A Novel Approach of Congestion Management in Deregulated Power System Using an Advanced and Intelligently Trained Twin Extremity Chaotic Map Adaptive Particle Swarm Optimization Algorithm

2018 ◽  
Vol 44 (8) ◽  
pp. 6861-6886 ◽  
Author(s):  
Indu Batra ◽  
Smarajit Ghosh
Keyword(s):  
Particle Swarm Optimization ◽  
Power System ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Chaotic Map ◽  
Congestion Management ◽  
Swarm Optimization ◽  
Adaptive Particle Swarm Optimization ◽  
Deregulated Power System ◽  
Novel Approach

Self-Adaptive Particle Swarm Optimization Algorithm with Mutation Operation Based on K-Means

2013 ◽  
Vol 760-762 ◽  
pp. 2194-2198 ◽  
Author(s):  
Xue Mei Wang ◽  
Yi Zhuo Guo ◽  
Gui Jun Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Adaptive Particle Swarm Optimization ◽  
Inertia Weight ◽  
Clustering Quality ◽  
Mutation Operation

Adaptive Particle Swarm Optimization algorithm with mutation operation based on K-means is proposed in this paper, this algorithm Combined the local searching optimization ability of K-means with the gobal searching optimization ability of Particle Swarm Optimization, the algorithm self-adaptively adjusted inertia weight according to fitness variance of population. Mutation operation was peocessed for the poor performative particle in population. The results showed that the algorithm had solved the poblems of slow convergence speed of traditional Particle Swarm Optimization algorithm and easy falling into the local optimum of K-Means, and more effectively improved clustering quality.

Intelligent Optimization of Hybrid Wind/PV/pumped-Storage Power System

2012 ◽  
Vol 512-515 ◽  
pp. 719-722
Author(s):  
Yan Ren ◽  
Yuan Zheng ◽  
Chong Li ◽  
Bing Zhou ◽  
Zhi Hao Mao
Keyword(s):  
Global Optimization ◽  
Particle Swarm Optimization ◽  
Power System ◽  
Hybrid System ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Good Convergence ◽  
Pumped Storage

The hybrid wind/PV/pumped-storage power system was the hybrid system which combined hybrid wind/PV system and pumped-storage power station. System optimization was very important in the system design process. Particle swarm optimization algorithm was a stochastic global optimization algorithm with good convergence and high accuracy, so it was used to optimize the hybrid system in this paper. First, the system reliability model was established. Second, the particle swarm optimization algorithm was used to optimize the system model in Nanjing. Finally, The results were analyzed and discussed. The optimization results showed that the optimal design method of wind/PV/pumped-storage system based on particle swarm optimization could take into account both the local optimization and the global optimization, which has good convergence high precision. The optimal system was that LPSP (loss of power supply probability) was zero.

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