APPLICATION OF PARTICLE SWARM OPTIMIZATION ALGORITHM FOR OPTIMAL REACTIVE POWER PLANNING

2007 ◽  
Vol 35 (1) ◽  
Author(s):  
Z. Al-Hamouz ◽  
S.F. Faisal ◽  
S. Al-Sharif
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Reactive Power ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Reactive Power Planning

Reactive Power Compensation of Distribution Network Based on Immune and Particle Swarm Optimization Algorithm

2012 ◽  
Vol 229-231 ◽  
pp. 1030-1033
Author(s):  
Wei Cui ◽  
Lin Chuan Li ◽  
Lei Zhang ◽  
Qian Sun
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Reactive Power ◽  
Distribution Network ◽  
Particle Swarm ◽  
Reactive Power Compensation ◽  
Swarm Optimization ◽  
Operation Costs ◽  
Sweep Algorithm

The reactive power compensation optimization in distribution network has the important meaning in maintaining system voltage stability, decreasing network loss and reducing operation costs. In order to meet factual conditions, we assume the system operates in minimum, normal and maximum three load modes and the objective function of problem includes the costs of power loss and the dynamic reactive power compensation devices allocated. In this paper we use Artificial Immune Algorithm(AIA) and Particle Swarm Optimization Algorithm(PSO) to determine compensate nodes and use the back/forward sweep algorithm calculate load flows. After applied into 28-nodes system, the result demonstrates the method is feasible and effective.

Multi-Objective Reactive Power Optimization Based on Refined Chaos Particle Swarm Optimization Algorithm

2014 ◽  
Vol 494-495 ◽  
pp. 1857-1860
Author(s):  
Ying Ai ◽  
Hong Wei Nie ◽  
Yi Xin Su ◽  
Dan Hong Zhang ◽  
Yao Peng
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Reactive Power ◽  
Power Optimization ◽  
Particle Swarm ◽  
Reactive Power Optimization ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Chaotic Mapping

In order to reduce the active network loss, increase the power quality and voltage static stability of power system, an index function of multi-objective reactive power optimization is established. Then, an improved adaptive chaotic particle swarm optimization algorithm is proposed to solve the problem. Through the using of cubic chaotic mapping, the particle population is initialized to enhance the diversity of its value; In the optimization process, poor fitness particles are updated with chaos disturbance, and their inertia weight are adjusted dynamically with particles fitness value so as to avoid local convergence. Simulation of IEEE 30 bus system shows that the proposed algorithm for reactive power optimization can avoid premature convergence effectively, and converge to optimal solution rapidly.

scholarly journals TAILORED PARTICLE SWARM OPTIMIZATION ALGORITHM FOR SOLVING OPTIMAL REACTIVE POWER PROBLEM

2020 ◽  
Vol 5 (12) ◽  
pp. 246-255
Author(s):  
K. Lenin
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Membrane Computing ◽  
Particle Swarm Optimization Algorithm ◽  
Reactive Power ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Neighbourhood Search ◽  
A Cell ◽  
Power Problem

This paper presents Tailored Particle Swarm Optimization (TPSO) algorithm for solving optimal reactive power problem. Particle Swarm optimization algorithm based on Membrane Computing is proposed to solve the problem. Tailored Particle Swarm Optimization (TPSO) algorithm designed with the framework and rules of a cell-like P systems, and particle swarm optimization with the neighbourhood search.  In order to evaluate the efficiency of the proposed algorithm, it has been tested on standard IEEE 118 & practical 191 bus test systems and compared to other specified algorithms. Simulation results show that Tailored Particle Swarm Optimization (TPSO) algorithm is superior to other algorithms in reducing the real power loss.

scholarly journals Progressive Particle Swarm Optimization Algorithm for Solving Reactive Power Problem

2015 ◽  
Vol 1 (3) ◽  
pp. 125
Author(s):  
Kanagasabai Lenin ◽  
Bhumanapally Ravindhranath Reddy ◽  
Munagala Surya Kalavathi
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Reactive Power ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Test System ◽  
Position Vector ◽  
Swarm Optimization ◽  
Power Problem

In this paper a Progressive particle swarm optimization algorithm (PPS) is used to solve optimal reactive power problem. A Particle Swarm Optimization algorithm maintains a swarm of particles, where each particle has position vector and velocity vector which represents the potential solutions of the particles. These vectors are modernized from the information of global best (Gbest) and personal best (Pbest) of the swarm. All particles move in the search space to obtain optimal solution. In this paper a new concept is introduced of calculating the velocity of the particles with the help of Euclidian Distance conception. This new-fangled perception helps in finding whether the particle is closer to Pbest or Gbest and updates the velocity equation consequently. By this we plan to perk up the performance in terms of the optimal solution within a rational number of generations. The projected PPS has been tested on standard IEEE 30 bus test system and simulation results show clearly the better performance of the proposed algorithm in reducing the real power loss with control variables are within the limits.

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