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A hybrid wavelet–APSO–ANN-based protection scheme for six-phase transmission line with real-time validation

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Abstract

The ever-increasing load demand motivates the adoption of the six-phase transmission system as a viable alternative to the conventional three-phase double-circuit line because of its enhanced power transfer capability. However, the acceptance of six-phase transmission is highly dependent on its protection scheme, which is a challenging task due to the large number of possible faults. In this regard, this paper describes a novel concept based on the hybridization of wavelet transform, adaptive particle swarm optimization (APSO) and ANN for fault classification and location in the six-phase transmission line. The proposed scheme overcomes the difficulties associated with existing ANN-based protection schemes related to the selection of architecture and convergence to the non-global optimal set of weight vectors and biases post-training. From the features/attributes extracted using wavelet transform, the hybrid algorithm utilizes the strong exploration and mapping capability of APSO and ANN, respectively, for input–output mapping. It significantly increases the probability of global convergence for ANN training, thereby improving the performance of the protection scheme in terms of fault detection, classification and location estimation accuracy. The proposed scheme has been extensively tested for wide variation in different fault attributes. In addition to the offline simulations, the effectiveness of the proposed scheme has been validated in real-time environment by carrying out hardware in loop simulations using OPAL-RT digital simulator.

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References

  1. Dave K, Mohan N, Deng X, Gorur R, Olsen R (2012) Analyzing techniques for increasing power transfer in the electric grid. IEEE North American power symposium (NAPS), Champaign, IL, USA, pp 1–6

  2. Stewart JR, Grant IS (1982) High phase order—ready for application. IEEE Trans Power Appar Syst 101(6):1757–1767

    Article  Google Scholar 

  3. Venkata SS, Guyker WC, Booth WH, Kondragunta J, Bhatt NB, Saini NK (1982) EPPC—a computer program for six-phase transmission line design. IEEE Trans Power Appar Syst 101(7):1859–1869

    Article  Google Scholar 

  4. Billinton R, Faried SO, Fotuhi-Firuzabad M (2003) Composite system reliability evaluation incorporating a six-phase transmission line. IEE Proc Gen Transm Distrib 150(4):413–419

    Article  Google Scholar 

  5. Apostolov A, George W (1992) Protecting NYSEG’s six-phase transmission line. IEEE Comput Appl Power 5(4):33–36

    Article  Google Scholar 

  6. Apostolov AP, Raffensperger RG (1996) Relay protection operation for faults on NYSEG’S six-phase transmission line. IEEE Trans Power Deliv 11(1):191–196

    Article  Google Scholar 

  7. Rebbapragada RV, Panke H, Pierce HJ, Stewart JR, Oppel LJ (1992) Selection and application of relay protection for six phase demonstration project. IEEE Trans Power Deliv 7(4):1900–1911

    Article  Google Scholar 

  8. Redfem MA, Angove HT, Barrett JI (1993) The application of non-unit protection to six-phase transmission lines. In: IEE 2nd international conference on advances in power system control, operation and management, Hong Kong, pp 607–612

  9. Hajjar AA, Mansour MM (2006) A microprocessor and wavelets based relaying approach for on line six-phase transmission lines protection. In: IEEE proceedings of the 41st international universities power engineering conference, pp 819–823

  10. Hajjar AA, Mansour MM (2007) Fault location for six-phase transmission lines based on the wavelet transform of the fault induced high frequency transients. In: Proceedings of 42nd international universities power engineering conference UPEC 2007, pp 252–256

  11. Stamp JE, Girgis AA (1999) Fault location technique for six phase transmission lines with unsynchronized phasors. In: Proceedings of the IEEE transmission and distribution conference, pp 663–667

  12. Koley E, Verma K, Ghosh S (2015) An improved fault detection classification and location scheme based on wavelet transform and artificial neural network for six phase transmission line using single end data only. SpringerPlus 4:551

    Article  Google Scholar 

  13. Koley E, Yadav A, Thoke AS (2015) A new single-ended artificial neural network-based protection Scheme for shunt faults in six-phase transmission line. Int Trans Electr Energy Syst 25(7):1257–1280

    Article  Google Scholar 

  14. Park TS, Lee JH, Choi B (2009) Optimization for artificial neural network with adaptive inertial weight of particle swarm optimization. In: Proceedings of 8th IEEE international conference on cognitive informatics, Kowloon, Hong Kong, China, pp 481–485

