Fault analysis (detection, classification and location) of transmission network is of great importance in power system. A Wavelet-Alienation-Neural (WAN) technique has been developed for the fault ...analysis of Unified Power Flow Controller (UPFC) compensated transmission network. The detection and classification of various outages are accomplished by alienation of wavelet based approximate coefficients computed from current signals. The precise location of faults is carried out by an Artificial Neural Network fed from estimated approximate coefficients computed from voltage and current signals of the same quarter cycle. The robustness of the algorithm is proved with the case studies of varying fault locations, sampling frequency, system parameters, effects of noise, fault incipient angle, different control strategies and fault path impedances.
This study presents a wavelet-alienation based algorithm for protection of two terminal transmission line. The proposed algorithm makes use of wavelet transform based approximate coefficients of ...three-phase voltage and current signals, obtained over a quarter cycle to detect, classify and locate various faults. Detection and classification of these faults are achieved with the help of alienation coefficients of current signals from both the terminals of the transmission line. The approximate coefficients of voltage and current signals, obtained over a quarter cycle, are fed to artificial neural network to locate the faults precisely. The proposed algorithm has been tested successfully for various types of faults with the variations in location, inception angle and fault impedance.
•A wavelet-alienation based protection algorithm has been proposed for multi-terminal transmission lines.•The detection and classification of faults have been done, within a quarter cycle, using ...approximate decomposition based alienation coefficients.•Estimation of faulty section and fault location has been done using quarter cycle data with the help of ANN.
This paper presents a wavelet-alienation based protection scheme for multi-terminal transmission system. The proposed algorithm makes use of wavelet transform based approximate coefficients of three-phase voltage and current signals, obtained over a quarter cycle to detect, classify and locate various faults. Detection and classification of faults are achieved with the help of approximate coefficients based alienation coefficients of current signals, measured from all the buses of system. The approximate coefficients of voltage and current signals, obtained over a quarter cycle, are fed to artificial neural network to locate the faults precisely, from respective buses. This algorithm has been tested successfully for all the types of faults with variations in location, inception angle and fault impedance. The proposed protection scheme is generalized for N-terminal system followed by successful testing on three-terminal and five-terminal systems.
This study presents an algorithm for detection, classification, and location of transmission line faults. A fault index based on features extracted from current signals using the alienation ...coefficient and Wigner distribution function has been proposed for the detection and classification of faults. Double line and double line to ground faults have been classified from each other using ground fault index based on negative sequence current. Statistical relations are proposed for the estimation of fault location using peak values of the proposed fault index. The results of different case studies established the effectiveness of the algorithm. The algorithm is found to be effective for providing protection to transmission line against various faults. This is achieved using current signals recorded on one terminal of the line. This makes the protection scheme less complex, fast and more economic due to the elimination of the requirement of communication channel and global positioning system synchronisation. The proposed protection scheme is also validated on a real-time network of transmission utility. The effectiveness of the algorithm is established by comparing performance with reported algorithms.
The custom power devices play important role for enhancing the power transfer capacity of transmission system. However, these devices introduce challenges of under reach or over reach, in the ...protection of transmission system. This article introduces a novel, protection algorithm based on wavelet-alienation-neural technique for STATCOM-compensated transmission system. For detecting and classifying faults, approximate coefficients are computed from the postfault quarter cycle current waveforms. Fault index, which is summation of alienation coefficients (computed by approximate coefficients) of both the buses, is computed and compared with the threshold magnitude for detecting and classifying the different faults. For the determination of fault location, artificial neural network is applied, with input as three-phase approximate coefficients, evaluated from the voltage and current signals over a time duration of a quarter cycle. Robustness of the developed scheme has been validated for various faults at different locations with varying fault impedances and angles of fault incidence.
This article presents a <inline-formula><tex-math notation="LaTeX">k</tex-math></inline-formula>-means clustering and weighted <inline-formula><tex-math ...notation="LaTeX">k</tex-math></inline-formula>-nearest neighbor (<inline-formula><tex-math notation="LaTeX">k</tex-math></inline-formula>-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. <inline-formula><tex-math notation="LaTeX">k</tex-math></inline-formula>-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 <inline-formula><tex-math notation="LaTeX">k</tex-math></inline-formula>-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.
This article presents a Formula Omitted-means clustering and weighted Formula Omitted-nearest neighbor (Formula Omitted-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. Formula Omitted-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 Formula Omitted-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.
A quick, double-sided, and hybrid protection system for identification and phase-type categorization of fault generated noisy signals on a 500 kV, 300 km long transmission line (TL) is introduced in ...this paper. The proposed scheme takes advantage of Fourier analysis and haar wavelet analysis and makes use of currents at equally relaying sides of a TL. Haar wavelet is used in the wavelet analysis of current signals. The simulation effects attained show that the scheme is quick and flawless as the phase-type is classified within 15 ms time.
In this paper, application of alienation technique has been presented for fault analysis on three-terminal transmission line system. For evaluation of Alienation Coefficients, samples of post-fault ...differential current (of successive cycles), are compared, for a quarter cycle window. This Alienation Coefficient is compared with the threshold, to detect and classify the faults. The proposed protection scheme is able to detect and classify the faults, within quarter-cycle. The performance of the proposed scheme has been established by case studies, in which fault locations, incipient angles and impedances are varied.
This paper deals with the application of wavelet based alienation technique for the detection and classification of faults on transmission lines. The three phase current signals of both the ends are ...synchronized with the help of Global Positioning System clock. These signals are decomposed with Haar wavelet to obtain approximate coefficients over a moving window of half cycle. Approximate Coefficients obtained over a half cycle are compared with the previous half cycle of same polarity to compute alienation coefficients at each end. A Fault Index, computed by adding alienation coefficients of local and remote end, is compared with the threshold to detect and classify the faults. The proposed algorithm has been tested successfully for various types of faults at different fault locations and different fault incidence angles.
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