Research Area:  Metaheuristic Computing

Abstract:

This article discusses the calculation and enhancement of available transfer capability (ATC) of different test systems with a unique condition. ATC is a strongly nonlinear AC optimal power-flow-based problem. In this article, biogeography-based optimization (BBO), grey wolf optimizer (GWO), selfish herd optimizer (SHO) and chaotic-selfish herd optimizer (CSHO) algorithm frameworks are implemented on different IEEE test systems, namely, IEEE 30-bus and IEEE 118-bus and Indian Northern Region Power Grid (NRPG) 246 bus systems under a variant condition to calculate ATC and enchantment of the ATC with and without unified power flow controller (UPFC) and contingency cases. The authors also propose to implement the concept of opposition-based learning (OBL), which is integrated with the SHO algorithm in this paper for improving the convergence characteristic and simulation study concerning the limitation of the conventional SHO algorithm. The effectiveness and feasibility of the proposed oppositional SHO (OSHO) algorithm are also tested with the help of the aforesaid test systems, and Friedmans test also has been performed.

Keywords:  
Available transfer capability
Biogeography-based optimization
Chaotic-selfish herd optimizer
Grey wolf optimizer
Friedmans testOpposition-based learning
Optimum power flow
Selfish herd optimizer
Unified power flow controller

Author(s) Name:  Kingsuk Majumdar, Provas Kumar Roy, Subrata Banerjee

Publisher name:  Taylor & Francis

DOI:  10.1080/03772063.2021.1960207

Volume Information:  

Paper Link:   https://www.tandfonline.com/doi/abs/10.1080/03772063.2021.1960207