The Initial Guess Estimation Newton Method for Power Flow in Distribution Systems

Qiuye Sun; Ling Liu; Dazhong Ma; Huaguang Zhang

doi:10.1109/JAS.2017.7510514

Volume 4 Issue 2

Apr. 2017

IEEE/CAA Journal of Automatica Sinica

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Qiuye Sun, Ling Liu, Dazhong Ma and Huaguang Zhang, "The Initial Guess Estimation Newton Method for Power Flow in Distribution Systems," IEEE/CAA J. Autom. Sinica, vol. 4, no. 2, pp. 231-242, Apr. 2017. doi: 10.1109/JAS.2017.7510514

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Qiuye Sun, Ling Liu, Dazhong Ma and Huaguang Zhang, "The Initial Guess Estimation Newton Method for Power Flow in Distribution Systems," IEEE/CAA J. Autom. Sinica, vol. 4, no. 2, pp. 231-242, Apr. 2017. doi: 10.1109/JAS.2017.7510514

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The Initial Guess Estimation Newton Method for Power Flow in Distribution Systems

doi: 10.1109/JAS.2017.7510514

Qiuye Sun^{1
,
,},
Ling Liu^2
,,
Dazhong Ma¹,
Huaguang Zhang^{1, 3
,}

1.
School of Information Science and Engineering, Northeastern University, Shenyang 110819, China
2.
School of College of Science, Northeastern University, Shenyang 110819, China
3.
Key Laboratory of Integrated Automation of Process Industry (Northeastern University) of the National Education Ministry, Shenyang 110819, China

Funds:

National Natural Science Foundation of China (NSFC) Key Program 61573094

the Fundamental Research Funds for the Central Universities N140402001

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Author Bio:
Ling Liu graduated from the Northeastern University, in 2014. She received the B.S. degree in electrical engineering and automation, and the M.S. degree in power system and automation, from the Northeastern University, Shenyang, China, in 2014 and 2017. Since 2017, she has been a assistant experimentalist with the School of College of Science, Northeastern University, China. Her current research interests include power flow in distribution system and power flow calculation with distributed generations.(e-mail: 13998257932@163.com)

Dazhong Ma graduated from Northeastern University, in 2004. He received the B.S. degree in automation, and the Ph.D. degree in control theory and control engineering, from Northeastern University, Shenyang, China, in 2004 and 2011, respectively. He is currently a professor with the School of Information Science and Engineering, Northeastern University, China. He is currently a lecturer with Northeastern University. His current research interests include fault diagnosis, fault-tolerant controls, and nonlinear controls.(e-mail: madazhong@ise. neu.edu.cn)

Huaguang Zhang graduated from Northeast Dianli University. He received the B. S. degree and the M. S. degree in control engineering from Northeast Dianli University, in 1982 and 1985, respectively. He received the Ph. D. degree in thermal power engineering and automation from Southeast University, Nanjing, China, in 1991. He has authored and coauthored over 280 journal and conference papers, six monographs and co-invented 90 patents. Dr. Zhang is chair of the Adaptive Dynamic Programming & Reinforcement Learning Technical Committee on IEEE Computational Intelligence Society. He is an associate editor of Automatica, IEEE Transactions on Neural Networks, IEEE Transactions on Cybernetics, and Nerocomputing, respectively. He was an associate editor of IEEE Transactions on Fuzzy Systems (2008-2013). He was awarded the Outstanding Youth Science Foundation Award from the National Natural Science Foundation Committee of China in 2003. He was named the Cheung Kong Scholar by the Education Ministry of China in 2005. He is a recipient of the IEEE Transactions on Neural Networks 2012 Outstanding Paper Award. His research interests include fuzzy control, stochastic system control, neural networks based control, nonlinear control, and their applications.(e-mail: zhanghuaguang@ise.neu.edu.cn)
Corresponding author: Qiuye Sun graduated from Northeast Dianli University, in 2000. He received the M.S. degree in power electronics and drives and the Ph.D. degree in control theory and control engineering from Northeastern University, Shenyang, China, in 2004 and 2007, respectively. Since 2014, he has been a full professor with the School of Information Science and Engineering, Northeastern University, China. His research interests include analysis and diagnosis of uncertain information in power distribution network, optimization analysis technology of power distribution network and network control of distributed generation system. He has authored and coauthored over 280 journal and conference papers, six monographs and co-invented 90 patents. Corresponding author of this paper.(e-mail: sunqiuye@mail.neu.edu.cn)
Received Date: 2015-11-03
Accepted Date: 2016-04-01

Abstract

Abstract

With the increasing integration of distributed generations (DGs), there is a demand for DGs to play a more important role on the voltage regulation. Meanwhile, the high penetration of DGs could raise a technical problem that the distribution system may operate with bi-directional power flow, leading to the inadequacy of the traditional power flow. Considering this new scenario in distribution system power flow, the convergence theorem is proposed, which contributes to develop a novel selection method of the initial guess closed to the convergent solution. Moreover, to ensure the fast rate of power flow convergence, the theorem of the maximum iterations estimation is also proposed. Based on the two proposed theorems, an Initial Guess Estimation Newton method is proposed, considering different operational status of DGs and initial guess sensitivity simultaneously. Based on the standard node systems, Tongliao grid, and 69 system of USA, three simulation examples are provided to illustrate the effectiveness of the proposed method.
- Convergent theorem,
- high penetration distributed generations (DGs),
- initial Guess estimation Newton method,
- maximum iteration times

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References(36)

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The Initial Guess Estimation Newton Method for Power Flow in Distribution Systems

doi: 10.1109/JAS.2017.7510514

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