Path: Top -> Journal -> Telkomnika -> 2020 -> Vol 18, No 4, August

Power system state estimation using teaching learning-based optimization algorithm

Journal from gdlhub / 2021-01-20 15:24:37
By : Surender Reddy Salkuti, Telkomnika
Created : 2021-01-18, with 1 files

Keyword : meta-heuristic algorithms; nonlinear programming; observability; power flow analysis; state estimation;
Url : http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/14159
Document Source : web

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.

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Publisher IDgdlhub
OrganizationTelkomnika
Contact NameHerti Yani, S.Kom
AddressJln. Jenderal Sudirman
CityJambi
RegionJambi
CountryIndonesia
Phone0741-35095
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