Capacity-based optimization using whale optimization technique of a power distribution network

被引:2
作者
Simachew, Bawoke [1 ]
Khan, Baseem [2 ]
Guerrero, Josep M. [3 ]
Padmanaban, Sanjeevikumar [4 ]
Mahela, Om Prakash [5 ]
Alhelou, Hassan Haes [6 ]
机构
[1] Natl Taipei Univ Technol, Dept Elect Engn, Taipei, Taiwan
[2] Hawassa Univ, Dept Elect & Comp Engn, Awasa, Ethiopia
[3] Aalborg Univ, Ctr Res Microgrids CROM, Aalborg, Denmark
[4] Aarhus Univ, CTiF Global Capsule, Dept Business Dev & Technol, Herning, Denmark
[5] Rajasthan Rajya Vidyut Prasaran Nigam Ltd, Power Syst Planning Div, Jaipur, Rajasthan, India
[6] Tishreen Univ, Dept Elect Power Engn, Latakia, Syria
关键词
capacitor placement; distributed generation; loss minimization; particle swarm optimization; whale optimization algorithm; DISTRIBUTION-SYSTEMS; PLACEMENT; RECONFIGURATION; GENERATION; MINIMIZATION; ALGORITHM; LOCATION; DG;
D O I
10.1002/eng2.12455
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
In the power distribution network, real power loss and voltage profile management are critical issues. By providing active and reactive power support, both of these issues can be managed. Distributed generation (DG) and capacitor bank (QG) can be utilized to solve these issues. Therefore, this paper utilized optimally placed and sized DG and capacitor (QG) to minimize losses and improve the voltage profile. The overall problem is optimized using an upgraded method of the fitness assignment and solution chasing based on the aggregate approach called multi-objective whale optimization algorithm (MWOA). Wind and solar photovoltaic sources with biomass are utilized as the DG sources with their probabilistic outputs. The developed method is tested using two practical feeders of Bahir Dar city distribution network, Ethiopia. The results of loss minimization and voltage profile enhancement with MWOA are compared with multi-objective particle swam optimization (MPSO) with an equal number of iterations to show the superiority of the developed method.
引用
收藏
页数:27
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