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Application of modified pigeon-inspired optimization algorithm and constraint -objective sorting rule on multi-objective optimal power flow problem

多客观的最佳的力量流动问题上的修改启发鸽子的优化算法和限制目的排序统治的申请

作     者:Chen, Gonggui Qian, Jie Zhang, Zhizhong Li, Shuaiyong 

作者机构:Chongqing Univ Posts & Telecommun Key Lab Ind Internet Things & Networked Control Minist Educ Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Chongqing Key Lab Complex Syst & Bion Control Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Key Lab Commun Network & Testing Technol Chongqing 400065 Peoples R China 

出 版 物:《APPLIED SOFT COMPUTING》 (应用软计算)

年 卷 期:2020年第92卷

页      面:106321-106321页

核心收录:

学科分类:08[工学] 0812[工学-计算机科学与技术(可授工学、理学学位)] 

基  金:National Natural Science Foundation Project of China Natural Science Foundation Project of Chongqing, China [cstc2018jcyjAX0536] Innovation Team Program of Chongqing Education Committee, China [CXTDX201601019] Chongqing University Innovation Team, China [KJTD201312] 

主  题:Modified pigeon-inspired optimization algorithm Optimal power flow problem Constraint-objective sorting rule Penalty function method 

摘      要:To solve the non-differentiable optimal power flow (OPF) problems with multiple contradictory objectives, a modified pigeon-inspired optimization algorithm (MPIO) is put forward in this paper. Combining with the common-used penalty function method (PFM), the MPIO-PFM algorithm is proposed and applied to optimize the active power loss, emission and fuel cost (with valve-point loadings) of power system. Eight simulation trials carried out on MATLAB software validate MPIO-PFM algorithm can obtain superior Pareto Frontier (PF) comparing with the typical NSGA-II algorithm. Nevertheless, some Pareto solutions obtained by MPIO-PFM algorithm cannot satisfy all system constraints due to the difficulty in choosing the proper penalty coefficients. Thus, an innovative approach named as constraint-objective sorting rule (COSR) is presented in this paper. The bi-objective and tri-objective trials implemented on IEEE 30-node, 57-node and 118-node systems demonstrate that the Pareto optimal set (POS) obtained by MPIO-COSR algorithm realizes zero-violation of various system constraints. Furthermore, the generational-distance and hyper-volume indexes quantitatively illustrate that in contrast to NSGA-II and MPIO-PFM methods, the MPIO-COSR algorithm can determine the evenly-distributed PFs with satisfactory-diversity. The intelligent MPIO-COSR algorithm provides an effective way to handle the non-convex MOOPF problems. (C) 2020 Elsevier B.V. All rights reserved.

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