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CSEE Journal of Power and Energy Systems 2024, 10(1): 41-50 https://doi.org/10.17775/CSEEJPES.2023.00440
Special Section Paper | Open Access | Issue | Published: 28 December 2023

System Strength Assessment Based on Multi-task Learning

Show Author's Information Baoluo Li1,  Shiyun Xu2,  Huadong Sun2( ),  Zonghan Li2,  Lin Yu2
Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education, Shandong University, Jinan 250014, China
China Electric Power Research Institute, Beijing 100192, China
Keywords:
multi-task learning, Critical short circuit ratio, hybrid intelligence enhancement, system strength
Cite this article:
Li B, Xu S, Sun H, et al. System Strength Assessment Based on Multi-task Learning. CSEE Journal of Power and Energy Systems, 2024, 10(1): 41-50. https://doi.org/10.17775/CSEEJPES.2023.00440
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Abstract Full text About this article

Increase in permeability of renewable energy sources (RESs) leads to the prominent problem of voltage stability in power system, so it is urgent to have a system strength evaluation method with both accuracy and practicability to control its access scale within a reasonable range. Therefore, a hybrid intelligence enhancement method is proposed by combining the advantages of mechanism method and data driven method. First, calculation of critical short circuit ratio (CSCR) is set as the direction of intelligent enhancement by taking the multiple renewable energy station short circuit ratio as the quantitative indicator. Then, the construction process of CSCR dataset is proposed, and a batch simulation program of samples is developed accordingly, which provides a data basis for subsequent research. Finally, a multi-task learning model based on progressive layered extraction is used to simultaneously predict CSCR of each RESs connection point, which significantly reduces evaluation error caused by weak links. Predictive performance and anti-noise performance of the proposed method are verified on the CEPRI-FS-102 bus system, which provides strong technical support for real-time monitoring of system strength.


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About this article

System Strength Assessment Based on Multi-task Learning

Show Author's information Baoluo Li1, Shiyun Xu2, Huadong Sun2( ), Zonghan Li2, Lin Yu2
Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education, Shandong University, Jinan 250014, China
China Electric Power Research Institute, Beijing 100192, China

Abstract

Increase in permeability of renewable energy sources (RESs) leads to the prominent problem of voltage stability in power system, so it is urgent to have a system strength evaluation method with both accuracy and practicability to control its access scale within a reasonable range. Therefore, a hybrid intelligence enhancement method is proposed by combining the advantages of mechanism method and data driven method. First, calculation of critical short circuit ratio (CSCR) is set as the direction of intelligent enhancement by taking the multiple renewable energy station short circuit ratio as the quantitative indicator. Then, the construction process of CSCR dataset is proposed, and a batch simulation program of samples is developed accordingly, which provides a data basis for subsequent research. Finally, a multi-task learning model based on progressive layered extraction is used to simultaneously predict CSCR of each RESs connection point, which significantly reduces evaluation error caused by weak links. Predictive performance and anti-noise performance of the proposed method are verified on the CEPRI-FS-102 bus system, which provides strong technical support for real-time monitoring of system strength.

Keywords: multi-task learning, Critical short circuit ratio, hybrid intelligence enhancement, system strength

References(24)

[1]

S. Rahman, S. Saha, S. N. Islam, M. T. Arif, M. Mosadeghy, M. E. Haque, and A. M. T. Oo, “Analysis of power grid voltage stability with high penetration of solar PV systems,” IEEE Transactions on Industry Applications, vol. 57, no. 3, pp. 2245–2257, May/Jun. 2021.
DOI Google Scholar
[2]

V. Telukunta, J. Pradhan, A. Agrawal, M. Singh, and S. G. Srivani, “Protection challenges under bulk penetration of renewable energy resources in power systems: a review,” CSEE Journal of Power and Energy Systems, vol. 3, no. 4, pp. 365–379, Dec. 2017.
DOI Google Scholar
[3]

N. P. W. Strachan and D. Jovcic, “Stability of a variable-speed permanent magnet wind generator with weak AC grids,” IEEE Transactions on Power Delivery, vol. 25, no. 4, pp. 2779–2788, Oct. 2010.
DOI Google Scholar
[4]

L. J. Cai and I. Erlich, “Doubly fed induction generator controller design for the stable operation in weak grids,” IEEE Transactions on Sustainable Energy, vol. 6, no. 3, pp. 1078–1084, Jul. 2015.
DOI Google Scholar
[5]

H. J. Gu, R. F. Yan and T. Saha, “Review of system strength and inertia requirements for the national electricity market of Australia,” CSEE Journal of Power and Energy Systems, vol. 5, no. 3, pp. 295–305, Sep. 2019.
Google Scholar
[6]

F. Zhang, H. H. Xin, D. Wu, Z. Wang, and D. Q. Gan, “Assessing strength of multi-infeed LCC-HVDC systems using generalized short-circuit ratio,” IEEE Transactions on Power Systems, vol. 34, no. 1, pp. 467–480, Jan. 2019.
DOI Google Scholar
[7]

