Using machine learning algorithms for classifying transmission line faults

Öz

The faults in transmission lines should be identified for attaining high quality energy in electrical power systems. Savings can be made in both time and energy if the transmission line faults are classified accurately. The present study examined phase-ground, phase-phase-ground, phase-phase, phase-phase-phase and no fault cases. Support Vector Machine (SVM), K-Nearest Neighbours Algorithm (KNN), Decision Tree (DT), Ensemble, Linear discriminant analysis (LDA) classifiers were used for classifying the transmission line faults. These algorithms were compared with regard to parameters such as accuracy, error rate, prediction speed and training time. The accuracy and minimum error of SVM and KNN classifiers were 99.7 % and 0.0011 respectively. DT classifier is faster than the other classifiers with a predicted speed of 29000 obs/sec. Whereas LDA had the shortest training time of 0.76992 sec. The results have indicated that SVM, KNN classifiers have similar performances. In addition, the classifiers SVM, KNN acquired minimum error with the highest accuracy compared with the other classifiers. While DT has the highest estimation speed, LDA has the shortest training time.

Anahtar Kelimeler

• support vector machine
• k-nearest neighbors algorithm
• decision tree
• transmission line faults
• ensemble
• linear discriminant analysis

Using Machine Learning Algorithms For Classifying Transmission Line Faults

Abstract

The faults in transmission lines should be identified for attaining high quality energy in electrical power systems. Savings can be made in both time and energy if the transmission line faults are classified accurately. The present study examined phase-ground, phase-phase-ground, phase-phase, phase-phase-phase and no fault cases. Support Vector Machine (SVM), K-Nearest Neighbours Algorithm (KNN), Decision Tree (DT), Ensemble, Linear discriminant analysis (LDA) classifiers were used for classifying the transmission line faults. These algorithms were compared with regard to parameters such as accuracy, error rate, prediction speed and training time. The accuracy and minimum error of SVM and KNN classifiers were 99.7 % and 0.0011 respectively. DT classifier is faster than the other classifiers with a predicted speed of 29000 obs/sec. Whereas LDA had the shortest training time of 0.76992 sec. The results have indicated that SVM, KNN classifiers have similar performances. In addition, the classifiers SVM, KNN acquired minimum error with the highest accuracy compared with the other classifiers. While DT has the highest estimation speed, LDA has the shortest training time.

Keywords

• support vector machine
• k-nearest neighbors algorithm decision tree
• transmission line faults
• ensemble
• linear discriminant analysis

References

1. Reference1 M. N. Mahmud, M. N. Ibrahim, M. K. Osman & Z. Hussain, “A robust transmission line fault classification scheme using class-dependent feature and 2-Tier multilayer perceptron network”, Electrical Engineering, vol.100(2), pp.607-623,2018, doi:10.1007/s00202-017-0531-5.
2. Reference2 S. S. Gururajapathy, H. Mokhlis& H. A. Illias, “Fault location and detection techniques in power distribution systems with distributed generation: A review”, Renewable and sustainable energy reviews, vol.74, pp.949-958,2017, doi: 10.1016/j.rser.2017.03.021.
3. Reference3 J. C. A. Freire, A. R. G. Castro, M. S. Homci, B. S. Meiguins & J. M. De Morais, “Transmission line fault classification using hidden Markov models”. IEEE Access, vol.7, pp.113499-113510,2019, doi:10.1109/ACCESS.2019.2934938.
4. Reference4 H. Fathabadi, “Novel filter-based ANN approach for short-circuit faults detection, classification and location in power transmission lines”, International Journal of Electrical Power & Energy Systems, vol.74,pp.374-383,2016,doi: 10.1016/j.ijepes.2015.08.005.
5. Reference5 A. Rahmati, & R. Adhami, “A fault detection and classification technique based on sequential components”. IEEE Transactions on Industry Applications, vol.50(6), pp.4202-4209,2014, doi: 10.1109/TIA.2014.2313652.
6. Reference6 N. Huang, J. Qi, F. Li, D. Yang, G. Cai, G. Huang, ... & Z. Li, “Short-circuit fault detection and classification using empirical wavelet transform and local energy for electric transmission line”, Sensors, vol.17(9), pp. 2133,2017, doi:10.3390/s17092133.
7. Reference7 P. Ray, & D. P. Mishra, “Support vector machine-based fault classification and location of a long transmission line”, Engineering science and technology, an international journal, vol.19(3), pp.1368-1380,2016, doi: 10.1016/j.jestch.2016.04.001
8. Reference8 Z. He, S. Lin, Y. Deng, X. Li, & Q. Qian, “A rough membership neural network approach for fault classification in transmission lines”, International Journal of Electrical Power & Energy Systems, vol.61, pp.429-439,2014,doi: 10.1016/j.ijepes.2014.03.027.

