Please wait a minute...

Frontiers in Energy

Front. Energy    2015, Vol. 9 Issue (1) : 7-21     https://doi.org/10.1007/s11708-014-0338-2
RESEARCH ARTICLE |
Impact of wind power generating system integration on frequency stabilization in multi-area power system with fuzzy logic controller in deregulated environment
Y. K. BHATESHVAR1,*(),H. D. MATHUR1,H. SIGUERDIDJANE2
1. Department of Electrical and Electronics Engineering, Birla Institute of Technology and Science, Pilani, Rajasthan 333031, India
2. Automatic Control Department, Supélec, Gif-sur-Yvette 91192, France
Download: PDF(2215 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
Abstract

Among the available options for renewable energy integration in existing power system, wind power is being considered as one of the suited options for future electrical power generation. The major constraint of wind power generating system (WPGS) is that it does not provide inertial support because of power electronic converters between the grid and the WPGS to facilitate frequency stabilization. The proposed control strategy suggests a substantial contribution to system inertia in terms of short-term active power support in a two area restructured power system. The control scheme uses fuzzy logic based design and takes frequency deviation as input to provide quick active power support, which balances the drop in frequency and tie-line power during transient conditions. This paper presents a comprehensive study of the wind power impact with increasing wind power penetration on frequency stabilization in restructured power system scenario. Variation of load conditions are also analyzed in simulation studies for the same power system model with the proposed control scheme. Simulation results advocates the justification of control scheme over other schemes.

Keywords two area power system      automatic generation control      wind power generating system (WPGS)      deregulated environment      fuzzy logic controller (FLC)     
Corresponding Authors: Y. K. BHATESHVAR   
Just Accepted Date: 17 November 2014   Online First Date: 17 December 2014    Issue Date: 02 March 2015
 Cite this article:   
Y. K. BHATESHVAR,H. D. MATHUR,H. SIGUERDIDJANE. Impact of wind power generating system integration on frequency stabilization in multi-area power system with fuzzy logic controller in deregulated environment[J]. Front. Energy, 2015, 9(1): 7-21.
 URL:  
http://journal.hep.com.cn/fie/EN/10.1007/s11708-014-0338-2
http://journal.hep.com.cn/fie/EN/Y2015/V9/I1/7
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
Y. K. BHATESHVAR
H. D. MATHUR
H. SIGUERDIDJANE
Fig.1  
Fig.2  Complete system model of load frequency control of two area thermal-hydro power system in deregulated environment
Parameter Value
Rating/MW 2000
Tg1,Tg2,Tg3 and Tg4/s 0.08
Tt1,Tt2,Tt3 and Tt4/s 0.3
Tr1,Tr2,Tr3 and Tr4/s 10
Kr1,Kr2,Kr3 and Kr4 0.5
b1,b2,b3 and b4 0.425
R1,R2,R3 and R4 2.4
Kp1,Kp2,Kp3 and Kp4 120
Tp1,Tp2,Tp3 and Tp4/s 20
T12 0.545
a12 -1
Tr1, Tr2 5
Tw1, Tw2 1
Rp1, Rp2 0.05
Rt1, Rt2 0.38
Tab.1  System parameters
Fig.3  Simplified block diagram of wind turbine
GAI Ki1 Ki2
Without WPGS support 0.37533 0.10
With WPGS support 0.90579 0.11033
Tab.2  Integral gains with GA in both areas
Fig.4  MISO type FLC
ΔACE
VVL VL L Z H VH VVH
ACE VVL VVL VVL VL VL L L Z
VL VVL VL VL L L Z H
L VL VL L L Z H H
Z VL L L Z H H VH
H L L Z H H VH VH
VH L Z H H VH VH VVH
VVH Z H H VH VH VVH VVH
Tab.3  Rule base for FLC
WPGS without freq. support WPGS with freq. support
GAI FLC GAI FLC
Peak undershoot Δf1 -0.006244 -0.004530 -0.001909 -0.001181
Δf2 -0.024975 -0.024468 -0.008426 -0.008955
ΔPtie12 -0.019263 -0.013099 -0.017579 -0.003104
Settling time Δf1 61.114581 50.959788 43.068700 8.795149
Δf2 49.695922 44.819118 34.009168 11.039225
ΔPtie12 >100 >100 >100 69.172200
Tab.4  Comparison of GAI and FLC controller with or without wind with step load change
Fig.5  Comparison of GAI without wind, GAI with wind, FLC without wind and FLC with wind at nominal values of system parameters with step load change

(a) Frequency deviation in Area 1; (b) frequency deviation in Area 2; (c) tie-line power deviation

Fig.6  Variable load change profile for which comparison done at 10%, 15% and 20% penetration
Fig.7  10% wind penetration with varying load in test Case B

(a) Frequency deviation in Area 1; (b) frequency deviation in Area 2; (c) tie-line power deviation

Fig.8  15% wind penetration with varying load

(a) Frequency deviation in Area 1; (b) frequency deviation in Area 2; (c) tie-line power deviation

