Hybrid PSO-Optimized ANFIS-Based Model to Improve Dynamic Voltage Stability

Authors

  • D. N. Truong Faculty of Electrical and Electronics Engineering, Ho Chi Minh City University of Technology and Education, Vietnam
  • V. T. Bui Faculty of Electrical and Electronics Engineering, Ho Chi Minh City University of Technology and Education, Vietnam
Volume: 9 | Issue: 4 | Pages: 4384-4388 | August 2019 | https://doi.org/10.48084/etasr.2833

Abstract

The objective of this paper is to perform a hybrid design for an Adaptive Neuro-Fuzzy Inference System (ANFIS) optimized by Particle Swarm Optimization (PSO) to improve the dynamic voltage stability of a grid-connected wind power system. An onshore 99.2MW wind farm using Doubly Fed Induction Generator (DFIG) is studied. To compensate the reactive power absorbed from the power grid of the wind farm, a Static VAR Compensator (SVC) is proposed. To demonstrate the performance of the proposed hybrid PSO–ANFIS controller, simulations of the voltage response in time-domain are performed in Matlab to evaluate the effectiveness of the designed controller. From the results, it can be concluded that the proposed hybrid PSO-optimized ANFIS-based model can be applied to enhance the dynamic voltage stability of the studied grid-connected wind power system.

Keywords:

adaptive neuro-fuzzy inference system, particle swarm optimization, static var compensator, voltage stability

Downloads

Download data is not yet available.

References

R. Pena, J. C. Clare, G. M. Asher, “Doubly fed induction generator using back-to-back PWM converters and its application to variable-speed wind-energy generation”, Electric Power Applications, Vol. 143, No. 3, pp. 231-241, 1996 DOI: https://doi.org/10.1049/ip-epa:19960288

L. Wang, L. Y. Chen, “Reduction of power fluctuations of a large-scale grid-connected offshore wind farm using a variable frequency transformer”, Transactions on Sustainable Energy, Vol. 2, No. 3, pp. 226-234, 2011 DOI: https://doi.org/10.1109/TSTE.2011.2142406

Q. Xia, Z. Wang, F. Liu, Y. Li, Y. Peng, Z. Xu, “Study on Power Quality Issues of Wind Farm”, 36th Chinese Control Conference, Dalian, China, July 26-28, 2017 DOI: https://doi.org/10.23919/ChiCC.2017.8029028

M. Latka, M. Nowak, “Analysis of Electrical Power Quality Parameters in the Power Grid with Attached Wind Farm”, Progress in Applied Electrical Engineering, Koscielisko, Poland, June 25-30, 2017 DOI: https://doi.org/10.1109/PAEE.2017.8008990

G. A. Ramos, M. A. Riosm, D. F. Gomez, H. Palacios, L. A. Posada, “Power Quality Study of a Large-Scale Wind Farm with Battery Energy Storage System”, Industry Applications Society Annual Meeting, Cincinnati, USA, October 1-5, 2017 DOI: https://doi.org/10.1109/IAS.2017.8101876

A. Jain, P. P. Singh, S. N. Singh, “Control Strategies for Output Power Smoothening of DFIG with SVC in Wind Conversion System”, Region 10 Humanitarian Technology Conference, Agra, India, December 21-23, 2016 DOI: https://doi.org/10.1109/R10-HTC.2016.7906840

E. A. Awad, E. A. Badran, F. H. Youssef, “Mitigation of switching overvoltages in microgrids based on SVC and supercapacitor”, IET Generation, Transmission & Distribution, Vol. 12, No. 2, pp. 355–362, 2018 DOI: https://doi.org/10.1049/iet-gtd.2017.0503

Y. Chang, Z. Xu, G. Chen, J. Xie, “A Novel SVC Supplementary Controller Based on Wide Area Signals”, Power Engineering Society General Meeting, Montreal, Canada, June 18–22, 2006

A. Jalilvand, M. D. Keshavarzi, “Adaptive SVC Damping Controller Design, Using Residue Method in a Multi-Machine System”, 6th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, Pattaya, Thailand, May 6–9, 2009 DOI: https://doi.org/10.1109/ECTICON.2009.5136986

