基于粒子群算法的同步定相振动控制仿真研究

Simulation Research on Synchrophasing Vibration Control Based on Phase Optimization by Using Particle Swarm Optimization

传统的同步定相技术采用螺旋桨特征理论,通过遍历各个相位差的组合来寻找最优相位差,但随着设备数目增多,其计算量大大增加,不利于工程应用。利用遗传算法可计算设备间的最优相位差,但其计算复杂且收敛速度较慢,而安装过多传感器作为振动评价点来衡量系统整体振动水平则会导致数据冗余并增加系统复杂度。为了提高计算效率和精度,提出一种基于粒子群算法(PSO)的同步定相振动控制方法,通过粒子群算法对振动评价传感器在数量约束条件下进行布放位置优化,建立振源设备和振动评价点之间的数学模型,并再次使用粒子群算法更新粒子的位置和速度,实现参数空间中的最优相位求解。根据计算结果调整激励力间的相位差,实现同步定相振动控制。仿真结果表明,采用粒子群算法能使传感器数量大大减少,使用计算得到的最优相位调整旋转机械设备激励力间的相位差,能有效减小振动向船体结构的传递。与遗传算法相比,粒子群算法在计算效率和收敛速度方面表现出更好的性能。

The traditional synchronous phasing technology adopts the propeller characteristic theory to find the optimal phase difference by traversing the combination of each phase difference,but with the increase of the number of equipment,the amount of calculation increases greatly,which is not conducive to engineering applications.The genetic algorithm can be used to calculate the optimal phase difference between devices,but its calculation is complex and the convergence speed is slow,and installing too many sensors as vibration evaluation points to measure the overall vibration level of the system will lead to data redundancy and increase system complexity.In order to improve the computational efficiency and accuracy,a synchronous phase-based vibration control method based on Particle Swarm Optimization(PSO)is proposed,in which the placement position of the vibration evaluation sensor is optimized under the condition of quantity constraint through the particle swarm optimization algorithm,the mathematical model between the vibration source equipment and the vibration evaluation point is established,and the particle swarm optimization is used again to update the position and velocity of the particles,so as to realize the optimal phase solution in the parameter space.According to the calculation results,the phase difference between the excitation forces is adjusted to achieve synchronous phase vibration control.The simulation results show that the particle swarm optimization can greatly reduce the number of sensors,and the phase difference between the excitation forces of the rotating machinery equipment can be adjusted by using the calculated optimal phase,which can effectively reduce the transmission of vibration to the hull structure.Compared with genetic algorithms,particle swarm optimization exhibits better performance in terms of computational efficiency and convergence speed.