Journal ArticleDOI
Reactive Power Optimization in Power System Based on Adaptive Particle Swarm Optimization
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TLDR
By testing on IEEE30 bus system simulation, comparing different algorithm optimization results show the effectiveness and superiority of APSO algorithm.Abstract:
This paper summarizes the reactive power optimization of power system characteristics and requirements, proposed to target the active power loss of reactive power optimization mathematical model, And the traditional classical algorithm can not handle the limitations of discrete variables, using the adaptive particle swarm optimization algorithm to solve the problem of reactive power optimization. By testing on IEEE30 bus system simulation, comparing different algorithm optimization results show the effectiveness and superiority of APSO algorithm.read more
References
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Proceedings ArticleDOI
Particle swarm optimization
TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
Journal ArticleDOI
The particle swarm - explosion, stability, and convergence in a multidimensional complex space
M. Clerc,James Kennedy +1 more
TL;DR: This paper analyzes a particle's trajectory as it moves in discrete time, then progresses to the view of it in continuous time, leading to a generalized model of the algorithm, containing a set of coefficients to control the system's convergence tendencies.
Proceedings ArticleDOI
Fitness-distance-ratio based particle swarm optimization
TL;DR: A modification of the particle swarm optimization algorithm (PSO) intended to combat the problem of premature convergence observed in many applications of PSO, which is shown to perform significantly better than the original PSO algorithm and some of its variants, on many different benchmark optimization problems.
Journal ArticleDOI
An efficient Differential Evolution based algorithm for solving multi-objective optimization problems
TL;DR: The proposed algorithm, named Multi-Objective Differential Evolution Algorithm (MODEA) utilizes the advantages of Opposition-Based Learning for generating an initial population of potential candidates and the concept of random localization in mutation step to introduce a new selection mechanism for generating a well distributed Pareto optimal front.
Proceedings ArticleDOI
Vector evaluated adaptive immune particle swarm optimization algorithm for multi-objective reactive power optimization
TL;DR: The vector evaluated adaptive immune particle swarm optimization (VEAIPSO) algorithm based on evaluation vector is applied to multi-objective reactive power optimization in this paper which provides an effective method for solving the problem.