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Detection and classification of short-circuit faults on a transmission line using current signal

Melih Coban Suleyman S. Tezcan
Bulletin of the Polish Academy of Sciences Technical Sciences | 2021 | 69 | 4 | e137630 | DOI: 10.24425/bpasts.2021.137630
Keywords transmission line fault detection fault classification support vector machine
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Abstract

This study offers two Support Vector Machine (SVM) models for fault detection and fault classification, respectively. Different short circuit events were generated using a 154 kV transmission line modeled in MATLAB/Simulink software. Discrete Wavelet Transform (DWT) is performed to the measured single terminal current signals before fault detection stage. Three level wavelet energies obtained for each of three-phase currents were used as input features for the detector. After fault detection, half cycle (10 ms) of three-phase current signals was recorded by 20 kHz sampling rate. The recorded currents signals were used as input parameters for the multi class SVM classifier. The results of the validation tests have demonstrated that a quite reliable, fault detection and classification system can be developed using SVM. Generated faults were used to training and testing of the SVM classifiers. SVM based classification and detection model was fully implemented in MATLAB software. These models were comprehensively tested under different conditions. The effects of the fault impedance, fault inception angle, mother wavelet, and fault location were investigated. Finally, simulation results verify that the offered study can be used for fault detection and classification on the transmission line.
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Bibliography

