Optimal Reconfiguration and Distributed Generator allocation in Distribution Network using an advanced Adaptive Differential Evolution

被引:0
|
作者
Biswas, Partha P. [1 ]
Mallipeddi, R. [2 ]
Suganthan, P. N. [1 ]
Amaratunga, Gehan A. J. [3 ]
机构
[1] Nanyang Technol Univ, Singapore, Singapore
[2] Kyungpook Natl Univ, Daegu, South Korea
[3] Univ Cambridge, Cambridge, England
基金
新加坡国家研究基金会;
关键词
Distribution network; real power loss; distributed generator; optimal reconfiguration; L-SHADE algorithm; POWER LOSS MINIMIZATION; SYSTEM RECONFIGURATION; OPTIMAL PLACEMENT; CUCKOO SEARCH; OPTIMIZATION; ALGORITHM; HYBRID;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Power loss in an electrical network is unavoidable due to its inherent resistance. However, for economical and efficient operation, the network loss shall be minimized to the extent possible. Construction of the distribution network is usually closed loop, though operation is radial with opening of a suitable sectionalizing switch that disconnects a branch in the loop. This process of reconfiguration i.e. selection of sectionalizing switch is done in a way such that system loss in minimized. Another effective way of reducing power loss is addition of distributed generators (DGs) locally to the system nodes (buses). DG size and location also need to be optimized for minimization of real power loss. This paper presents an application of a metaheuristic to simultaneously allocate DGs and perform reconfiguration of a couple of standard radial distribution networks. Both location and placement of the DG are optimized by the algorithm. As an obvious fact, the optimization problem is a combination of discrete (location) and continuous (rating) variables. Linear population size reduction technique of success history based adaptive differential evolution (L-SHADE) is implemented to perform the optimization task with objective of minimizing network real power loss. The algorithm is tested on standard IEEE 33-bus and 69-bus radial distribution networks. The simulation results are found to be promising and highly competitive when compared with results of other equivalent algorithms.
引用
收藏
页码:648 / 654
页数:7
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