Solving Reactive Power Optimization Problem Using Weight Improved PSO Algorithm

2021 ◽  
Author(s):  
Shaima Hamdan Shri ◽  
Mohammed B. Essa ◽  
Ayad Fadhil Mijbas
Keyword(s):  
Optimization Problem ◽  
Reactive Power ◽  
Power Optimization ◽  
Pso Algorithm ◽  
Reactive Power Optimization ◽  
Improved Pso

Research on the Reactive Power Optimization

2013 ◽  
Vol 385-386 ◽  
pp. 991-994
Author(s):  
Yan Yan Wang ◽  
Yan Song Li
Keyword(s):  
Power System ◽  
Reactive Power ◽  
Power Optimization ◽  
Low Voltage ◽  
Pso Algorithm ◽  
Reactive Power Optimization ◽  
Voltage Level ◽  
Line Network ◽  
Improved Pso

The power system is facing line losses, low voltage level and some other issues, this article begin with the point of the reactive power optimization, and through with the improved PSO algorithm, we find a way to reduce the line network loss.

A Novel Reactive Power Compensation Approach Based on Particle Swarm Optimization

2015 ◽  
Vol 740 ◽  
pp. 401-404
Author(s):  
Yun Zhi Li ◽  
Quan Yuan ◽  
Yang Zhao ◽  
Qian Hui Gang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problem ◽  
Reactive Power ◽  
Power Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Particle Swarm Algorithm ◽  
Reactive Power Optimization ◽  
Swarm Optimization ◽  
Swarm Algorithm

The particle swarm optimization (PSO) algorithm as a stochastic search algorithm for solving reactive power optimization problem. The PSO algorithm converges too fast, easy access to local convergence, leading to convergence accuracy is not high, to study the particle swarm algorithm improvements. The establishment of a comprehensive consideration of the practical constraints and reactive power regulation means no power optimization mathematical model, a method using improved particle swarm algorithm for reactive power optimization problem, the algorithm weighting coefficients and inactive particles are two aspects to improve. Meanwhile segmented approach to particle swarm algorithm improved effectively address the shortcomings evolution into local optimum and search accuracy is poor, in order to determine the optimal reactive power optimization program.

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.

Optimal Reactive Power Dispatch Based on Mixed Bacterial Chemotaxis Algorithm

2014 ◽  
Vol 494-495 ◽  
pp. 1849-1852 ◽  
Author(s):  
Xiao Ying Zhang ◽  
Chen Li ◽  
Zhen Li
Keyword(s):  
Reactive Power ◽  
Power Optimization ◽  
Bacterial Chemotaxis ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Premature Convergence ◽  
Local Optimum ◽  
Reactive Power Optimization ◽  
Optimal Reactive Power Dispatch ◽  
Elite Strategy

Particle Swarm Optimization (PSO) algorithm converges fast but it is easy to fall into local optimum, and bacterial chemotaxis (BC) algorithm prevents premature convergence and prevents falling into local optimum, so a new mixed bacterial chemotaxis (MBC) algorithm is proposed by combining the PSO with BC. The novel algorithm is applied to reactive power optimization on power system. First the PSO is used to find best solution, then BC is used to find the optimal solution among the selected area of previous step, the reserving elite strategy is introduced to enhance the efficiency of the algorithm, and then the optimal solution is obtained. Through the comparison with PSO and BCC in the reactive power optimization of IEEE30-bus system, the results indicate that MBC not only prevents premature convergence to a large extent, but also keeps a more rapid convergence rate than PSO and BCC.

Interval Reactive Power Optimization for Distribution System with DGs

2014 ◽  
Vol 1008-1009 ◽  
pp. 421-425
Author(s):  
Yong Jin Chen ◽  
Jie He Su ◽  
Yong Jun Zhang ◽  
Ying Qi Yi
Keyword(s):  
Interval Arithmetic ◽  
Distribution System ◽  
Power Flow ◽  
Reactive Power ◽  
Power Optimization ◽  
Pso Algorithm ◽  
Optimization Method ◽  
Reactive Power Optimization ◽  
Load Fluctuation ◽  
Proposed Model

A reactive power optimization method based on interval arithmetic is presented to solve the uncertainty of the output of distributed generation (DG) and the effects of load fluctuation. The concept of interval number and interval arithmetic is introduced to model the interval power flow of distribution system, which is iterated by using the Krawczyk-Moore operator. The objective function is to minimize the interval midpoint value of system’s power loss, with taking the interval voltage constraints into consideration for the interval reactive power optimization model. A modified IEEE 14-bus system is used to validate the proposed model and its Particle Swarm Optimization (PSO) algorithm. The simulation results show that the proposed method is effective.

Power System Reactive Power Optimization Based on Improved Genetic Agorithm

2012 ◽  
Vol 614-615 ◽  
pp. 1361-1366
Author(s):  
Ai Ning Su ◽  
Hui Qiong Deng ◽  
Tian Wei Xing
Keyword(s):  
Genetic Algorithm ◽  
Power System ◽  
Optimization Problem ◽  
Reactive Power ◽  
Power Optimization ◽  
Fitness Function ◽  
Reactive Power Optimization ◽  
Penalty Factor ◽  
Mutation Strategy ◽  
Optimization Search

Reactive power optimization is an effective method for improving the electricity quality and reducing the power loss in power system, and it is a mixed nonlinear optimization problem, so the optimization process becomes very complicated. Genetic algorithm is a kind of adaptive global optimization search algorithm based on simulating biological genetic in the natural environment and evolutionary processes, can be used to solve complex optimization problems such as reactive power optimization. Genetic algorithm is used to solve reactive power optimization problem in this study, improved the basic genetic algorithm, included the select, crossover and mutation strategy, and proposed a individual fitness function with penalty factor. The proposed algorithm is applied to the IEEE9-bus system to calculate reactive power. The results show the superiority of the proposed model and algorithm.

Reactive Power Optimization Problem Research Based on Node-Voltage-Based and Branch-Current-Based Hybrid Electric Power Network Equations

2012 ◽  
Vol 614-615 ◽  
pp. 751-760
Author(s):  
Guo You Wang ◽  
Xi Lin Zhang ◽  
Yu Shi ◽  
Yang Liu ◽  
Cheng Min Wang ◽  
...  
Keyword(s):  
Electric Power ◽  
Computational Efficiency ◽  
Optimization Problem ◽  
Reactive Power ◽  
Power Optimization ◽  
Electric Power System ◽  
Power Network ◽  
Reactive Power Optimization ◽  
Hybrid Electric ◽  
Node Voltage

The electric power system is a specific example among various networks in nature and human society, in which the network flow models and arithmetic can be applied. The node-voltage-based and branch-current-based hybrid electric power network equations are established in this paper, and the reactive power optimization problem is modeled based on the established network equations. It is respectively solved while the reactive power optimization problem is decomposed as two sub-problems, among which a sub-problem is described by quadric minimum cost flow model and another one is expressed by a linear equations. Thereby, the complexity and dimensions of reactive power optimization problem are distinctly reduced due to the two decomposed sub-problems are easy to solve. It is proved that found optimal solution is closed to global by the computational efficiency analysis. The case study is made at IEEE-30 system and it is indicated that proposed approach could improve the computational efficiency of reactive power optimization problem by comparing with traditional optimal power flow arithmetic.

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