C-Vine Pair Copula Based Wind Power Correlation Modelling in Probabilistic Small Signal Stability Analysis

Jin Xu; Wei Wu; Keyou Wang; Guojie Li

doi:10.1109/JAS.2020.1003267

Volume 7 Issue 4

Jun. 2020

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2020 > 7(4): 1154-1160

Jin Xu, Wei Wu, Keyou Wang and Guojie Li, "C-Vine Pair Copula Based Wind Power Correlation Modelling in Probabilistic Small Signal Stability Analysis," IEEE/CAA J. Autom. Sinica, vol. 7, no. 4, pp. 1154-1160, July 2020. doi: 10.1109/JAS.2020.1003267

Citation:

Jin Xu, Wei Wu, Keyou Wang and Guojie Li, "C-Vine Pair Copula Based Wind Power Correlation Modelling in Probabilistic Small Signal Stability Analysis," IEEE/CAA J. Autom. Sinica, vol. 7, no. 4, pp. 1154-1160, July 2020. doi: 10.1109/JAS.2020.1003267

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C-Vine Pair Copula Based Wind Power Correlation Modelling in Probabilistic Small Signal Stability Analysis

doi: 10.1109/JAS.2020.1003267

the Key Laboratory of Control of Power Transmission and Conversion, Ministry of Education, Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Funds:

the National Natural Science Foundation of China 51307107

the National Natural Science Foundation of China 51477098

the National Natural Science Foundation of China 51877133

SRFDP 20130073120034

State Grid Corporation of China Science and Technology Project Hybrid AC/DC Power Grid Planning and Optimization Study Under the Framework of GE

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Author Bio:

Jin Xu (M'20) received the B.S. degree in electrical engineering from Sichuan University, China, in 2013, and received the Ph.D. degree in electrical engineering from Shanghai Jiao Tong University, China, in 2019. He is currently a postdoctor at Shanghai Jiao Tong University. His research interests include power system stability analysis and real-time simulation. e-mail: xujin20506@foxmail.com

Wei Wu received the B.S. degree in electrical engineering from Huazhong University of Science and Technology, China, in 2012, and received the Ph.D. degree in electrical engineering from Shanghai Jiao Tong University, China, in 2017. His research interests include power system stability analysis and operation optimization.e-mail:wuweiceee@gmail.com

Keyou Wang(S'05-M'09) received the B.S. and M.S. degrees in electrical engineering from Shanghai Jiao Tong University, China, in 2001 and 2004, respectively, and the Ph.D. degree in electrical engineering from Missouri University of Science and Technology (formerly University of Missouri-Rolla), USA, in 2008. He is currently a Professor and Deputy Department Head of Electrical Engineering with Shanghai Jiao Tong University. His research interests include power system dynamics and stability, and renewable energy integration.e-mail:wangkeyou@sjtu.edu.cn

Guojie Li(M'09-SM'12) received the B.Eng. and M.E. degrees in electrical engineering from Tsinghua University, China, in 1989 and 1993, respectively, and the Ph.D. degree in electrical engineering from Nanyang Technological University, Singapore, in 1999. He was an Associate Professor with the Department of Electrical Engineering, Tsinghua University. He is currently a Professor with the Department of Electrical Engineering, Shanghai Jiao Tong University, China. His research interests include power system analysis and control, wind and PV power control and integration, and microgrid.e-mail:liguojie@sjtu.edu.cn
Corresponding author:
Keyou Wang, e-mail:wangkeyou@sjtu.edu.cn
Recommended by Associate Editor Changshui Zhang.

Abstract

Abstract

The increasing integration of wind power generation brings more uncertainty into the power system. Since the correlation may have a notable influence on the power system, the output powers of wind farms are generally considered as correlated random variables in uncertainty analysis. In this paper, the C-vine pair copula theory is introduced to describe the complicated dependence of multidimensional wind power injection, and samples obeying this dependence structure are generated. Monte Carlo simulation is performed to analyze the small signal stability of a test system. The probabilistic stability under different correlation models and different operating conditions scenarios is investigated. The results indicate that the probabilistic small signal stability analysis adopting pair copula model is more accurate and stable than other dependence models under different conditions.
- Monte Carlo simulation,
- pair copula,
- small signal stability,
- wind power correlation

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Recommended by Associate Editor Changshui Zhang.

References(30)

References

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In this paper, the C-vine pair copula theory is introduced to describe the complicated dependence of multidimensional wind power injection, and samples obeying this dependence structure are generated.
The probabilistic stability of power system integrated with six wind farms is investigated by performing the Monte Carlo simulations under different correlation models and different operating conditions scenarios.
In the case study of a modified New England test system, the simplified pair copula construction (sPCC) with C-vine structure proves to have a better reflection of the actual dependence than the linear correlation coefficient (LCC) model and multivariate normal copula model.

C-Vine Pair Copula Based Wind Power Correlation Modelling in Probabilistic Small Signal Stability Analysis

doi: 10.1109/JAS.2020.1003267

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