L. O. Mak, Y. X. Ni, C. M. Shen, “STATCOM with fuzzy controllers for interconnected power systems”, Electric Power Systems Research, Vol. 55, No. 2, pp. 87–95, 2000 DOI: https://doi.org/10.1016/S0378-7796(99)00100-5

I. Mansour, D. O. Abdeslam, P. Wira, J. Merckle, “Fuzzy Logic Control of an SVC to Improve the Transient Stability of AC Power Systems”, 35th Annual Conference of IEEE Industrial Electronics, Porto, Portugal, November 3–5, 2009 DOI: https://doi.org/10.1109/IECON.2009.5415212

J. S. R. Jang, “ANFIS: Adaptive-network-based fuzzy inference system”, Transactions on Systems, Man, and Cybernetics, Vol. 23, No. 3, pp. 665–685, 1993 DOI: https://doi.org/10.1109/21.256541

A. Albakkar, O. P. Malik, “Adaptive Neuro-Fuzzy FACTS Controller for Transient Stability Enhancement”, 16th National Power System Conference, Hyderabad, India, December 15-17, 2010

C. G. Martos, J. Rodriguez, M. J. Sanchez, “Mixed models for short-run forecasting of electricity prices: Application for the Spanish market”, Transactions on Power Systems, Vol. 22, No. 2, pp. 544–552, 2007 DOI: https://doi.org/10.1109/TPWRS.2007.894857

H. M. I. Pousinho, V. M. F. Mendes, J. P. S. Catalao, “A hybrid PSO-ANFIS approach for short-term wind power prediction in Portugal”, Energy Conversion and Management, Vol. 52, No. 1, pp. 397-402, 2011 DOI: https://doi.org/10.1016/j.enconman.2010.07.015

The WindPower, 1.6xle, available at: https://www.thewindpower.net/

turbine_en_670_ge-energy_1.6xle.php, 2019

J. P. S. Catalao, H. M. I. Pousinho, V. M. F. Mendes, “Short-term electricity prices forecasting in a competitive market by a hybrid intelligent approach”, Energy Conversion and Management, Vol. 52, No. 2, pp. 1061–1065, 2011 DOI: https://doi.org/10.1016/j.enconman.2010.08.035

D. N. Truong, “STATCOM Based Fuzzy Logic Damping Controller For Improving Dynamic Stability Of A Grid Connected Wind Power System”, International Conference On System Science And Engineering, Puli, Taiwan, July 7-9, 2016 DOI: https://doi.org/10.1109/ICSSE.2016.7551632

V. T. Bui, D. N. Truong, “Voltage Stability Enhancement of Bac Lieu Wind Power by ANFIS Controlled Static Var Compensator”, 4th International Conference on Green Technology and Sustainable Development, Ho Chi Minh City, Vietnam, November 23-24, 2018 DOI: https://doi.org/10.1109/GTSD.2018.8595629

M. A. Shoorehdeli, M. Teshnehlab, A. K. Sedigh, M. A. Khanesar, “Identification using ANFIS with intelligent hybrid stable learning algorithm approaches and stability analysis of training methods”, Applied Soft Computing, Vol. 9, No. 2, pp. 833–850, 2009 DOI: https://doi.org/10.1016/j.asoc.2008.11.001

S. P. Singh, S. C. Sharma, “A novel energy efficient clustering algorithm for wireless sensor networks”, Engineering, Technology & Applied Science Research, Vol. 7, No. 4, pp. 1775-1780, 2017 DOI: https://doi.org/10.48084/etasr.1277

Downloads

How to Cite

[1]
D. N. Truong and V. T. Bui, “Hybrid PSO-Optimized ANFIS-Based Model to Improve Dynamic Voltage Stability”, Eng. Technol. Appl. Sci. Res., vol. 9, no. 4, pp. 4384–4388, Aug. 2019.

Metrics

Abstract Views: 404
PDF Downloads: 267

Metrics Information
Bookmark and Share

Most read articles by the same author(s)