  15. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: IEEE international conference on neural networks, Perth, WA, Australia, pp 1942–1948

  16. Mousavi SM, Sadeghi J, Niaki STA, Alikar N, Bahreininejad A, Metselaar HSC (2014) Two parameter-tuned meta-heuristics for a discounted inventory control problem in a fuzzy environment. Inf Sci 276:42–62

    Article  MathSciNet  Google Scholar 

  17. Mousavi SM, Bahreininejad A, Musa SN, Yusof F (2017) A modified particle swarm optimization for solving the integrated location and inventory control problems in a two-echelon supply chain network. J Intell Manuf 28(1):191–206

    Article  Google Scholar 

  18. Chatterjee S, Sarkar S, Hore S, Dey N, Ashour AS, Balas VE (2017) Particle swarm optimization trained neural network for structural failure prediction of multistoried RC buildings. Neural Comput Appl 28(8):2005–2016

    Article  Google Scholar 

  19. Sheikhan M, Sha’bani AA (2013) PSO-optimized modular neural network trained by OWO-HWO algorithm for fault location in analog circuits. Neural Comput Appl 23(2):519–530

    Article  Google Scholar 

  20. Armaghani DJ, Raja RSNSB, Faizi K, Rashid ASA (2017) Developing a hybrid PSO–ANN model for estimating the ultimate bearing capacity of rock-socketed piles. Neural Comput Appl 28(2):391–405

    Article  Google Scholar 

  21. Upendar J, Gupta CP, Singh GK, Ramakrishna G (2010) PSO and ANN-based fault classification for protective relaying. IET Gen Transm Distrib 4(10):1197–1212

    Article  Google Scholar 

  22. Panigrahi BK, Pandi V, Ravikumar Das Sanjoy (2008) Adaptive particle swarm optimization approach for static and dynamic economic load dispatch. Energy Convers Manag 49(6):1407–1415

    Article  Google Scholar 

  23. Mousavi SM, Sadeghi J, Niaki STA, Tavana M (2016) A bi-objective inventory optimization model under inflation and discount using tuned Pareto-based algorithms: NSGA-II, NRGA, and MOPSO. Appl Soft Comput 43:57–72

    Article  Google Scholar 

  24. Alikar N, Mousavi SM, Ghazilla RAR, Tavana M, Olugu EU (2017) Application of the NSGA-II algorithm to a multi-period inventory-redundancy allocation problem in a series-parallel system. Reliab Eng Syst Saf 160:1–10

    Article  Google Scholar 

  25. Alikar N, Mousavi SM, Ghazilla RAR, Tavana M, Olugu EU (2017) A bi-objective multi-period series-parallel inventory-redundancy allocation problem with time value of money and inflation considerations. Comput Ind Eng 104:51–67

    Article  Google Scholar 

  26. Youssef OAS (2003) Online applications of wavelet transforms to power system relaying. IEEE Trans Power Deliv 18(4):1158–1165

    Article  MathSciNet  Google Scholar 

  27. Sushma V (2015) Most suited mother wavelet for localization of transmission line faults. Int J Sci Technol Res 4(6):288–293

    Google Scholar 

  28. Hashemi SM, Hagh M, Tarafdar Seyedi H (2013) Transmission-line protection: a directional comparison scheme using the average of superimposed components. IEEE Trans Power Deliv 28(2):955–964

    Article  Google Scholar 

  29. Powering Real-Time Simulation. https://www.opal-rt.com/software-rt-lab/. Accessed 15 May 2017

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Authors and Affiliations

  1. Department of Electrical Engineering, National Institute of Technology, Raipur, G.E. Road, Raipur, 492010, India

    Sunil Kumar Shukla, Ebha Koley & Subhojit Ghosh

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  1. Sunil Kumar Shukla
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  2. Ebha Koley
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  3. Subhojit Ghosh
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Correspondence to Subhojit Ghosh.

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Shukla, S.K., Koley, E. & Ghosh, S. A hybrid wavelet–APSO–ANN-based protection scheme for six-phase transmission line with real-time validation. Neural Comput & Applic 31, 5751–5765 (2019). https://doi.org/10.1007/s00521-018-3400-x

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