Fig.9  15% wind penetration with varying load in test Case B

(a) Frequency deviation in Area 1; (b) frequency deviation in Area 2; (c) tie-line power deviation

Fig.10  20% wind penetration with varying load in test Case B
Fig.11  Simulation results of Case C. 1 (TT-TTW)

(a) Frequency deviation Area 1—TT-TTW system; (b) frequency deviation Area 2—TT-TTW system; (c) tie-line power deviation—TT-TTW system

Fig.12  An interconnected two Areas, Area 1 consisting of thermal and thermal power system and Area 2 consisting of hydro and hydro power system with WPGS in Area 2
Fig.13  Simulation results of Case C.2 (TT-HHW)

(a) Frequency deviation Area 1—TT-TTW system; (b) frequency deviation Area 2—TT-TTW system; (c) tie-line power deviation—TT-TTW system

Fig.14  An interconnected two Areas, Area 1 consisting of thermal and thermal power system and Area 2 consisting of thermal and thermal power system with WPGS in both areas
Fig.15  Simulation results of Case C. 3 (TTW-TTW)
Fig.16  An interconnected two Areas, Area 1 consisting of thermal and thermal power system and Area 2 consisting of hydro and hydro power system with WPGS in both Areas 1 and 2
Fig.17  Simulation results of Case C. 4 (TTW-HHW)

(a) Frequency deviation Area 1—TT-TTW system; (b) frequency deviation Area 2—TT-TTW system; (c) tie-line power deviation—TT-TTW system

1 Ekanayake J, Jenkins N. Comparison of the response of doubly fed and fixed-speed induction generator wind turbines to changes in network frequency. IEEE Transactions on Energy Conversion, 2004, 19(4): 800–802
https://doi.org/10.1109/TEC.2004.827712
2 Rabelo B, Hofmann W. Optimal active and reactive power control with the doubly-fed induction generator in the MW-class wind-turbines. 2013–<month>10</month>–<day>05</day>, http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=975283
3 Slootweg J G, de Haan S W H, Polinder H, Kling W L. General model for representing variable speed wind turbines in power system dynamics simulations. IEEE Transactions on Power Systems, 2003, 18(1): 144–151
https://doi.org/10.1109/TPWRS.2002.807113
4 Lalor G, Ritchie J, Rourke S, Flynn D, O’Malley M J. Dynamic frequency control with increasing wind generation. In: Procceedings of IEEE Power Engineering Society General Meeting. Denver, 2004, 1715–1720
5 de Almeida R G, Pecas Lopes J A. Participation of doubly fed induction wind generators in system frequency regulation. IEEE Transactions on Power Systems, 2007, 22(3): 944–950
https://doi.org/10.1109/TPWRS.2007.901096
6 Mullane A, O’Malley M. The inertial response of induction-machine-based wind turbines. IEEE Transactions on Power Systems, 2005, 20(3): 1496–1503
https://doi.org/10.1109/TPWRS.2005.852081
7 Tan W, Zhang H, Yu M. Decentralized load frequency control in deregulated environments. International Journal of Electrical Power & Energy Systems, 2012, 41(1): 16–26
https://doi.org/10.1016/j.ijepes.2012.02.013
8 Kundur P. Power System Stability and Control. McGraw-Hill Professional, 2006, 739
9 Bhatt P, Roy R. Optimized automatic generation control by SSSC and TCPS in coordination with SMES for two-area hydro-hydro power system. In: Proceedings of 2009 International Conference on Advances in Computing, Control, and Telecommunication Technologies. Trivandrum, 2009, 474–480
10 Miller N W, Sanchez-Gasca J J, Price W W, Delmerico R W. Dynamic modeling of GE 1.5 and 3.6 MW wind turbine-generators for stability simulations. IEEE Power Engineering Society General Meeting, 2003, 3: 1977–1983
11 Mauricio J M, Marano A, Gómez-Expósito A, Martinez Ramos J L. Frequency regulation contribution through variable-speed wind energy conversion systems. IEEE Transactions on Power Systems, 2009, 24(1): 173–180
https://doi.org/10.1109/TPWRS.2008.2009398
12 Nanda J, Mishra S, Saikia L C. Maiden application of bacterial foraging-based optimization technique in multiarea automatic generation control. IEEE Transactions on Power Systems, 2009, 24(2): 602–609
https://doi.org/10.1109/TPWRS.2009.2016588
13 Roy R, Ghoshal S P, Bhatt P. Evolutionary computation based four-area automatic generation control in restructured environment. In: Proceedings of 2009 3rd International Conference on Power Systems. Kharagpur, 2009, 25–30
14 Goldberg D E. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, 1989, 432
Related articles from Frontiers Journals
[1] Abdelhak DIDA,Djilani BENATTOUS. A complete modeling and simulation of DFIG based wind turbine system using fuzzy logic control[J]. Front. Energy, 2016, 10(2): 143-154.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed