COMBINATION OF DISCRETE WAVELET TRANSFORM AND PROBABILISTIC NEURAL NETWORK ALGORITHM FOR DETECTING FAULT LOCATION ON TRANSMISSION SYSTEM
|
|
- Samuel Snow
- 5 years ago
- Views:
Transcription
1 International Journal of Innovative Computing, Information and Control ICIC International c 2011 ISSN Volume 7, Number 4, April 2011 pp COMBINATION OF DISCRETE WAVELET TRANSFORM AND PROBABILISTIC NEURAL NETWORK ALGORITHM FOR DETECTING FAULT LOCATION ON TRANSMISSION SYSTEM Atthapol Ngaopitakkul and Chaiyan Jettanasen Faculty of Engineering King Mongkut s Institute of Technology Ladkrabang Chalongkrung Rd., Ladkrabang, Bangkok 10520, Thailand knatthap@kmitl.ac.th Received December 2009; revised May 2010 Abstract. This paper proposes a new algorithm for detecting faults in an electrical power transmission system, using discrete wavelet transform (DWT) and probabilistic neural network (PNN). Fault conditions are simulated using ATP/EMTP to obtain current signals. The algorithm used to analyze fault locations is developed on MATLAB. Fault detection is processed using the positive sequence current signals. The comparison among the maximum coefficients in first scale of each bus, which can detect fault, is performed in order to detect the faulty bus. The first peak time obtained from the faulty bus is used as an input for training pattern. Various cases based on Thailand electricity transmission systems are studied to verify the validity of the proposed technique. The result shows that the algorithm is capable of performing the fault locations with accuracy. Keywords: Discrete wavelet transform, Fault location, Probabilistic neural network 1. Introduction. Nowadays, transmission lines are more complicated as a large grid owing to increasing demand of electric power. In an interconnected-electrical transmission system, a precise protection scheme is required in order to ensure the extreme level of the system reliability. Generally, when fault occurs on transmission lines, detecting fault is very necessary in order to clear fault before it generates the damage to the power system. The traditional method of signal analysis is based on Fourier transform, but the fault signals are non-stationary transient so the signal analysis methods with Fourier transform are not quite efficient. Recently, the development of an algorithm for detecting faults in the transmission lines has been progressed, resulting in transient-based techniques [1]. For the transient-based protection to be accurately applied in operation, the application of wavelet transform is used [1-9]. The advantage of the wavelet transform is that the band of analysis can be adjusted to allow high-frequency and low-frequency components to be precisely detected. As a result, the wavelet transform is not intended to replace the Fourier transform in analyzing steady state signals. It is an alternative tool for analyzing non-stationary or non-steady state signals. This is due to that the wavelet transform is very effective in detecting transient signals generated by the faults. The wavelet transform was initially proposed by Magnago et al. [10]. In the literature for fault location, most researches [4,10-16] have only considered the fault location for single bus and two-bus systems but not for multi-terminal. The location of the fault was normally calculated using travelling wave approach, as presented in [10]. In addition, artificial intelligence (AI) has been also reported in the literature for fault location. In [11], the paper describes an artificial neural network-based algorithm for fault location. The inputs are phasors of pre-fault and superimposed voltages and currents from all phases 1861
2 1862 A. NGAOPITAKKUL AND C. JETTANASEN of the transmission line. Nowadays, fault diagnosis for the transmission line has also been progressed with the applications of wavelet transform (WT) and artificial intelligent [17-19]. In [17], the paper proposes a fault location method employing wavelet fuzzy neural network to use post-fault transient and steady-state measurements. Several decision algorithms for locating fault on multi-terminal have been proposed [20,21], but their solutions and techniques are different. In [21], this paper presents a new method for locating faults on three-terminal power lines using three-phase current from all three-terminals and additionally three-phase voltage from the terminal at which a fault locator is installed. Conventional method for fault location, employed by Electricity Generating Authority of Thailand (EGAT), is the Line Fault Locator (LFL) Type c. As the devices of LFL are complicated and expensive, a new technique has to be investigated. Therefore, this paper is focused on the ANNs algorithm for the location of fault along the transmission systems in order to identify the fault location. This is due to that ANNs are a useful tool for solving and selecting a precision algorithm for a protection unit. There are currently many types of ANNs being commercially used. Back-propagation neural network (BPNN) is a type of ANNs, which is widely applied in such a system today. However, it is partly limited by the slow training performance. This drawback should be improved; otherwise the other types of neural network would be developed instead. Finally, the probabilistic neural network (PNN) is selected in the algorithm because it uses less training data and time compared with BPNN. Although, the PNN has not been yet fully evaluated in comparison to BPNN however the PNN approach offers several major advantages such as rapid training, added or deleted data from training set without lengthy retaining, and etc. As a result, it is useful to be able to perform fault location on the transmission line using wavelet transform and PNN. This paper is aimed to propose a combination of wavelet transforms and PNN to detect the faults on transmission systems. The fault conditions will be simulated using ATP/EMTP. The analysis and diagnosis were performed using MATLAB on a PC Pentium IV 2.4 GHz 512 MB. The systems under consideration have a radial and loop structure in order to show the advantage of the proposed method. Fault signals in each case are extracted to several scales on the wavelet transforms, and then are used as an input for a training process on the neural networks. A new technique to identify fault locations on the transmission system is discussed. In addition, the construction of the decision algorithm is detailed and implemented with various case studies, based on Thailand electricity transmission systems. 2. Power System Simulation Using EMTP. The ATP/EMTP [22] is employed to simulate fault signals at a sampling rate of 200 khz (corresponding to the chosen sampling time used in ATP/EMTP, which is equal to 5 µs). The fault types are chosen based on the Thailand s transmission system as shown in Figures 1 and 2. To avoid complexity, the fault resistance is assumed to be 10 Ω. Fault patterns in the simulation are performed with various changes of system parameters as follows: - Fault types under consideration, namely: single phase to ground (SLG: AG, BG, CG), double-line to ground (DLG: ABG, BCG, CAG), line to line (L-L: AB, BC, CA) and three-phase fault (3-P: ABC). - Fault locations on the each transmission lines were at the distance of 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80% and 90%, measured from the sending end. - Inception angle on a voltage waveform was varied between 0 330, with the increasing step of 30. Phase A was used as a reference. The example of original and ATP/EMTP simulated fault signals for phase A to ground fault (AG) in each phase at the sending end (MM3) of the transmission lines is illustrated
3 WAVELET TRANSFORM AND PROBABILISTIC NEURAL NETWORK ALGORITHM 1863 Figure 1. The system used in simulations studies for single circuit structure (System 1) [23] Figure 2. The system used in simulations studies for loop structure (System 2) [23] in Figure 3(a) and Figure 3(b) respectively. This is a fault occurring in phase A to ground (AG) at the length of 35% measured from the bus MM3 as depicted in Figure 1. The similarity between the original and simulated fault signals waveforms can be seen obviously. The fault signals generated using ATP/EMTP are interfaced to the MATLAB in order to analyse the transient high frequency components by using wavelet toolbox. 3. Fault Detection Algorithm. Fault detection decision algorithm is processed using the positive sequence current signals as illustrated in Figure 4. The fault signals generated using ATP/EMTP, is extracted to several scales with the Wavelet transform. The mother wavelet, daubechies4 (db4) [4,19,24], is employed to decompose high frequency components from the signals. Coefficients obtained using DWT of signals, then, are squared so that the abrupt change in the spectra can be clearly found. It is obviously seen that when fault occurs, the coefficients of high frequency components have a sudden change compared with those before an occurrence of the faults as illustrated in Figure 5. After applying the Wavelet transform to the positive sequence currents, the comparison of the
4 1864 A. NGAOPITAKKUL AND C. JETTANASEN (a) (b) Figure 3. (a) Example of original fault signals for AG fault; (b) Example of ATP/EMTP simulated fault signals for AG fault
5 WAVELET TRANSFORM AND PROBABILISTIC NEURAL NETWORK ALGORITHM 1865 coefficients from each scale is considered. The fault detection can be then analyzed, as illustrated in Figure 4. Figure 4. Flowchart for fault detection
6 1866 A. NGAOPITAKKUL AND C. JETTANASEN Figure 5. Wavelet transform from scale 1 to 5 for the positive sequence of current signals shown in Figure 3 Figure 6. Example of wavelet transform for the positive sequence of phase A to ground fault at the transmission system (section WN-CBG) where, WN1T, WN2T are WN bus section WN-CBG circuit 1 and circuit 2 respectively.
7 WAVELET TRANSFORM AND PROBABILISTIC NEURAL NETWORK ALGORITHM 1867 WN1O, WN2O are WN bus section WN-SNO circuit 1 and circuit 2 respectively. CBG1T, CBG2T are CBG bus section WN-CBG circuit 1 and circuit 2 respectively. CBG1N, CBG2N are CBG bus section SNO-CBG circuit 1 and circuit 2 respectively. SNO1O, SNO2O are SNO bus section WN-SNO circuit 1 and circuit 2 respectively. SNO1N, SNO2N are SNO bus section SNO-CBG circuit 1 and circuit 2 respectively. From Figure 5 and Figure 6 show that the coefficient at each scale of the wavelet transform does change. Therefore, the result obtained from the fault detection algorithm presumes that these signals are in their fault condition. However, when carefully considering, Figure 6 is found that all coefficients obtained from the positive sequence currents at every bus have a change of more than 5 times of a normal value during the faults due to the effect of a loop structure of the transmission network. The comparison among the maximum coefficients in first scale of each bus [25], which can detect fault, is carried out in order to detect the faulty bus. In case of double circuit, the maximum coefficients obtained from same buses are also compared in order to detect the faulty circuit. The first peak times obtained from the faulty bus are used as an input data for neural network as shown in Figures 7 and 8. Figure 7. First peak in the scale 1 at both ends of transmission lines for the positive sequence of current signal shown in Figure 4 4. Neural Network Decision Algorithm and Results. Probabilistic neural network (PNN) is developed by Donald Specht, to perform pattern classification using Gaussian potential functions and Bayes decision theory [26]. The PNN consists of three layers which are an input layer, a hidden radial basis layer and a competitive layer as illustrated in Figure 9. Each layer is interconnected by weights. Radial basis function and competitive function, which are activation function, are comprised in hidden radial basis layer and competitive layer respectively. Moreover, the number of neurons in radial basis layer is always equal to the number of training sets.
8 1868 A. NGAOPITAKKUL AND C. JETTANASEN Figure 8. The first peak in the scale 1 at the faulty bus for the signal shown in Figure 5 Figure 9. Probabilistic neural network [27] A training process of PNN [27] involves two stages as follows: 1. Input values are propagated to each neuron in the first layer. The radial basis layer computes distance from the input vector to weight vector, and produces output in radial basis layer as in Equation (1). φ (p) = exp ( p IW 1,1 2 σ 2 j where, p is the input pattern vector, IW 1,1 is the center vector of radial basis layer, σ is the spread constant for radial basis layer, which corresponds to bias value (b = ), Spread φ(p) is the output of radial basis layer. ) (1)
9 WAVELET TRANSFORM AND PROBABILISTIC NEURAL NETWORK ALGORITHM Each neuron in the competitive layer receives all radial basis layer outputs associated with a given class, and produces, as its net output, a vector of probabilities. Finally, a competitive activation function on the output of the competitive layer picks the maximum of these probabilities, and produces a 1 for that class and a 0 for the other classes as shown in Equation (2) [27]. o/p ANN = f 4 (LW 2,1 φ (p)) (2) where, LW 2,1 = weight vector between radial basis layer and competitive layer, f 4 = competitive activation function. From the simulated signals, the coefficients of scale 1, which are obtained using the wavelet transforms, are used for training and test processes of the PNN. Input data sets are normalized and divided into 4176 sets for training, and 2736 sets for tests. Before the training process, PNN structure consists of 2 neurons input and 1 neuron output while the number of neurons in radial basis layer are 4176 neurons (due to that number of neurons is always equal to the number of training sets). The inputs pattern is the first peak time in first scale of faulty buses at 1 / 4 cycle of positive sequence for post-fault currents as mentioned in the previous section. The output variables of the PNN are designated range 1 to 9, corresponding to various locations of faults as shown in Table 1. Table 1. Output patterns from neural networks Fault location Distance measured Distance measured from the sen- Output (Distance measured from the sending ding end for loop structure (km) of PNN from the sending end) end for radial (%) structure (km) Section T Section O Section N 1 10% % % % % % % % % Figure 10 shows an algorithm used in the training process for the PNN. During the training process, PNN begins with the random initial weight and, increasing spread in radial basis layer which corresponds to bias value (b = ) from until 0.1. Spread The step of increase is at to compute the minimum value of MAPE as shown in Equation (3). This procedure is repeated until the maximum number of spread is reached, or MAPE of test set is equal to zero. Results from the training process are shown in Table 2. MAP E = 1 n n o/p ANNi o/p T ARGET i o/p T ARGET i 100% (3) i=1 where, n = number of test set. After the training process, the algorithm was employed in order to calculate the distance of fault along the transmission systems. Case studies were varied to verify the algorithm capability. The system under consideration is shown in Figures 1 and 2. The fault location is defined at the distance between 10% and 90% of the transmission length measured from
10 1870 A. NGAOPITAKKUL AND C. JETTANASEN Figure 10. Process of training
11 WAVELET TRANSFORM AND PROBABILISTIC NEURAL NETWORK ALGORITHM 1871 Table 2. Results of training process Information for comparison Radial structure Loop structure Spread Iterations Number of Error for Test set 0 0 Best time of training process (second) Total time of training process (minute) the sending end and receiving end respectively. Various case studies were performed with various types of faults at each location on the transmission network including the variation of fault inception angles and locations on each transmission line. The total number of the case studies was 8,208. The comparison between an average error in fault locations obtained from the PNN algorithm proposed in this paper and that of the former wavelet algorithm developed by Markming et al. [4] is shown in Table 3. It can be seen that the new algorithm can provide a better performance in predicting the fault locations. Table 3. Comparison of average error for fault locations at various types of faults Section Average error (km) Types of Number of Fault Wavelet based on