doi: 10.3934/dcdss.2020104

A new iterative identification method for damping control of power system in multi-interference

1. 

School of Mechanical-electronic and Automobile Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China

2. 

Beijing Key Laboratory of Service Performance of Urban Rail Transit Vehicles, Beijing University of Civil Engineering and Architecture, Beijing 100044, China

3. 

School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Perth, 6845, Australia

* Corresponding author: Miao Yu

Received  August 2018 Revised  October 2018 Published  September 2019

Fund Project: The first author is supported by the Scholarship for Young University Teachers granted by China Scholarship Council (201709960017); National Natural Science Foundation of China (No.51407201); Research Funds for Beijing University of Civil Engineering and Architecture (No.X18121); The second author is supported by BUCEA Post Graduate Innovation Project (No.PG2012085)

In this paper, we consider the closed-loop model of a power system in a multi-interference environment. For a multi-interference power system, the closed-loop identification is a difficult task. Yet, the model identification error can degrade the effect of the damping control. This could lead to instability of the power grid. Thus, for the closed-loop identification, we propose an iterative online identification algorithm based on the recursive least squares method and the v-gap distance. The convergence of the algorithm is proved by using direct method. The proposed algorithm is applied to the New England system, for which the results obtained are compared with those obtained using the prediction error method and the Runge-Kutta method. From the simulation study being carried out on the IEEE 39-bus New England system, we observe that by using the iterative identification algorithm proposed in this paper, the output response time is reduced by about half when compared with those obtained by using the prediction error method and the Runge-Kutta method. Also, the number of oscillations in the output response is less. These clearly indicate that the algorithm proposed can effectively suppress low frequency oscillation. As for the amplitudes of the output responses produced by the three methods, they are basically the same.

Citation: Miao Yu, Haoyang Lu, Weipeng Shang. A new iterative identification method for damping control of power system in multi-interference. Discrete & Continuous Dynamical Systems - S, doi: 10.3934/dcdss.2020104
References:
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L. Q. DouQ. ZongZ. S. Zhao and Y. H. Ji, Iterative identification and control design with optimal excitation signals based on v-gap, Sci. in China, 52 (2009), 1120-1128. Google Scholar

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R. Goldoost-SolootY. Mishra and G. Ledwich, Wide-area damping control for inter-area oscillations using inverse filtering technique, IET Gener. Tran. and Distr., 9 (2015), 1534-1543. doi: 10.1049/iet-gtd.2015.0027. Google Scholar

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Y. ShenW. YaoJ. Y. Wen and H. B. He, Adaptive wide-area power oscillation damper design for photovoltaic plant considering delay compensation, IET Gener. Tran. and Distr., 11 (2017), 4511-4519. doi: 10.1049/iet-gtd.2016.2057. Google Scholar

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F. Z. SongY. LiuJ. X. XuX. F. YangP. He and Z. L. Yang, Iterative learning identification and compensation of space-periodic disturbance in PMLSM systems with time delay, IEEE Trans. on Ind. Electron., 65 (2018), 7579-7589. doi: 10.1109/TIE.2017.2777387. Google Scholar

[20]

C. Wu, Identification of Dominant Dynamic Characteristics of Power System Based on Ambient Signals, Ph.D thesis, Tsinghua University in Beijing, 2010.Google Scholar

[21]

C. Zhang and D. Shen, Zero-error convergence of iterative learning control based on uniform quantisation with encoding and decoding mechanism, IET Contr. Theory and Appl., 12 (2018), 1907-1915. doi: 10.1049/iet-cta.2017.0919. Google Scholar

[22]

S. ZhuX. J. Wang and H. Liu, Observer-based iterative and repetitive learning control for a class of nonlinear systems, IEEE/CAA J. Autom. Sinica, 5 (2018), 990-998. doi: 10.1109/JAS.2017.7510463. Google Scholar

[23]

H. Zhang and M. Gou, Convergence analysis of compressive sensing based on SCAD iterative thresholding algorithm, Chinese J. Eng. Math., 33 (2016), 243-258. Google Scholar

show all references

References:
[1]