  1.  M.M. Saha, J. Izykowski, and E. Rosolowski, Fault location on power networks. London: Springer, 2010.
  2.  M.B. Chatterjee and S. Debnath, “Cross correlation aided fuzzy based relaying scheme for fault classification in transmission lines,” Eng. Sci. Technol. Int J., vol. 23, no. 3, pp. 534–543, 2020, doi: 10.1016/j.jestch.2019.07.002.
  3.  A. Mukherjee, P.K. Kundu, and A. Das, “Classification and localization of transmission line faults using curve fitting technique with Principal component analysis features,” Electr. Eng., 2021, doi: 10.1007/s00202-021-01285-7.
  4.  R. Godse and S. Bhat, “Mathematical Morphology-Based Feature-Extraction Technique for Detection and Classification of Faults on Power Transmission Line,” IEEE Access, vol. 8, pp. 38459–38471, 2020, doi: 10.1109/access.2020.2975431.
  5.  Y. Liu, Y. Zhu, and K.Wu, “CNN-Based Fault Phase Identification Method of Double Circuit Transmission Lines,” Electr. Power Compon. Syst., vol. 48, no. 8, pp. 833–843, 2020, doi: 10.1080/15325008.2020.1821836.
  6.  M. Paul and S. Debnath, “Fault Detection and Classification Scheme for Transmission Lines Connecting Windfarm Using Single end Impedance,” IETE J. Res., pp. 1–13, 2021, doi: 10.1080/03772063.2021.1886601.
  7.  Y. Aslan and Y. E. Yağan, “Artificial neural-networkbased fault location for power distribution lines using the frequency spectra of fault data,” Electr. Eng., vol. 99, no. 1, pp. 301–311, 2016, doi: 10.1007/s00202-016-0428-8.
  8.  S. Ekici, “Support Vector Machines for classification and locating faults on transmission lines,” Appl. Soft Comput., vol. 12, no. 6,pp. 1650– 1658, 2012, doi: 10.1016/j.asoc.2012.02.011.
  9.  S.R. Samantaray, “A systematic fuzzy rule based approach for fault classification in transmission lines,” Appl. Soft Comput., vol. 13, no. 2, pp. 928–938, 2013, doi: 10.1016/j.asoc.2012.09.010.
  10.  S.R. Samantaray, P.K. Dash, and G. Panda, “Fault classification and location using HS-transform and radial basis function neural network,” Electr. Power Syst. Res., vol. 76, no. 9‒10, pp. 897–905, 2006, doi: 10.1016/j.epsr.2005.11.003.
  11.  A.A. Girgis and E.B. Makram, “Application of adaptive Kalman filtering in fault classification, distance protection, and fault location using microprocessors,” IEEE Trans. Power Syst., vol. 3, no. 1, pp. 301–309, 1988, doi: 10.1109/59.43215.
  12.  N. Ramesh Babu and B. Jagan Mohan, “Fault classification in power systems using EMD and SVM,” Ain Shams Eng. J., vol. 8, no. 2, pp. 103–111, 2017, doi: 10.1016/j.asej.2015.08.005.
  13.  F. Martin and J.A. Aguado, “Wavelet-based ann approach for transmission line protection,” IEEE Trans. Power Deliv., vol. 18, no. 4, pp. 1572–1574, 2003, doi: 10.1109/tpwrd.2003.817523.
  14.  O.A.S. Youssef, “Combined Fuzzy-Logic Wavelet-Based Fault Classification Technique for Power System Relaying,” IEEE Trans. Power Deliv., vol. 19, no. 2, pp. 582–589, 2004, doi: 10.1109/tpwrd.2004.826386.
  15.  A. Yadav and Y. Dash, “An Overview of Transmission Line Protection by Artificial Neural Network: Fault Detection, Fault Classification, Fault Location, and Fault Direction Discrimination,” Adv. Artif. Neural Syst., vol. 2014, pp. 1–20, 2014, doi: 10.1155/2014/230382.
  16.  M. Fikri and M.A.H. El-Sayed, “New algorithm for distance protection of high voltage transmission lines,” IEE Proc. C Gener. Transm. Distrib., vol. 135, no. 5, p. 436, 1988, doi: 10.1049/ip-c.1988.0056.
  17.  S. Mallat, “A Theory for Multiresolution Signal Decomposition: The Wavelet Representation,” Fundamental Papers in Wavelet Theory, 2009, pp. 494–513, doi: 10.1109/34.192463.
  18.  R. Salat and S. Osowski, “Accurate Fault Location in the Power Transmission Line Using Support Vector Machine Approach,” IEEE Trans. Power Syst., vol. 19, no. 2, pp. 979–986, 2004, doi: 10.1109/tpwrs.2004.825883.
  19.  P.K. Dash, S.R. Samantaray, and G. Panda, “Fault Classification and Section Identification of an Advanced Series-Compensated Transmission Line Using Support Vector Machine,” IEEE Trans. Power Deliv., vol. 22, no. 1, pp. 67–73, 2007, doi: 10.1109/tpwrd.2006. 876695.
  20.  V. Vapnik, “The Support Vector Method of Function Estimation,” Nonlinear Modeling, 1998, pp. 55–85, doi: 10.1007/978-1-4615-5703- 6_3.
  21.  Chih-Wei Hsu and Chih-Jen Lin, “A comparison of methods for multiclass support vector machines,” IEEE Trans. Neural Netw., vol. 13, no. 2, pp. 415–425, 2002, doi: 10.1109/72.991427.
  22.  S. Knerr, L. Personnaz, and G. Dreyfus, “Single-layer learning revisited: a stepwise procedure for building and training a neural network,” Neurocomputing, pp. 41–50, 1990, doi: 10.1007/978-3-642-76153-9_5.
  23.  J. Manit and P. Youngkong, Neighborhood components analysis in sEMG signal dimensionality reduction for gait phase pattern recognition. 7th Int. Conf. on Broadband Communications and Biomedical Applications, 2011, doi: 10.1109/IB2Com.2011.6217897.
  24.  M. Akdag and S. Rustemli, “Transmission line fault location: Simulation of real faults using wavelet transform based travelling wave methods,” Bitlis Eren Univ. J. Sci. Technol., vol. 9, no. 2, pp. 88–98, 2019, doi: 10.17678/beuscitech.653273.
  25.  N. Perera and A.D. Rajapakse, “Recognition of Fault Transients Using a Probabilistic Neural-Network Classifier,” IEEE Trans. Power Deliv., vol. 26, no. 1, pp. 410–419, 2011, doi: 10.1109/tpwrd.2010.2060214.
  26.  D. Gogolewski and W. Makiela, “Problems of Selecting the Wavelet Transform Parameters in the Aspect of Surface Texture Analysis,” TEH VJESN, vol. 28, no. 1, 2021, doi: 10.17559/tv-20190312141348.
  27.  J. Ypsilantis et al., “Adaptive, rule based fault diagnostician for power distribution networks,” IEE Proc. C Gener. Transm. Distrib., vol. 139, no. 6, p. 461, 1992, doi: 10.1049/ip-c.1992.0064.
  28.  F.B. Costa, “Fault-Induced Transient Detection Based on Real-Time Analysis of the Wavelet Coefficient Energy,” IEEE Trans. Power Deliv., vol. 29, no. 1, pp. 140–153, 2014, doi: 10.1109/tpwrd.2013.2278272.
  29.  P. Kapler, “An application of continuous wavelet transform and wavelet coherence for residential power consumer load profiles analysis,” Bull. Pol. Acad Sci. Tech. Sci., vol. 69, no. 1, 2021, doi: 10.24425/bpasts.2020.136216.
  30.  Y.Q. Chen, O. Fink, and G. Sansavini, “Combined Fault Location and Classification for Power Transmission Lines Fault Diagnosis With Integrated Feature Extraction,” IEEE Trans. Ind. Electron., vol. 65, no. 1, pp. 561–569, 2018, doi: 10.1109/TIE.2017.2721922.
  31.  G. Revati and B. Sunil, “Combined morphology and SVM-based fault feature extraction technique for detection and classification of transmission line faults,” Turk. J. Electr. Eng. Comput. Sci., vol. 28, no. 5, pp. 2768–2788, 2020, doi: 10.3906/elk-1912-7.
  32.  B.Y. Vyas, R.P. Maheshwari, and B. Das, “Versatile relaying algorithm for detection and classification of fault on transmission line,” Electr. Power Syst. Res., vol. 192, p. 106913, 2021, doi: 10.1016/j.epsr.2020.106913.
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Authors and Affiliations

