Optimal Reactive Power Dispatch problem using exchange market based Butterfly Optimization Algorithm

被引:9
作者
Dora, Bimal Kumar [1 ]
Rajan, Abhishek [1 ]
Mallick, Sourav [1 ]
Halder, Sudip [2 ]
机构
[1] Natl Inst Technol Sikkim, Ravangla 737139, Sikkim, India
[2] Visvesvaraya Natl Inst Technol, Nagpur 440010, India
关键词
Optimal Power Flow; Metaheuristic algorithm; Butterfly Optimization Algorithm; Optimal Reactive Power Dispatch; WOLF OPTIMIZER; FLOW; SYSTEMS;
D O I
10.1016/j.asoc.2023.110833
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The Butterfly Optimization Algorithm (BOA) is a swarm-based optimization technique which takes its inspiration from the butterflies' foraging activity. BOA has gained extensive popularity among the research community and is now utilized to tackle a variety of optimization challenges. However, literatures suggest that its exploration and exploitation are not properly balanced. To overcome this problem, BOA is fused with the crossover of Exchange Market Algorithm (EMA) and with non-uniform mutation. In this paper, enhanced BOA (EBOA) is used for solving the Optimal Reactive Power Dispatch (ORPD) problems. ORPD is traditionally a power system optimization tool that regulates control variables such as Generator bus Voltage, tap settings of tap-changing transformers, and VAR output of compensating devices in order to reduce real power loss, improve voltage deviation, and increase voltage stability. The suggested technique is evaluated using the IEEE 30 bus standard test system, IEEE 118 bus standard test system and Indian 62 bus system. To determine the efficacy of the algorithm, the findings from BOA and EBOA are compared to those produced by other researchers and published in the literature. From the results it can be observed that the proposed algorithm is able to reduce the active power loss by 22%, voltage deviation by 91.6% and L-Index by 42.02 % from their corresponding initial value for IEEE 30 bus system. Similar results can be seen from other test system also. The proposed algorithm is also tested in solving benchmark problems. The simulated results confirm the efficiency and robustness of the algorithm for solving ORPD problems.(c) 2023 Elsevier B.V. All rights reserved.
引用
收藏
页数:25
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