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Published in: Energy Systems 1/2015

01-03-2015 | Review

Methodologies in power systems fault detection and diagnosis

Authors: Saad Abdul Aleem, Nauman Shahid, Ijaz Haider Naqvi

Published in: Energy Systems | Issue 1/2015

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Abstract

Power systems frequently experience variations in their operation, which are mostly manifested as transmission line faults. Over the past decade, various techniques of fault diagnosis have been developed to ensure reliable and stable operation of power systems. This paper reviews the current literature on advanced application of fault diagnosis in power systems. Application of different fault diagnosis schemes is presented, with emphasis on reliable fault detection and classification of power system faults. The motivation behind applications of emerging process history, or pattern recognition, techniques in power system fault diagnosis has been reviewed. An extensive review of advanced mathematical techniques, in pattern recognition methods, involving wavelet transform, artificial neural networks and support vector machines has been presented. The paper also introduces a novel unsupervised technique of quarter-sphere support vector machine for power system fault detection and classification and reviews its application as future research in the developing area of fault diagnosis.

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Literature
1.
go back to reference Karthikeyan, M., Malathi, V.: Wavelet-support vector machine approach for classification of power quality disturbances. Int. J. Recent Trends Eng. 1(3) (2009) Karthikeyan, M., Malathi, V.: Wavelet-support vector machine approach for classification of power quality disturbances. Int. J. Recent Trends Eng. 1(3) (2009)
2.
go back to reference Kalyani, S., Swarup, K.: Binary svm approach for security assessment and classification in power systems. In: India Conference (INDICON), Annual IEEE, pp. 1–4. IEEE Press, New York (2009) Kalyani, S., Swarup, K.: Binary svm approach for security assessment and classification in power systems. In: India Conference (INDICON), Annual IEEE, pp. 1–4. IEEE Press, New York (2009)
3.
go back to reference Koval, D.: Power system disturbance patterns. IEEE Trans. Ind. Appl. 26(3), 556–562 (1990)CrossRef Koval, D.: Power system disturbance patterns. IEEE Trans. Ind. Appl. 26(3), 556–562 (1990)CrossRef
4.
go back to reference Santoso, S., Powers, E., Grady, W., Hofmann, P.: Power quality assessment via wavelet transform analysis. IEEE Trans. Power Deliv. 11(2), 924–930 (1996)CrossRef Santoso, S., Powers, E., Grady, W., Hofmann, P.: Power quality assessment via wavelet transform analysis. IEEE Trans. Power Deliv. 11(2), 924–930 (1996)CrossRef
5.
go back to reference Stones, J., Collinson, A.: Power quality. Power Eng. J. 15(2), 58–64 (2001)CrossRef Stones, J., Collinson, A.: Power quality. Power Eng. J. 15(2), 58–64 (2001)CrossRef
6.
go back to reference Andersson, G.: Modelling and analysis of electric power systems. EEH-Power Systems Laboratory, ETH, Zürich (2004) Andersson, G.: Modelling and analysis of electric power systems. EEH-Power Systems Laboratory, ETH, Zürich (2004)
7.
go back to reference Tleis, N.: Power systems modelling and fault analysis: theory and practice. Newnes (2007) Tleis, N.: Power systems modelling and fault analysis: theory and practice. Newnes (2007)
8.
go back to reference Elhaffar, A.: Power transmission line fault location based on current traveling waves. Praca doktorska, Department of Electrical Engineering, Helsinki University of Technology, Helsinki (2008) Elhaffar, A.: Power transmission line fault location based on current traveling waves. Praca doktorska, Department of Electrical Engineering, Helsinki University of Technology, Helsinki (2008)
9.
go back to reference Bunnoon, P.: Fault detection approaches to power system: state-of-the-art article reviews for searching a new approach in the future. IJECE 3(4), 553–560 (2013)CrossRef Bunnoon, P.: Fault detection approaches to power system: state-of-the-art article reviews for searching a new approach in the future. IJECE 3(4), 553–560 (2013)CrossRef
10.
go back to reference Singh, M., Panigrahi, B., Maheshwari, R.: Transmission line fault detection and classification. In: International Conference on Emerging Trends in Electrical and Computer Technology (ICETECT), pp. 15–22. IEEE Press, New York (2011) Singh, M., Panigrahi, B., Maheshwari, R.: Transmission line fault detection and classification. In: International Conference on Emerging Trends in Electrical and Computer Technology (ICETECT), pp. 15–22. IEEE Press, New York (2011)
11.
go back to reference Farhat, I.: Fault detection, classification and location in transmission line systems using neural networks. Ph.D. dissertation, Concordia University (2003) Farhat, I.: Fault detection, classification and location in transmission line systems using neural networks. Ph.D. dissertation, Concordia University (2003)
12.
go back to reference Ayyagari, S.: Artificial neural network based fault location for transmission line (2011) Ayyagari, S.: Artificial neural network based fault location for transmission line (2011)
13.
go back to reference Jiang, J., Yang, J., Lin, Y., Liu, C., Ma, J.: An adaptive pmu based fault detection/location technique for transmission lines. I. Theory and algorithms. IEEE Trans. Power Deliv. 15(2), 486–493 (2000)CrossRef Jiang, J., Yang, J., Lin, Y., Liu, C., Ma, J.: An adaptive pmu based fault detection/location technique for transmission lines. I. Theory and algorithms. IEEE Trans. Power Deliv. 15(2), 486–493 (2000)CrossRef
14.
go back to reference Xiangjun, Z., Yuanyuan, W., Yao, X.: Faults detection for power systems, pp. 71–118 (2010) Xiangjun, Z., Yuanyuan, W., Yao, X.: Faults detection for power systems, pp. 71–118 (2010)
15.
go back to reference Sanaye-Pasand, M., Khorashadi-Zadeh, H.: Transmission line fault detection and phase selection using ann. In: International Conference on Power Systems Transients, New Orleans (2003) Sanaye-Pasand, M., Khorashadi-Zadeh, H.: Transmission line fault detection and phase selection using ann. In: International Conference on Power Systems Transients, New Orleans (2003)
16.