Melih Coban
1 2
ORCID: ORCID
Suleyman S. Tezcan
2
ORCID: ORCID
  1. Bolu Abant Izzet Baysal University, Bolu, Turkey
  2. Gazi University, Ankara, Turkey

Data irregularities in discretisation of test sets used for evaluation of classification systems: A case study on authorship attribution

Urszula Stańczyk Beata Zielosko
Bulletin of the Polish Academy of Sciences Technical Sciences | 2021 | 69 | 4 | e137629 | DOI: 10.24425/bpasts.2021.137629
Keywords discretisation data irregularities evaluation and test sets rough sets authorship attribution stylometry
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Abstract

When patterns to be recognised are described by features of continuous type, discretisation becomes either an optional or necessary step in the initial data pre-processing stage. Characteristics of data, distribution of data points in the input space, can significantly influence the process of transformation from real-valued into nominal attributes, and the resulting performance of classification systems employing them. If data include several separate sets, their discretisation becomes more complex, as varying numbers of intervals and different ranges can be constructed for the same variables. The paper presents research on irregularities in data distribution, observed in the context of discretisation processes. Selected discretisation methods were used and their effect on the performance of decision algorithms, induced in classical rough set approach, was investigated. The studied input space was defined by measurable style-markers, which, exploited as characteristic features, facilitate treating a task of stylometric authorship attribution as classification
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Bibliography