faults case studies detection DWT and PNN Traveling wave [4] SLG % MM3-TTK DLG % L-L % ABC % SLG % WN-CBG DLG % L-L % ABC % SLG % WN-SNO DLG % L-L % ABC % SLG % SNO-CBG DLG % L-L % ABC % Average % Conclusions. This paper proposes an algorithm based on a combination of DWT and PNN algorithm to identify fault location on the transmission systems. Daubechies4 (db4) is selected as a mother wavelet. The DWT has been employed to decompose high frequency components from fault signals. Positive sequence current signals are used in fault detection. The maximum coefficients of the positive sequence current obtained from all buses are compared in order to detect the faulty bus on the transmission system. It is found that the fault detection algorithm can detect fault with the accuracy of 100% using scale 1 only. PNN has been selected in the decision algorithm for predicting the location of fault. The first peak times obtained from the faulty bus are used as an input for the training process of a neural network in a decision algorithm. The results show
12 1872 A. NGAOPITAKKUL AND C. JETTANASEN that the proposed algorithm is able to detect the faulty bus with the accuracy of 100% and identify fault location with the average error of 0 km. Acknowledgment. This work is partially supported by King Mongkut s Institute of Technology Ladkrabang Research fund. The authors also gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the presentation. REFERENCES [1] Z. Q. Bo, F. Jiang, Z. Chen, X. Z. Dong, G. Weller and M. A. Redfern, Transient based protection for power transmission systems, IEEE Power Engineering Society Winter Meeting, vol.3, pp , [2] C. H. Kim and R. Aggarwal, Wavelet transforms in power systems. I. General introduction to the wavelet transforms, IEE Power Engineering Journal, vol.14, no.2, pp.81-87, [3] O. A. S. Youssef, Fault classification based on wavelet transforms, IEEE/PES Transmission and Distribution Conference and Exposition, vol.1, pp , [4] P. Maknimg, S. Bunjongjit, A. Kunakorn, S. Jiriwibhakorn and M. Kando, Fault diagnosis in transmission lines using wavelet transform analysis, IEEE/PES Transmission and Distribution Conference and Exhibition 2002: Asia Pacific, vol.3, pp , [5] A. Ngaopitakkul and A. Kunakorn, Internal fault classification in transformer windings using combination of discrete wavelet transforms and back-propagation neural networks, International Journal of Control, Automation, and Systems, vol.4, no.3, pp , [6] Z. Zhang, Y. Ohara, H. Toda, T. Miyake and T. Imamura, De-noising method by combining adaptive line enhancer and complex discrete wavelet transform, ICIC Express Letters, vol.1, no.2, pp , [7] S. P. Lee and C. H. Loh, Object-oriented design metrics as early quality indicators of faulty classes and components, ICIC Express Letters, vol.3, no.3(a), pp , [8] O. Mustapha, D. Lefebvre, M. Khalil, G. Hoblos and H. Chafouk, Fault detection algorithm using DCS method combined with filters bank derived from the wavelet transform, International Journal of Innovative Computing, Information and Control, vol.5, no.5, pp , [9] I. Usman, A. Khan, A. Ali and T.-S. Choi, Reversible watermarking based on intelligent coefficient selection and integer wavelet transform, International Journal of Innovative Computing, Information and Control, vol.5, no.12(a), pp , [10] F. H. Magnago and A. Abur, Fault location using wavelets, IEEE Trans. on Power Delivery, vol.13, no.4, pp , [11] Z. Chen and J.-C. Maun, Artificial neural network approach to single-ended fault locator for transmission lines, IEEE Trans. Power Systems, vol.15, pp , [12] A. J. Mazon, I. Zamora, J. F. Minambres, M. A. Zorrozua, J. J. Barandiaran and K. Sagastabeitia, A new approach to fault location in two-terminal transmission lines using artificial neural networks, Electric Power Systems Research, vol.56, pp , [13] T. Kawady and J. Stenzel, A practical fault location approach for double circuit transmission lines using single end data, IEEE Trans. on Power Delivery, vol.18, no.4, pp , [14] J. Izykowski, E. Rosolowski and M. M. Saha, Locating faults in parallel transmission lines under availability of complete measurements at one end, IEE Proc. on Generation, Transmission and Distribution, vol.151, no.2, pp , [15] G. Song, J. Suonan, Q. Xu, P. Chen and Y. Ge, Parallel transmission lines fault location algorithm based on differential component net, IEEE Trans. on Power Delivery, vol.20, no.4, pp , [16] L. S. Martins, J. F. Martins, F. V. Pires and C. M. Alegria, A neural space vector fault location for parallel double-circuit distribution lines, International Journal of Electrical Power & Energy Systems, vol.27, no.3, pp , [17] C. Fan, K. K. Li, W. L. Chan, W. Yu and Z. Zhang, Application of wavelet fuzzy neural network in locating single line to ground fault (SLG) in distribution lines, International Journal of Electrical Power & Energy Systems, vol.29, no.6, pp , [18] M. J. Reddy and D. K. Mohanta, A wavelet-fuzzy combined approach for classification and location of transmission line faults, Electrical Power and Energy Systems, vol.29, no.9, pp , 2007.
13 WAVELET TRANSFORM AND PROBABILISTIC NEURAL NETWORK ALGORITHM 1873 [19] P. Chiradeja and A. Ngaopitakkul, Identification of fault types for single circuit transmission line using discrete wavelet transform and artificial neural networks, Proc. of 2009 International Conference on Electrical Engineering, Hong Kong, [20] Y.-H. Lin, C.-W. Liu and C.-S. Yu, A new fault locator for three-terminal transmission lines-using two-terminal synchronized voltage and current phasors, IEEE Trans. on Power Delivery, pp , [21] J. Izykowski, E. Rosolowski, M. M. Saha, M. Fulczyk and P. Balcerek, A fault-location method for application with current differential relays of three-terminal lines, IEEE Trans. on Power Delivery, vol.22, no.4, pp , [22] D. V. Dommelen, Alternative Transient Program Rule Book, Leuven EMTP Center, Belgium, [23] Switching and Transmission Line Diagram, Electricity Generation Authorisation Thailand (EGAT). [24] N. S. D. Brito, B. A. Souza and F. A. C. Pires, Daubechies wavelets in quality of electrical power, Proc. of IEEE International Conference on Harmonics and Quality of Power, pp , [25] T. Patcharoen, A. Ngaopitakkul and A. Kunakorn, Identification of fault types for a three-bus transmission network using discrete wavelet transform and probabilistic neural networks, Proc. of the 8th International Power Engineering Conference, Singapore, pp , [26] S. Haykin, Neural Networks: A Comprehensive Foundation, Prentice-Hall, [27] H. Demuth and M. Beale, Neural Network Toolbox User s Guide, The Math Work Inc., 2001.
