Multi-Stable Stochastic Resonance Based Protection Scheme for Parallel Transmission Lines with UPFC
DOI:
https://doi.org/10.24297/jac.v12i23.5Keywords:
complex wavelet transform, unified power flow controller, collective sum technique, spectral energy, double line transmission system, multi-scale stochastic resonanceAbstract
This paper presents a multi-stable stochastic resonance (MSR) based on complex wavelet transform (CWT) for protecting a double line transmission system with unified power flow controller (UPFC) in one line. The fault detection at the sending end is recognized by the collective sum technique (CST) using the current signals of all the three-phases with heavy background noise. The noisy signal is processed by parameter compensation and the processed signal is decomposed by CWT with different scale frequencies. The spectral energies of each phase can be used to identify the faulty phases. The CWT is used to compute the spectral energies of each phase current. The proposed scheme has been studied for wide variation of operating parameters and compared with two other fault extraction methods such as EMD-based spectral analysis and wavelet transform with post spectral analysis. The test results of the proposed CWT based MSR algorithm indicates that it can accurately detect and classify the fault with in one cycle from fault inception.
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References
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29. Samantaray S. R, Tripathy L. N, Dash P. K. Differential equation-based fault locator for uniï¬ed power flow controller-based transmission line using synchronized phasor measurements. IET Generation Transmission and Distribution. 2009;3(1):86-98.
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Hari V. N, Anand C. V, Premkumar A. B, Madhukumar A. S. Design and performance analysis of asignal detector based on superthreshold stochasics resonance. Signal Processing. 2012;92:1745
2. Masahide Hojo, Yasunori Mitani, Toshifumi Ise, and Kiichiro Tsuji. Quantitative evaluation of generator power control effect of FACTS controllers for power system stabilization. Elect Engineering in Japan. 2002;138:43-51. DOI 10.1002/eej.1127
3. Rajabi-Ghahnavieh A, Fotuhi-Firuzabad M, Shahidehpour M, Feuillet R. UPFC for Enhancing power system reliability. IEEE Trans Power Delivery. 2010; 25:2881-2890.
4. Wei Xuan, Chow Joe H, Fardanesh Behruz, Edris Abdel-Aty. A common modeling framework of voltage-sourced converters for load flow, sensitivity, and dispatch analysis. IEEE Trans on Power System. 2004;19(2):934-941.
5. Sheng-Huei Lee, Jiang-Hong Liu and Chia-Chi Chu. Modelling and locating unified power-flow controllers for static voltage stability enhancements. Int Trans on Elect Energy System. 2012;24(11):1524-1540. DOI:10.1002/etep.1700
6. Song Y. H. and Liu J. Y. Steady-state power flow and voltage control by unified power-flow controllers, part 1: Modelling and algorithms. European Trans on Elect Power 2000;10(1):53-57. DOI: 10.1002/etep.4450100108
7. Chong B X. P, Zhang K. R, Godfrey L. Yao and Bazargan M. Optimal location of unified power flow controller for congestion management. European Trans on Elect Power. 2010;20:600-610. DOI: 10.1002/etep.341
8. Suman Bhowmick, Biswarup Das, and Narendra Kumar. An Advanced IPFC Model to Reuse Newton Power Flow Codes. IEEE Trans on Power System. 2009;24:525-532. DOI: 10.1109/TPWRS.2009.2016643
9. Taher Niknam, Mohammad Rasoul Narimani, Masoud Jabbari. Dynamic optimal power flow using hybrid particle swarm optimization and simulated annealing. European Trans on Elect Power. 2013;23(7):975-1001. DOI: 10.1002/etep.1633
10. EI-Zonkoly AM, Desouki H. Wavelet entropy based algorithm for fault detection and classificatin in FACTS compensated transmission line. Int J Electr Power Energy System. 2011;33(8):1368-74.
11. He Z, Fu L, Lin S. Fault detection and classification in EHV transmission line based on wavelet singular entropy. IEEE Trans Power Delivery. 2010;25(4):2156-2163.
