Transient Stability Constrained Optimal Power Flow Using Teaching Learning Based Optimization

被引：0

作者：

Oubbati, Youcef ^{[1
]}

Arif, Salem ^{[1
]}

机构：

[1] Univ Amar Telidji Laghouat, LACoSERE, Laghouat, Algeria

来源：

PROCEEDINGS OF 2016 8TH INTERNATIONAL CONFERENCE ON MODELLING, IDENTIFICATION & CONTROL (ICMIC 2016) | 2016年

关键词：

Power System; Transient Stability Constrained Optimal Power Flow (TSCOPF); Teaching-Learning-Based Optimization (TLBO); Optimal Power Flow (OPT); Power System Contingencies; DIFFERENTIAL EVOLUTION; GENETIC ALGORITHM; SELECTION;

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：

Transient Stability Constrained Optimal Power Flow (TSCOPF) constitutes one of the most computational intensive applications; it is used for power system preventive control against blackouts triggered by transient instability after a contingency. In this paper, a novel Optimal Power Flow (OPF) is proposed by adding the Transient Stability (TS) constraints into the conventional OPF problem, a Teaching-Learning-Based Optimization (TLBO) is proposed to solve the OPF problem. The objective function is to minimize the total cost of fuel for all generators. The proposed methodology has been tested on standard test systems the IEEE 30-bus network model. The simulation results are compared to those obtained with other conventional and new methods found in recent works.)

引用

页码：284 / 289

页数：6

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