pp 21-25
Padm Bhushan Singh1, Rahul Sharma2, Nagendra Kr. Swarnkar3, Gaurav Kapoor4
1Department of Electrical Engineering, Suresh Gyan Vihar University, Jaipur
padm.singh123@gmail.com
2Department of Electrical Engineering, Suresh Gyan Vihar University, Jaipur
rahul.sharma@mygyanvihar.com
3Department of Electrical Engineering, Suresh Gyan Vihar University, Jaipur
nagendra.swarnkar@mygyanvihar.com
4Department of Electrical Engineering, Modi Institute of Technology, Kota, India
gaurav.kapoor019@gmail.com
Abstract—Protection of transmission lines using a quick and reliable fault detection technique is an important necessity in electrical power transmission and distribution systems to maintain reliable supply of power flow. Transmission line faults should be quickly detected and faulty phases should be accurately identified in order to distinguish between the faulty and the healthy phase (s). This paper presents a complete review on the approaches used for fault detection, faulty section identification, fault classification and fault location evaluation in different configurations of transmission lines.
Keywords—Transmission Line Protection, Fault Detection, Fault Classification, Fault Location Estimation, Wavelet Transform, Artificial Neural Network, Support Vector Machines.
I. Introduction
Techniques for the detection, classification and location estimation of faults have been intensively studied and applied over the years. Detection and location of faults on the present extra high voltage transmission lines are necessary to supply reliable flow of power. Detection, classification and location of faults on the transmission lines are done by many researchers using different methods. The advancement in artificial intelligence have facilitated the researchers across the world to bring out research with high strength in that limits of old fault detection schemes can be extended. This review paper will gives an idea about the various techniques in the fault detection and classification by picking reputed research papers. This review also gives the details of different software used by the researchers for carrying out the simulation studies.
This paper is prepared as follows: Section-I presents an introduction. Section-II presents the type of transmission line faults. Section-III describes a powerful review of various techniques used by the researchers for the transmission line protection. Section-IV presents the conclusions of the paper.
II. Transmission Line Faults
The fault in a transmission line (also known as abnormal situation) distracts the current from its intended path. A fault generates the abnormal situation in a power system. The fault is mainly divided in to two types. They are series faults (open conductor) and short circuit faults (shunt). The series (open conductor) faults may be further classified into one conductor, two conductors or three conductors open fault. Similarly, the short circuit fault is classified in to two parts. The first type of fault in known as symmetrical and another fault is known as unsymmetrical fault. Symmetrical fault is also known as three phase fault with or without ground. Unsymmetrical faults are of three type’s viz. single line to ground fault, double line to ground faults, and line to line faults. Clearing of faults is necessary for the reliable operation of a power system.
III. Review of Various Approaches used for Transmission Line Protection
A conjunction of wavelet transforms and support vector machine has been used for fault zone detection in a three phase series compensated transmission line in [1]. A fault classification and faulty section identification technique based on artificial neural network has been used for the protection of teed transmission lines in [2]. Wavelet transform based series compensated transmission line protection technique has been proposed in [3]. For the protection of parallel transmission lines, approach based on naïve bayes classifier is proposed in [4]. Wavelet transform has been used for series compensated transmission line protection [5]. A digital relaying scheme is introduced for the protection of first zone of series compensated double circuit transmission line in [6]. A fault loop model is proposed for the location of inter-circuit faults on double circuit transmission line [7]. Artificial neural network has been used for the location and classification of faults on double circuit transmission line [8].
A new Harmony search algorithm based technique has been proposed for the classification of transmission line faults in [9]. In [10], fault classification of double circuit transmission line using artificial neural network has been discussed. In [11] wavelet transform based fault detection and fault discrimination technique has been proposed for the protection of the four terminal transmission lines connected to SVC using photo voltaic and wind energy source. In [12], wavelet transform has been used for the protection of series compensated transmission lines. In [13], wavelet transform is used for the detection of high impedance arching faults in three phase transmission lines. In [14], Hilbert huang transform is applied for the protection of transmission line against faults. Support vector machine based technique has been introduced for the detection, classification and location of HVDC transmission line faults in [15]. Discrete wavelet transform has been used for the location of faults and evaluation of fault distance in three phase transmission lines [16]. In [17], fuzzy multi-sensor data fusion approach has been applied for the estimation of fault location in transmission lines. Wavelet transform is used for the classification of faults in double circuit transmission lines in [18]. In [19], performance evaluation of various types of fault detection techniques has been described for an NYSEG six phase transmission system. Adaptive Cumulative Sum-Based approach has been applied for the selection of faulty phase on double circuit transmission lines [20].
