Applied Mechanics and Materials Vol. 818 (2016) pp 47-51
© (2016) Trans Tech Publications, Switzerland
doi:10.4028/www.scientific.net/AMM.818.47

Submitted: 2014-08-27
Accepted: 2015-01-22

Single-Line to Ground-Fault Detection for Unit Generator-Transformerbased on Wavelet Transform and Neural Networks

Ahmad Rizal Sultan1,a *, Mohd Wazir Mustafa2,b, Makmur Saini3,c
1,3.Politeknik Negeri Ujung Pandang, Makassar, South Sulawesi, 90245 Indonesia
1,2,3.Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Johor, 81300 Malaysia
a.rizal.sultan@fkegraduate.utm.my, b.wazir@fke.utm.my, c.makmur.saini@fkegraduate.utm.my

Keywords: 

ground-fault detection; unit generator-transformer; dispersion factor of the wavelet

Abstract.

 This paper proposes an approach for the detection of the single line to ground fault on a unit generator-transformer, based on the extraction of statistical parameters from wavelet transform based neural network. In the simulation, the current and voltage signals were found decomposed over wavelet analysis into several approximations and details. The simulation of the unit generatortransformer was carried out using the Sim-PowerSystem Blockset of MATLAB. The statistical parameters analysis involved measurement of the dispersion factors (range and standard deviation)of wavelet coefficients. Regarding the pattern recognition of neural networks performance, the accuracy of SLG-fault detection of neural networks was 97.45 %. The results indicated that dispersion factor feature of wavelet transforms was accurate enough in distinguishing a single line to ground-fault and normal condition for a unit generator-transformer.

Introduction

Small current Ground-Fault (GF) detection has been a major concern in protective relaying for a long time. Relaying engineers and researchers often faces the challenge of developing the most suitable technique that can detect faults with reasonable reliability to secure the run of a power system [1]. In general, a step up transformer at an electric power station can be categorized either as a unit generator-transformer configuration, a unit generator-transformer configuration with generator breaker, a cross-compound generator or a generator involving a unit transformer [2, 3]. A GF on the transmission line or busbar can disturb the system configuration of the generator. Numerous feature extraction methods based on Wavelet Transform (WT) have been used for the detection and classification of faults. Reference [4] proposes a new technique for arcing fault location by using Discrete Wavelet Transform (DWT) and wavelet networks. Fault classification procedure based on wavelet in transmission is suggested in [5]. Reference [6, 7, 8] describes the feature extraction technique based on fast WT, a fault index and wavelet power for use to detect the stator faults in the synchronous generator and transformer. Abstraction of a statistical parameter as fault detection has been used for fault detection in previous studies, but they only consisted of standard deviation, kurtosis and skewness [9]. Meanwhile, the statistical feature parameters include kurtosis, skewness, crest factor, clearance factor, shape factor, impulse factor, variance, square root amplitude value and absolute mean amplitude value to fault diagnosis in rotating machine, as defined in reference [10]. The new approach as proposed in this paper uses a dispersion factors of statistical parameters for single-line to ground (SLG) detection. In the analysis, the GF signals were calculated by using DWT. The GF detection was carried out including the analysis on significance of Range (R) and Standard Deviation (STD) of the current and voltage wavelet coefficients, as well as the detail andapproximate of wavelet coefficients to distinguish SLG-fault.A WT is an instrument which functions for the extraction of the transient signals. The best select of the mother wavelet plays an important part for detecting different types of transient signals. The finest wavelet for extracting signal information is that which can produce as many coefficients as possible to characterize the individual signals.

Conclusion

Concerning the ANN performance, the accuracy of the SLG-fault detection was 97.45 %. In this paper, analysis of a dispersion factor of a statistical parameters on wavelet coefficients as ANN input successfully detection of SLG-fault at the unit generator-transformer.