  1.  G. Franzini, M. Kestemont, G. Rotari, M. Jander, J. Ochab, E. Franzini, J. Byszuk, and J. Rybicki, “Attributing Authorship in the Noisy Digitized Correspondence of Jacob and Wilhelm Grimm,” Front. Digital Humanit., vol. 5, p. 4, 2018, doi: 10.3389/fdigh.2018.00004.
  2.  A. Fernández, S. García, M. Galar, R. C. Prati, B. Krawczyk, and F. Herrera, “Data level preprocessing methods,” in Learning from Imbalanced Data Sets. Cham: Springer International Publishing, 2018, pp. 79–121, doi: 10.1007/978-3-319-98074-4_5.
  3.  S. Garcia, J. Luengo, J. Saez, V. Lopez, and F. Herrera, “A survey of discretization techniques: Taxonomy and empirical analysis in supervised learning,” IEEE Trans. Knowl. Data Eng., vol. 25, no. 4, pp. 734–750, 2013, doi: 10.1109/TKDE.2012.35.
  4.  S. Das, S. Datta, and B.B. Chaudhuri, “Handling data irregularities in classification: Foundations, trends, and future challenges,” Pattern Recognit., vol. 81, pp. 674–693, 2018, doi: 10.1016/j.patcog.2018.03.008.
  5.  U. Stańczyk, “Evaluating importance for numbers of bins in discretised learning and test sets,” in Intelligent Decision Technologies 2017: Proceedings of the 9th KES International Conference on Intelligent Decision Technologies (KES-IDT 2017) – Part II, ser. Smart Innovation, Systems and Technologies, I. Czarnowski, J.R. Howlett, and C.L. Jain, Eds. Springer International Publishing, 2018, vol. 72, pp. 159–169, doi: 10.1007/978-3-319-59421-7_15.
  6.  G. Baron, “On approaches to discretization of datasets used for evaluation of decision systems,” in Intelligent Decision Technologies 2016, ser. Smart Innovation, Systems and Technologies, I. Czarnowski, A. Caballero, R. Howlett, and L. Jain, Eds. Springer, 2016, vol. 56, pp. 149–159, doi: 10.1007/978-3-319-39627-9_14.
  7.  U. Stańczyk and B. Zielosko, “On approaches to discretisation of stylometric data and conflict resolution in decision making,” in Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 23rd International Conference KES-2019, Budapest, Hungary, 4‒6 September 2019, ser. Procedia Computer Science, I. J. Rudas, J. Csirik, C. Toro, J. Botzheim, R.J. Howlett, and L.C. Jain, Eds. Elsevier, 2019, vol. 159, pp. 1811– 1820, doi: 10.1016/j.procs.2019.09.353.
  8.  J. Bazan, H. Nguyen, S. Nguyen, P. Synak, and J. Wróblewski, “Rough set algorithms in classification problem,” in Rough Set Methods and Applications: New Developments in Knowledge Discovery in Information Systems, L. Polkowski, S. Tsumoto, and T. Lin, Eds. Heidelberg: Physica-Verlag HD, 2000, pp. 49–88, doi: 10.1007/978-3-7908-1840-6_3.
  9.  J. Bazan and M. Szczuka, “The rough set exploration system,” in Transactions on Rough Sets III, ser. Lecture Notes in Computer Science, J. F. Peters and A. Skowron, Eds. Berlin, Heidelberg: Springer, 2005, vol. 3400, pp. 37–56, doi: 10.1007/11427834_2.
  10.  I. Chikalov, V. Lozin, I. Lozina, M. Moshkov, H. Nguyen, A. Skowron, and B. Zielosko, Three Approaches to Data Analysis – Test Theory, Rough Sets and Logical Analysis of Data, ser. Intelligent Systems Reference Library. Berlin, Heidelberg: Springer, 2013, vol. 41, doi: 10.1007/978-3-642-28667-4.
  11.  Z. Pawlak and A. Skowron, “Rudiments of rough sets,” Inf. Sci., vol. 177, no. 1, pp. 3–27, 2007, doi: 10.1016/j.ins.2006.06.003.
  12.  J. Rybicki, M. Eder, and D. Hoover, “Computational stylistics and text analysis,” in Doing Digital Humanities: Practice, Training, Research, 1st ed., C. Crompton, R. Lane, and R. Siemens, Eds. Routledge, 2016, pp. 123–144, doi: 10.4324/9781315707860.
  13.  M. Eder, “Style-markers in authorship attribution a crosslanguage study of the authorial fingerprint,” Stud. Pol. Ling., vol. 6, no. 1, pp. 99–114, 2011.
  14.  H. Craig, “Stylistic analysis and authorship studies,” in A companion to digital humanities, S. Schreibman, R. Siemens, and J. Unsworth, Eds. Oxford: Blackwell, 2004, doi: 10.1002/9780470999875.ch20.
  15.  G. Baron, “Comparison of cross-validation and test sets approaches to evaluation of classifiers in authorship attribution domain,” in Proceedings of the 31st International Symposium on Computer and Inf. Sci., ser. Communications in Computer and Information Science, T. Czachórski, E. Gelenbe, K. Grochla, and R. Lent, Eds. Cracow: Springer, 2016, vol. 659, pp. 81–89, doi: 10.1007/978-3-319-47217- 1_9.
  16.  S.S. Mullick, S. Datta, S.G. Dhekane, and S. Das, “Appropriateness of performance indices for imbalanced data classification: An analysis,” Pattern Recognit., vol. 102, pp. 107–197, 2020, doi: 10.1016/j.patcog.2020.107197.
  17.  J.M. Johnson and T.M. Khoshgoftaar, “Survey on deep learning with class imbalance,” J. Big Data, vol. 6, no. 27, pp. 1–54, 2019, doi: 10.1186/s40537-019-0192-5.
  18.  N. Basurto, C. Cambra, and Á. Herrero, “Improving the detection of robot anomalies by handling data irregularities,” Neurocomputing, 2020, doi: 10.1016/j.neucom.2020.05.101, in press.