In [21], a scheme for high speed protective relaying using ANN by digital signal processing for fault classification in real time by measuring three phase voltages and currents is proposed. In [22], artificial neural network is implemented for the detection of single line to ground fault with non-linear arcing resistance. Simulation was done under load and fault conditions with various system situations. According to them proposed ANN effectively works under conditions of non-linear resistance and variable source impedance where the conventional relays fail to work. In [23], a scheme for the relaying of transmission line based on artificial neural network and feed-forward neural network has been described. The proposed technique use the value of voltage and current signals frequency spectrum for the decision making. EMTP was used for the evaluation of the performance of the proposed scheme for both symmetrical and unsymmetrical faults. Faults have been detected accurately within a quarter cycle after the inception of fault. MATLAB was used for the simulation of 160 miles transmission line.
In [24], neural network is applied for the transmission line distance protection by using samples of voltage and current signals. The effects of fault resistance and source impedance were considered during the simulation studies. EMTDC was used for the simulation of a three phase, 230 kV power system. Sampling rate of 20 samples per cycle was selected. Simulation results concluded that the pattern recognition and fault classification using artificial neural network based relay is immune to overreach and under reach, extending to 80-90%. In [25], artificial neural network has been applied using back propagation neural network for the detection and classification of faults. MATLAB and Neuro shell-2 was used for the simulation of 110 kV, 145 km long transmission line. In [26], artificial neural network is used for fault detection and classification in real time to extra high voltage transmission line for digital protection. The proposed work was based on measurement of voltage and current signals of each phase. MATLAB was used for the simulation of a 400 kV, 50 Hz and 100 km long transmission line. The test results concluded that artificial neural network works satisfactorily at a high speed for fault type identification with various fault parameters variation.
In [27], radial basis function neural network is used for the adaptive digital distance protection of transmission line which provided accurate results than old feed forward neural network using the approach of back propagation. PSCAD/EMTDC was used for the simulation of 400 kV, 128 km long transmission line. Simulation results demonstrated that maximum zone relaying with high resistance faults with reduced training data and RBFNN is suitable for the real time application. In [28], artificial neural network was used for fault detection and classification in extra high voltage transmission line protection. A combination of three types of relays viz. differential, over current and distance relays has been used for fault detection and classification. Classification of faults was done by recorded data using digital signal processor and artificial neural network. A feed forward ANN uses RMS values of phase voltage, phase current and phase voltage angle. MATLAB was used for the simulation of 600 km long transmission line with sampling frequency of 333.333 samples per cycle. In [29], ANN based fault detector using feed forward ANN (FFNN) is proposed. Simulation was done using MATLAB and EMTP software. Sampling frequency of 960 Hz using a single hidden layer with different types of filters was used. Response time of one-half of cycle had been found.
In [30], RBFNN (radial basis feed forward neural network) is applied for high speed distance relaying of transmission lines which provided good efficiency in training and computation. In [31], wavelet transform is used for the detection and location of faults in high voltage direct current transmission system. MATLAB is used for the simulation of HVDC 12-pulse system. Simulation was carried out by considering a 300 km long DC transmission line having system voltage level of 500 kV. The HVDC system test system was simulated for various types of DC as well as AC faults like short circuit on the AC side of inverter station, single line to ground, double line to ground, line to line, three phase faults and triple line to ground faults. The faults have been detected with 99% correctness. In [32], wavelet transform based technique is proposed for the transmission line fault detection in the presence of STATCOM. MATLAB is used for the simulation of transmission line test system. The proposed scheme was tested for different types of faults, fault inception angles and fault resistances with and without connecting the STATCOM. Wavelet transform has been proved as a reliable and computationally efficient tool for the detection of fault on transmission line. In [33], wavelet transform is used for the distance protection of transmission line. ATP has been used for the simulation of 200 km long transmission line test system. The real time digital relaying scheme was implemented on a DSP board. Wavelet transform has been proved as an efficient and reliable tool for fault detection. Wavelet transform decomposes the voltage and current signals for extracting the features of fundamental frequency phasors which are needed to evaluate the impedance of the fault.
