Title: Optimal power flow using clustered adaptive teaching learning-based optimisation

Authors: S. Surender Reddy; B.K. Panigrahi

Addresses: Department of Railroad and Electrical Engineering, Woosong University, Daejeon – 300718, South Korea ' Department of Electrical Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, India

Abstract: In this paper, optimal power flow (OPF) with non-convex and non-smooth generator cost characteristics is presented using clustered adaptive teaching learning-based optimisation (CATLBO) algorithm. The proposed OPF formulation includes active and reactive power constraints; prohibited zones, and valve point loading (VPL) effects of generators. In the problem formulation, transformer tap settings and reactive power compensating devices settings are also considered as control variables. OPF is a complicated optimisation problem, hence there is a need to solve this problem with an accurate algorithm. In the proposed CATLBO algorithm, the class is divided into different sections, and allot different teacher to every section depending on the performance of that particular section. This sectioning of the class makes the proposed technique more robust and less prone to trapping in local optima. The OPF solution is obtained by considering generator fuel cost, transmission loss and voltage stability index as objective functions. The effectiveness of proposed CATLBO algorithm is validated on IEEE 30 bus test system, and the simulation results obtained with CATLBO algorithm are compared with other optimisation techniques presented in the literature.

Keywords: evolutionary algorithms; optimal power flow; OPF; voltage profile; voltage stability.

DOI: 10.1504/IJBIC.2017.084316

International Journal of Bio-Inspired Computation, 2017 Vol.9 No.4, pp.226 - 234

Accepted: 31 May 2016
Published online: 05 Jun 2017 *

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