9. Reference9 P. Ray, & D. P. Mishra, “Support vector machine based fault classification and location of a long transmission line”, Engineering science and technology, an international journal, vol.19(3), pp.1368-1380,2016,doi: 10.1016/j.jestch.2016.04.001.
10. Reference10 Y. Q. Chen, O. Fink & G. Sansavini, “Combined fault location and classification for power transmission lines fault diagnosis with integrated feature extraction”, IEEE Transactions on Industrial Electronics, vol. 65(1), pp. 561-569,2017,doi: 10.1109/TIE.2017.2721922.
11. Reference11 S. Ekici, “Support Vector Machines for classification and locating faults on transmission lines”, Applied soft computing, vol.12(6), pp. 1650-1658,2012,doi: 10.1016/j.asoc.2012.02.011.
12. Reference12 J. C. A.Freire, A. R. G. Castro, M. S. Homci, B. S. Meiguins & J. M. De Morais, “Transmission line fault classification using hidden Markov models”, IEEE Access, vol.7, pp.113499-113510,2019,doi:10.1109/ACCESS.2019.2934938.
13. Reference13 [13] A. Jamehbozorg & S. M. Shahrtash, “A decision-tree-based method for fault classification in single-circuit transmission lines”, IEEE Transactions on Power Delivery, vol.25(4), pp.2190-2196,2010,doi: 10.1109/TPWRD.2010.2053222.
14. Reference14 S. R. Samantaray, “A systematic fuzzy rule based approach for fault classification in transmission lines”, Applied soft computing, vol.13(2), pp.928-938,2013, doi: 10.1016/j.asoc.2012.09.010.
15. Reference15 R. N., Mahanty, &, P. B. Dutta Gupta . “Comparison of fault classification methods based on wavelet analysis and ANN”, Electric Power Components and Systems, vol.34(1), pp.47-60,2006.doi:10.1080/15325000691001485
16. Reference16 T. Nguyen & Y. Liao, “Transmission line fault type classification based on novel features and neuro-fuzzy system”, Electric Power Components and Systems, vol.38(6), pp.695-709,2010,doi:10.1080/15325000903489702.
17. Reference17 I. Aljarah, A. Z. Ala’M, H. Faris, M. A Hassonah, S. Mirjalili, S., & H. Saadeh, “Simultaneous feature selection and support vector machine optimization using the grasshopper optimization algorithm. Cognitive Computation, 10(3), 478-495,2018,doi:10.1007/s12559-017-9542-9.
18. Reference18 A. Zendehboudi, M. A. Baseer & R. Saidur, “Application of support vector machine models for forecasting solar and wind energy resources: A review”, Journal of cleaner production, vol.199, pp.272-285,2018, doi: 10.1016/j.jclepro.2018.07.164.
19. Reference19 N. Ali, D. Neagu & P. Trundle, “Evaluation of k-nearest neighbour classifier performance for heterogeneous data sets”, SN Applied Sciences, vol.1(12), pp.1-15,2019, doi:10.1007/s42452-019-1356-9.
20. Reference20 Y Guo, H. Cao, S. Han, Y. Sun, & Y. Bai, “Spectral–spatial hyperspectralimage classification with k-nearest neighbor and guided filter”, IEEE Access, vol.6, pp.18582-18591,2018, doi:10.1109/ACCESS.2018.2820043.
21. Reference21 T. Lan, H. Hu, C. Jiang, G. Yang & Z. Zhao, “A comparative study of decision tree, random forest, and convolutional neural network for spread-F identification”, Advances in Space Research, vol.65(8), pp.2052-2061,2020, doi: 10.1016/j.asr.2020.01.036.