Discrete Wavelet Transform and Support Vector Machines Algorithm for Classification of Fault Types on Transmission Line
Discrete Wavelet Transform and Support Vector Machines Algorithm for Classification of Fault Types on Transmission Line K. Kunadumrongrath and A. Ngaopitakkul, Member, IAENG Abstract This paper proposes
More informationIDENTIFYING TYPES OF SIMULTANEOUS FAULT IN TRANSMISSION LINE USING DISCRETE WAVELET TRANSFORM AND FUZZY LOGIC ALGORITHM
International Journal of Innovative Computing, Information and Control ICIC International c 2013 ISSN 1349-4198 Volume 9, Number 7, July 2013 pp. 2701 2712 IDENTIFYING TYPES OF SIMULTANEOUS FAULT IN TRANSMISSION
More informationInternal Fault Classification in Transformer Windings using Combination of Discrete Wavelet Transforms and Back-propagation Neural Networks
International Internal Fault Journal Classification of Control, in Automation, Transformer and Windings Systems, using vol. Combination 4, no. 3, pp. of 365-371, Discrete June Wavelet 2006 Transforms and
More informationDetection and classification of faults on 220 KV transmission line using wavelet transform and neural network
International Journal of Smart Grid and Clean Energy Detection and classification of faults on 220 KV transmission line using wavelet transform and neural network R P Hasabe *, A P Vaidya Electrical Engineering
More informationOnline Diagnosis and Monitoring for Power Distribution System
Energy and Power Engineering, 1,, 59-53 http://dx.doi.org/1.3/epe.1. Published Online November 1 (http://www.scirp.org/journal/epe) Online Diagnosis and Monitoring for Power Distribution System Atef Almashaqbeh,
More informationArtificial Neural Network Based Fault Locator for Single Line to Ground Fault in Double Circuit Transmission Line
DOI: 10.7763/IPEDR. 2014. V75. 11 Artificial Neural Network Based Fault Locator for Single Line to Ground Fault in Double Circuit Transmission Line Aravinda Surya. V 1, Ebha Koley 2 +, AnamikaYadav 3 and
More informationA Fast and Accurate Fault Detection Approach in Power Transmission Lines by Modular Neural Network and Discrete Wavelet Transform
Comput. Sci. Appl. Volume 1, Number 3, 2014, pp. 152-157 Received: July 10, 2014; Published: September 25, 2014 Computer Science and Applications www.ethanpublishing.com A Fast and Accurate Fault Detection
More informationFault Location Technique for UHV Lines Using Wavelet Transform
International Journal of Electrical Engineering. ISSN 0974-2158 Volume 6, Number 1 (2013), pp. 77-88 International Research Publication House http://www.irphouse.com Fault Location Technique for UHV Lines
More informationTRANSIENT STABILITY ENHANCEMENT OF POWER SYSTEM USING INTELLIGENT TECHNIQUE
TRANSIENT STABILITY ENHANCEMENT OF POWER SYSTEM USING INTELLIGENT TECHNIQUE K.Satyanarayana 1, Saheb Hussain MD 2, B.K.V.Prasad 3 1 Ph.D Scholar, EEE Department, Vignan University (A.P), India, ksatya.eee@gmail.com
More informationDetection of fault location on transmission systems using Wavelet transform
International Academic Institute for Science and Technology International Academic Journal of Science and Engineering Vol. 3, No. 4, 2016, pp. 23-32. ISSN 2454-3896 International Academic Journal of Science
More informationISSN: [Taywade* et al., 5(12): December, 2016] Impact Factor: 4.116
IJESRT INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY DETECTION AND CLASSIFICATION OF TRANSMISSION LINES FAULTS USING DISCRETE WAVELET TRANSFORM AND ANN AS CLASSIFIER Dhanashri D.
More informationA DWT Approach for Detection and Classification of Transmission Line Faults
IJIRST International Journal for Innovative Research in Science & Technology Volume 3 Issue 02 July 2016 ISSN (online): 2349-6010 A DWT Approach for Detection and Classification of Transmission Line Faults
More informationFAULT DETECTION, CLASSIFICATION AND LOCATION ON AN UNDERGROUND CABLE NETWORK USING WAVELET TRANSFORM
90 FAULT DETECTION, CLASSIFICATION AND LOCATION ON AN UNDERGROUND CABLE NETWORK USING WAVELET TRANSFORM Hashim Hizam, Jasronita Jasni, Mohd Zainal Abidin Ab Kadir, Wan Fatinhamamah Wan Ahmad Department
More informationWavelet Based Fault Detection, Classification in Transmission System with TCSC Controllers
ISSN: 2248-9622, Vol. 5, Issue 8, (Part - 3) August 215, pp.25-29 RESEARCH ARTICLE OPEN ACCESS Wavelet Based Fault Detection, Classification in Transmission System with TCSC Controllers 1 G.Satyanarayana,
More informationAccurate Hybrid Method for Rapid Fault Detection, Classification and Location in Transmission Lines using Wavelet Transform and ANNs
From the SelectedWorks of Innovative Research Publications IRP India Summer May 1, 215 Accurate Hybrid Method for Rapid Fault Detection, Classification and Location in Transmission Lines using Wavelet
More informationImproved first zone reach setting of artificial neural network-based directional relay for protection of double circuit transmission lines
Published in IET Generation, Transmission & Distribution Received on 5th April 2013 Revised on 17th September 2013 Accepted on 24th September 2013 ISSN 1751-8687 Improved first zone reach setting of artificial
More informationFault Detection and Classification for Transmission Line Protection System Using Artificial Neural Network
Journal of Electrical and Electronic Engineering 16; 4(5): 89-96 http://www.sciencepublishinggroup.com/j/jeee doi: 1.11648/j.jeee.1645.11 ISSN: 39-1613 (Print); ISSN: 39-165 (Online) Fault Detection and
More informationSERIES (OPEN CONDUCTOR) FAULT DISTANCE LOCATION IN THREE PHASE TRANSMISSION LINE USING ARTIFICIAL NEURAL NETWORK
1067 SERIES (OPEN CONDUCTOR) FAULT DISTANCE LOCATION IN THREE PHASE TRANSMISSION LINE USING ARTIFICIAL NEURAL NETWORK A Nareshkumar 1 1 Assistant professor, Department of Electrical Engineering Institute
