Research of active vibration control optimal disposition based on CMQPSO

2021 ◽  
pp. 1-8
Author(s):  
Xiangzhong Meng ◽  
Ying Ma ◽  
Qiang Guo
Keyword(s):  
Particle Swarm Optimization ◽  
Control System ◽  
Vibration Control ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Cloud Model ◽  
Active Vibration Control ◽  
Sensors And Actuators ◽  
Swarm Optimization ◽  
Active Vibration

The adaptive quantum particle swarm optimization algorithm based on cloud model and the multi-island genetic algorithm [15] have obvious advantages in convergence speed to solve the sensor optimization problem, and can effectively achieve global optimization. Due to the installation of sensors and actuators, the electromechanical coupling coefficient of intelligent structures is changed, which affects the vibration energy of structures. In this paper, the reserved energy index of structural vibration control system is taken as the objective optimization function. The position, number, length and control gain of sensors and actuators of active vibration control system are optimized. The adaptive Quantum-behaved Particle Swarm Optimization algorithm in cloud model(CMQPSO) is used as the optimization strategy, and the cantilever beam is taken as an example. This approach is verified its effectiveness and feasibility. It is found that excellent optimization results are obtained.

Cloud Adapt Particle Swarm Optimization Algorithm for Distribution Network Planning with Distributed Generation

2013 ◽  
Vol 427-429 ◽  
pp. 1136-1140 ◽  
Author(s):  
Bu Quan Xu ◽  
Li Chen Zhang ◽  
Bu Zhen Xu
Keyword(s):  
Particle Swarm Optimization ◽  
Distributed Generation ◽  
Optimization Algorithm ◽  
Distribution Systems ◽  
Particle Swarm Optimization Algorithm ◽  
Cloud Model ◽  
Particle Swarm ◽  
Value Assessment ◽  
General Group ◽  
Swarm Optimization

Distributed Generation (DG) can be used to improve power quality, power supply reliability and reduce network loss et. Meanwhile Particle Swarm Optimization algorithm (PSO) is easy to fall into the local minimum. In this paper we propose a Cloud Adaptive Particle Swarm Optimization algorithm (CAPSO) to optimize the site and size of DG based on cloud model which has a tendency and randomness property. Judged by two dynamic value assessment, particle belongs to which group, excellent, general and poor. The inertia weight in general group is adaptively varied depending on X-conditional cloud generation. Taking the minimum network loss as the objective function, simulation on the IEEE 33BUS distribution systems to validate the methodology. Analysis and simulations indicate that it has good convergence speed and exactness.

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