首页    期刊浏览 2024年11月30日 星期六
登录注册

文章基本信息

  • 标题:Power system state estimation using teaching learning-based optimization algorithm
  • 本地全文:下载
  • 作者:Surender Reddy Salkuti
  • 期刊名称:TELKOMNIKA (Telecommunication Computing Electronics and Control)
  • 印刷版ISSN:2302-9293
  • 出版年度:2020
  • 卷号:18
  • 期号:4
  • 页码:2125-2131
  • DOI:10.12928/telkomnika.v18i4.14159
  • 出版社:Universitas Ahmad Dahlan
  • 摘要:The main goal of this paper is to formulate power system state estimation (SE) problem as a constrained nonlinear programming problem with various constraints and boundary limits on the state variables. SE forms the heart of entire real time control of any power system. In real time environment, the state estimator consists of various modules like observability analysis, network topology processing, SE and bad data processing. The SE problem formulated in this work is solved using teaching leaning-based optimization (TLBO) technique. Difference between the proposed TLBO and the conventional optimization algorithms is that TLBO gives global optimum solution for the present problem. To show the suitability of TLBO for solving SE problem, IEEE 14 bus test system has been selected in this work. The results obtained with TLBO are also compared with conventional weighted least square (WLS) technique and evolutionary based particle swarm optimization (PSO) technique.
  • 关键词:meta-heuristic algorithms; nonlinear programming; observability; power flow analysis; state estimation;
国家哲学社会科学文献中心版权所有