Opposition based red wolf algorithm for solving optimal reactive power problem

Lenin Kanagasabai

doi:10.11591/ijaas.v10.i3.pp193-197

Abstract

<p>This paper presents an opposition based red wolf optimization (ORWO) algorithm for solving optimal reactive power problem. Each red wolf has a flag vector in the algorithm, and length is equivalent to the whole sum of numbers which features in the dataset of the wolf optimization (WO). In this proposed algorithm, red wolf optimization algorithm has been intermingled with opposition-based learning (OBL). By this amalgamate procedure the convergence speed of the proposed algorithm will be increased. To discover an improved candidate solution, the concurrent consideration of a probable and its corresponding opposite are estimated which is closer to the global optimum than an arbitrary candidate solution. Proposed algorithm has been tested in standard IEEE 14-bus and 300-bus test systems. The simulation results show that the proposed algorithm reduced the real power loss considerably.</p>

Highlights

In this work the key objective is actual power loss reduction
This paper presents an opposition based red wolf optimization (ORWO) algorithm for solving optimal reactive power problem
Proposed algorithm has been tested in standard IEEE 14-bus and 300-bus test systems

Summary

INTRODUCTION

In this work the key objective is actual power loss reduction. Optimal reactive power problem has been solved by a variety of methods [1]-[6]. This paper proposes an opposition based red wolf optimization (ORWO) algorithm for solving optimal reactive power problem. Each red wolf has a flag vector in the algorithm and length is equivalent to the whole sum of numbers which features in the dataset of the wolf optimization (WO) [17]-[19]. In this proposed algorithm red wolf optimization algorithm has been intermingled with opposition based learning (OBL) [20]. The simulation results show that the proposed algorithm reduced the real power loss considerably

PROBLEM FORMULATION

OPPOSITION BASED RED WOLF OPTIMIZATION

SIMULATION RESULTS

CONCLUSION