More informationAnalysis of Distance Protection for EHV Transmission Lines Using Artificial Neural Network
Analysis of Distance Protection for EHV Transmission Lines Using Artificial Neural Network Ezema C.N 1, Iloh J.P.I 2, Obi P.I. 3 1, 2 Department of Electrical /Electronic Engineering, Chukwuemeka Odumegwu
More informationA Novel Fuzzy Neural Network Based Distance Relaying Scheme
902 IEEE TRANSACTIONS ON POWER DELIVERY, VOL. 15, NO. 3, JULY 2000 A Novel Fuzzy Neural Network Based Distance Relaying Scheme P. K. Dash, A. K. Pradhan, and G. Panda Abstract This paper presents a new
More informationAPERFECT transmission line protection scheme is expected
IEEE TRANSACTIONS ON POWER DELIVERY, VOL. 22, NO. 2, APRIL 2007 859 Transmission Line Boundary Protection Using Wavelet Transform and Neural Network Nan Zhang, Member, IEEE, and Mladen Kezunovic, Fellow,
More informationCLASSIFICATION OF POWER QUALITY DISTURBANCES USING WAVELET TRANSFORM AND S-TRANSFORM BASED ARTIFICIAL NEURAL NETWORK
CLASSIFICATION OF POWER QUALITY DISTURBANCES USING WAVELET TRANSFORM AND S-TRANSFORM BASED ARTIFICIAL NEURAL NETWORK P. Sai revathi 1, G.V. Marutheswar 2 P.G student, Dept. of EEE, SVU College of Engineering,
More informationANFIS Approach for Locating Faults in Underground Cables
Vol:8, No:6, 24 ANFIS Approach for Locating Faults in Underground Cables Magdy B. Eteiba, Wael Ismael Wahba, Shimaa Barakat International Science Index, Electrical and Computer Engineering Vol:8, No:6,
More informationAutomatic Detection and Positioning of Power Quallity Disturbances using a Discrete Wavelet Transform
Automatic Detection and Positioning of Power Quallity Disturbances using a Discrete Wavelet Transform Ramtin Sadeghi, Reza Sharifian Dastjerdi, Payam Ghaebi Panah, Ehsan Jafari Department of Electrical
More information[Nayak, 3(2): February, 2014] ISSN: Impact Factor: 1.852
IJESRT INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY Classification of Transmission Line Faults Using Wavelet Transformer B. Lakshmana Nayak M.TECH(APS), AMIE, Associate Professor,
More informationKeywords: Power System Computer Aided Design, Discrete Wavelet Transform, Artificial Neural Network, Multi- Resolution Analysis.
GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES IDENTIFICATION AND CLASSIFICATION OF POWER QUALITY DISTURBANCES BY AN EFFECTIVE WAVELET BASED NEURAL CLASSIFIER Prof. A. P. Padol Department of Electrical
More informationDetection and Classification of Faults on Parallel Transmission Lines using Wavelet Transform and Neural Network
Detection and Classification of s on Parallel Transmission Lines using Wavelet Transform and Neural Networ V.S.Kale, S.R.Bhide, P.P.Bedear and G.V.K.Mohan Abstract The protection of parallel transmission
More informationClassification of Voltage Sag Using Multi-resolution Analysis and Support Vector Machine
Journal of Clean Energy Technologies, Vol. 4, No. 3, May 2016 Classification of Voltage Sag Using Multi-resolution Analysis and Support Vector Machine Hanim Ismail, Zuhaina Zakaria, and Noraliza Hamzah
More informationReview of Performance of Impedance Based and Travelling Wave Based Fault Location Algorithms in Double Circuit Transmission Lines
Journal of Electrical and Electronic Engineering 2015; 3(4): 65-69 Published online July 3, 2015 (http://www.sciencepublishinggroup.com/j/jeee) doi: 10.11648/j.jeee.20150304.11 ISSN: 2329-1613 (Print);
More informationTraveling-Waves-Based Ground Fault Location Using Zero-Sequence Detection and Wavelet Transform
Journal of Electrical Engineering, Electronics, Control and Computer Science JEEECCS, Volume 3, Issue 7, pages 7-12, 2017 Traveling-Waves-Based Ground Fault Location Using Zero-Sequence Detection and Wavelet
More informationA COMPARATIVE STUDY: FAULT DETECTION METHOD ON OVERHEAD TRANSMISSION LINE
Volume 118 No. 22 2018, 961-967 ISSN: 1314-3395 (on-line version) url: http://acadpubl.eu/hub ijpam.eu A COMPARATIVE STUDY: FAULT DETECTION METHOD ON OVERHEAD TRANSMISSION LINE 1 M.Nandhini, 2 M.Manju,
More informationClassification of Faults on Transmission lines using EMTP and Wavelet Multiresolution Analysis
IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE) e-issn: 2278-1676,p-ISSN: 2320-3331, Volume 9, Issue 5 Ver. II (Sep Oct. 2014), PP 79-86 Classification of Faults on Transmission lines
More informationIDENTIFICATION OF POWER QUALITY PROBLEMS IN IEEE BUS SYSTEM BY USING NEURAL NETWORKS
Fourth International Conference on Control System and Power Electronics CSPE IDENTIFICATION OF POWER QUALITY PROBLEMS IN IEEE BUS SYSTEM BY USING NEURAL NETWORKS Mr. Devadasu * and Dr. M Sushama ** * Associate
More informationISSN Vol.05,Issue.06, June-2017, Pages:
WWW.IJITECH.ORG ISSN 2321-8665 Vol.05,Issue.06, June-2017, Pages:1061-1066 Fuzzy Logic Based Fault Detection and Classification of Unsynchronized Faults in Three Phase Double Circuit Transmission Lines
More informationDistribution System Faults Classification And Location Based On Wavelet Transform
Distribution System Faults Classification And Location Based On Wavelet Transform MukeshThakre, Suresh Kumar Gawre & Mrityunjay Kumar Mishra Electrical Engg.Deptt., MANIT, Bhopal. E-mail : mukeshthakre18@gmail.com,
More informationFault Classification and Faulty Section Identification in Teed Transmission Circuits Using ANN
International Journal of Computer and Electrical Engineering, Vol. 3, No. 6, December Classification and y Section Identification in Teed Transmission Circuits Using ANN Prarthana Warlyani, Anamika Jain,
More informationPower Quality Monitoring of a Power System using Wavelet Transform
