USE OF A MULTI-OBJECTIVE TEACHING-LEARNING ALGORITHM FOR REDUCTIONOF POWER LOSSES IN A POWER TEST SYSTEM

MIGUEL A. MEDINA; JUAN M. RAMIREZ; CARLOS A. COELLO; SWAGATAM DAS

首页> 外文期刊>Dyna >USE OF A MULTI-OBJECTIVE TEACHING-LEARNING ALGORITHM FOR REDUCTIONOF POWER LOSSES IN A POWER TEST SYSTEM

【24h】

USE OF A MULTI-OBJECTIVE TEACHING-LEARNING ALGORITHM FOR REDUCTIONOF POWER LOSSES IN A POWER TEST SYSTEM

机译：多目标教学法在减少电力测试系统中的电力损耗中的应用

获取原文

掌桥外文数据库（机构版） >>

开具论文收录证明 >>

文献代查 >>

页面导航

摘要
著录项
相似文献
相关主题

摘要

This paper presents a multi-objective teaching learning algorithm based on decomposition for solving the optimal reactive powerdispatch problem (ORPD). The effectiveness and performance of the proposed algorithm are compared with respect to a multi-objectiveevolutionary algorithm based on decomposition (MOEA/D) and the NSGA-II. A benchmark power system model is used to test thealgorithms? performance. The results of the power losses reduction as well as the performance metrics indicate that the proposedalgorithm is a reliable choice for solving the problem.

机译：本文提出了一种基于分解的多目标教学学习算法，用于求解最优无功分配问题。相对于基于分解的多目标进化算法（MOEA / D）和NSGA-II，比较了所提算法的有效性和性能。使用基准电源系统模型来测试算法？性能。功率损耗降低的结果以及性能指标表明，所提出的算法是解决问题的可靠选择。

著录项

来源
《Dyna》 |2014年第185期|共页
作者
MIGUEL A. MEDINA; JUAN M. RAMIREZ; CARLOS A. COELLO; SWAGATAM DAS;
展开▼
作者单位

展开▼
收录信息
原文格式 PDF
正文语种
中图分类矿业工程;
关键词

相似文献

外文文献
中文文献
专利

1. Use of a multi-objective teaching-learning algorithm for reduction of power losses in a power test system [J] . Miguel A. Medina, Juan M. Ramirez, Carlos A. Coello, Dyna . 2014,第185期

机译：使用多目标教学算法来减少功率测试系统中的功率损耗
2. Applications of multi-objective dimension-based firefly algorithm to optimize the power losses, emission, and cost in power systems [J] . Chen Gonggui, Yi Xingting, Zhang Zhizhong, Applied Soft Computing . 2018,第期

机译：基于多目标维思萤火虫算法优化电力系统功率损耗，发射和成本的应用
3. Multi-objective enhanced PSO algorithm for optimizing power losses and voltage deviation in power systems [J] . Chen Gonggui, Liu Lilan, Guo Yanyan, Compel . 2016,第1期

机译：多目标增强型PSO算法，用于优化电力系统的功率损耗和电压偏差
4. Comparative Application of Multi-Objective Evolutionary Algorithms to the Voltage and Reactive Power Optimization Problem in Power Systems [C] . S.B.D.V.P.S. Anauth, Robert T.F. Ah King Simulated evolution and learning . 2010

机译：多目标进化算法在电力系统电压和无功优化问题中的比较应用
5. Optimal operational strategies for a day-ahead electricity market in the presence of market power using multi-objective evolutionary algorithms. [D] . Rodrigo, Deepal. 2007

机译：使用多目标进化算法，在存在市场力量的情况下，针对日间电力市场的最优运营策略。
6. Low-Power Testing of Losses in Millimeter-Wave Transmission Lines for High-Power Applications [O] . S. T. Han, E. N. Comfoltey, M. A. Shapiro, -1

机译：针对大功率应用的毫米波传输线中的损耗的低功率测试
7. Use of a multi-objective teaching-learning algorithm for reduction of power losses in a power test system [O] . Miguel A. Medina, Juan M. Ramirez, Carlos A. Coello, 2014

机译：使用多目标教学算法来降低功率测试系统中的功率损耗

USE OF A MULTI-OBJECTIVE TEACHING-LEARNING ALGORITHM FOR REDUCTIONOF POWER LOSSES IN A POWER TEST SYSTEM

摘要

著录项

相似文献

相关主题

期刊订阅