In [34], wavelet transform is used for the transmission line fault classification and identification. The energy of detail coefficients of the signal and the ratio of the energy change of every type of fault have been used for distinguishing the faulty phases. MATLAB was used for the simulation of 6.6 kV transmission line test system. Numerous types of fault at various locations on transmission line were simulated. In [35], the wavelet based technique for the detection of transmission line symmetrical faults was proposed. MATLAB has been used for the simulation of 400 kV, 300 km long parallel transmission line test model. Various types of symmetrical faults at different locations on parallel transmission lines and inception angles were simulated. In [36], wavelet packet transform in conjunction with dq-axis components is used for the current differential protection of transmission lines. In their work they described and implemented hybrid scheme by localizing frequency sub-bands of dq-axis components of the differential currents using wavelet packet transform for calculating the signature of the internal faults. Simulation was carried out using MATLAB. Discrimination of internal and external faults was done by using proposed method. Fast fault clearing speed and excellent accuracy are some advantages of the above proposed technique.
In literature [37], PNN is used for the protection of series capacitor compensated multi bus extra high voltage transmission system. The classification of different types of transmission line faults is done by using PNN technique. MATLAB software was used for simulating 60 Hz, 735 kV, 600 km long capacitor compensated transmission line. Wavelet transform was utilized for the feature extraction of current signals due of its multi-resolution analysis feature. Numerous types of fault situations at various fault positions, different fault inception angles with different values of fault resistance and numerous loading conditions was considered during the simulation study. The conjunction of wavelet transform and PNN technique classifies different fault types with huge correctness. In [38], Wavelet transform is used for the multi-terminal transmission line protection combined with wind energy source. Wavelet transform has been used for the analysis of different types of faults with different fault inception angle, different values of fault impedance and at different fault location. A 200 km long, 400 kV, 1000 MVA with 5° angle difference transmission line test system is simulated using MATLAB. In [39], harmony search algorithm (HSA) is used for the classification of transmission line faults. A 230 kV, 60 Hz transmission line test system is simulated using PSCAD / EMTDC. Total 528 fault cases have been tested using HAS method. In [40], wavelet transform in combination with neural network is used for the classification of fault generated transients. The fault current features have been extracted using wavelet transform and then artificial neural network has been used for the fault classification.
Test results exemplify the accurate fault classification within 1/8th of a cycle. Simulation studies have been conducted using EMTP. In [41], discrete wavelet transform is used for the transmission line fault detection. MATLAB / Simulink software was used for the simulation study. In this work, a 25 kV, 100 km long, 50 Hz transmission line has been simulated. Fault detection was carried out using daubeshies-4 mother wavelet. In [42], principle of travelling wave based directional comparison is described. The method utilized high frequency current and low frequency voltage signals. Dyadic wavelet transform is used for the identification of fault direction and for extracting the voltage and current polarities. EMTP has been utilized for testing of 750 kV transmission system. In [43], travelling wave theory has been presented for the protection of ultra high voltage transmission line.
Dyadic wavelet transform is used for the detection of fault direction and the extraction of the polarities of voltage and current travelling waves. Simulation study has been carried out using EMTP. A 750 kV double circuit transmission line test system was simulated using EMTP. In [44], a combination of wavelet singular entropy and Euclidean norm has been applied for the detection and classification of faults. Simulation study was carried using Dlg SILENT Power Factory. The performance of proposed technique has been evaluated for different fault types, fault resistances and various values of fault inception time. In [45], wavelet singular entropy (WSE) is used for the detection and classification of transmission line faults. The classification of fault has been done using the Euclidean norm value of all the phases. A transient based protection (TBP) method has been utilized which use the transient information of the current signals. Discrete wavelet transform is used for the feature extraction of current signals. Simulation study has been carried out using Dig SILENT Power Factory.
