New Efficient Evolutionary Algorithm Applied to Optimal Reactive Power Dispatch

New Efficient Evolutionary Algorithm Applied to Optimal Reactive Power Dispatch

Provas Kumar Roy (Dr. B. C. Roy Engineering College, India)
DOI: 10.4018/978-1-4666-6252-0.ch016
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Abstract

Evolutionary Algorithms (EAs) are well-known optimization techniques to deal with nonlinear and complex optimization problems. However, most of these population-based algorithms are computationally expensive due to the slow nature of the evolutionary process. To overcome this drawback and to improve the convergence rate, this chapter employs Quasi-Opposition-Based Learning (QOBL) in conventional Biogeography-Based Optimization (BBO) technique. The proposed Quasi-Oppositional BBO (QOBBO) is comprehensively developed and successfully applied for solving the Optimal Reactive Power Dispatch (ORPD) problem by minimizing the transmission loss when both equality and inequality constraints are satisfied. The proposed QOBBO algorithm's performance is studied with comparisons of Canonical Genetic Algorithm (CGA), five versions of Particle Swarm Optimization (PSO), Local Search-Based Self-Adaptive Differential Evolution (L-SADE), Seeker Optimization Algorithm (SOA), and BBO on the IEEE 30-bus, IEEE 57-bus, and IEEE 118-bus power systems. The simulation results show that the proposed QOBBO approach performed better than the other listed algorithms and can be efficiently used to solve small-, medium-, and large-scale ORPD problems.
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1. Introduction

Optimal reactive power dispatch (ORPD) has a growing impact on secure and economical operation of power systems. It is an effective method to minimize the transmission losses and maintain the power system running under normal conditions. All controllable variables, such as voltage of generators, tap ratio of transformers, Var injection of shunt compensators, are determined which minimizes real power losses, satisfying a given set of operational constraints. It is an effective method to improve voltage level, decrease power losses and maintain the power system running under normal conditions.

Different classical techniques have been reported in the literature pertaining to ORPD problem, including conventional approaches such as linear programming (LP) (Aoki, Fan & Nishikor, 1988), interior point methods (Granville, 1994; Yan, Yu, Yu, & Bhattarai,2006) and dynamic programming (DP) (Lu, & Hsu, 1995). These methods are local optimizers in nature, i.e., they might converge to local solutions instead of global ones if the initial guess happens to be in the neighborhood of a local solution. DP method may cause the dimensions of the problem to become extremely large, thus requiring enormous computational efforts.

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