Reactive Power Optimization Based on CAGA Algorithm

2012 ◽  
Vol 616-618 ◽  
pp. 2210-2213
Author(s):  
Li Jun Chen ◽  
Ran Ran Hai ◽  
Ya Hong Zhang ◽  
Gang Gang Xu
Keyword(s):  
Genetic Algorithm ◽  
Reactive Power ◽  
Power Optimization ◽  
Cloud Model ◽  
Optimal Solution ◽  
Population Diversity ◽  
Adaptive Genetic Algorithm ◽  
Reactive Power Optimization ◽  
Global Optimal ◽  
Crossover And Mutation

Reactive power optimization is a typical high-dimensional, nonlinear, discontinuous problem. Traditional Genetic algorithm(GA) exists precocious phenomenon and is easy to be trapped in local minima. To overcome this shortcoming, this article will introduce cloud model into Adaptive Genetic Algorithm (AGA), adaptively adjust crossover and mutation probability according to the X-condition cloud generator to use the randomness and stable tendency of droplets in cloud model. The article proposes the cloud adaptive genetic algorithm(CAGA) ,according to the theory, which probability values have both stability and randomness, so, the algorithm have both rapidity and population diversity. Considering minimum network loss as the objective function, make the simulation in standard IEEE 14 node system. The results show that the improved CAGA can achieve a better global optimal solution compared with GA and AGA.

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.

scholarly journals Ant colony search algorithm for optimal reactive power optimization

2006 ◽  
Vol 3 (1) ◽  
pp. 77-88 ◽  
Author(s):  
K. Lenin ◽  
M.R. Mohan
Keyword(s):  
Genetic Algorithm ◽  
Power Systems ◽  
Optimization Problem ◽  
Reactive Power ◽  
Power Optimization ◽  
Search Algorithm ◽  
Ant Colony ◽  
Adaptive Genetic Algorithm ◽  
Reactive Power Optimization ◽  
Ant Colonies

The paper presents an (ACSA) Ant colony search Algorithm for Optimal Reactive Power Optimization and voltage control of power systems. ACSA is a new co-operative agents? approach, which is inspired by the observation of the behavior of real ant colonies on the topic of ant trial formation and foraging methods. Hence, in the ACSA a set of co-operative agents called "Ants" co-operates to find good solution for Reactive Power Optimization problem. The ACSA is applied for optimal reactive power optimization is evaluated on standard IEEE, 30, 57, 191 (practical) test bus system. The proposed approach is tested and compared to genetic algorithm (GA), Adaptive Genetic Algorithm (AGA).

Research on Algorithm of Distributed Reactive Power Optimization Based on Cloud Computing and Improved NSGA-II

2014 ◽  
Vol 644-650 ◽  
pp. 1927-1930
Author(s):  
Bao Yi Wang ◽  
Hao Yin ◽  
Shao Min Zhang
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Reactive Power ◽  
Power Optimization ◽  
Cloud Model ◽  
Local Optimum ◽  
Reactive Power Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Crossover And Mutation

A distributed reactive power optimization algorithm is put forward based on cloud computing and improved NSGA-II (fast non-dominated sorting genetic algorithm) in this paper. It is designed to solve problem of multi-objective reactive power optimization with huge amounts of data in power grid, whose difficulties lie in local optimum and slow processing speed. First, NSGA-II's crossover and mutation operator are improved based on Cloud Model, so as to satisfy the adaptive characteristics. In this way, we improved global optimization ability and convergence speed when dealing with large-scale reactive power optimization. Second, we introduced cloud computing, parallelized the proposed algorithm based on MapReduce programming framework. In this way, we achieved distributed improved NSGA-II algorithm, effectively improved the calculation speed of handling massive high-dimensional reactive power optimization. Through theoretical study demonstrated the superiority of the algorithm to solve the Multi-Objective reactive power optimization.

Reactive Power Optimization of Hydropower Station Based on Improved Genetic Algorithm

2011 ◽  
Vol 71-78 ◽  
pp. 2214-2217
Author(s):  
Wei Tian ◽  
Jin Chan Wang ◽  
Yu Cheng Ye ◽  
Wei Chen
Keyword(s):  
Genetic Algorithm ◽  
Reactive Power ◽  
Power Optimization ◽  
Hydropower Station ◽  
Reactive Power Optimization ◽  
Improved Genetic Algorithm ◽  
C Language ◽  
Crossover Probability ◽  
Mutation Strategy ◽  
Crossover And Mutation

In this paper, an improved GA was proposed to minimum the disadvantages of classic GA. This modified GA improved the crossover and mutation strategy by using of sort program to arrange chromosome from big to small. The crossover probability and mutation probability were decided by the numbers of the order. Samples were chosen to take part in interbreeding. The convergence speed and results can be improved in this way. Moreover, premature convergence and local convergence were avoided at the same time. The modified GA was implemented by an optimization program compiled in visual C++ language. It has been successfully applied in the reactive power optimization of a Hydropower station. The results showed the feasibility and validity of this modified genetic algorithm.

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