go back to reference Gu, I., Styvaktakis, E.: Bridge the gap: signal processing for power quality applications. Electr. Power Syst. Res. 66(1), 83–96 (2003)CrossRef Gu, I., Styvaktakis, E.: Bridge the gap: signal processing for power quality applications. Electr. Power Syst. Res. 66(1), 83–96 (2003)CrossRef
17.
go back to reference Das, D., Singh, N., Sinha, A.: A comparison of fourier transform and wavelet transform methods for detection and classification of faults on transmission lines. In: 2006 IEEE Power India Conference, p. 7. IEEE Press, New York (2006) Das, D., Singh, N., Sinha, A.: A comparison of fourier transform and wavelet transform methods for detection and classification of faults on transmission lines. In: 2006 IEEE Power India Conference, p. 7. IEEE Press, New York (2006)
18.
go back to reference Abdollahi, A., Seyedtabaii, S.: Comparison of fourier and wavelet transform methods for transmission line fault classification. In: 4th International Power Engineering and Optimization Conference (PEOCO), pp. 579–584. IEEE Press, New York (2010) Abdollahi, A., Seyedtabaii, S.: Comparison of fourier and wavelet transform methods for transmission line fault classification. In: 4th International Power Engineering and Optimization Conference (PEOCO), pp. 579–584. IEEE Press, New York (2010)
19.
go back to reference Li, K., Lai, L., David, A.: Application of artificial neural network in fault location technique. In: Proceedings. DRPT 2000. International Conference on Electric Utility Deregulation and Restructuring and Power Technologies, pp. 226–231. IEEE Press, New York (2000) Li, K., Lai, L., David, A.: Application of artificial neural network in fault location technique. In: Proceedings. DRPT 2000. International Conference on Electric Utility Deregulation and Restructuring and Power Technologies, pp. 226–231. IEEE Press, New York (2000)
20.
go back to reference Ekici, S., Yildirim, S., Poyraz, M.: Energy and entropy-based feature extraction for locating fault on transmission lines by using neural network and wavelet packet decomposition. Expert Syst. Appl. 34(4), 2937–2944 (2008)CrossRef Ekici, S., Yildirim, S., Poyraz, M.: Energy and entropy-based feature extraction for locating fault on transmission lines by using neural network and wavelet packet decomposition. Expert Syst. Appl. 34(4), 2937–2944 (2008)CrossRef
21.
go back to reference Isermann, R.: Supervision, fault-detection and fault-diagnosis methods-an introduction. Control Eng. Pract. 5(5), 639–652 (1997)CrossRef Isermann, R.: Supervision, fault-detection and fault-diagnosis methods-an introduction. Control Eng. Pract. 5(5), 639–652 (1997)CrossRef
22.
go back to reference Venkatasubramanian, V., Rengaswamy, R., Kavuri, S., Yin, K.: A review of process fault detection and diagnosis: Part I: Quantitative model-based methods. Comput. Chem. Eng. 27(3), 293–311 (2003)CrossRef Venkatasubramanian, V., Rengaswamy, R., Kavuri, S., Yin, K.: A review of process fault detection and diagnosis: Part I: Quantitative model-based methods. Comput. Chem. Eng. 27(3), 293–311 (2003)CrossRef
23.
go back to reference Katipamula, S., Brambley, M.: Review article: methods for fault detection, diagnostics, and prognostics for building systemsa review, part I. HVAC&R Res. 11(1), 3–25 (2005)CrossRef Katipamula, S., Brambley, M.: Review article: methods for fault detection, diagnostics, and prognostics for building systemsa review, part I. HVAC&R Res. 11(1), 3–25 (2005)CrossRef
24.
go back to reference Venkatasubramanian, V., Rengaswamy, R., Kavuri, S., Yin, K.: A review of process fault detection and diagnosis: Part II: Qualitative models and search strategies. Comput. Chem. Eng. 27(3), 313–326 (2003)CrossRef Venkatasubramanian, V., Rengaswamy, R., Kavuri, S., Yin, K.: A review of process fault detection and diagnosis: Part II: Qualitative models and search strategies. Comput. Chem. Eng. 27(3), 313–326 (2003)CrossRef
25.
go back to reference Venkatasubramanian, V., Rengaswamy, R., Kavuri, S., Yin, K.: A review of process fault detection and diagnosis: Part III: Process history based methods. Comput. Chem. Eng. 27(3), 327–346 (2003)CrossRef Venkatasubramanian, V., Rengaswamy, R., Kavuri, S., Yin, K.: A review of process fault detection and diagnosis: Part III: Process history based methods. Comput. Chem. Eng. 27(3), 327–346 (2003)CrossRef
26.
go back to reference Sorsa, T., Koivo, H., Koivisto, H.: Neural networks in process fault diagnosis. IEEE Trans. Syst. Man Cybern. 21(4), 815–825 (1991)CrossRef Sorsa, T., Koivo, H., Koivisto, H.: Neural networks in process fault diagnosis. IEEE Trans. Syst. Man Cybern. 21(4), 815–825 (1991)CrossRef
27.
go back to reference Frank, P., Köppen-Seliger, B.: New developments using AI in fault diagnosis. Eng. Appl. Artif. Intell. 10(1), 3–14 (1997)CrossRef Frank, P., Köppen-Seliger, B.: New developments using AI in fault diagnosis. Eng. Appl. Artif. Intell. 10(1), 3–14 (1997)CrossRef
28.
go back to reference Wang, C.: Methodologies and algorithms for fault locators in modern power systems. Ph.D. dissertation, University of the West of England, UK (2002) Wang, C.: Methodologies and algorithms for fault locators in modern power systems. Ph.D. dissertation, University of the West of England, UK (2002)
29.