Applied Mechanics and Materials Vol. 818 (2016) pp 156-165 · JANUARY 2016
Impact Factor: 0.15 · DOI: 10.4028/www.scientific.net/AMM.818.156

Transmission Line Using Discrete Wavelet Transform and Back-Propagation Neural Network Based on Clarke’sTransformation

Makmur SAINI1,3,a, Abdullah Asuhaimi Bin MOHD ZIN 1,b,
Mohd Wazir Bin MUSTAFA1,c, Ahmad Rizal SULTAN1,3,d, Rahimuddin 2,e
1. Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru,
2. Faculty of Mechanical Engineering, Universiti Teknologi Malaysia
3. Politeknik Negeri Ujung Pandang, South Sulawesi, Indonesia 90245
a. saini@fkegraduate.utm.my, b, abdullah@fke.utm.my, c. wazir@fke.utm.my,
d. ahmad.rizal@fkegraduate.utm.my, e. rahimnav@gmail.com

Keywords: 

Wavelet Transformation, back-propagation neural network, Fault Classification, Fault

detection, Clarke’s Transformation, PSCAD/EMTDC

Abstract. 

1. paper proposes a new technique of using discrete wavelet transform (DWT) and back-propagation neural network (BPNN) based on Clarke’s transformation for fault classification and detection on a single circuit transmission line. Simulation and training process for the neural network are done by using PSCAD / EMTDC and MATLAB. Daubechies4 mother wavelet (DB4) is used to decompose the high frequency components of these signals. The wavelet transform coefficients (WTC) and wavelet energy coefficients (WEC) for classification fault and detect patterns used as input for neural network training back-propagation (BPNN). This information is then fed into a neural network to classify the fault condition. A DWT with quasi optimal performance for preprocessing stage are presented. This study also includes a comparison of the results of training BPPN and DWT with and without Clarke’s transformation, where the results show that using Clarke transformation in training will give in a smaller mean square error (MSE) and mean absolute error (MAE). The simulation also shows that the new algorithm is more reliable and accurate.

Introduction

The transmission line is vital elements in power system since this electrical energy can be transferred from are placed to another. However, this transmission line part of power system has often impaired. Most of the disturbances on the power system come from the interference on thetransmission line. Therefore, speed and accuracy in the determination of fault detection and classification of disturbances in the transmission line have a very important role in electric power systems. This paper proposes a method of using discrete wavelet transform (DWT) and back-propagation neural network (BPNN) based on Clarke’s transformation to determine the fault detection and classification on single circuit transmission line. This study presents a different approach called alpha-beta transformation based on the Clarke’s transformation, which is also a transformation of a three-phase system into a two-phase system [1, 2], where the result of the Clarke’s transformation is transformed into discrete wavelets transform.. In recent years, several methods of fault classification have been proposed. Some of them are based on artificial neural network [3,4], wavelet transform [5,6] and also combination of these techniques [7-8]. This paper proposes a novel method for fault classification in transmission lines using discrete wavelet transform (DWT) and back-propagation neural network (BPNN). The key idea of the method is to use wavelet coefficient detail and the wavelet energy coefficient of the currents as the input patterns to create a simple multi-layer perception network (MLP). This paper presents the development of a new decision algorithm for use in the protective relay for fault detection and classification. To validate this technique, the fault conditions had been simulated using EMTDC / PSCAD [9].

Conclusion

This paper developed the technique which is the linking discrete wavelet transform (DWT) and back-propagation neural network (BPNN) based on the Clarke transformation for fault classification and detect on single circuit transmission lines. This study also includes comparison on the results of training BPPN and DWT with and without Clarke’s transformation, where the results show that using the Clarke’s transformation in training will produce smaller MSE and MAE  compared with without transformation Clarke’s, among the three structures, the Architecture result was the best, which was 12 – 10 – 20 - 4. This technique applies Daubechies4 (db4) as a mother wavelet using in this paper, the performance shows that the proposed technique gives good accuracy of transient classification.

Acknowledgment

The authors would like to express their gratitude to Universiti Teknologi Malaysia, The State Polytechnic of Ujung Pandang, PT. PLN (Persero) of South Sulawesi and the Government of South

Sulawesi, Indonesia for providing the financial and technical support for this research.

