14 G until failure at an ambient temperature of 55 degree Celsius. Strain measurements of the electronic assemblies were measured using the voltage outputs from the strain gauges which are fixed at different locations on the Printed Circuit Board (PCB). Various analysis was conducted on the strain signals include Time-frequency analysis (TFA), Joint Time-Frequency analysis (JTFA) and Statistical techniques like Principal component analysis (PCA), Independent component analysis (ICA) to monitor the health of the packages during the experiment. Frequency analysis techniques were used to get a detailed understanding of the different frequency components before and after the failure of the electronic assemblies. Different filtering algorithms and frequency quantization techniques gave insight about the change in the frequency components with the time of vibration and the energy content of the strain signals was also studied using the joint time-frequency analysis. It is seen that as the vibration time increases the occurrence of new high-frequency components increases and further the amplitude of the high-frequency components also has increased compared to the before failure condition. Statistical techniques such as PCA and ICA were primarily used to reduce the dimensions of the larger data sets and provide a pattern without losing the different characteristics of the strain signals during the course of vibration of electronic assemblies till failure. This helps to represent the complete behavior of the electronic assemblies and to understand the change in the behavior of the strain components till failure. The principal components which were calculated using PCA discretely separated the before failure and after failure strain components and this behavior were also seen by the independent components which were calculated using the Independent Component Analysis (ICA). To quantify the prognostics and hence the health of the electronic assemblies, different statistical prediction algorithms were applied to the coefficients of principal and independent components calculated from PCA and ICA analysis. The instantaneous frequency of the strain signals was calculated and PCA and ICA analysis on the instantaneous frequency matrix was done. The variance of the principal components of instantaneous frequency showed an increasing trend during the initial hours of vibration and after attaining a maximum value it then has a decreasing trend till before failure. During the failure of components, the variance of the principal component decreased further and attained a lowest value. This behavior of the instantaneous frequency over the period of vibration is used as a health monitoring feature.

scholarly journals Reflected Traveling Wave Based Single-Ended Fault Location in Distribution Networks

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 3917 ◽  
Author(s):  
Yangang Shi ◽  
Tao Zheng ◽  
Chang Yang
Keyword(s):  
Traveling Wave ◽  
Power Distribution ◽  
Fault Location ◽  
Single Phase ◽  
Distribution Network ◽  
Distribution Networks ◽  
Power Distribution Networks ◽  
Reflected Waves ◽  
Ground Fault ◽  
Location Methods

Traveling wave (TW)-based fault-location methods have been used to determine single-phase-to-ground fault distance in power-distribution networks. The previous approaches detected the arrival time of the initial traveling wave via single ended or multi-terminal measurements. Regarding the multi-branch effect, this paper utilized the reflected waves to obtain multiple arriving times through single ended measurement. Potential fault sections were estimated by searching for the possible traveling wave propagation paths in accordance with the structure of the distribution network. This approach used the entire propagation of a traveling wave measured at a single end without any prerequisite of synchronization, which is a must in multi-terminal measurements. The uniqueness of the fault section was guaranteed by several independent single-ended measurements. Traveling waves obtained in a real 10 kV distribution network were used to determine the fault section, and the results demonstrate the significant effectiveness of the proposed method.

Research on a Distribution Line Zero Mode Current Traveling Wave Fault Location Method Immune from Wave Velocity Affection

2014 ◽  
Vol 1049-1050 ◽  
pp. 621-625
Author(s):  
Liang Chen ◽  
Xiao Feng Zhang ◽  
Geng Li
Keyword(s):  
Wave Velocity ◽  
Traveling Wave ◽  
Fault Location ◽  
Zero Mode ◽  
Clock Synchronization ◽  
Research Process ◽  
Initial Distribution ◽  
Location Method ◽  
Mathematical Expressions ◽  
Distribution Line

This paper presented a research on zero mode current traveling wave based distribution line fault location method which is not affected by wave velocity. The mathematical expressions of initial distribution line fault current traveling wave, reflection wave of the fault location and the refraction wave of opposite ends are derived. And the directivity relationship and identification method of these three element are fully analyzed. Based on the research process mentioned above, the fault location method introduced in this paper eliminates the error produced both by velocity propagation and clock synchronization. In this way, the veracity and reliability of short distribution line fault location was improved.

scholarly journals Ensemble EMD based Time-Frequency Analysis of Continuous Adventitious Signal Processing