International Journal of Electrical Engineering. ISSN 0974-2158 Volume 3, Number 3 (2010), pp. 189--199 International Research Publication House http://www.irphouse.com Power Quality Monitoring of a Power
More informationWavelet Transform for Classification of Voltage Sag Causes using Probabilistic Neural Network
International Journal of Electrical Engineering. ISSN 974-2158 Volume 4, Number 3 (211), pp. 299-39 International Research Publication House http://www.irphouse.com Wavelet Transform for Classification
More informationDwt-Ann Approach to Classify Power Quality Disturbances
Dwt-Ann Approach to Classify Power Quality Disturbances Prof. Abhijit P. Padol Department of Electrical Engineering, abhijit.padol@gmail.com Prof. K. K. Rajput Department of Electrical Engineering, kavishwarrajput@yahoo.co.in
More informationFault Detection Using Hilbert Huang Transform
International Journal of Research in Advent Technology, Vol.6, No.9, September 2018 E-ISSN: 2321-9637 Available online at www.ijrat.org Fault Detection Using Hilbert Huang Transform Balvinder Singh 1,
More informationIMPLEMENTATION OF NEURAL NETWORK IN ENERGY SAVING OF INDUCTION MOTOR DRIVES WITH INDIRECT VECTOR CONTROL
IMPLEMENTATION OF NEURAL NETWORK IN ENERGY SAVING OF INDUCTION MOTOR DRIVES WITH INDIRECT VECTOR CONTROL * A. K. Sharma, ** R. A. Gupta, and *** Laxmi Srivastava * Department of Electrical Engineering,
More informationDetection of Fault in Fixed Series Compensated Transmission Line during Power Swing Using Wavelet Transform
International Journal of Scientific and Research Publications, Volume 4, Issue 5, May 24 Detection of Fault in Fixed Series Compensated Transmission Line during Power Swing Using Wavelet Transform Rohan
More informationA fast and accurate distance relaying scheme using an efficient radial basis function neural network
Electric Power Systems Research 60 (2001) 1 8 www.elsevier.com/locate/epsr A fast and accurate distance relaying scheme using an efficient radial basis function neural network A.K. Pradhan *, P.K. Dash,
More informationLabVIEW Based Condition Monitoring Of Induction Motor
RESEARCH ARTICLE OPEN ACCESS LabVIEW Based Condition Monitoring Of Induction Motor 1PG student Rushikesh V. Deshmukh Prof. 2Asst. professor Anjali U. Jawadekar Department of Electrical Engineering SSGMCE,
More informationComparison of Wavelet Transform and Fourier Transform based methods of Phasor Estimation for Numerical Relaying
Comparison of Wavelet Transform and Fourier Transform based methods of Phasor Estimation for Numerical Relaying V.S.Kale S.R.Bhide P.P.Bedekar Department of Electrical Engineering, VNIT Nagpur, India Abstract
More informationWavelet Transform Based Islanding Characterization Method for Distributed Generation
Fourth LACCEI International Latin American and Caribbean Conference for Engineering and Technology (LACCET 6) Wavelet Transform Based Islanding Characterization Method for Distributed Generation O. A.
More informationDetermination of Fault Location and Type in Distribution Systems using Clark Transformation and Neural Network
International Journal of Applied Power Engineering (IJAPE) Vol., No., August, pp. 75~86 ISSN: 5879 75 Determination of Fault Location and Type in Distribution Systems using Clark Transformation and Neural
More informationFAULT DETECTION AND DIAGNOSIS OF HIGH SPEED SWITCHING DEVICES IN POWER INVERTER
FAULT DETECTION AND DIAGNOSIS OF HIGH SPEED SWITCHING DEVICES IN POWER INVERTER R. B. Dhumale 1, S. D. Lokhande 2, N. D. Thombare 3, M. P. Ghatule 4 1 Department of Electronics and Telecommunication Engineering,
More informationImproving Current and Voltage Transformers Accuracy Using Artificial Neural Network
Improving Current and Voltage Transformers Accuracy Using Artificial Neural Network Haidar Samet 1, Farshid Nasrfard Jahromi 1, Arash Dehghani 1, and Afsaneh Narimani 2 1 Shiraz University 2 Foolad Technic
More informationDetection and Classification of One Conductor Open Faults in Parallel Transmission Line using Artificial Neural Network
Detection and Classification of One Conductor Open Faults in Parallel Transmission Line using Artificial Neural Network A.M. Abdel-Aziz B. M. Hasaneen A. A. Dawood Electrical Power and Machines Eng. Dept.
More informationApproach for High voltage transmission line protection by using line trap network & ANN over SVM
Approach for High voltage transmission line protection by using line trap network & ANN over SVM 1 Aaditya P.Agarkar, 2 Dr.Swapnil B.Mohod 1 PG student, 2 Assistant Professor 1,2 Department of Electrical
More informationMATLAB/GUI Simulation Tool for Power System Fault Analysis with Neural Network Fault Classifier
MATLAB/GUI Simulation Tool for Power System Fault Analysis with Neural Network Fault Classifier Ph Chitaranjan Sharma, Ishaan Pandiya, Dipak Swargari, Kusum Dangi * Department of Electrical Engineering,
More informationFaults Detection in Single-Core Symmetrical Phase Shifting Transformers Based on Wavelets
Faults Detection in Single-Core Symmetrical Phase Shifting Transformers Based on Wavelets 1 Meenakshi Sahu, 2 Rahul Rahangdale 1,2 Department Of Electronics And Communication Engineering School of Engineering
More informationVOLTAGE and current signals containing information
Impact of Instrument Transformers and Anti-Aliasing Filters on Fault Locators R. L. A. Reis, W. L. A. Neves, and D. Fernandes Jr. Abstract Butterworth and Chebyshev anti-aliasing filters assembled in instrument
More informationConsidering Characteristics of Arc on Travelling Wave Fault Location Algorithm for the Transmission Lines without Using Line Parameters
Considering Characteristics of Arc on Travelling Wave Fault Location Algorithm for the Transmission Lines without Using Line Parameters M. Bashir mohsenbashir@ieee.org I. Niazy ismail_niazy@ieee.org J.
