An Adaptive Neuro Fuzzy Inference System based Method for DC Fault Recognition in VSC-MTDC System

Document Type : Research Paper

Authors

Department of Electrical Engineering, National Institute of Technology Raipur, Chhattisgarh, India.

Abstract

This paper presents an Adaptive Neuro Fuzzy Inference System (ANFIS) method for recognizing the fault in a Voltage Source Converter-Multiterminal HVDC (VSC-MTDC) system. A four-terminal VSC-based HVDC system is designed in MATLAB software and used for the validation of research. The proposed scheme has advanced features that overcome the limitations of a fuzzy inference system, as it does not need an expert to provide the best performance. Artificial Neural Networks (ANNs) depend only on input and output data through the training process. ANFIS is a very effective method that combines the strengths of artificial neural networks (ANN) in learning from processes and the ability of fuzzy inference systems to deal with uncertain input. In order to protect against faults, two distinct FIS models have been developed to recognize pole-to-ground and pole-to-pole faults. The results indicate a trip signal when a fault is present, hence increasing the reliability of the system. This approach provides quick outcomes without taking any feedback from the remote end of the system.

Keywords

Main Subjects

References

[1]   G. D. Kamalapur, V. R. Sheelavant, S. Hyderabad, A. Pujar, S. Baksi, and A. Patil, “HVDC transmission in India,” IEEE Potentials, vol. 33, no. 1, pp. 22–27, 2014, doi: 10.1109/MPOT.2012.2220870.
[2]   P. K. Tharani, A. Gupta, and A. Gupta, “An Overview to HVDC links in India,” Int. J. Electr. Electron. Comput. Eng., vol. 2, no. 1, pp. 94–98, 2013.
[3]   S. Barman, A. R. Paul, and H. J. Bahirat, “Control strategy for ± 800 kV 6000 MW NER-Agra Multi-Terminal HVDC Project,” 2018 20th Natl. Power Syst. Conf. NPSC 2018, 2018, doi: 10.1109/NPSC.2018.8771761.
[4]   L. Xini, “The Introduction and Analysis of MTDC System,” vol. 82, no. Snce, pp. 405–408, 2017, doi: 10.2991/snce-17.2017.81.
[5]   A. Imani, Z. Moravej, and M. Pazoki, “A novel MODWT-based fault detection and classification scheme in VSC-HVDC transmission line,” Electr. Power Syst. Res., vol. 221, no. January, p. 109434, 2023, doi: 10.1016/j.epsr.2023.109434.
[6]   V. Ashok, A. Yadav, and V. K. Naik, Fault Detection and Classi fi cation of Multi-location and Evolving Faults in Double-Circuit Transmission Line Using ANN. Springer Singapore, 2019. doi: 10.1007/978-981-13-0514-6.
[7]   P. K. Mishra and A. Yadav, A Single Ended Fuzzy Based Directional Relaying Scheme for Transmission Line Compensated by Fixed Series Capacitor, vol. 1. Springer International Publishing, 2020. doi: 10.1007/978-3-030-16660-1.
[8]   J. Li, Q. Yang, H. Mu, S. Le Blond, and H. He, “A new fault detection and fault location method for multi-terminal high voltage direct current of offshore wind farm,” Appl. Energy, vol. 220, no. December 2017, pp. 13–20, 2018, doi: 10.1016/j.apenergy.2018.03.044.
[9]   R. Li, L. Xu, and L. Yao, “DC fault detection and location in meshed multiterminal HVDC systems based on DC reactor voltage change rate,” IEEE Trans. Power Deliv., vol. 32, no. 3, pp. 1516–1526, 2017, doi: 10.1109/TPWRD.2016.2590501.
[10] M. J. Perez-Molina, D. M. Larruskain, P. E. López, and A. Etxegarai, “Analysis of local measurement-based algorithms for fault detection in a multi-terminal HVDC grid,” Energies, vol. 12, no. 24, pp. 1–20, 2019, doi: 10.3390/en12244808.
[11] Q. Yang, S. Le Blond, R. Aggarwal, Y. Wang, and J. Li, “New ANN method for multi-terminal HVDC protection relaying,” Electr. Power Syst. Res., vol. 148, pp. 192–201, 2017, doi: 10.1016/j.epsr.2017.03.024.
[12] U. Sahu, A. Yadav, and M. Pazoki, “A protection method for multi-terminal HVDC system based on fuzzy approach,” MethodsX, vol. 10, no. October 2022, p. 102018, 2023, doi: 10.1016/j.mex.2023.102018.
