Abstract
In a dual-circuit transmission line, the location of evolving faults (EVFs) is more tiresome due to its multifaceted nature. In this paper, a novel data-mining-based scheme is proposed for location of EVFs by using an ensemble of regression trees, that is, bagged regression trees and boosted regression trees. This ensemble of regression tree modules is trained with optimized hyperparameters such as minimum leaf size, leaning cycles, and learning rate by using Bayesian optimization. A practical power transmission network of Chhattisgarh state is modeled/simulated in MATLAB software to employ the proposed fault location scheme. Exclusive datasets are provided by performing extensive simulation studies at a wide range of fault scenarios, thereby applying discrete wavelet transform as an explanatory signal processing technique. Further performance assessment is carried out by comparing different error metrics such as mean absolute error, mean absolute relative error, mean square error, and root mean square error. The simulation results confirm the applicability of the proposed scheme for fault location estimation, and it makes a research insight while designing relaying schemes to practical power transmission networks.
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References
Spoor DJ, Zhu J (2005) Inter circuit faults and distance relaying of dual-circuit lines. IEEE Trans Power Deliv 20(3):1846–1852. https://doi.org/10.1109/TPWRD.2004.833899
Jain A, Thoke AS, Patel RN, Koley E (2010) Intercircuit and cross-country fault detection and classification using artificial neural network. In: 2010 Annual IEEE India conference (INDICON), Kolkata, pp 1–4. https://doi.org/10.1109/INDCON.2010.5712601
Solak K, Rebizant W (2010) Analysis of differential protection response for cross-country faults in transmission lines. In: 2010 Modern electric power systems, Wroclaw, pp 1–4
Zin AAM, Omar NA, Yusof AM, Karim SPA (2012) Effect of 132 kV cross-country fault on distance protection system. In: 2012 Sixth Asia modelling symposium, Bali, pp 167–172. https://doi.org/10.1109/AMS.2012.17
Xu ZY, Li W, Bi TS, Xu G, Yang QX (2011) First-zone distance relaying algorithm of parallel transmission lines for cross-country nonearthed faults. IEEE Trans Power Deliv 26(4):2486–2494. https://doi.org/10.1109/TPWRD.2011.2158455
Bi T, Li W, Xu Z, Yang Q (2012) First-zone distance relaying algorithm of parallel transmission lines for cross-country grounded Faults. IEEE Trans Power Deliv 27(4):2185–2192. https://doi.org/10.1109/TPWRD.2012.2210740
Swetapadma A, Yadav A (2015) Improved fault location algorithm for multi-location faults, transforming faults and shunt faults in thyristor-controlled series capacitor compensated transmission line. IET Gener Transm Distrib 9(13):1597–1607. https://doi.org/10.1049/iet-gtd.2014.0981
Swetapadma A, Yadav A (2015) All shunt fault location including cross-country and evolving faults in transmission lines without fault type classification. Electr Power Syst Res 123:1–12. https://doi.org/10.1016/j.epsr.2015.01.014
Singh S, Vishwakarma DN (2018) A novel methodology for identifying cross-country faults in series-compensated double circuit transmission line. Procedia Comput Sci 125:427–433. https://doi.org/10.1016/j.procs.2017.12.056
Codino A, Gatta FM, Geri A, Lauria S, Maccioni M, Calone R (2017) Detection of cross-country faults in medium voltage distribution ring lines. In: 2017 AEIT International annual conference, Cagliari, pp 1–6. https://doi.org/10.23919/AEIT.2017.8240493
Gatta FM, Geri A, Lauria S, Maccioni M (2018) An equivalent circuit for evaluation of cross-country fault currents in medium voltage (MV) distribution networks. Energies 11(8):1929. https://doi.org/10.3390/en11081929
Swetapadma A, Yadav A (2018) An artificial neural network-based solution to locate the multilocation faults in double circuit series capacitor compensated transmission lines. Int Trans Electr Energ Syst 28:e2517. https://doi.org/10.1002/etep.2517