go back to reference Swarup, K., Chandrasekharaiah, H.: Fault detection and diagnosis of power systems using artificial neural networks. In: Proceedings of the First International Forum on Applications of Neural Networks to Power Systems, pp. 102–106. IEEE Press, New York (1991) Swarup, K., Chandrasekharaiah, H.: Fault detection and diagnosis of power systems using artificial neural networks. In: Proceedings of the First International Forum on Applications of Neural Networks to Power Systems, pp. 102–106. IEEE Press, New York (1991)
30.
go back to reference Vazquez, E., Altuve, H., Chacon, O.: Neural network approach to fault detection in electric power systems. In: IEEE International Conference on Neural Networks, vol. 4, pp. 2090–2095. IEEE Press, New York (1996) Vazquez, E., Altuve, H., Chacon, O.: Neural network approach to fault detection in electric power systems. In: IEEE International Conference on Neural Networks, vol. 4, pp. 2090–2095. IEEE Press, New York (1996)
31.
go back to reference Rodrigues, M., Souza, J., Schilling, M., Do Coutto Filho, M.: Fault diagnosis in electrical power systems using artificial neural networks. In: PowerTech Budapest 99: International Conference on Electric Power Engineering, p. 130. IEEE Press, New York (1999) Rodrigues, M., Souza, J., Schilling, M., Do Coutto Filho, M.: Fault diagnosis in electrical power systems using artificial neural networks. In: PowerTech Budapest 99: International Conference on Electric Power Engineering, p. 130. IEEE Press, New York (1999)
32.
go back to reference Stergiopoulos, K., Pipe, A., Nouri, H.: Intelligent control architectures for fault diagnosis in electrical power distribution networks. In: IEEE International Symposium on Intelligent Control, pp. 569–573. IEEE Press, New York (2003) Stergiopoulos, K., Pipe, A., Nouri, H.: Intelligent control architectures for fault diagnosis in electrical power distribution networks. In: IEEE International Symposium on Intelligent Control, pp. 569–573. IEEE Press, New York (2003)
33.
go back to reference Uyar, M., Yildirim, S., Gencoglu, M.: An effective wavelet-based feature extraction method for classification of power quality disturbance signals. Electr. Power Syst. Res. 78(10), 1747–1755 (2008)CrossRef Uyar, M., Yildirim, S., Gencoglu, M.: An effective wavelet-based feature extraction method for classification of power quality disturbance signals. Electr. Power Syst. Res. 78(10), 1747–1755 (2008)CrossRef
34.
go back to reference Yusuff, A., Jimoh, A., Munda, J.: Determinant-based feature extraction for fault detection and classification for power transmission lines. Gener. Transm. Distrib. IET 5(12), 1259–1267 (2011)CrossRef Yusuff, A., Jimoh, A., Munda, J.: Determinant-based feature extraction for fault detection and classification for power transmission lines. Gener. Transm. Distrib. IET 5(12), 1259–1267 (2011)CrossRef
35.
go back to reference Phadke, A., Thorp, J.: Computer Relaying for Power Systems. Wiley, New York (1992) Phadke, A., Thorp, J.: Computer Relaying for Power Systems. Wiley, New York (1992)
36.
go back to reference Bollen, M., Gu, I., Axelberg, P., Styvaktakis, E.: Classification of underlying causes of power quality disturbances: deterministic versus statistical methods. EURASIP J. Adv. Signal Process. (2007) Bollen, M., Gu, I., Axelberg, P., Styvaktakis, E.: Classification of underlying causes of power quality disturbances: deterministic versus statistical methods. EURASIP J. Adv. Signal Process. (2007)
37.
go back to reference Huang, S., Hsieh, C.: Feasibility of fractal-based methods for visualization of power system disturbances. Int. J. Electr. Power Energy Syst. 23(1), 31–36 (2001)CrossRef Huang, S., Hsieh, C.: Feasibility of fractal-based methods for visualization of power system disturbances. Int. J. Electr. Power Energy Syst. 23(1), 31–36 (2001)CrossRef
38.
go back to reference Dash, P., Panigrahi, B., Panda, G.: Power quality analysis using s-transform. IEEE Trans. Power Deliv. 18(2), 406–411 (2003)CrossRef Dash, P., Panigrahi, B., Panda, G.: Power quality analysis using s-transform. IEEE Trans. Power Deliv. 18(2), 406–411 (2003)CrossRef
39.
go back to reference Wang, M., Ochenkowski, P., Mamishev, A.: Classification of power quality disturbances using time-frequency ambiguity plane and neural networks. In: Power Engineering Society Summer Meeting, vol. 2, pp. 1246–1251. IEEE Press, New York (2001) Wang, M., Ochenkowski, P., Mamishev, A.: Classification of power quality disturbances using time-frequency ambiguity plane and neural networks. In: Power Engineering Society Summer Meeting, vol. 2, pp. 1246–1251. IEEE Press, New York (2001)
40.
go back to reference Chowdhury, F., Christensen, J., Aravena, J.: Power system fault detection and state estimation using kalman filter with hypothesis testing. IEEE Trans. Power Deliv. 6(3), 1025–1030 (1991)CrossRef Chowdhury, F., Christensen, J., Aravena, J.: Power system fault detection and state estimation using kalman filter with hypothesis testing. IEEE Trans. Power Deliv. 6(3), 1025–1030 (1991)CrossRef
41.
go back to reference Heydt, G., Fjeld, P., Liu, C., Pierce, D., Tu, L., Hensley, G.: Applications of the windowed FFT to electric power quality assessment. IEEE Trans. Power Deliv. 14(4), 1411–1416 (1999)CrossRef Heydt, G., Fjeld, P., Liu, C., Pierce, D., Tu, L., Hensley, G.: Applications of the windowed FFT to electric power quality assessment. IEEE Trans. Power Deliv. 14(4), 1411–1416 (1999)CrossRef
42.
go back to reference Schegan, C.: A hardware/software platform for fault detection and identification in electric power distribution systems for testing various detection schemes. Ph.D. dissertation, Drexel University (2008) Schegan, C.: A hardware/software platform for fault detection and identification in electric power distribution systems for testing various detection schemes. Ph.D. dissertation, Drexel University (2008)
43.
