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author:

Shao, Zhenguo (Shao, Zhenguo.) [1] | Huang, Gengye (Huang, Gengye.) [2] | Zhang, Yan (Zhang, Yan.) [3] | Chen, Feixiong (Chen, Feixiong.) [4]

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EI PKU

Abstract:

Affine power flow algorithm is critical for solving the uncertain power flow. It is necessary to improve the computational efficiency and reduce the conservatism of the algorithm. In the paper, based on the fixed noise element, a predictor-corrector affine equation iterative model was proposed. Meanwhile, the optimal correction strategy was proposed to adjust the noise element coefficient to ensure the completeness while reducing the conservatism. Furthermore, the power-voltage affine model with noise element correlation was established based on the characteristics of load demand. Simultaneously, the affine power flow equation was established based on the power-voltage affine model. Finally, a Gauss-Seidel and a Newton-Raphson affine power flow algorithm based on predictor-corrector method were proposed. It is verified that the proposed algorithm can reduce the conservatism with higher computational efficiency and accelerate convergence speed. © 2021 Chin. Soc. for Elec. Eng.

Keyword:

Computational efficiency Efficiency Electric load flow Iterative methods

Community:

  • [ 1 ] [Shao, Zhenguo]Fujian Smart Electrical Engineering Technology Research Center, College of Electrical Engineering and Automation, Fuzhou University, Fuzhou; 350108, China
  • [ 2 ] [Huang, Gengye]Fujian Smart Electrical Engineering Technology Research Center, College of Electrical Engineering and Automation, Fuzhou University, Fuzhou; 350108, China
  • [ 3 ] [Zhang, Yan]Fujian Smart Electrical Engineering Technology Research Center, College of Electrical Engineering and Automation, Fuzhou University, Fuzhou; 350108, China
  • [ 4 ] [Chen, Feixiong]Fujian Smart Electrical Engineering Technology Research Center, College of Electrical Engineering and Automation, Fuzhou University, Fuzhou; 350108, China

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Source :

Proceedings of the Chinese Society of Electrical Engineering

ISSN: 0258-8013

Year: 2021

Issue: 7

Volume: 41

Page: 2331-2340

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 2

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 3

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