H. K. AbdulkhaderJ. Jacob and A. T. Mathew, Fractional-order lead-lag compensator-based multi-band power system stabiliser design using a hybrid dynamic GA-PSO algorithm, IET Gener. Tran. and Distr., 12 (2018), 1515-1521. Google Scholar

[2]

P. Albertos and A. Sala, Iterative Identification and Control: Advances in Theory and Applications, Springer-Verlag, New York, 2002.Google Scholar

[3]

X. H. Bu and Z. S. Hou, Adaptive iterative learning control for linear systems with binary-valued observations, IEEE Trans. on Neur. Net. and Lear. Syst., 29 (2018), 232-237. doi: 10.1109/TNNLS.2016.2616885. Google Scholar

[4]

X. M. Chen and G. R. Guo, The convergence analysis of the WCE iterative algorithm, J. Natl. Univ. of Def. Tech., 3 (1986), 16-25. Google Scholar

[5]

M. Darabian and A. Jalilvand, Designing a wide area damping controller to coordinate FACTS devices in the presence of wind turbines with regard to time delay, IET Rene. Power Gener., 12 (2018), 1523-1534. doi: 10.1049/iet-rpg.2017.0602. Google Scholar

[6]

Z. L. DengX. H. Qin and M. B. Zhang, Frequency-domain analysis of robust monotonic convergence of norm-optimal iterative learning control, IEEE Trans. on Contr. Syst. Tech., 26 (2018), 637-651. Google Scholar

[7]

L. Q. DouQ. ZongZ. S. Zhao and Y. H. Ji, Iterative identification and control design with optimal excitation signals based on v-gap, Sci. in China, 52 (2009), 1120-1128. Google Scholar

[8]

R. Goldoost-SolootY. Mishra and G. Ledwich, Wide-area damping control for inter-area oscillations using inverse filtering technique, IET Gener. Tran. and Distr., 9 (2015), 1534-1543. doi: 10.1049/iet-gtd.2015.0027. Google Scholar

[9]

Q. Guo, The Iterative Methods to Solve Systems of Nonlinear Equations, Ph.D thesis, Hefei University of Technology in Anhui Province, 2015.Google Scholar

[10]

Z. X. Liu, Y. Z. Sun, X. Li, B. Song, Z. S. Liu and F. M. Feng, Wide-area damping control system in China Southern Power Grid and its operation analysis, Auto. Electric. Power Syst., 38 (2014), 152–159 and 183.Google Scholar

[11]

K. LiuY. M. ZhangX. Y. LiR. Jiang and Q. Zeng, Design of VSC-HVDC bilateral fuzzy logic reactive power damping controller based on oscillation transient energy decrease, Power Syst. Tech., 40 (2016), 1030-1036. Google Scholar

[12]

W. C. MengX. Y. WangB. FanQ. M. Yang and I. Kamwa, Adaptive nonlinear neural control of wide-area power systems, IET Gener. Tran. and Distr., 11 (2017), 4531-4536. Google Scholar

[13]

D. H. Owens and K. Feng, Parameter optimization in iterative learning control, Int. J. Contr., 76 (2003), 1059-1069. doi: 10.1080/0020717031000121410. Google Scholar

[14]

D. RimorovA. HenicheI. KamwaS. BabaeiG. Stefopolous and B. Fardanesh, Dynamic performance improvement of New York state power grid with multi-functional multi-band power system stabiliser-based wide-area control, IET Gener. Tran. and Distr., 11 (2017), 4537-4545. doi: 10.1049/iet-gtd.2017.0288. Google Scholar

[15]

X. RuanZ. Z. Bien and Q. Wang, Convergence characteristics of proportional-type iterative learning control in the sense of Lebesgue-p norm, IET Contr. Theory and Appl., 6 (2012), 707-714. doi: 10.1049/iet-cta.2010.0388. Google Scholar

[16]

G. SebastianY. TanD. Oetomo and I. Mareels, Feedback-based iterative learning design and synthesis with output constraints for robotic manipulators, IEEE Contr. Syst. Lett., 2 (2018), 513-518. doi: 10.1109/LCSYS.2018.2842186. Google Scholar