Neurocomputing 168 (2015) 983–993 (ELSEVIER) Impact Factor =  2.08

New algorithm for detection and fault classification on parallel
transmission line using DWT and BPNN based on Clarke’s
transformation

Abdullah Asuhaimi Mohd Zin a, Makmur Saini a,c,*, Mohd Wazir Mustafa a,

Ahmad Rizal Sultan a,c, Rahimuddin b

a Faculty of Electrical Engineering, Universiti Teknologi Malaysia (UTM), Johor Bahru 81310, Malaysia

b Faculty of Engineering, Universitas Hasanuddin, Makassar 90245, South Sulawesi, Indonesia

c Politeknik Negeri Ujung Pandang, Makassar 90245, South Sulawesi, Indonesia

* makmur.saini@fkegraduate.utm.my 

Article history:

Received 2 July 2014

Received in revised form

23 February 2015

Accepted 10 May 2015

Communicated by Hongli Dong

Available online 19 May 2015

a b s t r a c t

This paper presents a new algorithm for fault detection and classification using discrete wavelet

transform (DWT) and back-propagation neural network (BPNN) based on Clarke’s transformation on

parallel transmission. Alpha and beta (mode) currents generated by Clarke’s transformation were used to convert the signal of discrete wavelet transform (DWT) to get the wavelet transform coefficients (WTC) and the wavelet energy coefficient (WEC). Daubechies4 (Db4) was used as a mother wavelet to decompose the high frequency components of the signal error. The simulation was performed using PSCAD/EMTDC for transmission system modeling. Simulation was performed at different locations along the transmission line with different types of fault and fault resistance, fault location and fault initial angle on a given power system model. Four statistic methods utilized are in the present study to determine the accuracy of detection and classification faults. The results show that the best Clarke transformation occurred on the configuration of 12-24-48-4, respectively. For instance, the errors usingmean square error method, the errors of BPNN, Pattern Recognition Network and Fit Network are0.03721, 0.13115 and 0.03728, respectively. This indicates that the BPNN results are the lowest error.

& 2015 Elsevier B.V. All rights reserved

Introduction

Parallel transmission lines have been widely used in modern power systems to improve power transfer, reliability and security for the transmission of electrical energy. The possibility of different

configurations of parallel lines, combined with mutual coupling effects, makes their protection a challenging problem, therefore a fast and reliable protection is needed for rapid fault detection and accurate estimation of fault location errors. This is vital to support the maintenance and restoration services to improve the continuity and reliability of supply. Therefore, a parallel transmission line requires special consideration in comparison with the single transmission line, due to the effect of mutual coupling on the parallel transmission line. It must also comply with the standards of IEEE.STD.114 2004 [1]. One major advantage of parallel transmission is availability of transmission

network during and after the fault. This paper applies discrete wavelet transform (DWT) and backpropagation neural network (BPNN) using Clarke’s transformation to determine the fault detection and classification on the parallel transmission line. This study presents a different approach called alpha-beta transformation based on Clarke’s transformation;which is also a transformation of a three-phase system into a two-phase system [2,3], where the result of the Clarke’s transformation

is changed into discrete wavelets transform. Recently, some applications of wavelet transforms in power systems are power system protection, power system transients, partial discharge, transformer protection and condition monitoring. Among all, the power system protection continues to be a major application area of wavelet transform in power systems [4], while Artificial Neural Network (ANN) continues as an efficientpattern recognition, classification and generalization tool that motivates many algorithms based on ANN to be used for fault detection and classification [5]. In recent years, the combination of ANN and wavelet has been applied on researches regarding various power system planning and operation problems [6,7], as well as power quality [8], fault classification [9], state estimation and control system [10,11]. This paper presents the development of a new decision algorithm for use in the protective relay for fault detection and classification. In this method, fault conditions are simulated using EMTDC/PSCAD [12]. Current waveforms obtained from the simulation are then extracted using Clarke transformation and wavelet transformation. Decision algorithm, therefore, is built based on back-propagation neural network. In this study, the validity of the proposed algorithm had been tested using various initial error angles, location and broken phase

errors. In addition, the results of the proposed algorithms were compared with and without wavelet transform based Clarke transformation.

Conclusion

This paper proposes a technique of using a combination of discrete wavelet transform (DWT) and back-propagation neural networks (BPPN) with and without Clarke’s transformation, in order to identify fault classification and detection on parallel circuit transmission lines. This technique applies Daubechies4 (Db4) as a mother wavelet. Various case studies have been studied, including variation distance, the initial angle and fault resistance. This study also includes comparison of the results of training BPPN and DWT with and without Clarke’s transformation, where the results show that using Clarke’s transformation will produce smaller MSE and MAE, compared to without Clarke’s transformation. Among the three structuresArchitects result was the best, which was 12-24-48-12. Four statistical methods are utilized in the present study to determine the accuracy of detection and classification faults, suggesting that the Back Propagation Neural Network results in the lowest error thus it is the best compared with Pattern Recognition Network and Fit Network.

