Reactive power optimization model based on multi-objective bi-level programming and mixed algorithm

Author(s):  
Shufen Wang ◽  
Zhongping Wan ◽  
Heng Fan
2014 ◽  
Vol 494-495 ◽  
pp. 1857-1860
Author(s):  
Ying Ai ◽  
Hong Wei Nie ◽  
Yi Xin Su ◽  
Dan Hong Zhang ◽  
Yao Peng

In order to reduce the active network loss, increase the power quality and voltage static stability of power system, an index function of multi-objective reactive power optimization is established. Then, an improved adaptive chaotic particle swarm optimization algorithm is proposed to solve the problem. Through the using of cubic chaotic mapping, the particle population is initialized to enhance the diversity of its value; In the optimization process, poor fitness particles are updated with chaos disturbance, and their inertia weight are adjusted dynamically with particles fitness value so as to avoid local convergence. Simulation of IEEE 30 bus system shows that the proposed algorithm for reactive power optimization can avoid premature convergence effectively, and converge to optimal solution rapidly.


2013 ◽  
Vol 5 (7) ◽  
pp. 731-735
Author(s):  
Shengqing Li ◽  
Lilin Zeng ◽  
Xiaodong Luo ◽  
Yongan Li ◽  
Zhengping He

2014 ◽  
Vol 971-973 ◽  
pp. 979-982
Author(s):  
Yan Hong Li ◽  
Zhi Rong Zhang

Automatic voltage control(AVC) is the highest form of current power grid voltage and reactive power control,during the implementation of AVC, the whole network reactive power optimization isthe core and foundation. Thispaper researches and discuses the application of reactive power optimization inpower grid AVC. In the traditional reactive power optimization, the reactivepower limits of synchronous generators are fixed. In this paper, thesynchronous generator PQ operating limits change with external conditions,thus establishes reactive power optimization model in accordance with therequirements of AVC. Thispaper presents reactive power optimization method based on the principle ofpartition. The method decomposes the system to several partitions. Eachpartition separately optimized, thus reduces the system scale.And the convergence of the algorithm, the calculation speed and the discretevariable processing etc. improve. At the same time, this method reflects theclassification, hierarchical, partition, characteristics of coordinated controlof AVC.


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