scholarly journals Decentralized Dynamic Optimization for Power Network Voltage Control

Author(s):  
Hao Jan Liu ◽  
Wei Shi ◽  
Hao Zhu
Author(s):  
Aristide Tolok Nelem ◽  
Pierre Ele ◽  
Papa Alioune Ndiaye ◽  
Salomé Ndjakomo Essiane ◽  
Mathieu Jean Pierre Pesdjock

Author(s):  
Ariel Antonowicz ◽  
Piotr Derbis ◽  
Mariusz Nowak ◽  
Andrzej Urbaniak

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1470 ◽  
Author(s):  
Amedeo Andreotti ◽  
Bianca Caiazzo ◽  
Alberto Petrillo ◽  
Stefania Santini ◽  
Alfredo Vaccaro

Modern power distribution systems require reliable, self-organizing and highly scalable voltage control systems, which should be able to promptly compensate the voltage fluctuations induced by intermittent and non-programmable generators. However, their deployment in realistic operation scenarios is still an open issue due, for example, to the presence of non-ideal and unreliable communication systems that allow each component within the power network to share information about its state. Indeed, due to technological constraints, time-delays in data acquisition and transmission are unavoidable and their effects have to be taken into account in the control design phase. To this aim, in this paper, we propose a fully distributed cooperative control protocol allowing the voltage control to be achieved despite the presence of heterogeneous time-varying latencies. The idea is to exploit the distributed intelligence along the network, so that it is possible to bring out an optimal global behavior via cooperative distributed control action that leverages both local and the outdated information shared among the devices within the power network. Detailed simulation results obtained on the realistic case study of the IEEE 30-bus test system are presented and discussed in order to prove the effectiveness of the proposed approach in the task of solving complex voltage control problems. Finally, a robustness analysis with respect to both loads variations and hard communication delays was also carried to disclose the efficiency of the approach.


2019 ◽  
Vol 217 ◽  
pp. 01002
Author(s):  
Aleksandr Domyshev ◽  
Alexey Osak ◽  
Kirill Zamula

The subsystem for optimal control of voltage and reactive power of EPS is developed. The proposed solution uses state of art methods for state estimation, forecasting and dynamic optimization. A new architecture of an artificial neural network is proposed – a neuro-analytical network. Algorithms are proposed that allow reliable combination of classical automatic control methods and methods using machine learning. The proposed methodology is designed for use in a real power system for automatic voltage control.


2020 ◽  
Vol 16 (1) ◽  
pp. 67-77
Author(s):  
Kohei Murakami ◽  
Shinya Yoshizawa ◽  
Hideo Ishii ◽  
Yasuhiro Hayashi ◽  
Hiroshi Kondo ◽  
...  

Author(s):  
Antoine Gravel-Savard ◽  
Maarouf Saad ◽  
Pierre-Jean Lagacé ◽  
Dalal Asber ◽  
Serge Lefebvre

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