22. Reference22 J. Dou, A. P. Yunus, D.T. Bui, A. Merghadi, M. Sahana, Z. Zhu, ... & B.T. Pham, “Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan”, Science Of The Total Environment, vol.662, pp.332-346,2019,doi: 10.1016/j.scitotenv.2019.01.221
23. Reference23 M. Czajkowski &M. Kretowski, “Decision tree underfitting in mining of gene expression data. An evolutionary multi-test tree approach”, Expert Systems with Applications, vol.137, pp.392-404,2019,doi: 10.1016/j.eswa.2019.07.019.
24. Reference24 M.M. Fraz, W. Jahangir, S. Zahid, M.M. Hamayun & S.A. Barman, “Multiscale segmentation of exudates in retinal images using contextual cues and ensemble classification”, Biomedical Signal Processing and Control, vol.35, pp.50-62,2017,doi: 10.1016/j.bspc.2017.02.012.
25. Reference25 G. Singh, B. Singh, & M. Kaur, “Grasshopper optimization algorithm–based approach for the optimization of ensemble classifier and feature selection to classify epileptic EEG signals”, Medical & biological engineering & computing, vol.57(6), pp.1323-1339,2019,doi:10.1007/s11517-019-01951-w
26. Reference26 B. Chithra, & R. Nedunchezhian, “Dynamic neutrosophic cognitive map with improved cuckoo search algorithm (DNCM-ICSA) and ensemble classifier for rheumatoid arthritis (RA) disease”, Journal of King Saud University-Computer and Information Sciences,2020,doi: 10.1016/j.jksuci.2020.06.011
27. Reference27 I. Cárdenas-Gallo, C.A. Sarmiento, G. A. Morales, M. A. Bolivar & R. Akhavan-Tabatabaei, “An ensemble classifier to predict track geometry degradation”, Reliability Engineering & System Safety, vol.161, pp.53-60,2017. Doi:10.1016/j.ress.2016.12.012
28. Reference28 A. Yadav & A. Swetapadma, “A novel transmission line relaying scheme for fault detection and classification using wavelet transform and linear discriminant analysis”, Ain Shams Engineering Journal, vol.6(1), pp.199-209,2015,doi: 10.1016/j.ress.2016.12.012
29. Reference29 H. Wang, Y. Fan, B. Fang & S. Dai, “Generalized linear discriminant analysis based on euclidean norm for gait recognition”, International Journal of Machine Learning and Cybernetics, vol.9(4), pp.569-576,2018,doi: 10.1016/j.ress.2016.12.012
30. Reference30 M.M. Ghiasi, S. Zendehboudi, & A.A. Mohsenipour, “Decision tree-based diagnosis of coronary artery disease: CART model”, Computer methods and programs in biomedicine, vol.192, pp.105400,2020,.doi: 10.1016/j.cmpb.2020.105400
31. Reference31 G. Zeng, “On the confusion matrix in credit scoring and its analytical properties”, Communications in Statistics-Theory and Methods, vol. 49(9), pp. 2080-2093,2020, doi:10.1080/03610926.2019.1568485
32. Reference32 Electrical Fault detection and classification, A collection of line currents and voltages for different fault conditions, https://www.kaggle.com/esathyaprakash/electrical-fault-detection-and-classification? select=detect_dataset.csv