Optimization of Distributed Generation in Radial Distribution Network for Active Power Loss Minimization using Jellyfish Search Optimizer Algorithm

被引:0
作者
Ranga, Jarabala [1 ]
Thiagarajan, Y. [2 ]
Kesavan, D. [3 ]
Deglus, Jovin [4 ]
Reddy, Sibbala Bhargava [5 ]
Rajakumar, P. [6 ]
Priya, R. A. [6 ]
机构
[1] Ramachandra Coll Engn, Dept Elect & Elect Engn, Eluru, Andhra Pradesh, India
[2] Christ Coll Engn & Technol, Dept Elect & Elect Engn, Pondicherry, India
[3] Sona Coll Technol, Dept Elect & Elect Engn, Salem, Tamil Nadu, India
[4] Acharya Inst Technol, Dept Artificial Intelligence & Machine Learning, Bangalore, Karnataka, India
[5] Srinivasa Ramanujan Inst Technol, Dept Elect & Elect Engn, Anantapur, Andhra Pradesh, India
[6] Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & T, Dept Elect & Elect Engn, 400 Feet Outer Ring Rd, Chennai, Tamil Nadu, India
关键词
Distributed Generation; Radial Distribution Network; Jellyfish Search Optimizer; Active Power Losses; OPTIMAL ALLOCATION; DG;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The inclusion of distributed generation (DG) units in the distribution network (DN) effectively cuts down the power losses (PL) and strengthens the voltage profile (VP). This paper examines the effect of allocating different distributed generation (DG) in radial distribution networks (RDN) through an implementation of an optimization technique using a recently introduced bioinspired algorithm known as a jellyfish search optimizer (JSO). Unlike the other optimization algorithms, the JSO algorithm evades the local optimal trap and reaches the optimal solution in less time. The DG position(s) and size(s) are optimized for active power loss (APL) minimization with respect to several constraints. The effectiveness and robustness of the proposed optimization technique using JSO algorithm is investigated on a balanced IEEE RDNs with 33, 69 and 118 -buses. The simulation outcomes are obtained for different types (type I, II and III) of DG placement. Additionally, a comprehensive comparative study has been performed for the JSO and other algorithms. The comparison exemplifies that the proposed JSO optimization approach produces a better optimal solution with steady convergence than other techniques reported in the literature. Also, the simulation findings show the potentiality of JSO optimization method for solving complex optimization problems.
引用
收藏
页码:215 / 223
页数:9
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