scholarly journals A Fast Reactive Power Optimization in Distribution Network Based on Large Random Matrix Theory and Data Analysis

2016 ◽  
Vol 6 (6) ◽  
pp. 158 ◽  
Author(s):  
Wanxing Sheng ◽  
Keyan Liu ◽  
Hongyan Pei ◽  
Yunhua Li ◽  
Dongli Jia ◽  
...  
Keyword(s):  
Data Analysis ◽  
Random Matrix Theory ◽  
Random Matrix ◽  
Reactive Power ◽  
Power Optimization ◽  
Matrix Theory ◽  
Distribution Network ◽  
Reactive Power Optimization

scholarly journals Reactive power optimization system of distribution network based on edge calculation

2021 ◽  
Vol 1914 (1) ◽  
pp. 012033
Author(s):  
Jinbo Huang ◽  
Jiangxiao Fang ◽  
Liexiang Hu ◽  
Bolong Shi ◽  
Suirong Li ◽  
...  
Keyword(s):  
Reactive Power ◽  
Power Optimization ◽  
Distribution Network ◽  
Reactive Power Optimization ◽  
Optimization System

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.

Research on Reactive Power Optimization Based on Genetic Algorithms in Distribution Network

2014 ◽  
Vol 644-650 ◽  
pp. 2476-2478
Author(s):  
Lin Yuan Wang
Keyword(s):  
Genetic Algorithms ◽  
Mutation Rate ◽  
Exponential Function ◽  
Reactive Power ◽  
Power Optimization ◽  
Distribution Network ◽  
Adaptive Mutation ◽  
Initial Population ◽  
Reactive Power Optimization ◽  
Slow Convergence

The reactive power optimization is formulated based on genetic algorithms in distribution net work. SGA has defects of slow convergence and being prone to immature convergence. In order to eliminate the defects, an improved GA is proposed in this thesis. CIP scheme is presented, which can guarantee diversity of the population by designing the initial population to obtain all the values within the definition area. A parameter called individual distributing degree is defined to describe how individuals are distributed in the definition area. Adaptive mutation rate is defined as an exponential function of the retained generations of the Elitism, and it is in inverse proportion to individual distribution degree. It accelerates the convergent process.

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