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Proceedings ArticleDOI

Detection and classification of faults on transmission line using time-frequency approach of current transients

TL;DR: This paper presents an algorithm for detection and classification of transmission line faults using time-frequency analysis (Stock-Well Transform) on current signal obtained from both the ends of Transmission line, which has been successfully tested for types of fault, fault impedance, fault incidence angle and fault location.
Abstract: This paper presents an algorithm for detection and classification of transmission line faults using time-frequency analysis (Stock-Well Transform) on current signal obtained from both the ends of transmission line. The current samples are synchronized with GPS clock and their absolute values are added at each end, to obtained resultant current signal. The cumulative differential sum of the resultant signal over a moving window of half-cycle is compared with the disturbance threshold, to detect a disturbance. Subsequently energy of the signal over a half-cycle prior to the detection of disturbance is computed based on (Stock-Well Transform) and compared with fault threshold, to classify the disturbance into faulty and non-faulty transients. Finally, the faults are classified by computing energy of three-phase currents and zero sequence current in comparing with fault threshold. The proposed algorithm has been successfully tested for types of fault, fault impedance, fault incidence angle and fault location.
Citations
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Journal ArticleDOI
TL;DR: This article presents a regression-based algorithm for the protection of transmission line with means clustering and weighted weighted nearest neighbor and the robustness of the algorithm for different fault parameters such as fault impedance and fault location is validated.
Abstract: This article presents a $k$ -means clustering and weighted $k$ -nearest neighbor ( $k$ -NN) regression-based algorithm for the protection of transmission line. Three-phase current signals of both the terminals are synchronized and sampled with a sampling frequency of 3.84 kHz. Cumulative differential sum (CDS) is computed by subtracting the samples of current cycle from the previous cycle at both the terminals of transmission line. $k$ -means clustering is applied on CDS to compute two centroids using moving window of width, equal to one cycle. Difference between the absolute values of centroids is computed at both the terminals and represented by the centroid difference (CD). The CD of both the terminals is added to compute the fault index. The computed fault index is used to detect and classify the types of faults. The location of the fault is estimated by the weighted $k$ -NN regression method. Various case studies are performed to validate the robustness of the algorithm for different fault parameters such as fault impedance and fault location. The effect of noise is also considered to check the accuracy of the proposed algorithm in the noisy environment.

16 citations


Cites methods from "Detection and classification of fau..."

  • ...The authors in [7] used the S-transform and the cumulative differential sum (CDS)based protection scheme for the two terminal lines....

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Proceedings ArticleDOI
01 Feb 2020
TL;DR: In this paper, the authors used K-means clustering to detect and classify shunt faults in power transmission line, where the synchronized current signals at both the bus of the transmission line and the approximate wavelet coefficients of the both the buses are added to get resultant approximate coefficients.
Abstract: In this paper, shunt fault is detected, classified and located in power transmission line. The synchronized Current signals at both the bus of the transmission line are used for obtaining approximate coefficients using wavelet transform. The proposed algorithm is used K-means clustering to detect and classify the faults. Fault location is estimated using linear regression method. The approximate wavelet coefficients of the both the buses are added to get resultant approximate coefficients. K-means clustering is applied on these resultant approximate coefficients to computes two centroid in a half cycle. The centroid difference (C.D) is computed, and the basis of the centroid difference the fault is detected and classified. Various case studies such as vary fault location, fault incidence angle and fault impedance to verify the robustness of the algorithm.

5 citations


Cites methods from "Detection and classification of fau..."

  • ...[17] author used cumulative sum techique to detect and classify the fault....