12. Dubey R, Samantaray S. R. Wavelet singular entropy-based symmetrical fault-detection and out-of-step protection during power swing. IET Generation Transmission and Dstribution. 2013;7(10):1123-1134.
13. Usama Y, Xiaomin L, Imam H. Design and implementation of a wavelet analysis-based shunt fault detection and identification module for transmission line aplications. IET Generation Transmission and Distribution. 2014;8(3):431-441.
14. Costa F.B. Fault-induced detection based on real-time analysis of the wavelet coefficient energy. IEEE Trans Power Delivery. 2013;29(1):140-153.
15. Benzi R, Sutera A, Vulpiana A. The mechanism of stochastic resonance. J Phys A: Math Gen. 1981;14(11):453-457.
16. Duan F. B, Chapeau-Blonduau F, Abbott D, Exploring weak-periodic-sugnal stochastic resonance un locally optimal processors with a Fisher information metric. Signal Processing. 2012;92:3049-3055.
17. He Q, Wang J, Liu Y, Dai D, Kong F. Multiscale noise tuning of stochastic resonance for enhanced fault diagnosis in rotating machines. Mech Syst Signal Processing. 2012;28:443-457.
18. Tao Z, Lu C, Cha Z. Multi-frequency periodic weak signal detection based on single-well potential stochastic resonance. J Electon Meas Instrumentation. 2014;28:171-176.
19. Mao Q, Lin M, Zheng Y. Study of weak multi-frequency signal detection based on stochastic resonance. J Basic Sci Eng. 2008;16:86-91.
20. Gammaitoni L, Hanggi P, Jung P, Marchesoni F. Stochastic resonance Review. Mod Phys. 1998;70:223-287.
21. Lie Y, Han D, Lin J. New adaptive stochastic resonance method and its application to fault diagnosis. J Mech Eng. 2012;48:62-67.
22. Li N, Zhou R, Hu Q, Liu X. Mechanical fault diagnosis based on redundant second generation wavelet packet transform, neighbourhood rough set and support vector machine. Mech Sys Signal Processing. 2012;28:608-621.
23. Riske H. The Fokker-PlancksEquation methods of solution and applications, Springer, Berlin, 1989. 1-20.
24. El-Zonkoly A.M, Desouki H. Wavelet entropy based algorithm for fault detection and classification in FACTS compensated transmission line. Int J Elect Power and Energy System. 2011;33(8):1368-1374.
25. He Q, Wang J. Effect of multi scale noise tuning on stchastic resonance for weak signal detection. Digital Signal Processing. 2012;22:614-621.
26. Lu S, He Q, Kong F. Sequential multi scale noise tuning stochastic resonance for train bearing fault diagnosis in an embedded system. IEEE Trans Instrumentation and Measurement. 2014;63:106-116.
27. He Q, Liu Y, Kong F. Machine fault signature analysis by midpoint based empirical mode decomposition. Meas Sci Technology. 2011;22: 015702.
28. Hrishnanand K. R, Dash P.K. A new real time fast discreate S-transform for cross differential protection for shunt compensated power system. IEEE Trans on Power Delivery. 2013;28(1):402-410.
29. Samantaray S. R, Tripathy L. N, Dash P. K. Differential equation-based fault locator for uniï¬ed power flow controller-based transmission line using synchronized phasor measurements. IET Generation Transmission and Distribution. 2009;3(1):86-98.
30. Wang C, Gao R, Yan R. Unified time-scale-frequency analysis for machine defect signature extraction: Theoritical framework. Mech Syst Signal Processing. 2009;23:226-235.
Hari V. N, Anand C. V, Premkumar A. B, Madhukumar A. S. Design and performance analysis of asignal detector based on superthreshold stochasics resonance. Signal Processing. 2012;92:1745
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Published
2016-12-15
How to Cite
Ezhil, M. A., Jawhar S, J., & C, C. (2016). Multi-Stable Stochastic Resonance Based Protection Scheme for Parallel Transmission Lines with UPFC. JOURNAL OF ADVANCES IN CHEMISTRY, 12(23), 5458–5471. https://doi.org/10.24297/jac.v12i23.5
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