go back to reference Falifla, A., AbuBeker, H.: On line fault detection for transmission line using power system stabilizer signals. Ph.D. dissertation, Universiti Teknologi Malaysia, Faculty of Electrical Engineering (2007) Falifla, A., AbuBeker, H.: On line fault detection for transmission line using power system stabilizer signals. Ph.D. dissertation, Universiti Teknologi Malaysia, Faculty of Electrical Engineering (2007)
44.
go back to reference Mao, P., Aggarwal, R.: A novel approach to the classification of the transient phenomena in power transformers using combined wavelet transform and neural network. IEEE Trans. Power Deliv. 16(4), 654–660 (2001)CrossRef Mao, P., Aggarwal, R.: A novel approach to the classification of the transient phenomena in power transformers using combined wavelet transform and neural network. IEEE Trans. Power Deliv. 16(4), 654–660 (2001)CrossRef
45.
go back to reference Santoso, S., Powers, E., Grady, W., Parsons, A.: Power quality disturbance waveform recognition using wavelet-based neural classifier. I. Theoretical foundation. IEEE Trans. Power Deliv. 15(1), 222–228 (2000)CrossRef Santoso, S., Powers, E., Grady, W., Parsons, A.: Power quality disturbance waveform recognition using wavelet-based neural classifier. I. Theoretical foundation. IEEE Trans. Power Deliv. 15(1), 222–228 (2000)CrossRef
46.
go back to reference Santoso, S., Powers, E., Grady, W., Parsons, A.: Power quality disturbance waveform recognition using wavelet-based neural classifier. II. Application. IEEE Trans. Power Deliv. 15(1), 229–235 (2000)CrossRef Santoso, S., Powers, E., Grady, W., Parsons, A.: Power quality disturbance waveform recognition using wavelet-based neural classifier. II. Application. IEEE Trans. Power Deliv. 15(1), 229–235 (2000)CrossRef
47.
go back to reference Bhowmik, P., Purkait, P., Bhattacharya, K.: A novel wavelet assisted neural network for transmission line fault analysis. In: Annual IEEE India Conference (INDICON), vol. 1, pp. 223–228. IEEE Press, New York (2008) Bhowmik, P., Purkait, P., Bhattacharya, K.: A novel wavelet assisted neural network for transmission line fault analysis. In: Annual IEEE India Conference (INDICON), vol. 1, pp. 223–228. IEEE Press, New York (2008)
48.
go back to reference Chun-Lin, L.: A tutorial of the wavelet transform (2010) Chun-Lin, L.: A tutorial of the wavelet transform (2010)
49.
go back to reference Borras, D., Castilla, M., Moreno, N., Montano, J.: Wavelet and neural structure: a new tool for diagnostic of power system disturbances. IEEE Trans. Ind. Appl. 37(1), 184–190 (2001)CrossRef Borras, D., Castilla, M., Moreno, N., Montano, J.: Wavelet and neural structure: a new tool for diagnostic of power system disturbances. IEEE Trans. Ind. Appl. 37(1), 184–190 (2001)CrossRef
50.
go back to reference Chanda, D., Kishore, N., Sinha, A.: Application of wavelet multiresolution analysis for classification of faults on transmission lines. In: TENCON 2003. Conference on Convergent Technologies for Asia-Pacific Region, vol. 4, pp. 1464–1469. IEEE Press, New York (2003) Chanda, D., Kishore, N., Sinha, A.: Application of wavelet multiresolution analysis for classification of faults on transmission lines. In: TENCON 2003. Conference on Convergent Technologies for Asia-Pacific Region, vol. 4, pp. 1464–1469. IEEE Press, New York (2003)
51.
go back to reference Kale, V., Bhide, S., Bedekar, P., Mohan, G.: Detection and classification of faults on parallel transmission lines using wavelet transform and neural network. Int. J. Electr. Electron. Eng. 1(4), 364–368 (2008) Kale, V., Bhide, S., Bedekar, P., Mohan, G.: Detection and classification of faults on parallel transmission lines using wavelet transform and neural network. Int. J. Electr. Electron. Eng. 1(4), 364–368 (2008)
52.
go back to reference Parikh, U., Das, B., Maheshwari, R.P.: Combined wavelet-svm technique for fault zone detection in a series compensated transmission line. IEEE Trans. Power Deliv. 23(4), 1789–1794 (2008)CrossRef Parikh, U., Das, B., Maheshwari, R.P.: Combined wavelet-svm technique for fault zone detection in a series compensated transmission line. IEEE Trans. Power Deliv. 23(4), 1789–1794 (2008)CrossRef
53.
go back to reference Megahed, A., Moussa, A., Bayoumy, A.: Usage of wavelet transform in the protection of series-compensated transmission lines. IEEE Trans. Power Deliv. 21(3), 1213–1221 (2006)CrossRef Megahed, A., Moussa, A., Bayoumy, A.: Usage of wavelet transform in the protection of series-compensated transmission lines. IEEE Trans. Power Deliv. 21(3), 1213–1221 (2006)CrossRef
54.
go back to reference Zhang, N., Kezunovic, M.: Transmission line boundary protection using wavelet transform and neural network. IEEE Trans. Power Deliv. 22(2), 859–869 (2007)CrossRefMATH Zhang, N., Kezunovic, M.: Transmission line boundary protection using wavelet transform and neural network. IEEE Trans. Power Deliv. 22(2), 859–869 (2007)CrossRefMATH
55.
go back to reference Mahmood, F., Qureshi, S., Kamran, M.: Application of wavelet multi-resolution analysis & perceptron neural networks for classification of transients on transmission line. In: Australasian Universities Power Engineering Conference (AUPEC’08), pp. 1–5. IEEE Press, New York (2008) Mahmood, F., Qureshi, S., Kamran, M.: Application of wavelet multi-resolution analysis & perceptron neural networks for classification of transients on transmission line. In: Australasian Universities Power Engineering Conference (AUPEC’08), pp. 1–5. IEEE Press, New York (2008)
56.