IV. Conclusion
This paper presents a review on the techniques used for fault detection, classification and location in transmission lines. Numerous schemes are introduced and representative works are described in brief. Various types of transforms are presented. It is found that the fault detection technique depends on the feature extraction. Fault classification technique mainly uses a combination of two different approaches. For the location of faults, various schemes have been discussed in this paper. So there is a need for developing more new techniques using some advanced approaches that have superior computational accurateness and efficacy for the real time applications.
References
[1] R. P. Maheshwari, U. Parikh and B. Das, “Combined Wavelet-SVM Technique for Fault Zone Detection in a Series Compensated Transmission Line”, IEEE Transactions on Power Delivery, vol. 23, no. 4, pp. 1789-1794, October 2008.
[2] Prarthana Warlyani, Anamika Jain, A.S.Thoke, and R.N.Patel, “Fault Classification and Faulty Section Identification in Teed Transmission Circuits Using ANN”, International Journal of Computer and Electrical Engineering, vol. 3, no. 6, pp. 807-811, December 2011.
[3] Ashraf I. Megahed, A. M. Moussa, and A. E. Bayoumy, “Usage of wavelet transform in the protection of series-compensated transmission lines”, IEEE Transactions on Power Delivery, vol. 21, no.3, pp. 1213-1221, July 2006.
[4] Aleena Swetapadma and Anamika Yadav, “Protection of Parallel Transmission Lines Including Inter-Circuit Faults using Naïve Bayes Classifier”, ELSEVIER-AEJ, no.-55, pp. 1411-1419, April 2016.
[5] V. J. Pandya and S.A. Kanitkar, “A Novel Unit Protection Scheme for Protection of Series Compensated Transmission Line Using Wavelet Transform”, IEEE PES CIGRE AUPEC-2007, pp. 9-12, December 2007
[6] Ranjeet Kumar, Amrita Sinha and G. K. Choudhary, “A New Digital Distance Relaying Algorithm for First Zone Protection for Series compensated Double Circuit Transmission Lines”, Proc. IEEE-ICACC, pp. 102-106, August 2013
[7] M. M. Saha, G. Smetek, J. Izykowski, E. Rosolowski, and P. Pierz, “Location of Inter-Circuit Faults on Double Circuit Transmission Line”, Proceedings of IEEE Modern Electric Power Systems, pp. 1-7, 2015.
[8] N. Saravanan and A. Rathinam, “A Comparative Study on ANN Based Fault Location and Classification Technique for Double Circuit Transmission Line”, Proceedings of IEEE 4th International Conference on Computational Intelligence and Communication Networks, pp. 824-830, 2012.
[9] T. S. Abdelgayed, W. G. Morsi, and T. S. Sidhu, “A New Harmony Search Approach for Optimal Wavelets Applied to Fault Classification”, IEEE Transactions on Smart Grid, Vol. 9, No.-2, pp. 521-529, 2018.
[10] H. K. Zadeh, “Artificial Neural Network Approach to Fault Classification for Double Circuit Transmission Lines”, Proceedings of IEEE/PES Transmission and Distribution Conference and Exposition, pp. 859-862, 2004.
[11] Y. M. Sree, R. K. Goli, G. V. Priya, and V. Ramaiah, “A Four Terminal Transmission Line Protection by Wavelet Approach in the Presence of SVC using Hybrid Generation”, Proceedings of IEEE Innovations in Power and Advanced Computing Technologies, pp. 1-6, 2017.
[12] A. I. Megahed, A. M. Moussa, and A. E. Bayoumy, “Usage of wavelet transform in the protection of series-compensated transmission lines”, IEEE Transactions on Power Delivery, Vol. 21, No.3, pp. 1213-1221, 2006.
[13] Chul-Hwan Kim, “A Novel Fault Detection Technique of High Impedance Arching Faults in Transmission Lines Using the Wavelet Transform”, IEEE Transactions on Power Delivery, Vol. 17, No.4, pp. 921-929, Oct. 2002.
