Maximum power tracking of wave power generation system based on improved particle swarm optimization

In a wave power generation system, traditional particle swarm optimization (PSO) is prone to premature convergence and local optimal solution when it is used to control the maximum power tracking. Therefore, an improved PSO is proposed, and simulated annealing adaptive particle swarm optimization (S...

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Bibliographic Details
Published in:Journal of physics. Conference series 2023-06, Vol.2520 (1), p.12004
Main Authors: Lan, Ying, Gao, Guiyu
Format: Article
Language:English
Subjects:
Adaptive control
Algorithms
Electric power generation
Hyperbolic functions
Learning
Maximum power tracking
Optimization
Particle swarm optimization
Physics
Random numbers
Simulated annealing
Simulation
Tracking control
Wave power
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Summary:In a wave power generation system, traditional particle swarm optimization (PSO) is prone to premature convergence and local optimal solution when it is used to control the maximum power tracking. Therefore, an improved PSO is proposed, and simulated annealing adaptive particle swarm optimization (SAAPSO) is constructed. The algorithm controls the inertia weight coefficient factor by hyperbolic tangent function and makes the nonlinear adaptive change. Using a linear change strategy to control social learning factors and self-learning factors, a simulated annealing operation is introduced, and a temperature is set according to the initial state of the population. The population is guided to compare the fitness value with the random number in the Metropolis criterion and temperature, and whether a new solution is generated is judged, to improve the problem that the algorithm falls into a locally optimal solution. The simulation results show that the algorithm can effectively prevent the wave power generation system from falling into the local maximum power point, and the maximum wave energy capture ability is significantly improved.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2520/1/012004