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Patent
14 Jan 2020
TL;DR: In this article, the authors proposed a method for detecting the start time of a waveform fault of a fault indicator of a distribution network, and the method comprises the following steps of step 1, reading the waveform file of waveform recording equipment; step 2, taking a sampling point of the wave-form file, calculating a time change rate B of the sampling point and the latter point, comparing the time change rates B with a threshold value B, and indicating that the sampling points may be the moment of failure; continuing to step 3, and proceeding to the next sampling
Abstract: The invention discloses a method for judging start time of a waveform fault of a fault indicator of a distribution network, and the method comprises the following steps of step 1, reading a waveform file of waveform recording equipment; step 2, taking a sampling point of the waveform file, calculating a time change rate B of the sampling point and the latter point, comparing the time change rate Bwith a threshold value B, and if the time change rate B exceeds the threshold value B, indicating that the sampling point may be the moment of failure; continuing to step 3, and if the time change rate B dose not exceed the threshold value B, proceeding to the next sampling point; and step 3, calculating a half-wave current area integration Iarea of the sampling point, comparing the Iarea with Iset, returning to the step 2 if Iarea is less than Iset, and determining to find the starting point of the fault if Iarea is greater than Iset, thereby solving the problems in the prior art such as theadoption of a method of determining the starting point of a fault based on a sudden change in the slope has large limitations, so that the noise is easily to be misjudged as a fault.
Proceedings ArticleDOI
01 Oct 2018
TL;DR: A Wavelet based transmission line protection algorithm which uses centroid difference for fault detection and support vector regression for the fault location and a large number of case studies involving changes in fault impedance, fault incidence angle and fault location have been conducted to establish the performance of the proposed algorithm.
Abstract: This paper presents a Wavelet based transmission line protection algorithm which uses centroid difference for fault detection and support vector regression for the fault location. The sample of three phase currents signals of both the terminals of the line are synchronized and decomposed with Wavelet Transform to obtain the absolute values of approximate coefficients over moving window of a cycle. For the decomposition of current signal dbl mother wavelet is used. Two centroid at each cycle is computed using k-means clustering. The centroid difference is computed at both terminal added to obtain fault index. The fault index is compared with a threshold to detect the faulty phase and to classify the fault. The same approximate coefficients of post fault current transient obtain over half a cycle are used for the estimation of fault location with the help of support vector regression. A large number of case studies involving changes in fault impedance, fault incidence angle and fault location have been conducted to establish the performance of the proposed algorithm.

Cites background from "Detection and classification of fau..."

  • ...[12] present cumulative differential sum based protection scheme for transmission line....

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Proceedings ArticleDOI
01 Dec 2018
TL;DR: A new on-line self-learning evolutionary fault detection method based on the FFT algorithm is designed and simulation shows that the method has good detection performance for a weak fault signal.
Abstract: The article first analyzes many existing machine running fault diagnosis methods, which may not be good for on-line detection in view of the complexity of the algorithm. At the same time, studies the characteristics of random noise modulation. Then, designs a new on-line self-learning evolutionary fault detection method based on the FFT algorithm. The simulation shows that the method has good detection performance for a weak fault signal.
References
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Journal Article
TL;DR: The S transform as discussed by the authors is an extension to the ideas of the Gabor transform and the Wavelet transform, based on a moving and scalable localising Gaussian window and is shown here to have characteristics that are superior to either of the transforms.
Abstract: The S transform, an extension to the ideas of the Gabor transform and the Wavelet transform, is based on a moving and scalable localising Gaussian window and is shown here to have characteristics that are superior to either of the transforms. The S transform is fully convertible both forward and inverse from the time domain to the 2-D frequency translation (time) domain and to the familiar Fourier frequency domain. Parallel to the translation (time) axis, the S transform collapses as the Fourier transform. The amplitude frequency-time spectrum and the phase frequency-time spectrum are both useful in defining local spectral characteristics. The superior properties of the S transform are due to the fact that the modulating sinusoids are fixed with respect to the time axis while the localising scalable Gaussian window dilates and translates. As a result, the phase spectrum is absolute in the sense that it is always referred to the origin of the time axis, the fixed reference point. The real and imaginary spectrum can be localised independently with a resolution in time corresponding to the period of the basis functions in question. Changes in the absolute phase ofa constituent frequency can be followed along the time axis and useful information can be extracted. An analysis of a sum of two oppositely progressing chirp signals provides a spectacular example of the power of the S transform. Other examples of the applications of the Stransform to synthetic as well as real data are provided.

2,323 citations

Journal ArticleDOI
TL;DR: A new approach to fault classification for high speed protective relaying based on the use of neural network architecture and implementation of digital signal processing concepts is presented and its effectiveness in computer simulations on parallel transmission lines is shown.
Abstract: This paper presents a new approach to fault classification for high speed protective relaying and show its effectiveness in computer simulations on parallel transmission lines. The scheme is based on the use of neural network architecture and implementation of digital signal processing concepts. We begin by classifying several fault types like 1-phase-to-ground, 2-phase-to-ground and 3-phase-to-ground faults. We proceed with classification of arcing and nonarcing faults in order to obtain a successful automatic reclosing. Encouraging results are shown and indicate that this approach can be used for supporting a new generation of very high speed protective relaying systems. >

