.png){kind=icon}
返回
Real coded genetic algorithm based transmission system loss estimation in dynamic economic dispatch problem
.png){kind=icon}
OA
AI
DOI：10.1016/j.aej.2018.01.014.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Estimation of transmission loss is vital in scheduling, optimization and planning of power systems. The conventional transmission loss evaluation methods used in power system scheduling problems are not accurate as the transmission network parameters in the system operator database are erroneous and not updated periodically. The conventional techniques rely on the precise network model. Moreover, loss evaluation gains significant importance as it affects the revenues of several utilities. In this context, this article proposes a method to evaluate transmission losses in a scheduling problem without relying on the network model. The proposed method uses samples of real power generation, consumption and losses collected at various operating conditions. From these data, genetic algorithm based loss coefficients (GALCs) are obtained by minimizing the mean absolute error between actual and calculated loss values using real coded genetic algorithm. Then, GALCs are used to evaluate losses in a dynamic economic dispatch problem and its performance is compared with conventional loss estimation techniques. The proposed GALC is validated on the IEEE 30 bus system and using the real time data of the Ontario power system. The performance analysis is also carried out for change in system operating conditions, transmission network modifications and outages. (C) 2018 Faculty of Engineering, Alexandria University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keyword:
Sample based transmission system loss coefficients
Real coded genetic algorithm
Transmission network model
Dynamic economic dispatch
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.8
论文数:
6.3K
被引数:
2.6W
机构
引用论文
.png){kind=icon}
分享收藏
分享收藏
查看更多内容