2018 ◽  
Vol 7 (4.10) ◽  
pp. 896
Author(s):  
B. B Shankar ◽  
D. Jayadevappa
Keyword(s):  
Empirical Mode Decomposition ◽  
Instantaneous Frequency ◽  
Ensemble Empirical Mode Decomposition ◽  
Sound Waves ◽  
Lung Sound ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Marginal Spectrum ◽  
Frequency Components ◽  
Nonlinear Signal

The importance of lung sound analyses is increasing day by day very rapidly. In this paper, we present a new method for analysis of two classes of lung signals namely wheezes and crackles. The procedure used in this article is based on improved Empirical Mode Decomposition (EMD) called Ensemble Empirical Mode Decomposition (EEMD) to analyze and compare continuous and discontinuous adventitious sounds with EMD. These two proposed procedures decompose the lung signals into a set of instantaneous frequency components. Function (IMF). The continuous and discontinuous adventitious sounds are present in an asthmatic patient, produces a non-stationary and nonlinear signal pattern. The empirical mode decomposition (EMD) decomposes such characteristic signals. The instantaneous frequency and spectral analysis related to dual techniques specified above are utilized by IMF to investigate and present the outcome in the time-frequency distribution to investigate the qualities of inbuilt properties of lung sound waves. The Hilbert marginal spectrum has been used to represent total amplitude and energy contribution from every frequency value. Finally, the resultant EEMD analysis is better for wheezes that solves mode mixing issues and improvisation is seen over the EMD method.   

scholarly journals A Fault Feature Extraction Method for Rolling Bearing Based on Pulse Adaptive Time-Frequency Transform

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Jinbao Yao ◽  
Baoping Tang ◽  
Jie Zhao
Keyword(s):  
Instantaneous Frequency ◽  
Pulse Method ◽  
Rolling Bearing ◽  
Shock Pulse ◽  
Feature Extraction Method ◽  
Time Frequency ◽  
Fault Features ◽  
Frequency Components ◽  
Adaptive Time ◽  
Fault Feature Extraction

Shock pulse method is a widely used technique for condition monitoring of rolling bearing. However, it may cause erroneous diagnosis in the presence of strong background noise or other shock sources. Aiming at overcoming the shortcoming, a pulse adaptive time-frequency transform method is proposed to extract the fault features of the damaged rolling bearing. The method arranges the rolling bearing shock pulses extracted by shock pulse method in the order of time and takes the reciprocal of the time interval between the pulse at any moment and the other pulse as all instantaneous frequency components in the moment. And then it visually displays the changing rule of each instantaneous frequency after plane transformation of the instantaneous frequency components, realizes the time-frequency transform of shock pulse sequence through time-frequency domain amplitude relevancy processing, and highlights the fault feature frequencies by effective instantaneous frequency extraction, so as to extract the fault features of the damaged rolling bearing. The results of simulation and application show that the proposed method can suppress the noises well, highlight the fault feature frequencies, and avoid erroneous diagnosis, so it is an effective fault feature extraction method for the rolling bearing with high time-frequency resolution.

HHT-Based Selection of Optimal Time-Frequency Patterns for Motor Imagery

2013 ◽  
Vol 380-384 ◽  
pp. 3522-3525 ◽  
Author(s):  
Ping Gong ◽  
Min You Chen ◽  
Li Zhang ◽  
Wen Juan Jian
Keyword(s):  
Motor Imagery ◽  
Instantaneous Frequency ◽  
Optimal Time ◽  
Instantaneous Amplitude ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Frequency Components ◽  
Novel Method ◽  
Selection Of

In this paper, a novel method based on Hilbert-Huang transform (HHT) is presented to select optimal timefrequency patterns for single-trial motor imagery electroencephalograph (EEG). The method comprises three progressive steps: 1) employ Empirical Mode Decomposition (EMD) method to decompose EEG signal into a superposition of components or functions called IMFs, and then apply Hilbert transform to the IMFs to calculate the instantaneous frequency and instantaneous amplitude; 2) select the IMFs including the most useful frequency components 3) the optimal timefrequency patterns can be selected according to the instantaneous frequency and instantaneous amplitude of the selected IMFs. After selecting the optimal timefrequency patterns, the features extracted by different methods are classified by Fisher linear discriminator. The results showed that the proposed method could improve the classification accuracy.

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