Research on Improved Bat Algorithm for Multi-objective Optimal Power Flow Problem

被引：0
|
作者：
Chen, Gonggui [1 ]
Qian, Jie [1 ]
Zhang, Zhizhong [2 ]
机构：
[1] Chongqing Univ Posts & Telecommun, Coll Automat, Chongqing, Peoples R China
[2] Chongqing Univ Posts & Telecommun, Key Lab Commun Network & Testing Technol, Chongqing, Peoples R China
来源：
2018 CHINESE AUTOMATION CONGRESS (CAC) | 2018年
关键词：
MOIBA algorithm; MOOPF problem; PDM; DISPATCH;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
To handle multi-objective optimal power flow (MOOPF) problem while satisfying various system constraints, a new multi-objective improved bat algorithm (MOIBA) is proposed. MOIBA algorithm can improve the shortcoming of basic bat algorithm which has weak global exploration and unsatisfactory diversity. To validate the performance of MOIBA algorithm, IEEE30 bus system is implemented on MATLAB software to optimize, respectively, power loss and fuel cost concurrently, emission and fuel cost concurrently, power loss and emission concurrently. A Pareto-dominant Method with constraint (PDM) is employed to satisfy inequality constraints of state variables. The results obtained by MOIBA algorithm, compared with MOPSO algorithm, show the effectiveness and superiority of proposed MOIBA algorithm in solving MOOPF problem.
引用
收藏
页码：1263 / 1268
页数：6
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