Quasi Oppositional Teaching-Learning based Optimization for Optimal Power Flow Incorporating FACTS

Quasi Oppositional Teaching-Learning based Optimization for Optimal Power Flow Incorporating FACTS

Susanta Dutta (Department of Electrical Engineering, Dr. B.C. Roy Engineering College, Durgapur, India), Provas Kumar Roy (Department of Electrical Engineering, Jalpaiguri Government Engineering College, Jalpaiguri, India) and Debashis Nandi (Department of Information Technology, National Institute of Technology Durgapur, Durgapur, India)
Copyright: © 2016 |Pages: 21
DOI: 10.4018/IJEOE.2016040104


In this paper, quasi-oppositional teaching-learning based optimization (QOTLBO) is introduced and successfully applied for solving an optimal power flow (OPF) problem in power system incorporating flexible AC transmission systems (FACTS). The main drawback of the original teaching-learning based optimization (TLBO) is that it gives a local optimal solution rather than the near global optimal one in limited iteration cycles. In this paper, opposition based learning (OBL) concept is introduced to improve the convergence speed and simulation results of TLBO. The effectiveness of the proposed method implemented with MATLAB and tested on modified IEEE 30-bus system in four different cases. The simulation results show the effectiveness and accuracy of the proposed QOTLBO algorithm over other methods like conventional BBO and hybrid biogeography-based optimization (HDE-BBO). This method gives better solution quality in finding the optimal parameter settings for FACTS devices to solve OPF problems. The simulation study also shows that using FACTS devices, it is possible to improve the quality of the electric power supply thereby providing an economically attractive solution to power system problems.
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1. Introduction

Optimal power flow (OPF) is an important tool for power system operators both in planning and operation in the present day power systems. The main aspect of OPF is to minimize the costs of meeting the load demand for the power system while satisfying all the security constraints like the various equality and inequality constraints. On the other hand, Flexible AC transmission systems (FACTS) devices are power electronic controlled devices, which are integrated in power systems to increase the transmission line capability to its thermal limit, control the power flow in specific lines and improve the security of transmission system. FACTS devices could also be used to minimize the total generation cost of OPF problem. These devices also reduce unwanted loop flows in the heavily loaded lines thereby resulting in an increase of load ability, improved security and stability of the network.

Some conventional approaches have been used to solve the OPF problem with FACTS like linear programming (LP) (Ge, & Chung, 1999); (Sundar, & Ravikumar, 2012), quadratic programming (QP) (Chung, & Yun, 1998), interior point method (IPM) (Rakpenthai, Premrudeepreechacharn, & Uatrongjit, 2009) and Newton’s method (NM) (Ambriz-Perez, Acha, Fuerte-Esquivel, & Torre, 1998); (Ambriz-Perez, Acha, Fuerte-Esquivel, 2000) by linearizing the objective function and the system constraints around an operating point assuming differentiable, continuous, analytical and monotonically increasing cost function. Unfortunately, the problems of FACTS based OPF are highly non-convex and non-linear optimization problems and so these methods tends to stuck at local optimal solutions.

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