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.

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.

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.

Reactive Power Optimization for Distribution Network with Distributed Generators Based on Semi-Definite Programming

2014 ◽  
Vol 1070-1072 ◽  
pp. 809-814
Author(s):  
Lei Dong ◽  
Ai Zhong Tian ◽  
Tian Jiao Pu ◽  
Zheng Fan ◽  
Ting Yu
Keyword(s):  
Mathematical Model ◽  
Reactive Power ◽  
Power Optimization ◽  
Distribution Network ◽  
Optimal Solution ◽  
Mixed Integer ◽  
Reactive Power Optimization ◽  
Global Optimal Solution ◽  
Distributed Generators ◽  
Global Optimal

Reactive power optimization for distribution network with distributed generators is a complicated nonconvex nonlinear mixed integer programming problem. This paper built a mathematical model of reactive power optimization for distribution network and a new method to solve this problem was proposed based on semi-definite programming. The original mathematical model was transformed and relaxed into a convex SDP model, to guarantee the global optimal solution within the polynomial times. Then the model was extended to a mixed integer semi-definite programming model with discrete variables when considering discrete compensation equipment such as capacitor banks. Global optimal solution of this model can be obtained by cutting plane method and branch and bound method. Numerical tests on the modified IEEE 33-bus system show this method is exact and can be solved efficiently.

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.

scholarly journals Multi-Mean Scout Particle Swarm Optimization (MMSCPSO) based Reactive Power Optimization in Large-Scale Power Systems

2021 ◽  
pp. 31-42
Author(s):  
Christophe Bananeza ◽  
Sylvère Mugemanyi ◽  
Théogène Nshimyumukiza ◽  
Jean Marie Vianney Niyodusenga ◽  
Jean De Dieu Munyaneza
Keyword(s):  
Particle Swarm Optimization ◽  
Reactive Power ◽  
Power Optimization ◽  
Particle Swarm ◽  
Active Power ◽  
Local Optimum ◽  
Reactive Power Optimization ◽  
Power Losses ◽  
Swarm Optimization ◽  
Active Power Losses

The particle swarm optimization (PSO) is a population-based algorithm belonging into metaheuristic algorithms and it has been used since many decades for handling and solving various optimization problems. However, it suffers from premature convergence and it can easily be trapped into local optimum. Therefore, this study presents a new algorithm called multi-mean scout particle swarm optimization (MMSCPSO) which solves reactive power optimization problem in a practical power system. The main objective is to minimize the active power losses in transmission line while satisfying various constraints. Control variables to be adjusted are voltage at all generator buses, transformer tap position and shunt capacitor.  The standard PSO has a better exploitation ability but it has a very poor exploration  ability. Consequently, to maintain the balance between these two abilities during the  search process by helping particles to escape from the local optimum trap, modifications were made where initial population was produced by tent and logistic maps and it was subdividing it into sub-swarms to ensure good distribution of particles within the search space. Beside this, the idle particles (particles unable to improve their personal best) were replaced by insertion of a scout phase inspired from the artificial bee colony in the standard PSO. This algorithm has been applied and tested on IEEE 118-bus system and it has shown a strong performance in terms of active power loss minimization and voltage profile improvement compared to the original PSO Algorithm, whereby the MMSCPSO algorithm reduced the active power losses at 18.681% then the PSO algorithm reduced the active power losses at 15.457%. Hence, the MMSCPSO could be a better solution for reactive power optimization in large-scale power systems.

Probabilistic Reactive Power Optimization of Distribution Network Considering Stochastic Wind Speed and Load

2013 ◽  
Vol 684 ◽  
pp. 676-679
Author(s):  
Shu Heng Chen ◽  
Luan Chen ◽  
Yong Chen
Keyword(s):  
Wind Speed ◽  
Reactive Power ◽  
Power Optimization ◽  
Distribution Network ◽  
Pso Algorithm ◽  
Sql Server ◽  
Particle Swarm Algorithm ◽  
Software System ◽  
Reactive Power Optimization ◽  
Loss Method

Based on the probabilistic loss model of distribution network and the improved hybrid particle swarm algorithm, a reactive power optimization algorithm is presented, which encompasses the effects of stochastic wind speed and load. Firstly, with the control vector dimension’s length augmented and with the probabilistic loss method built, the reactive power optimization model is presented. Secondly, with the Niche operations embedded into the original PSO, an improved hybrid PSO algorithm is presented. Lastly, the corresponding software system program is developed in VC++ language and on basis of SQL SERVER platform. While this software system being supplied into a case, the experimental data have proved that this algorithm possesses more adaptability. At the same time, compared with the RTS algorithm, the calculating process is speeded.

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