More informationCurrent Harmonic Estimation in Power Transmission Lines Using Multi-layer Perceptron Learning Strategies
Journal of Electrical Engineering 5 (27) 29-23 doi:.7265/2328-2223/27.5. D DAVID PUBLISHING Current Harmonic Estimation in Power Transmission Lines Using Multi-layer Patrice Wira and Thien Minh Nguyen
More informationA Novel Scheme of Transmission Line Faults Analysis and Detection by Using MATLAB Simulation
A Novel Scheme of Transmission Line Faults Analysis and Detection by Using MATLAB Simulation Satish Karekar 1, Varsha Thakur 2, Manju 3 1 Parthivi College of Engineering and Management, Sirsakala, Bhilai-3,
More informationFAULT LOCATION IN OVERHEAD TRANSMISSION LINE WITHOUT USING LINE PARAMETER
FAULT LOCATION IN OVERHEAD TRANSMISSION LINE WITHOUT USING LINE PARAMETER 1 JAY PRAKASH KESHRI, 2 HARPAL TIWARI 1,2 Electrical Engineering Department Malaviya National Institute of Technology Jaipur E-mail:
More informationAustralian Journal of Basic and Applied Sciences. Locatiing Faults in Radial Distribution Line Using Neural Network
AENSI Journals Australian Journal of Basic and Applied Sciences ISSN:1991-8178 Journal home page: www.ajbasweb.com Locatiing Faults in Radial Distribution Line Using Neural Network 1 S. Karunambigai and
More informationLocating Sub-Cycle Faults in Distribution Network Applying Half-Cycle DFT Method
Locating Sub-Cycle Faults in Distribution etwork Applying Half-Cycle DFT Method Po-Chen Chen, Student Member, IEEE, Vuk Malbasa, Member, IEEE, Mladen Kezunovic, Fellow, IEEE Department of Electrical Computer
More informationAn Enhanced Symmetrical Fault Detection during Power Swing/Angular Instability using Park s Transformation
Indonesian Journal of Electrical Engineering and Computer Science Vol., No., April 6, pp. 3 ~ 3 DOI:.59/ijeecs.v.i.pp3-3 3 An Enhanced Symmetrical Fault Detection during Power Swing/Angular Instability
More informationSVC Compensated Multi Terminal Transmission System Digital Protection Scheme using Wavelet Transform Approach
SVC Compensated Multi Terminal Transmission System Digital Protection Scheme using Wavelet Transform Approach J.Uday Bhaskar 1, S.S Tulasiram 2, G.Ravi Kumar 3 JNTUK 1, JNTUH 2, JNTUK 3 udayadisar@gmail.com
More informationHarmonic Analysis of Power System Waveforms Based on Chaari Complex Mother Wavelet
Proceedings of the 7th WSEAS International Conference on Power Systems, Beijing, China, September 15-17, 2007 7 Harmonic Analysis of Power System Waveforms Based on Chaari Complex Mother Wavelet DAN EL
More informationImplementation and Evaluation a SIMULINK Model of a Distance Relay in MATLAB/SIMULINK
Implementation and Evaluation a SIMULINK Model of a Distance Relay in MATLAB/SIMULINK Omar G. Mrehel Hassan B. Elfetori AbdAllah O. Hawal Electrical and Electronic Dept. Operation Department Electrical
More informationA New Adaptive Wide Area Protection Algorithm for Distribution Networks with Distributed Generation
41, Issue 1 (2018) 1-6 Journal of Advanced Research Design Journal homepage: www.akademiabaru.com/ard.html ISSN: 2289-7984 A New Adaptive Wide Area Protection Algorithm for Distribution Networks with Distributed
More informationPerformance Evaluation of Traveling Wave Fault Locator for a 220kV Hoa Khanh-Thanh My Transmission Line
Engineering, Technology & Applied Science Research Vol. 8, No. 4, 2018, 3243-3248 3243 Performance Evaluation of Traveling Wave Fault Locator for a 220kV Hoa Khanh-Thanh My Transmission Line Kim Hung Le
More informationInfluence of Coupling Capacitor Voltage Transformers on Travelling Wave-Based Fault Locators
Influence of Coupling Capacitor oltage Transformers on Travelling Wave-Based Fault Locators R. L. A. Reis, F.. Lopes, W. L. A. Neves and D. Fernandes Jr. Abstract-- The coupling capacitor voltage transformer
More informationPMU Based Monitoring of Inter-Area Oscillation in Thailand Power System via Home Power Outlets
PMU Based Monitoring of Inter-Area Oscillation in Thailand Power System via Home Power Outlets 199 PMU Based Monitoring of Inter-Area Oscillation in Thailand Power System via Home Power Outlets Issarachai
More informationPattern Recognition Techniques Applied to Electric Power Signal Processing Ghazi Bousaleh, Mohamad Darwiche, Fahed Hassoun
Pattern Recognition Techniques Applied to Electric Power Signal Processing Ghazi Bousaleh, Mohamad Darwiche, Fahed Hassoun Abstract: We propose in this paper an approach whose main objective is to detect
More informationA Single Monitor Method for Voltage Sag Source Location using Hilbert Huang Transform
Research Journal of Applied Sciences, Engineering and Technology 5(1): 192-202, 2013 ISSN: 2040-7459; e-issn: 2040-7467 Maxwell Scientific Organization, 2013 Submitted: May 15, 2012 Accepted: June 06,
More informationUltra Hight Voltge Transmission line Faults Identified and Analysis by using MATLAB Simulink
International Seminar On Non-Conventional Energy Sources for Sustainable Development of Rural Areas, IJAERD- International Journal of Advance Engineering & Research Development e-issn: 2348-4470, p-issn:2348-6406
More informationIdentification and Classification of Fault in an EHV Transmission line using S-Transform and Neural Network
I J C International Journal of lectrical, lectronics ISSN No. (Online) : 2277-2626 and Computer ngineering 2(2): 80-87(2013) Special dition for Best Papers of Michael Faraday IT India Summit-2013, MFIIS-13
More informationWavelet Based Transient Directional Method for Busbar Protection
Based Transient Directional Method for Busbar Protection N. Perera, A.D. Rajapakse, D. Muthumuni Abstract-- This paper investigates the applicability of transient based fault direction identification method
More informationEnhanced Real Time and Off-Line Transmission Line Fault Diagnosis Using Artificial Intelligence
Enhanced Real Time and Off-Line Transmission Line Fault Diagnosis Using Artificial Intelligence Okwudili E. Obi, Oseloka A. Ezechukwu and Chukwuedozie N. Ezema 0 Enhanced Real Time and Off-Line Transmission
More informationModeling and Testing of a Digital Distance Relay Using MATLAB/SIMULINK
Modeling and Testing of a Digital Distance Relay Using MATLAB/SIMULINK Li-Cheng Wu, Chih-Wen Liu,Senior Member,IEEE, Ching-Shan Chen,Member,IEEE Department of Electrical Engineering, National Taiwan University,
More informationCharacterization of Voltage Sag due to Faults and Induction Motor Starting
Characterization of Voltage Sag due to Faults and Induction Motor Starting Dépt. of Electrical Engineering, SSGMCE, Shegaon, India, Dépt. of Electronics & Telecommunication Engineering, SITS, Pune, India
More informationINTELLIGENT DETECTION OF SERIAL ARC FAULT ON LOW VOLTAGE POWER LINES
Journal of Marine Science and Technology, Vol 5, 1, pp 3-53 (17) 3 DOI: 119/JMST-1-111-1 INTELLIGENT DETECTION OF SERIAL ARC FAULT ON LOW VOLTAGE POWER LINES Chi-Jui Wu, Yu-Wei Liu, and Chen-Shung Hung
More informationPower System Failure Analysis by Using The Discrete Wavelet Transform
Power System Failure Analysis by Using The Discrete Wavelet Transform ISMAIL YILMAZLAR, GULDEN KOKTURK Dept. Electrical and Electronic Engineering Dokuz Eylul University Campus Kaynaklar, Buca 35160 Izmir
More informationSINGLE ENDED TRAVELING WAVE BASED FAULT LOCATION USING DISCRETE WAVELET TRANSFORM
University of Kentucky UKnowledge Theses and Dissertations--Electrical and Computer Engineering Electrical and Computer Engineering 4 SINGLE ENDED TRAVELING WAVE BASED FAULT LOCATION USING DISCRETE WAVELET
More informationA Transient Current Based Wavelet-Fuzzy Approach for the Protection of Six-Terminal Transmission System
Abstract International Journal of Exploration in Science and Technology A Transient Current Based Wavelet-Fuzzy Approach for the Protection of Six-Terminal Transmission System J.Uday Bhaskar 1, G.Ravi
More informationA new scheme based on correlation technique for generator stator fault detection-part π
International Journal of Energy and Power Engineering 2014; 3(3): 147-153 Published online July 10, 2014 (http://www.sciencepublishinggroup.com/j/ijepe) doi: 10.11648/j.ijepe.20140303.16 ISSN: 2326-957X
More informationKey-Words: - NARX Neural Network; Nonlinear Loads; Shunt Active Power Filter; Instantaneous Reactive Power Algorithm
Parameter control scheme for active power filter based on NARX neural network A. Y. HATATA, M. ELADAWY, K. SHEBL Department of Electric Engineering Mansoura University Mansoura, EGYPT a_hatata@yahoo.com
More informationClassification of Signals with Voltage Disturbance by Means of Wavelet Transform and Intelligent Computational Techniques.
