不平衡配电网中分布式发电机最优定位和最优规模的混合优化

Q2 Engineering
S. Mhetre, I. Korachagaon
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引用次数: 0

摘要

摘要本文的目标是通过将DG的最优放置问题作为一个受限的非线性优化问题来减少功率损耗和改善电压分布。本文提出的混合算法是将狮算法(LA)和海狮算法(Sea Lion Optimization algorithm, SLnO)的概念融合而成,称为基于多因素更新的混合模型(Multifactor Update-based hybrid Model, MUHM)。采用前向-后向扫描(FBSM)模型计算功率损耗。在使用DG的馈线组中,对电压分布和损耗最小化进行了三种测试用例:“情况1(DG单独提供实功率(P)),情况2 (DG单独提供无功功率(Q))和情况3 (DG同时提供实功率和无功功率)”。将建议的方法应用于各种IEEE测试系统,分别包括IEEE 33、IEEE 123和IEEE 69,以评估其有效性。结果表明,加载百分比为0时的功分别比SFF、BBO、BAT、LA和SLnO高12、15、135、4.65和8。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hybrid optimization for optimal positioning and sizing of distributed generators in unbalanced distribution networks
Abstract The goal of this work is to reduce power loss and improve voltage profile by formulating the optimal DG placement problem as a restricted nonlinear optimisation problem. As a novelty, the proposed hybrid algorithm, referred to as Multifactor Update-based Hybrid Model (MUHM) is constructed by merging the concepts of Lion Algorithm (LA) & Sea Lion Algorithm (Sea Lion Optimization Algorithm (SLnO). The Forward-Backward Sweep (FBSM) Model is used to calculate the power loss. Three test cases are examined for the voltage profile & loss minimization in the feeder team with DGs: “case 1(DG supplying real power alone (P), case 2 (DG supplying reactive power alone (Q) and Case 3 (DG supplying both real and reactive power)”. Application of the suggested method to various IEEE test systems, including IEEE 33, IEEE 123, and IEEE 69, respectively, is used to assess its efficacy. According, the results show that the presented work at loading percentage = 0 is 12, 15, 135, 4.65, and 8 superior to SFF, BBO, BAT, LA and SLnO, respectively.
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来源期刊
Energy Harvesting and Systems
Energy Harvesting and Systems Energy-Energy Engineering and Power Technology
CiteScore
2.00
自引率
0.00%
发文量
31
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