Reactive Power Optimization Algorithm of Active Distribution Network Based on Improved Quantum Particle Swarm Optimization

2019 ◽  
Author(s):  
Zhihao Ning ◽  
Can Wang ◽  
Wei Deng ◽  
Xu Jia Zhu ◽  
Longtao Jia
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Reactive Power ◽  
Power Optimization ◽  
Quantum Particle ◽  
Distribution Network ◽  
Reactive Power Optimization ◽  
Swarm Optimization ◽  
Quantum Particle Swarm Optimization ◽  
Active Distribution Network

Reactive Power Optimization in Distribution Network with Wind Farm

2012 ◽  
Vol 614-615 ◽  
pp. 1372-1376 ◽  
Author(s):  
Chuang Li ◽  
Min You Chen ◽  
Yong Wei Zhen ◽  
Ang Fu ◽  
Jun Jie Li
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Model ◽  
Wind Farm ◽  
Reactive Power ◽  
Power Optimization ◽  
Quantum Particle ◽  
Distribution Network ◽  
Reactive Power Optimization ◽  
Swarm Optimization ◽  
Quantum Particle Swarm Optimization

The traditional methods to adjust voltage in distribution network reactive power optimization is discretization，and it is difficult to realize the continuous voltage adjustment. A reactive power optimization model and algorithm in distribution network with wind farm is proposed. The network loss，deviation of voltage and stability of voltage are taken into account in the multi-objective reactive power optimization model. The quantum particle swarm optimization（QPSO）algorithm has been used to solve the reactive power optimization problem. The algorithm described particle state by wave function, not only increase the diversity of population，but also avoid premature convergence. The comparison of the simulation result between QPSO and PSO on the modified IEEE 33-bus system demonstrated the effectiveness and advantage of quantum particle swarm optimization.

Reactive Power Optimization with Chaotic Firefly Algorithm and Particle Swarm Optimization in A Distribution Subsystem Network

2016 ◽  
Vol 12 (1) ◽  
pp. 71-78
Author(s):  
Hamza Yapıcı ◽  
Nurettin Çetinkaya
Keyword(s):  
Particle Swarm Optimization ◽  
Firefly Algorithm ◽  
Optimization Problem ◽  
Reactive Power ◽  
Power Optimization ◽  
Distribution Network ◽  
Reactive Power Optimization ◽  
Power Losses ◽  
Swarm Optimization ◽  
Real Distribution

In this paper the minimization of power losses in a real distribution network have been described by solving reactive power optimization problem. The optimization has been performed and tested on Konya Eregli Distribution Network in Turkey, a section of Turkish electric distribution network managed by MEDAŞ (Meram Electricity Distribution Corporation). The network contains about 9 feeders, 1323 buses (including 0.4 kV, 15.8 kV and 31.5 kV buses) and 1311 transformers. This paper prefers a new Chaotic Firefly Algorithm (CFA) and Particle Swarm Optimization (PSO) for the power loss minimization in a real distribution network. The reactive power optimization problem is concluded with minimum active power losses by the optimal value of reactive power. The formulation contains detailed constraints including voltage limits and capacitor boundary. The simulation has been carried out with real data and results have been compared with Simulated Annealing (SA), standard Genetic Algorithm (SGA) and standard Firefly Algorithm (FA). The proposed method has been found the better results than the other algorithms.

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.

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