Outline

  • Abstract
  • Keywords
  • 1. Introduction
  • 2. Power Flow Equations
  • 2.1. Constraints
  • 2.1.1. Equality Constraints
  • 2.1.2. Inequality Constraints
  • 3. Particle Swarm Optimization
  • 3.1. Introduction
  • 3.2. Advantages and Disadvantages
  • 3.3. Algorithm and Flowchart
  • 3.4. Dynamic Weights
  • 4. Results
  • 5. Conclusion
  • References

رئوس مطالب

  • چکیده
  • کلید واژه ها
  • 1. مقدمه
  • 2. معادلات جریان قدرت
  • 2.1. محدودیت ها
  • 2.1.1. محدودیت های برابر
  • 2.1.2. محدودیت های نابرابری
  • 3. بهینه سازی اجتماع ذرات
  • 3.1. مقدمه
  • 3.2. مزایا و معایب
  • 3.3. الگوریتم و فلوچارت/نمودار جریان کار
  • 3.4. وزن های پویا
  • 4. نتایج
  • 5. نتیجه گیری

Abstract

This paper presents Particle Swarm Optimization Algorithm, with dynamic weights, applied to reduce the real power loss in a system. Particle Swarm Optimization with detailed study on weights for particle movements is used. Generator bus voltages, transformer tap positions and switch-able shunt capacitor banks are used as variables to control the reactive power flow. Particle Swarm Optimization has been applied to IEEE 6 bus system to present the case. The proposed dynamic weights show better, fast and consistent results with higher rate of convergence.

Keywords: - - -

Conclusions

Reactive power flow optimization is a complex combinatorial problem. Particle Swarm Optimization Algorithm with dynamic weights has been successfully used to minimize the active power losses in the system, while satisfying all power system constraints. The proposed algorithm was found to be better at reducing losses and convergence when compared to existing [14] methods. The PSO Algorithm has been coded using MATLAB.

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