go back to reference Gayathri, K., Kumarappan, N.: Comparative study of fault identification and classification on ehv lines using discrete wavelet transform and fourier transform based ann. Int. J. Electr. Comput. Syst. Eng. 2(2), 125–136 (2008) Gayathri, K., Kumarappan, N.: Comparative study of fault identification and classification on ehv lines using discrete wavelet transform and fourier transform based ann. Int. J. Electr. Comput. Syst. Eng. 2(2), 125–136 (2008)
57.
go back to reference Gaouda, A., Kanoun, S., Salama, M., Chikhani, A.: Pattern recognition applications for power system disturbance classification. IEEE Trans. Power Deliv. 17(3), 677–683 (2002)CrossRef Gaouda, A., Kanoun, S., Salama, M., Chikhani, A.: Pattern recognition applications for power system disturbance classification. IEEE Trans. Power Deliv. 17(3), 677–683 (2002)CrossRef
58.
go back to reference Huang, J., Hu, X., Geng, X.: An intelligent fault diagnosis method of high voltage circuit breaker based on improved emd energy entropy and multi-class support vector machine. Electr. Power Syst. Res. 81(2), 400–407 (2011)CrossRef Huang, J., Hu, X., Geng, X.: An intelligent fault diagnosis method of high voltage circuit breaker based on improved emd energy entropy and multi-class support vector machine. Electr. Power Syst. Res. 81(2), 400–407 (2011)CrossRef
59.
go back to reference Janik, P., Lobos, T.: Automated classification of power-quality disturbances using SVM and RBF networks. IEEE Trans. Power Deliv. 21(3), 1663–1669 (2006)CrossRef Janik, P., Lobos, T.: Automated classification of power-quality disturbances using SVM and RBF networks. IEEE Trans. Power Deliv. 21(3), 1663–1669 (2006)CrossRef
60.
go back to reference Saha, M., Rosolowski, E., Izykowski, J.: Artificial intelligent application to power system protection. Department of Electrical Engineering, University of Technology, Poland (1999) Saha, M., Rosolowski, E., Izykowski, J.: Artificial intelligent application to power system protection. Department of Electrical Engineering, University of Technology, Poland (1999)
62.
go back to reference Aggarwal, R., Song, Y.: Artificial neural networks in power systems. I. General introduction to neural computing. Power Eng. J. 11(3), 129–134 (1997)CrossRef Aggarwal, R., Song, Y.: Artificial neural networks in power systems. I. General introduction to neural computing. Power Eng. J. 11(3), 129–134 (1997)CrossRef
64.
go back to reference Aggarwal, R., Song, Y.: Artificial neural networks in power systems. II. Types of artificial neural networks. Power Eng. J. 12(1), 41–47 (1998)CrossRef Aggarwal, R., Song, Y.: Artificial neural networks in power systems. II. Types of artificial neural networks. Power Eng. J. 12(1), 41–47 (1998)CrossRef
65.
go back to reference Aggarwal, R., Song, Y.: Artificial neural networks in power systems. III. Examples of applications in power systems. Power Eng. J. 12(6), 279–287 (1998)CrossRef Aggarwal, R., Song, Y.: Artificial neural networks in power systems. III. Examples of applications in power systems. Power Eng. J. 12(6), 279–287 (1998)CrossRef
66.
go back to reference Haque, M., Kashtiban, A.: Application of neural networks in power system: a review. Trans. Eng. Comput. Technol. 6 (2000) Haque, M., Kashtiban, A.: Application of neural networks in power system: a review. Trans. Eng. Comput. Technol. 6 (2000)
67.
go back to reference Geethanjali, M., Priya, K.: Combined wavelet transfoms and neural network (WNN) based fault detection and classification in transmission lines. In: INCACEC. International Conference on Control, Automation, Communication and Energy Conservation, pp. 1–7. IEEE Press, New York (2009) Geethanjali, M., Priya, K.: Combined wavelet transfoms and neural network (WNN) based fault detection and classification in transmission lines. In: INCACEC. International Conference on Control, Automation, Communication and Energy Conservation, pp. 1–7. IEEE Press, New York (2009)
68.
go back to reference Koley, E., Jain, A., Thoke, A., Jain, A., Ghosh, S.: Detection and classification of faults on six phase transmission line using ANN. In: 2nd International Conference on Computer and Communication Technology (ICCCT), pp. 100–103. IEEE Press, New York (2011) Koley, E., Jain, A., Thoke, A., Jain, A., Ghosh, S.: Detection and classification of faults on six phase transmission line using ANN. In: 2nd International Conference on Computer and Communication Technology (ICCCT), pp. 100–103. IEEE Press, New York (2011)
69.
go back to reference Kasinathan, K.: Power system fault detection and classification by wavelet transforms and adaptive resonance theory neural networks (2007) Kasinathan, K.: Power system fault detection and classification by wavelet transforms and adaptive resonance theory neural networks (2007)
70.
go back to reference Srinivas, H., Srinivasan, K., Umesh, K.: Application of artificial neural network and wavelet transform for vibration analysis of combined faults of unbalances and shaft bow (2010) Srinivas, H., Srinivasan, K., Umesh, K.: Application of artificial neural network and wavelet transform for vibration analysis of combined faults of unbalances and shaft bow (2010)
71.
go back to reference Mahanty, R., Gupta, P.: Application of rbf neural network to fault classification and location in transmission lines. In: IEE Proceedings Generation, Transmission and Distribution, vol. 151, no. 2, pp. 201–212. IET, London (2004) Mahanty, R., Gupta, P.: Application of rbf neural network to fault classification and location in transmission lines. In: IEE Proceedings Generation, Transmission and Distribution, vol. 151, no. 2, pp. 201–212. IET, London (2004)
72.
go back to reference Dash, P., Pradhan, A., Panda, G.: Application of minimal radial basis function neural network to distance protection. IEEE Trans. Power Deliv. 16(1), 68–74 (Jan 2001) Dash, P., Pradhan, A., Panda, G.: Application of minimal radial basis function neural network to distance protection. IEEE Trans. Power Deliv. 16(1), 68–74 (Jan 2001)
73.