[14] Xiao’an Qin, Xiangjun Zeng, Yijie Zhang , Zhihua Wu, and Changsha, P. R., “HHT Based Non-Unit Transmission Line Protection using Travelling Wave”, Proc. IEEE-PES, pp. 1-5, July 2009.
[15] Jenifer Mariam Johnson and Anamika Yadav, “Complete Protection for Fault Detection, Classification and Location Estimation in HVDC Transmission Lines Using Support Vector Machines”, IET Science, Measurement and Technology, Vol. 11, No. 3, pp. 279-287, Nov. 2016.
[16] Sunusi Sani Adamu and Sada Iliya: Fault Location and Distance Estimation on Power Transmission Lines Using Discrete Wavelet Transform, International Journal of Advances in Engineering and Technology, Vol.1, No.-5, November 2011, pp. 69-76.
[17] Zaibin Jiao and Rundong Wu: New Method to Improve Fault Location Accuracy in Transmission Line based on Fuzzy Multi-Sensor Data Fusion, IEEE Trans. on Smart Grid, July 2018.
[18] F. Martin, J. A. Aguado, M. Medina, and J. Munoz: Classification of faults in double circuit lines using wavelet transforms, Proc. IEEE International Conference on Industrial Technology, April 2008, pp. 1-6.
[19] L. Oppel and E. Krizauskas, “Evaluation of the Performance of Line Protection Schemes on the NYSEG Six Phase Transmission System”, IEEE Transactions on Power Delivery, vol. 14, no. 1, pp. 110-115, January 1999.
[20] M. R. Noori and S. M. Shahrtash, “A Novel Faulted Phase Selector for Double Circuit Transmission Lines by Employing Adaptive Cumulative Sum-Based Method”, Proc. in International Conference on Environment and Electrical Engineering, Venice, 2012, pp. 365-370.
[21] T. Dalstein and B. Kulicke, “Neural network approach to fault classification for high speed protective relaying,” IEEE Transactions on Power Delivery, vol. 10, no.2, pp. 1002–1009, 1995.
[22] W. Qi, G. W. Swift, P. G. McLaren, and A. V. Castro, “Artificial neural network application to distance protection,” in Proceedings of the International Conference on Intelligent Systems Applications to Power Systems (ISAP ’96), pp. 226–230, February 1996.
[23] F. Zahra, B. Jeyasurya, and J. E. Quaicoe, “High-speed transmission line relaying using artificial neural networks,” Electric Power Systems Research, vol. 53, no. 3, pp. 173–179, 2000.
[24] Sanaye-Pasand M. and Kharashadi-Zadeh H., “An extended ANN-based high speed accurate distance protection algorithm”, Electrical Power and Energy Systems, vol. 28, no. 6, pp. 387-396,2006.
[25] E. B. M. Tayeb and O. A. A. A. Rhim, “Transmission line faults detection, classification and location using artificial neural network,” in Proceedings of the International Conference and Utility Exhibition on Power and Energy Systems: Issues and Prospects for Asia (ICUE ’11), pp. –5, September 2011.
[26] M. Ben Hessine, H. Jouini, and S. Chebbi, “Fault detection and classification approaches in transmission lines using artificial neural networks,” in Proceedings of the 17th IEEE Mediterranean Electro technical Conference (MELECON ’14), pp. 515– 519, April 2014.
[27] Bhalja B. R. and Maheshwari R. P., “High resistance faults on two terminal parallel transmission line by analysis, simulation studies, and an adaptive distance relaying scheme”, IEEE Trans. Power Delivery, vol. 22, no. 2, and pp.801-812, 2007.
[28] A. S. M. Altaie and J. Asumadu, “Fault detection and classification for compensating network using combination relay and ANN”, IEEE International Conference on Electro/Information Technology, May 2015.
[29] E. Vazquez, H. J. Altuve, and O. L. Chacon, “Neural network approach to fault detection in electric power systems,” in Proceedings of the IEEE International Conference on Neural Networks, vol. 4, pp. 2090–2095,Washington, DC, USA, June 1996.
[30] A. K. Pradhan, P. K. Dash, and G. Panda, “A fast and accurate distance relaying scheme using an efficient radial basis function neural network,” Electric Power Systems Research, vol. 60, no. 1, pp. 1–8, 2001.
