基于滑模优化与神经控制的船载风力发电系统最大功率跟踪方法

Maximum power tracking method of shipborne wind power generation system based on sliding mode optimization and neural control

[目的]旨在解决船载风力发电系统中传统的最大功率跟踪算法的稳定性和快速性差问题。[方法]在系统分析船舶运动合成风场特性的基础上,提出基于单神经元比例积分(SNPI)的最优机械角速度跟踪控制策略,用以提升风机重启的跟踪快速性。同时,采用功率滑模极值搜索(PSMES)算法取代依赖风速精确测量的叶尖速比(TSR)方法,实现机械角速度的快速优化与发电系统频繁重启下的最大功率搜索。[结果]仿真结果表明,相比于传统算法,采用机械角速度PSMES优化和SNPI控制的最大功率跟踪策略可将快速性能提升50%以上,且稳定性能得到了改善。[结论]研究表明,所提最大功率跟踪算法具有明显的快速性和稳定性优势。

[Objective]This study aims to solve the problems of poor tracking stability and low rapidity of traditional maximum power tracking algorithms in shipborne wind power generation systems.[Methods]On the basis of systematically analyzing the characteristics of the synthetic wind field of ship motion, an optimal mechanical angular velocity tracking control strategy based on single neuron proportional integral(SNPI) is proposed to improve the tracking speed of wind turbine restart. At the same time, the power sliding mode extremum seeking(PSMES) algorithm is used to replace the tip speed ratio(TSR) method which relies on accurate wind speed measurement to achieve the rapid optimization of mechanical angular velocity and cope with the maximum power search under frequent restarts of the power generation system. [Results]The simulation results show that using the maximum power tracking strategy of mechanical angular velocity PSMES optimization and SNPI control, compared with the traditional algorithm, improves rapidity performance by more than 50% while also enhancing stability performance. [Conclusion]The proposed maximum power tracking algorithm has obvious advantages in both rapidity and stability.