go back to reference Lin, W., Yang, C., Lin, J., Tsay, M.: A fault classification method by rbf neural network with ols learning procedure. In: PICA 2001. Innovative Computing for Power Electric Energy Meets the Market. 22nd IEEE Power Engineering Society International Conference on Power Industry Computer Applications, pp. 118–121 (2001) Lin, W., Yang, C., Lin, J., Tsay, M.: A fault classification method by rbf neural network with ols learning procedure. In: PICA 2001. Innovative Computing for Power Electric Energy Meets the Market. 22nd IEEE Power Engineering Society International Conference on Power Industry Computer Applications, pp. 118–121 (2001)
74.
go back to reference Ekici, S., Yildirim, S., Poyraz, M.: A neural network based approach for transmission line faults (2007) Ekici, S., Yildirim, S., Poyraz, M.: A neural network based approach for transmission line faults (2007)
75.
go back to reference Pradhan, A., Mohanty, S., Routray, A.: Neural fault classifier for transmission line protection-a modular approach. In: Power Engineering Society General Meeting, p. 5. IEEE Press, New York (2006) Pradhan, A., Mohanty, S., Routray, A.: Neural fault classifier for transmission line protection-a modular approach. In: Power Engineering Society General Meeting, p. 5. IEEE Press, New York (2006)
76.
go back to reference Wahab, N., Mohamed, A., Hussain, A.: Transient stability assessment of a power system using PNN and LS-SVM methods. J. Appl. Sci. 7(21), 3208–3216 (2007)CrossRef Wahab, N., Mohamed, A., Hussain, A.: Transient stability assessment of a power system using PNN and LS-SVM methods. J. Appl. Sci. 7(21), 3208–3216 (2007)CrossRef
77.
go back to reference Gaing, Z.: Wavelet-based neural network for power disturbance recognition and classification. IEEE Trans. Power Deliv. 19(4), 1560–1568 (2004)CrossRef Gaing, Z.: Wavelet-based neural network for power disturbance recognition and classification. IEEE Trans. Power Deliv. 19(4), 1560–1568 (2004)CrossRef
79.
go back to reference Hand, D., Mannila, H., Smyth, P.: Principles of Data Mining. MIT Press, New York (2001) Hand, D., Mannila, H., Smyth, P.: Principles of Data Mining. MIT Press, New York (2001)
80.
go back to reference Kecman, V.: Support vector machines-an introduction. Support vector machines: theory and applications, pp. 605–605 (2005) Kecman, V.: Support vector machines-an introduction. Support vector machines: theory and applications, pp. 605–605 (2005)
81.
go back to reference Abe, S.: Support vector machines for pattern classification. Springer, New York (2010) Abe, S.: Support vector machines for pattern classification. Springer, New York (2010)
82.
go back to reference Steinwart, I., Christmann, A.: Support vector machines (information science & statistics). Recherche 67, 02 (2008) Steinwart, I., Christmann, A.: Support vector machines (information science & statistics). Recherche 67, 02 (2008)
83.
go back to reference Melgani, F., Bazi, Y.: Classification of electrocardiogram signals with support vector machines and particle swarm optimization. IEEE Trans. Inf. Technol. Biomed. 12(5), 667–677 (2008)CrossRef Melgani, F., Bazi, Y.: Classification of electrocardiogram signals with support vector machines and particle swarm optimization. IEEE Trans. Inf. Technol. Biomed. 12(5), 667–677 (2008)CrossRef
84.
go back to reference Guler, I., Ubeyli, E.: Multiclass support vector machines for EEG-signals classification. IEEE Trans. Inf. Technol. Biomed. 11(2), 117–126 (2007)CrossRef Guler, I., Ubeyli, E.: Multiclass support vector machines for EEG-signals classification. IEEE Trans. Inf. Technol. Biomed. 11(2), 117–126 (2007)CrossRef
85.
go back to reference Chunling, C., Tongyu, X., Zailin, P., Ye, Y.: Power quality disturbances classification based on multi-class classification SVM. In: 2nd International Conference on Power Electronics and Intelligent Transportation System (PEITS), vol. 1, pp. 290–294. IEEE Press, New York (2009) Chunling, C., Tongyu, X., Zailin, P., Ye, Y.: Power quality disturbances classification based on multi-class classification SVM. In: 2nd International Conference on Power Electronics and Intelligent Transportation System (PEITS), vol. 1, pp. 290–294. IEEE Press, New York (2009)
86.
go back to reference Nguyen, X., Huang, L., Joseph, A.: Support vector machines, data reduction, and approximate kernel matrices. Mach. Learn. Knowl. Discov. Databases 137–153 (2008) Nguyen, X., Huang, L., Joseph, A.: Support vector machines, data reduction, and approximate kernel matrices. Mach. Learn. Knowl. Discov. Databases 137–153 (2008)
88.
go back to reference Yeung, D., Wang, D., Ng, W., Tsang, E., Wang, X.: Structured large margin machines: sensitive to data distributions. Mach. Learn. 68(2), 171–200 (2007)CrossRef Yeung, D., Wang, D., Ng, W., Tsang, E., Wang, X.: Structured large margin machines: sensitive to data distributions. Mach. Learn. 68(2), 171–200 (2007)CrossRef
89.
go back to reference Scholkopf, B., Smola, A.J.: Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT Press, Cambridge (2001) Scholkopf, B., Smola, A.J.: Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT Press, Cambridge (2001)
90.
go back to reference Herbrich, R.: Learning kernel classifiers: theory and algorithms. MIT Press, Cambridge (2002) Herbrich, R.: Learning kernel classifiers: theory and algorithms. MIT Press, Cambridge (2002)
91.
go back to reference Hofmann, T., Schölkopf, B., Smola, A.: Kernel methods in machine learning. Ann. Stat. 1171–1220 (2008) Hofmann, T., Schölkopf, B., Smola, A.: Kernel methods in machine learning. Ann. Stat. 1171–1220 (2008)
92.