[31] Pannala Krishna Murthy et al, “Wavelet Transform Approach for Detection and Location of Faults in HVDC System”, IEEE, December 2008.
[32] P. Venugopal Rao et al, “Detection of Transmission Line Faults in the Presence of STATCOM Using Wavelets”, IEEE India Conference (INDICON), 16-18, December 2011.
[33] A. H. Osman and O.P. Malik, “Transmission Line Distance Protection Based on Wavelet Transform”, IEEE Transactions on Power Delivery, vol. 19, no. 2, pp. 515-523, April 2004.
[34] S. A. Shaaban and Takashi Hiyama, “Transmission Line Faults Classification Using Wavelet Transform”, Proceedings of the 14th International MEPCON’10, vol. 15, pp. 532-537, December 2010.
[35] Rahul Dubey et al, “Wavelet Based Energy Function for Symmetrical Fault Detection during Power Swing”, IEEE Transaction Power Delivery, vol. 40, pp. 881-897, 2012.
[36] Adel Aktaibi et al, “A High-Speed Digital Current Differential Protection Algorithm for Power Transmission Lines in Smart Grids”, IEEE-EPEC-2016, October 2016.
[37] P. D. Rawal et al, “Accurate Fault Classification in Series Compensated Multi-Terminal Extra High Voltage Transmission Line using Probabilistic Neural Network”, ICEEOT-2016, pp. 1550-1554, 2016.
[38] Y. Manju Sree et al, “Multi-Terminal Transmission Line Protection using Wavelet Based Digital Relay in the Presence of Wind Energy Source”, ICEEOT-2016, pp. 4124-4128, 2016.
[39] Tamer S. Abdelgayed et al, “A New Harmony Search Approach for Optimal Wavelets Applied to Fault Classification”, IEEE Transactions on Smart Grid, pp. 1-9, 2016.
[40] Ahmad Abdullah, “Towards a New Paradigm for Ultrafast Transmission Line Relaying”, IEEE-PECI-2016, April 2016.
[41] Suman Devi et al, “Detection of Transmission Line Faults using Discrete Wavelet Transform”, IEEE-CASP-2016, pp. 133-138, June-2016.
[42] Xinzhou Dong et al, “Travelling Wave based Directional Comparison Protection Scheme and its Application in 750 kV Transmission Lines”, IEEE-PESGM-2015, August 2015.
[43] Xinzhou Dong et al, “Implementation and Application of Practical Travelling Wave based Directional Protection in UHV Transmission Lines”, IEEE Transactions on Power Delivery, pp. 1-9, 2015.
[44] Daniel Guillen et al, “Detection and Classification of Faults in Transmission Lines using the Maximum Wavelet Singular Value and Euclidean Norm”, IEEE-IET-GTD-2015, pp. 2294-2302, vol.-9, July-2015.
[45] Daniel Guillen et al, “Fault Detection and Classification in Transmission Line Using the Euclidian Norm of the Total WES”, IEEE/PES-PEST&D-LA-2014, November 2014.
[46] Alen BERNADIĆ and Zbigniew LEONOWICZ, “Power Line Fault Location using the Complex Space Phasor and Hilbert Huang Transform”, PRZEGLĄD ELEKTROTECHNICZNY, pp. 204-207, 2011.
[47] Xiao’an Qin, Xiangjun Zeng, Yijie Zhang , Zhihua Wu, and Changsha, P. R., “HHT Based Non-Unit Transmission Line Protection using Travelling Wave”, IEEE-PES, pp. 1-5, July 2009.
[48] Aashi Manglik, Wei Li, and S. U. Ahmad, “Fault Detection in Power System using the Hilbert Huang Transform”, IEEE-CCECE, pp. 1-4, May 2016.
[49] Zahra Moravej, Masoud Movahhedneya, Ghadir Radman, and Mohammad Pazoki, “Effective Fault Location Technique in Three Terminal Transmission Line using Hilbert and DWT”, IEEE-ICEIT, pp. 170-176, May 2015.
[50] Premlata Jena and Ashok Kumar Pradhan, “Detection of High Impedance Fault”, IEEE- Inter. Conf., January 2012.