286 citations

Journal ArticleDOI
TL;DR: In this paper, a threshold of eliminating the influences of noises is determined adaptively according to the background noises, and the abilities of the wavelet transform in detecting and localizing the disturbances can hence be restored.
Abstract: By means of the wavelet transform (WT), a power quality (PQ) monitoring system could easily and correctly detect and localize the disturbances in the power systems. However, the signal under investigation is often corrupted by noises, especially the ones with overlapping high-frequency spectrum of the transient signals. The performance of the WT in detecting the disturbance would be greatly degraded, due to the difficulty of distinguishing the noises and the disturbances. To enhance the capability of the WT-based PQ monitoring system, this paper proposes a de-noising approach to detection of transient disturbances in a noisy environment. In the proposed de-noising approach, a threshold of eliminating the influences of noises is determined adaptively according to the background noises. The abilities of the WT in detecting and localizing the disturbances can hence be restored. To test the effectiveness of the developed de-noising scheme, employed were diverse data obtained from the EMTP/ATP programs for the main transient disturbances in the power systems as well as from actual field tests. Using the approach proposed in this paper, remarkable efficiency of monitoring the PQ problems and high tolerance to the noises are approved.

191 citations


"Detection and classification of fau..." refers background in this paper

  • ...It provides good fault detection and classification accuracy in the noiseless conditions, but its accuracy deteriorates in the presence of noise and harmonics [5]....

    [...]

Journal ArticleDOI
TL;DR: In this article, the 2-dimensional S transform is introduced as a method of computing the local spectrum at every point of an image, which is used for spectral characterisation of aperiodic or random patterns.
Abstract: An image is a function, f(x, y) , of the independent space variables x and y . The global Fourier spectrum of the image is a complex function F(k x , k y ) of the wave numbers k x and k y . The global spectrum may be viewed as a construct of the spectra of an arbitrary number of segments of f(x, y) , leading to the concept of a local spectrum at every point of f(x, y) . The two-dimensional S transform is introduced here as a method of computation of the local spectrum at every point of an image. In addition to the variables x and y , the 2-D S transform retains the variables k x and k y , being a complex function of four variables. Visualisation of a function of four variables is difficult. We skirt around this by removing one degree of freedom, through examination of ‘slices’. Each slice of the 2-D S transform would then be a complex function of three variables, with separate amplitude and phase components. By ranging through judiciously chosen slice locations the entire S transform can be examined. Images with strictly periodic patterns are best analysed with a global Fourier spectrum. On the other hand, the 2-D S transform would be more useful in spectral characterisation of aperiodic or random patterns.

161 citations


"Detection and classification of fau..." refers background in this paper

  • ...Neural network based fault detection and classification have reported in [6][7][8] The neural network recognizes the fault pattern using current and voltage waveforms, but neural networks cannot produce accurate output due to inaccuracies in the input phasor dataset....

    [...]

Journal ArticleDOI
TL;DR: This paper presents a new approach to distance relaying using fuzzy neural network (FNM), which provides robust and accurate classification/location of faults for a variety of power system operating conditions even with resistance in the fault path.
Abstract: This paper presents a new approach to distance relaying using fuzzy neural network (FNM). The FNN can be viewed either as a fuzzy system, a neural network or fuzzy neural network. The structure is seen as a neural network for training and a fuzzy viewpoint is utilized to gain insight into the system and to simplify the model. The number of rules is determined by the data itself and therefore a smaller number of rules is produced. The network is trained with the backpropagation algorithm. A pruning strategy is applied to eliminate the redundant rules and fuzzification neurons, consequently a compact structure is achieved. The classification and location tasks are accomplished by using different FNN's. Once the fault type is identified by the FNN classifier the selected fault locating FNN estimates the location of the fault accurately. Normalized peaks of fundamental voltage and current waveforms are considered as inputs to all the networks and an additional input derived from the DC component is fed to fault locating networks. The peaks and DC component are extracted from sampled signals by the EKF. Test results show that the new approach provides robust and accurate classification/location of faults for a variety of power system operating conditions even with resistance in the fault path.

121 citations


"Detection and classification of fau..." refers background in this paper

  • ...Neural network based fault detection and classification have reported in [6][7][8] The neural network recognizes the fault pattern using current and voltage waveforms, but neural networks cannot produce accurate output due to inaccuracies in the input phasor dataset....

    [...]