Acknowledgments

The authors would like to express their gratitude to Universiti Teknologi Malaysia, the State Polytechnic of Ujung Pandang, PT. PLN (Persero) of South Sulawesi and the Government of South

Sulawesi, Indonesia for providing the financial and technical support for this research.

PRZEGLĄD ELEKTROTECHNICZNY, ISSN 0033-2097, R. 90 NR 11/2014

An accurate fault detection and location on transmission line  using wavelet based on Clarke’s transformation

Makmur SAINI 1,2
, Abdullah Asuhaimi Bin MOHD ZIN 1
, Mohd Wazir Bin MUSTAFA 1
Ahmad Rizal SULTAN 1,2

1.Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru
2.Politeknik Negeri Ujung Pandang, South Sulawesi, Indonesia 90245

Abstract.
 This paper presents accurate fault detection and location using wavelet based on Clarke’s transformation. This study was doneusing Clarke’s transformation method to convert current phase (three phase) signal into a two-phase current alpha and beta (current mode). The proposed method introduced the mode current to transform the signal using discrete wavelet transform (DWT) and was utilized to obtain the wavelet transform
coefficients. Analysis was also conducted for other mother wavelets. The most accurate parent was wavelet Db8, with the fastesttime of detection and the smallest error, whereas the largest error was found in Coil4 parent wavelet. The result for proposed method was compared with Db4, Sym4, Coil4 and Db8 and found to be very accurate

Streszczenie.W artykule opisano dokładnąmetodęwykrywania awarii w sieciach przesyłowych bazująca na falkowej transformacie Clarka. Sygnał trójfazowy jest przekształcany do postaci dwufazowej Za najbardziej siędo tego celu nadająca uznano falkęDb8 z najszybszym czasem wykrywania i najlepsządokładnością. Wyniki porównano z innymi typami falek. Dokładna metoda lokalizacji awarii w sieciach przesyłowych bazująca na wykorzystaniu transformaty falkowej Clarka

Keywords: Wavelet Transformation; Fault location; Fault detection; Clarke’s Transformation.

Słowa kluczowe: wykrywanie i lokalizacja awarii, transformata falkowa, transformata Clarka.
doi:10.12915/pe.2014.11.42

VOL. 9, NO. 8, AUGUST 2014  ISSN 1819-6608
ARPN Journal of Engineering and Applied Sciences
©2006-2014 Asian Research Publishing Network (ARPN). All rights reserved.

 

IDENTIFICATION AND LOCATION OF FAULT ON A TRANSMISSION  LINE USING WAVELET BASED ON CLARKE’S TRANSFORMATION 

Makmur Saini 1, 2 , Abdullah Asuhaimi Bin Mohd Zin 1, Mohd Wazir Bin Mustafa 1 and Ahmad Rizal Sultan1, 2
1 Faculty of Electrical Engineering, UniversitiTeknologi Malaysia, UTM Johor Bahru, Malaysia
2 Politeknik Negeri Ujung Pandang, South Sulawesi, Indonesia
E-Mail: makmur.saini@fkegraduate.utm.my

ABSTRACT
This paper presents a study on fault detection and location by using PSCAD to obtain the current signal in the transformation of signal interference. This study was done by using the Clarke’s transformation method to transfer the current signal phase (three phase) signal into a two-phase current, alpha current and gamma current (current Mode). New method with fault current approach is introduced in this paper. Mode current in transform signal using discrete wavelet transform (DWT) was utilized to obtain the wavelet transform coefficients (WTC)2, to determine the current time when the disturbance amplitude values (WTC) 2 reached a maximum point value. Mother wavelet was used to compare the Db4,  Sym4, Coil4 and Db8. The fault location was determined using the Clarke transformation, then transformed into wavelet, which was very accurate and thorough. Analysis was also conducted for some other mother wavelets. The error of the simulated wavelet fourth parent was found less than 2%. The most accurate parent was wavelet Db8 with the fastest time of detection and the most minor error, whereas the largest error was found in the parent wavelet Coil4.

Keywords:wavelet transformation, fault location, fault detection, Clarke’s transformation.