H. Xiao, Y. Zhang, X. Z. Duan, and Y. H. Li, “Evaluating strength of hybrid multi-infeed HVDC systems for planning studies using hybrid multi-infeed interactive effective short-circuit ratio,” IEEE Transactions on Power Delivery, vol. 36, no. 4, pp. 2129–2144, Aug. 2021.
DOI Google Scholar
[8]
P. F. de Toledo, B. Bergdahl, and G. Asplund, “Multiple infeed short circuit ratio-aspects related to multiple HVDC into one AC network,” in 2005 IEEE/PES Transmission & Distribution Conference & Exposition: Asia and Pacific, 2005, pp. 1–6.
DOI
[9]

H. D. Sun, S. Y. Xu, T. Xu, Q. Guo, J. B. He, B. Zhao, L. Yu, Y. Zhang, W. F. Li, Y. K. Zhou, Y. T. Zhang, and Y. Y. Zhu, “Definition and index of short circuit ratio for multiple renewable energy stations,” Proceedings of the CSEE, vol. 41, no. 2, pp. 497–505, Jan. 2021.
Google Scholar
[10]
J. Schmall, S. H. Huang, Y. Li, J. Billo, J. Conto, and Y. Zhang, “Voltage stability of large-scale wind plants integrated in weak networks: an ERCOT case study,” in 2015 IEEE Power & Energy Society General Meeting, 2015, pp. 1–5.
DOI
[11]
GE Energy Consulting. (2014, Oct.). Minnesota renewable energy integration and transmission study: final report[Online]. Available: https://mn.gov/commerce-stat/pdfs/mrits-report-2014.pdf.
[12]
J. Bech, “Connection of wind farms to weak AC networks,” CIGRE, Paris, Dec. 2016.
[13]

L. Yu, H. D. Sun, S. Y. Xu, B. Zhao, and J. Zhang, “A critical system strength evaluation of a power system with high penetration of renewable energy generations,” CSEE Journal of Power and Energy Systems, vol. 8, no. 3, pp. 710–720, May 2022.
Google Scholar
[14]

D. Wu, G. G. Li, M. Javadi, A. M. Malyscheff, M. G. Hong, and J. N. Jiang, “Assessing impact of renewable energy integration on system strength using site-dependent short circuit ratio,” IEEE Transactions on Sustainable Energy, vol. 9, no. 3, pp. 1072–1080, Jul. 2018.
DOI Google Scholar
[15]

W. Dong, H. H. Xin, D. Wu, and L. B. Huang, “Small signal stability analysis of multi-infeed power electronic systems based on grid strength assessment,” IEEE Transactions on Power Systems, vol. 34, no. 2, pp. 1393–1403, Mar. 2019.
DOI Google Scholar
[16]

J. J. Q. Yu, D. J. Hill, A. Y. S. Lam, J. T. Gu, and V. O. K. Li, “Intelligent time-adaptive transient stability assessment system,” IEEE Transactions on Power Systems, vol. 33, no. 1, pp. 1049–1058, Jan. 2018.
DOI Google Scholar
[17]

C. Ren, Y. Xu, J. H. Zhao, R. Zhang, and T. Wan, “A super-resolution perception-based incremental learning approach for power system voltage stability assessment with incomplete PMU measurements,” CSEE Journal of Power and Energy Systems, vol. 8, no. 1, pp. 76–85, Jan. 2022.
Google Scholar
[18]

J. Xie and W. Sun, “Distributional deep reinforcement learning-based emergency frequency control,” IEEE Transactions on Power Systems, vol. 37, no. 4, pp. 2720–2730, Jul. 2022.
DOI Google Scholar
[19]

J. Li, S. Chen, X. Wang and T. Pu, “Load shedding control strategyin power grid emergency state based on deep reinforcement learning,” CSEE Journal of Power and Energy Systems, vol. 8, no. 4, pp. 1175–1182, Jul. 2022
Google Scholar
[20]

T. J. Wang and Y. Tang, “Transient stability preventive control basedon graph convolution neural network and transfer deep reinforcementlearning,” CSEE Journal of Power and Energy Systems, doi: 10.17775/CSEEJPES.2022.05030.
DOI Google Scholar
[21]
D. H. A. Lee and G. Andersson, “An equivalent single-infeed model of multi-infeed HVDC systems for voltage and power stability analysis,” IEEE Transactions on Power Delivery, vol. 31, no. 1, pp. 303–312, Feb. 2016.
[22]
H. Y. Tang, J. N. Liu, M. Zhao, and X. D. Gong, “Progressive layered extraction (PLE): a novel multi-task learning (MTL) model for personalized recommendations,” in Proceedings of the 14th ACM Conference on Recommender Systems, 2020, pp. 269–278.
DOI
[23]
J. Q. Ma, Z. Zhao, X. Y. Yi, J. L. Chen, L. C. Hong, and E. H. Chi, “Modeling task relationships in multi-task learning with multi-gate mixture-of-experts,” in 2018 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018, pp. 1930–1939.
DOI
[24]

A. Gupta, G. Gurrala, and P. S. Sastry, “An online power system stability monitoring system using convolutional neural networks,” IEEE Transactions on Power Systems, vol. 34, no. 2, pp. 864–872, Mar. 2019.
DOI Google Scholar
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Publication history

Received: 27 January 2023
Revised: 27 March 2023
Accepted: 28 April 2023
Published: 28 December 2023
Issue date: January 2024

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© 2023 CSEE.

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This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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