go back to reference Huang, K., Yang, H., King, I., Lyu, M.: Learning large margin classifiers locally and globally. In: Proceedings of the twenty-first international conference on Machine learning, p. 51. ACM Press, New York (2004) Huang, K., Yang, H., King, I., Lyu, M.: Learning large margin classifiers locally and globally. In: Proceedings of the twenty-first international conference on Machine learning, p. 51. ACM Press, New York (2004)
93.
go back to reference Smola, A., Schölkopf, B.: Learning with kernels. Citeseer (1998) Smola, A., Schölkopf, B.: Learning with kernels. Citeseer (1998)
94.
go back to reference Tax, D.M.J., Duin, R.P.W.: Data domain description using support vectors. In: ESANN’99, pp. 251–256 (1999) Tax, D.M.J., Duin, R.P.W.: Data domain description using support vectors. In: ESANN’99, pp. 251–256 (1999)
95.
go back to reference Zhang, X., Gu, C., Lin, J.: Support vector machines for anomaly detection. In: The Sixth World Congress on Intelligent Control and Automation (WCICA), vol. 1, 2594–2598. IEEE Press, New York (2006) Zhang, X., Gu, C., Lin, J.: Support vector machines for anomaly detection. In: The Sixth World Congress on Intelligent Control and Automation (WCICA), vol. 1, 2594–2598. IEEE Press, New York (2006)
96.
go back to reference Gomez-Verdejo, V., Arenas-Garcia, J., Lazaro-Gredilla, M., Navia-Vazquez, A.: Adaptive one-class support vector machine. IEEE Trans. Signal Process. 59(6), 2975–2981 (June 2011) Gomez-Verdejo, V., Arenas-Garcia, J., Lazaro-Gredilla, M., Navia-Vazquez, A.: Adaptive one-class support vector machine. IEEE Trans. Signal Process. 59(6), 2975–2981 (June 2011)
97.
go back to reference Rajasegarar, S., Leckie, C., Bezdek, J., Palaniswami, M.: Centered hyperspherical and hyperellipsoidal one-class support vector machines for anomaly detection in sensor networks. IEEE Trans. Inf. Forensics Secur. 5(3), 518–533 (2010)CrossRef Rajasegarar, S., Leckie, C., Bezdek, J., Palaniswami, M.: Centered hyperspherical and hyperellipsoidal one-class support vector machines for anomaly detection in sensor networks. IEEE Trans. Inf. Forensics Secur. 5(3), 518–533 (2010)CrossRef
98.
go back to reference Rajasegarar, S., Leckie, C., Palaniswami, M.: Cesvm: Centered hyperellipsoidal support vector machine based anomaly detection. In: ICC ’08. IEEE International Conference on Communications, pp. 1610–1614 (2008) Rajasegarar, S., Leckie, C., Palaniswami, M.: Cesvm: Centered hyperellipsoidal support vector machine based anomaly detection. In: ICC ’08. IEEE International Conference on Communications, pp. 1610–1614 (2008)
99.
go back to reference Rajasegarar, S., Leckie, C., Palaniswami, M., Bezdek, J.: Quarter sphere based distributed anomaly detection in wireless sensor networks. In: ICC ’07. IEEE International Conference on Communications, pp. 3864–3869 (2007) Rajasegarar, S., Leckie, C., Palaniswami, M., Bezdek, J.: Quarter sphere based distributed anomaly detection in wireless sensor networks. In: ICC ’07. IEEE International Conference on Communications, pp. 3864–3869 (2007)
100.
go back to reference Shahid, N., Naqvi, I.H., Qaisar, S.B.: Quarter-Sphere SVM: attribute and Spatio-Temporal correlations based outlier and event detection in wireless sensor networks. In: 2012 IEEE Wireless Communications and Networking Conference: Mobile and Wireless Networks (IEEE WCNC 2012 Track 3 Mobile and Wireless). France (2012) Shahid, N., Naqvi, I.H., Qaisar, S.B.: Quarter-Sphere SVM: attribute and Spatio-Temporal correlations based outlier and event detection in wireless sensor networks. In: 2012 IEEE Wireless Communications and Networking Conference: Mobile and Wireless Networks (IEEE WCNC 2012 Track 3 Mobile and Wireless). France (2012)
101.
go back to reference Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, Burlington (2006) Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, Burlington (2006)
103.
go back to reference Aggarwal, C., Yu, P.: Outlier detection for high dimensional data. ACM Sigmod Rec. 30(2), 37–46 (2001)CrossRef Aggarwal, C., Yu, P.: Outlier detection for high dimensional data. ACM Sigmod Rec. 30(2), 37–46 (2001)CrossRef
104.
go back to reference Aly, M.: Survey on multiclass classification methods. Neural Netw. 1–9 (2005) Aly, M.: Survey on multiclass classification methods. Neural Netw. 1–9 (2005)
105.
go back to reference Mayoraz, E., Alpaydin, E.: Support vector machines for multi-class classification. Eng. Appl. Bioinspir. Artif. Neural Netw. 833–842 (1999) Mayoraz, E., Alpaydin, E.: Support vector machines for multi-class classification. Eng. Appl. Bioinspir. Artif. Neural Netw. 833–842 (1999)
106.
go back to reference Hsu, C., Lin, C.: A comparison of methods for multiclass support vector machines. IEEE Trans. Neural Netw. 13(2), 415–425 (2002)CrossRef Hsu, C., Lin, C.: A comparison of methods for multiclass support vector machines. IEEE Trans. Neural Netw. 13(2), 415–425 (2002)CrossRef
107.
go back to reference Xu, T., He, D., Luo, Y.: A new orientation for multi-class SVM. In: SNPD 2007. Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, vol. 3, pp. 899–904. IEEE Press, New York (2007) Xu, T., He, D., Luo, Y.: A new orientation for multi-class SVM. In: SNPD 2007. Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, vol. 3, pp. 899–904. IEEE Press, New York (2007)
108.