  19.  G. Shi, C. Feng,W. Xu, L. Liao, and H. Huang, “Penalized multiple distribution selection method for imbalanced data classification,” Knowledge-Based Syst., vol. 196, p. 105833, 2020, doi: 10.1016/j.knosys.2020.105833.
  20.  S. Au, R. Duan, S.G. Hesar, and W. Jiang, “A framework of irregularity enlightenment for data pre-processing in data mining,” Ann. Oper. Res., vol. 174, no. 1, pp. 47–66, 2010, doi: 10.1007/s10479-008-0494-z.
  21.  M. Koziarski, M. Wozniak, and B. Krawczyk, “Combined cleaning and resampling algorithm for multi-class imbalanced data with label noise,” Knowledge-Based Syst., vol. 204, p. 106223, 2020, doi: 10.1016/j.knosys.2020.106223.
  22.  N. Basurto, Á. Arroyo, C. Cambra, and Á. Herrero, “Imputation of missing values affecting the software performance of component-based robots,” Comput. Electr. Eng., vol. 87, p. 106766, 2020, doi: 10.1016/j.compeleceng.2020.106766.
  23.  S. Argamon, K. Burns, and S. Dubnov, Eds., The structure of style: Algorithmic approaches to understanding manner and meaning. Berlin: Springer, 2010, doi: 10.1007/978-3-642-12337-5.
  24.  S. Sbalchiero and M. Eder, “Topic modeling, long texts and the best number of topics. some problems and solutions,” Qual. Quant., vol. 54, pp. 1095–1108, 2020, doi: 10.1007/s11135-020-00976-w.
  25.  R. Peng and H. Hengartner, “Quantitative analysis of literary styles,” Am. Statistician, vol. 56, no. 3, pp. 15–38, 2002, doi: 10.1198/000313002100.
  26.  E. Stamatatos, “A survey of modern authorship attribution methods,” J. Am. Soc. Inf. Sci. Technol., vol. 60, no. 3, pp. 538–556, 2009, doi: 10.1002/asi.21001.
  27.  D. Khmelev and F. Tweedie, “Using Markov chains for identification of writers,” Lit. Linguist. Comput., vol. 16, no. 4, pp. 299–307, 2001, doi: 10.1093/llc/16.3.299.
  28.  M. Koppel, J. Schler, and S. Argamon, “Computational methods in authorship attribution,” J. Am. Soc. Inf. Sci. Technol., vol. 60, no. 1, pp. 9–26, 2009, doi: 10.1002/asi.20961.
  29.  M. Jockers and D. Witten, “A comparative study of machine learning methods for authorship attribution,” Lit. Linguist. Comput., vol. 25, no. 2, pp. 215–223, 2010, doi: 10.1093/llc/fqq001.
  30.  M. Eder and J. Rybicki, “Do birds of a feather really flock together, or how to choose training samples for authorship attribution,” Lit. Linguist. Comput., vol. 28, pp. 229–236, 8 2013, doi: 10.1093/llc/fqs036.
  31.  M. Eder, “Does size matter? Authorship attribution, small samples, big problem,” Digital Scholarsh. Humanit., vol. 30, pp. 167–182, 06 2015, doi: 10.1093/llc/fqt066.
  32.  K. Kalaivani and S. Kuppuswami, “Exploring the use of syntactic dependency features for document-level sentiment classification,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 67, no. 2, pp. 339–347, 2019, doi: 10.24425/bpas.2019.128608.
  33.  G. Rotari, M. Jander, and J. Rybicki, “The Grimm brothers: A stylometric network analysis,” Digital Scholarsh. Humanit., 02 2020, doi: 10.1093/llc/fqz088.
  34.  C. Jankowski, D. Reda, M. Mańkowski, and G. Borowik, “Discretization of data using Boolean transformations and information theory based evaluation criteria,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 63, no. 4, pp. 923–932, 2015, doi: 10.1515/bpasts-2015-0105.
  35.  U. Fayyad and K. Irani, “Multi-interval discretization of continuous valued attributes for classification learning,” in Proceedings of the 13th International Joint Conference on Artificial Intelligence, vol. 2. Morgan Kaufmann Publishers, 1993, pp. 1022–1027.
  36.  I. Kononenko, “On biases in estimating multi-valued attributes,” in Proceedings of the 14th International Joint Conference on Artificial Intelligence IJCAI’95, vol. 2. Morgan Kaufmann Publishers Inc., 1995, pp. 1034–1040.
  37.  J. Rissanen, “Modeling by shortest data description,” Automatica, vol. 14, no. 5, pp. 465–471, 1978, doi: 10.1016/0005-1098(78)90005-5.
  38.  S. Kotsiantis and D. Kanellopoulos, “Discretization techniques: A recent survey,” GESTS Int. Trans. Comput. Sci. Eng., vol. 32, no. 1, pp. 47–58, 2006.
  39.  B. Zielosko, “Application of dynamic programming approach to optimization of association rules relative to coverage and length,” Fundamenta Informaticae, vol. 148, no. 1-2, pp. 87–105, 2016, doi: 10.3233/FI-2016-1424.
  40.  S.G. Weidman and J. O’Sullivan, “The limits of distinctive words: Re-evaluating literature’s gender marker debate,” Digital Scholarsh. Humanit., vol. 33, pp. 374–390, 2018, doi: 10.1093/llc/fqx017.
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Authors and Affiliations

Urszula Stańczyk
1
Beata Zielosko
2
  1. Silesian University of Technology, ul. Akademicka 2A, 44-100 Gliwice, Poland
  2. University of Silesia in Katowice, ul. Będzińska 39, 41-200 Sosnowiec, Poland

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