[13] K. De Kerf et al., “Wavelet-based protection strategy for DC faults in multi-terminal VSC HVDC systems,” IET Gener. Transm. Distrib., vol. 5, no. 4, pp. 496–503, 2011, doi: 10.1049/iet-gtd.2010.0587.
[14] B. Li, J. He, Y. Li, and B. Li, “A review of the protection for the multi-terminal VSC-HVDC grid,” Prot. Control Mod. Power Syst., vol. 4, no. 1, 2019, doi: 10.1186/s41601-019-0136-2.
[15] C. Li, C. Zhao, J. Xu, Y. Ji, F. Zhang, and T. An, “A Pole-to-Pole Short-Circuit Fault Current Calculation Method for DC Grids,” IEEE Trans. Power Syst., vol. 32, no. 6, pp. 4943–4953, 2017, doi: 10.1109/TPWRS.2017.2682110.
[16] G. Zhou, X. Zhang, M. Han, S. Filizadeh, and Z. Geng, “Single-ended Fault Detection Scheme Using Support Vector Machine for Multi-terminal Direct Current Systems Based on Modular Multilevel Converter,” J. Mod. Power Syst. Clean Energy, vol. 11, no. 3, pp. 990–1000, 2023, doi: 10.35833/MPCE.2021.000404.
[17] C. Li, A. M. Gole, and C. Zhao, “A fast DC fault detection method using DC reactor voltages in HVdc grids,” IEEE Trans. Power Deliv., vol. 33, no. 5, pp. 2254–2264, 2018, doi: 10.1109/TPWRD.2018.2825779.
[18] L. Liu, Z. Liu, M. Popov, P. Palensky, and M. A. M. M. Van Der Meijden, “A fast protection of multi-terminal HVDC system based on transient signal detection,” IEEE Trans. Power Deliv., vol. 36, no. 1, pp. 43–51, 2021, doi: 10.1109/TPWRD.2020.2979811.
[19] S. Xue, J. Lian, J. Qi, and B. Fan, “Pole-to-ground fault analysis and fast protection scheme for HVDC based on overhead transmission lines,” Energies, vol. 10, no. 7, 2017, doi: 10.3390/en10071059.
[20] N. Bawane, A. G. Kothari, and D. P. Kothari, “ANFIS based HVDC control and fault identification of HVDC converter,” vol. 2, pp. 673–689, 2005.
[21] C. A. License, G. Dhiman, A. Sharma, M. Shabaz, P. Shukla, and M. Arora, “Retracted: Taxonomy of Adaptive Neuro-Fuzzy Inference System in Modern Engineering Sciences,” Comput. Intell. Neurosci., vol. 2023, pp. 1–1, 2023, doi: 10.1155/2023/9892072.
[22] A. Cruz, “ANFIS : Adaptive Neuro-Fuzzy Inference Systems ANFIS Architecture Hybrid Learning Algorithm ANFIS as a Universal Approximatior Simulation Examples,” pp. 1–33.
[23] B. Chaitanya, A. Yadav, and A. Soni, “Communication assisted fuzzy based adaptive protective relaying scheme for microgrid,” J. Power Technol., vol. 98, no. 1, pp. 57–69, 2018.
[24] L. Tightiz, M. A. Nasab, H. Yang, and A. Addeh, “An intelligent system based on optimized ANFIS and association rules for power transformer fault diagnosis,” ISA Trans., vol. 103, pp. 63–74, 2020, doi: 10.1016/j.isatra.2020.03.022.
[25] A. Belaout, F. Krim, A. Mellit, B. Talbi, and A. Arabi, “Multiclass adaptive neuro-fuzzy classifier and feature selection techniques for photovoltaic array fault detection and classification,” Renew. Energy, vol. 127, pp. 548–558, 2018, doi: 10.1016/j.renene.2018.05.008.
[26] M. A. Mohamed, M. A. Moustafa Hassan, F. Albalawi, S. S. M. Ghoneim, Z. M. Ali, and M. Dardeer, “Diagnostic modelling for induction motor faults via anfis algorithm and dwt-based feature extraction,” Appl. Sci., vol. 11, no. 19, 2021, doi: 10.3390/app11199115.
[27]      U. Sahu, A. Yadav, Fault detection in MTDC network utilising one end measurements, 2019, 8th Int. Conf. Power Syst. Transit. Towar. Sustain. Smart Flex. Grids, ICPS, 2019.
[28]      Elgeziry, M., Elsadd, M., Elkalashy, N., Kawady, T., Taalab, A.-M. and Izzularab, M.A., Non-pilot protection scheme for multi-terminal VSC–HVDC transmission systems. IET Renewable Power Generation, 2019, 13: 3033-3042. https://doi.org/10.1049/iet-rpg.2018.6265
 

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History

  • Receive Date: 28 May 2024
  • Revise Date: 01 August 2024
  • Accept Date: 08 September 2024

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