go back to reference Xu, T.: A new sphere-structure multi-class classifier. In: PACCS’09. Pacific-Asia Conference on Circuits, Communications and Systems, pp. 520–525. IEEE Press, New York (2009) Xu, T.: A new sphere-structure multi-class classifier. In: PACCS’09. Pacific-Asia Conference on Circuits, Communications and Systems, pp. 520–525. IEEE Press, New York (2009)
109.
go back to reference Liu, S., Liu, Y., Wang, B.: An improved hyper-sphere support vector machine. In: ICNC 2007. Third International Conference on Natural Computation, vol. 1, pp. 497–500. IEEE Press, New York (2007) Liu, S., Liu, Y., Wang, B.: An improved hyper-sphere support vector machine. In: ICNC 2007. Third International Conference on Natural Computation, vol. 1, pp. 497–500. IEEE Press, New York (2007)
110.
go back to reference Liu, C., Yang, Y., Tang, C.: An improved method for multi-class support vector machines. In: International Conference on Measuring Technology and Mechatronics Automation (ICMTMA), vol. 1, pp. 504–508. IEEE Press, New York (2010) Liu, C., Yang, Y., Tang, C.: An improved method for multi-class support vector machines. In: International Conference on Measuring Technology and Mechatronics Automation (ICMTMA), vol. 1, pp. 504–508. IEEE Press, New York (2010)
111.
go back to reference Salat, R., Osowski, S.: Accurate fault location in the power transmission line using support vector machine approach. IEEE Trans. Power Syst. 19(2), 979–986 (2004)CrossRef Salat, R., Osowski, S.: Accurate fault location in the power transmission line using support vector machine approach. IEEE Trans. Power Syst. 19(2), 979–986 (2004)CrossRef
112.
go back to reference Thukaram, D., Khincha, H., Ravikumar, B.: An intelligent approach using support vector machines for monitoring and identification of faults on transmission systems. In: Power India Conference, pp. 184–190. IEEE Press, New York (2006) Thukaram, D., Khincha, H., Ravikumar, B.: An intelligent approach using support vector machines for monitoring and identification of faults on transmission systems. In: Power India Conference, pp. 184–190. IEEE Press, New York (2006)
113.
go back to reference Thukaram, D., Khincha, H., Vijaynarasimha, H.: Artificial neural network and support vector machine approach for locating faults in radial distribution systems. IEEE Trans. Power Deliv. 20(2), 710–721 (2005)CrossRef Thukaram, D., Khincha, H., Vijaynarasimha, H.: Artificial neural network and support vector machine approach for locating faults in radial distribution systems. IEEE Trans. Power Deliv. 20(2), 710–721 (2005)CrossRef
114.
go back to reference Lukomski, R., Wilkosz, K.: Utilization of support vector machine classifiers to power system topology verification. In: Proceedings of the International Symposium Modern Electric Power Systems (MEPS), pp. 1–6. IEEE Press, New York (2010) Lukomski, R., Wilkosz, K.: Utilization of support vector machine classifiers to power system topology verification. In: Proceedings of the International Symposium Modern Electric Power Systems (MEPS), pp. 1–6. IEEE Press, New York (2010)
115.
go back to reference Kunadumrongrath, K., Ngaopitakkul, A.: Discrete wavelet transform and support vector machines algorithm for classification of fault types on transmission line. In: Proceedings of the International MultiConference of Engineers and Computer Scientists, vol. 2 (2012) Kunadumrongrath, K., Ngaopitakkul, A.: Discrete wavelet transform and support vector machines algorithm for classification of fault types on transmission line. In: Proceedings of the International MultiConference of Engineers and Computer Scientists, vol. 2 (2012)
116.
go back to reference Moulin, L., Da Silva, A., El-Sharkawi, M., Marks, R., et al.: Support vector machines for transient stability analysis of large-scale power systems. IEEE Trans. Power Syst. 19(2), 818–825 (2004)CrossRef Moulin, L., Da Silva, A., El-Sharkawi, M., Marks, R., et al.: Support vector machines for transient stability analysis of large-scale power systems. IEEE Trans. Power Syst. 19(2), 818–825 (2004)CrossRef
117.
go back to reference Ravikumar, B., Thukaram, D., Khincha, H.: Application of support vector machines for fault diagnosis in power transmission system. IET Gener. Transm. Distrib. 2(1), 119–130 (2008)CrossRef Ravikumar, B., Thukaram, D., Khincha, H.: Application of support vector machines for fault diagnosis in power transmission system. IET Gener. Transm. Distrib. 2(1), 119–130 (2008)CrossRef
118.
go back to reference Shahid, N., Aleem, S.A., Naqvi, I.H., Zaffar, N.: Support vector machine based fault detection and classification in smart grids. In: GC’12 Workshop: Smart Grid Communications: Design for Performance (GC’12 Workshop-SGComm 2012), Anaheim (2012) Shahid, N., Aleem, S.A., Naqvi, I.H., Zaffar, N.: Support vector machine based fault detection and classification in smart grids. In: GC’12 Workshop: Smart Grid Communications: Design for Performance (GC’12 Workshop-SGComm 2012), Anaheim (2012)
119.
go back to reference Shahid, N., Naqvi, I.H.: Energy efficient outlier detection in wsns based on temporal and attribute correlations. In: International Conference on Emerging Technologies (2011) Shahid, N., Naqvi, I.H.: Energy efficient outlier detection in wsns based on temporal and attribute correlations. In: International Conference on Emerging Technologies (2011)
Metadata
Title
Methodologies in power systems fault detection and diagnosis
Authors
Saad Abdul Aleem
Nauman Shahid
Ijaz Haider Naqvi
Publication date
01-03-2015
Publisher
Springer Berlin Heidelberg
Published in
Energy Systems / Issue 1/2015
Print ISSN: 1868-3967
Electronic ISSN: 1868-3975
DOI
https://doi.org/10.1007/s12667-014-0129-1

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