Proceedings of the 6th WSEAS International Conference on Power Systems, Lison, Portugal, Septemer 22-24, 2006 435 Classification of Signals with Voltage Disturance y Means of Wavelet Transform and Intelligent
More informationA New Fault Locator for Three-Terminal Transmission Lines Using Two-Terminal Synchronized Voltage and Current Phasors
452 IEEE TRANSACTIONS ON POWER DELIVERY, VOL. 17, NO. 2, APRIL 2002 A New Fault Locator for Three-Terminal Transmission Lines Using Two-Terminal Synchronized Voltage and Current Phasors Ying-Hong Lin,
More informationAnalysis of Fault location methods on transmission lines
University of New Orleans ScholarWorks@UNO University of New Orleans Theses and Dissertations Dissertations and Theses Spring 5-16-214 Analysis of Fault location methods on transmission lines Sushma Ghimire
More informationKeywords: Wavelet packet transform (WPT), Differential Protection, Inrush current, CT saturation.
IJESRT INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY Differential Protection of Three Phase Power Transformer Using Wavelet Packet Transform Jitendra Singh Chandra*, Amit Goswami
More informationDetection and Identification of PQ Disturbances Using S-Transform and Artificial Intelligent Technique
American Journal of Electrical Power and Energy Systems 5; 4(): -9 Published online February 7, 5 (http://www.sciencepublishinggroup.com/j/epes) doi:.648/j.epes.54. ISSN: 36-9X (Print); ISSN: 36-9 (Online)
More informationFault Detection in Double Circuit Transmission Lines Using ANN
International Journal of Research in Advent Technology, Vol.3, No.8, August 25 E-ISSN: 232-9637 Fault Detection in Double Circuit Transmission Lines Using ANN Chhavi Gupta, Chetan Bhardwaj 2 U.T.U Dehradun,
More informationApplication of ANFIS for Distance Relay Protection in Transmission Line
International Journal of Electrical and Computer Engineering (IJECE) Vol. 5, No. 6, December 2015, pp. 1311~1318 ISSN: 2088-8708 1311 Application of ANFIS for Distance Relay Protection in Transmission
More informationAn Ellipse Technique Based Relay For Extra High Voltage Transmission Lines Protection
Proceedings of the 14th International Middle East Power Systems Conference (MEPCON 10), Cairo University, Egypt, December 19-21, 2010, Paper ID 162. An Ellipse Technique Based Relay For Extra High Voltage
More informationFPGA implementation of DWT for Audio Watermarking Application
FPGA implementation of DWT for Audio Watermarking Application Naveen.S.Hampannavar 1, Sajeevan Joseph 2, C.B.Bidhul 3, Arunachalam V 4 1, 2, 3 M.Tech VLSI Students, 4 Assistant Professor Selection Grade
More informationDETECTION AND CLASSIFICATION OF POWER QUALITY DISTURBANCES
DETECTION AND CLASSIFICATION OF POWER QUALITY DISTURBANCES Ph.D. THESIS by UTKARSH SINGH INDIAN INSTITUTE OF TECHNOLOGY ROORKEE ROORKEE-247 667 (INDIA) OCTOBER, 2017 DETECTION AND CLASSIFICATION OF POWER
More informationIndirect Vector Control of Induction Motor Using Pi Speed Controller and Neural Networks
Vol.3, Issue.4, Jul - Aug. 2013 pp-1980-1987 ISSN: 2249-6645 Indirect Vector Control of Induction Motor Using Pi Speed Controller and Neural Networks C. Mohan Krishna M. Tech 1, G. Meerimatha M.Tech 2,
More informationSteady State versus Transient Signal for Fault Location in Transmission Lines
Journal of Physics: Conference Series PAPER OPEN ACCESS Steady State versus Transient Signal for Location in Transmission Lines To cite this article: M.N. Hashim et al 8 J. Phys.: Conf. Ser. 9 43 View
More informationDetection and Classification of Power Quality Event using Discrete Wavelet Transform and Support Vector Machine
Detection and Classification of Power Quality Event using Discrete Wavelet Transform and Support Vector Machine Okelola, Muniru Olajide Department of Electronic and Electrical Engineering LadokeAkintola
More informationUse of Neural Networks in Testing Analog to Digital Converters
Use of Neural s in Testing Analog to Digital Converters K. MOHAMMADI, S. J. SEYYED MAHDAVI Department of Electrical Engineering Iran University of Science and Technology Narmak, 6844, Tehran, Iran Abstract:
More informationTeaching Distance Relay Using Matlab/Simulink Graphical User Interface
Available online at www.sciencedirect.com Procedia Engineering 53 ( 2013 ) 264 270 Malaysian Technical Universities Conference on Engineering & Technology 2012, MUCET 2012 Part 1 - Electronic and Electrical
More informationPrediction of Missing PMU Measurement using Artificial Neural Network
Prediction of Missing PMU Measurement using Artificial Neural Network Gaurav Khare, SN Singh, Abheejeet Mohapatra Department of Electrical Engineering Indian Institute of Technology Kanpur Kanpur-208016,
More informationBusbar Differential Relaying Method Based on Combined Amplitude and Phase Information of High Frequency Transient Currents
Energy and Power Engineering, 2013, 5, 1288-1292 doi:10.4236/epe.2013.54b244 Published Online July 2013 (http://www.scirp.org/journal/epe) Busbar Differential Relaying Method Based on Combined Amplitude
More informationDETECTION AND CLASSIFICATION OF POWER QUALITY DISTURBANCE WAVEFORM USING MRA BASED MODIFIED WAVELET TRANSFROM AND NEURAL NETWORKS
Journal of ELECTRICAL ENGINEERING, VOL. 61, NO. 4, 2010, 235 240 DETECTION AND CLASSIFICATION OF POWER QUALITY DISTURBANCE WAVEFORM USING MRA BASED MODIFIED WAVELET TRANSFROM AND NEURAL NETWORKS Perumal
More information