Optimal reactive power planning using evolutionary algorithms: a comparative study for evolutionary programming, evolutionary strategy, genetic algorithm, and linear programming

1998 ◽  
Vol 13 (1) ◽  
pp. 101-108 ◽  
Author(s):  
K.Y. Lee ◽  
F.F. Yang
Keyword(s):  
Genetic Algorithm ◽  
Linear Programming ◽  
Evolutionary Algorithms ◽  
Comparative Study ◽  
Reactive Power ◽  
Evolutionary Programming ◽  
Evolutionary Strategy ◽  
Reactive Power Planning

Distribution Network Reactive Power Planning Based on Improved Evolutionary Strategy Theory

2013 ◽  
Vol 732-733 ◽  
pp. 1023-1028
Author(s):  
Si Qing Sheng ◽  
Xing Li ◽  
Yang Lu
Keyword(s):  
Reactive Power ◽  
Distribution Network ◽  
Load Condition ◽  
Optimal Solution ◽  
Evolution Strategy ◽  
Evolutionary Strategy ◽  
Initial Population ◽  
Fitness Value ◽  
Cauchy Mutation ◽  
Reactive Power Planning

In this paper a distribution network reactive power planning mathematical model was established, taking the minimized sum of electrical energy loss at the different load operation modes and the investment for reactive power compensation equipments as objective function to solve the planning question respectively and taking the transformer tap as equality constraint. The evolution strategy is improved, The Euclidean distance is introduced into the formation of the initial population, and the initial population under the max load operation mode is based on the optimal solution of the min load condition. The Cauchy mutation and variation coefficient are introduced into the evolution strategy method. By means of improvement of fitness to ensure diversity of population in early and accuracy of the fitness value.

scholarly journals Dynamic economic dispatch: a comparative study for differential evolution, particle swarm optimization, evolutionary programming, genetic algorithm, and simulated annealing

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Jagat Kishore Pattanaik ◽  
Mousumi Basu ◽  
Deba Prasad Dash
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Comparative Study ◽  
Differential Evolution ◽  
Economic Dispatch ◽  
Particle Swarm ◽  
Evolutionary Programming ◽  
Swarm Optimization ◽  
Dynamic Economic Dispatch

AbstractThis paper presents a comparative study for five artificial intelligent (AI) techniques to the dynamic economic dispatch problem: differential evolution, particle swarm optimization, evolutionary programming, genetic algorithm, and simulated annealing. Here, the optimal hourly generation schedule is determined. Dynamic economic dispatch determines the optimal scheduling of online generator outputs with predicted load demands over a certain period of time taking into consideration the ramp rate limits of the generators. The AI techniques for dynamic economic dispatch are evaluated against a ten-unit system with nonsmooth fuel cost function as a common testbed and the results are compared against each other.

Choosing the best parameters for method of deformed stars in n-dimensional space

2021 ◽  
pp. 24-28
Author(s):  
Maryna Antonevych ◽  
Anna Didyk ◽  
Nataliia Tmienova ◽  
Vitaliy Snytyuk
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Differential Evolution ◽  
Classical Method ◽  
Dimensional Space ◽  
New Method ◽  
Evolutionary Strategy ◽  
Point Groups ◽  
Best Parameters

This paper is devoted to the problem of optimization of a function in -dimensional space, which, in general case, is polyextreme and undifferentiated. The new method of deformed stars in n-dimensional space was proposed. It is built on the ideas and principles of the evolutionary paradigm. Method of deformed stars is based on the assumption of using potential solutions groups. There by it allows to increase the rate of the accuracy and the convergence of the achieved result. Populations of potential solutions are used to optimize the multivariable function. In contrast to the classical method of deformed stars, we obtained a method that solves problems in -dimensional space, where the population of solutions consists of 3-, 4-, and 5-point groups. The advantages of the developed method over genetic algorithm, differential evolution and evolutionary strategy as the most typical evolutionary algorithms are shown. Also, experiments were performed to investigate the best configuration of method of deformed stars parameters.

OPTIMAL REACTIVE POWER PLANNING USING GENETIC ALGORITHM

2002 ◽  
Vol 25 (2) ◽  
pp. 31-42
Author(s):  
G. E. M. Aly ◽  
a. El-Desouki ◽  
Amany El-Zonkoly
Keyword(s):  
Genetic Algorithm ◽  
Reactive Power ◽  
Reactive Power Planning
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