[17]

Y. ShenW. YaoJ. Y. Wen and H. B. He, Adaptive wide-area power oscillation damper design for photovoltaic plant considering delay compensation, IET Gener. Tran. and Distr., 11 (2017), 4511-4519. doi: 10.1049/iet-gtd.2016.2057. Google Scholar

[18]

T. D. SonG. Pipeleers and J. Swevers, Robust monotonic convergent iterative learning control, IEEE Trans. on Automat. Contr., 61 (2016), 1063-1068. doi: 10.1109/TAC.2015.2457785. Google Scholar

[19]

F. Z. SongY. LiuJ. X. XuX. F. YangP. He and Z. L. Yang, Iterative learning identification and compensation of space-periodic disturbance in PMLSM systems with time delay, IEEE Trans. on Ind. Electron., 65 (2018), 7579-7589. doi: 10.1109/TIE.2017.2777387. Google Scholar

[20]

C. Wu, Identification of Dominant Dynamic Characteristics of Power System Based on Ambient Signals, Ph.D thesis, Tsinghua University in Beijing, 2010.Google Scholar

[21]

C. Zhang and D. Shen, Zero-error convergence of iterative learning control based on uniform quantisation with encoding and decoding mechanism, IET Contr. Theory and Appl., 12 (2018), 1907-1915. doi: 10.1049/iet-cta.2017.0919. Google Scholar

[22]

S. ZhuX. J. Wang and H. Liu, Observer-based iterative and repetitive learning control for a class of nonlinear systems, IEEE/CAA J. Autom. Sinica, 5 (2018), 990-998. doi: 10.1109/JAS.2017.7510463. Google Scholar

[23]

H. Zhang and M. Gou, Convergence analysis of compressive sensing based on SCAD iterative thresholding algorithm, Chinese J. Eng. Math., 33 (2016), 243-258. Google Scholar

Figure 1.  Closed-loop Power System Model
Figure 2.  Closed-loop Power System Identification Model
Figure 3.  The flow chart of iterative identification algorithm based on RLS and $ v $-gap
Figure 4.  IEEE 39-bus New England test system
Figure 5.  The optimal parameters of the New England system being identified by the RLS parameter estimation
Figure 6.  The Bode diagrams of the identified model and the initial model for New England system
Figure 7.  Comparison of output responses for New England system
Figure 8.  The $ v $-gap distance between $ G $ and $ B_i $ for New England system
Table 1.  The output responses obtained by different identification methods for New England system
Runge-Kutta Iterative identification Prediction Error
Time/s 70 29 39
Amplitude/dB 0.912 0.984 0.883
Runge-Kutta Iterative identification Prediction Error
Time/s 70 29 39
Amplitude/dB 0.912 0.984 0.883
Table 2.  The frequency stability margin and the $ v $-gap distance corresponding to each identified data for New England system
Group 1 Group 2 Group 3 Group 4 Group 5 Group 6
The frequency stability margin 0.0447 0.0269 0.0325 0.0622 0.1272 0.1257
v-gap distance 0.5899 0.5660 0.4151 0.1462 0.1099 0.1048
Group 7 Group 8 Group 9 Group 10 Group 11 Group 12
The frequency stability margin 0.1258 0.1178 0.1177 0.1191 0.1193 0.1192
v-gap distance 0.1041 0.0645 0.0589 0.0590 0.0610 0.0645
Group 1 Group 2 Group 3 Group 4 Group 5 Group 6
The frequency stability margin 0.0447 0.0269 0.0325 0.0622 0.1272 0.1257
v-gap distance 0.5899 0.5660 0.4151 0.1462 0.1099 0.1048
Group 7 Group 8 Group 9 Group 10 Group 11 Group 12
The frequency stability margin 0.1258 0.1178 0.1177 0.1191 0.1193 0.1192
v-gap distance 0.1041 0.0645 0.0589 0.0590 0.0610 0.0645
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