Ashok V, Yadav A, Nayak VK (2019) Fault detection and classification of multi-location and evolving faults in double-circuit transmission line using ANN. In: Nayak J, Abraham A, Krishna B, Chandra Sekhar G, Das A (eds) Soft computing in data analytics: advances in intelligent systems and computing, vol 758. Springer, Berlin, pp 307–317. https://doi.org/10.1007/978-981-13-0514-6_31
Ashok V, Yadav A (2019) A protection scheme for cross-country faults and transforming faults in dual-circuit transmission line using real-time digital simulator: a case study of Chhattisgarh State Transmission Utility. Iran J Sci Technol Trans Electr Eng 43:941–967
Ashok V, Yadav A, Abdelaziz AY (2019) MODWT-based fault detection and classification scheme for cross-country and evolving faults. Electr Power Syst Res 175:105897. https://doi.org/10.1016/j.epsr.2019.105897
Ashok V, Yadav A (2019) A novel decision tree algorithm for fault location assessment in dual-circuit transmission line based on DCT-BDT approach. In: Abraham A, Cherukuri A, Melin P, Gandhi N (eds) Intelligent systems design and applications. ISDA 2018. Advances in intelligent systems and computing, vol 941, pp 801–809. https://doi.org/10.1007/978-3-030-16660-1_78
Naresh Kumar A, Sanjay C, Chakravarthy M (2019) Fuzzy inference system-based solution to locate the cross-country faults in parallel transmission line. Int J Electr Eng Educ. https://doi.org/10.1177/0020720919830905
Gao J, Wang B, Wang Z, Wang Y, Kong F (2019) A wavelet transform-based image segmentation method. Optik. https://doi.org/10.1016/j.ijleo.2019.164123
Gao J, Fan L, Xu L (2013) Median null (Sw)-based method for face feature recognition. Appl Math Comput 219(12):6410–6419. https://doi.org/10.1016/j.amc.2013.01.005
https://medium.com/machine-learning-researcher/dimensionality-reduction-pca-and-lda-6be91734f567. Accessed 25 Sept 2020
Martínez AM, Kak AC (2001) PCA versus LDA. IEEE Trans Pattern Anal Mach Intell 23(2):228–233
Breiman L (1996) Bagging predictors. Mach Learn 24:123. https://doi.org/10.1023/A:1018054314350
Polikar R (2006) Ensemble based systems in decision making. IEEE Circuits Syst Mag 6(3):21–45. https://doi.org/10.1109/mcas.2006.1688199
Loh W-Y (2011) Classification and regression trees. In: Overview article in WIREs data mining and knowledge discovery. Wiley, New York, vol 01, pp 14–23. https://doi.org/10.1002/widm.8
https://in.mathworks.com/help/stats/treebagger-class.html#bvfstrb
Zhang G, Liang G, Li W, Fang J, Wang J, Geng Y, Wang J-Y (2017) Learning convolutional ranking-score function by query preference regularization. In: Yin H et al (eds) Intelligent data engineering and automated learning—IDEAL 2017. IDEAL 2017. Lecture notes in computer science, vol 10585. Springer, Cham. https://doi.org/10.1007/978-3-319-68935-7_1
C37.114-2004 (2015) IEEE guide for determining fault location on AC transmission and distribution lines. In: IEEE Std C37.114-2014 (Revision of IEEE Std C37.114-2004), pp 1–76. https://doi.org/10.1109/ieeestd.2015.7024095
Acknowledgements
“The authors acknowledge the financial support of Central Power Research Institute, Bangalore for funding the Project No. RSOP/2016/TR/1/22032016, dated: 19.07.2016. The authors are grateful to the Head of the institution as well as the Head of the Department of Electrical Engineering, National Institute of Technology, Raipur, for providing the research amenities to carry this work. The authors are indebted to the local power utility (Chhattisgarh State Power Transmission Company Limited) for their assistance in component/equipment data of real power system network”.
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Valabhoju, A., Yadav, A., Pazoki, M. et al. Optimized ensemble of regression tree-based location of evolving faults in dual-circuit line. Neural Comput & Applic 33, 8795–8820 (2021). https://doi.org/10.1007/s00521-020-05628-6
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DOI: https://doi.org/10.1007/s00521-020-05628-6