AI 中文总结
针对现有HPPWM求解方法忽略数字控制器定时器分辨率导致的最优性偏差问题，提出GCSA-PSO策略，直接在离散空间搜索可实现脉冲序列，结合状态自适应评估提升效率，实验表明其计算时间更短、控制精度更高。
AI 中文摘要
谐波规划脉冲宽度调制（HPPWM）可实现灵活的谐波调节，是大功率能量转换系统的有前景解决方案。但现有多数方法在连续空间求解HPPWM，忽略了实际数字控制器的有限定时器分辨率，部署时存在潜在最优性偏差。为此，本文提出网格约束状态自适应粒子群优化（GCSA-PSO）策略，通过使解空间匹配实际定时器约束，直接在离散解空间搜索可实现的脉冲序列，提升部署一致性并降低搜索负担；还开发了状态自适应评估策略，根据粒子状态分配不同代价评估，避免不必要评估以提升计算效率。实验数据证实，与经典方法相比，所提方法减少了计算时间，且在实际数字控制器部署下实现了更高的控制精度。
英文摘要
Harmonic programmed pulse width modulation (HPPWM), offering flexible harmonic regulation, is a promising solution for high-power energy conversion systems. However, most existing methods solve HPPWM in a continuous space while ignoring the finite timer resolution of practical digital controllers. This leads to a potential optimality deviation during deployment. Motivated by this, this paper proposes a Grid-Constrained State-Adaptive Particle Swarm Optimization (GCSA-PSO) strategy. By matching the solution space with practical timer constraints, GCSA-PSO directly searches for implementable pulse sequences in the discrete solution space, thereby improving deployment consistency while reducing the search burden. Moreover, a state-adaptive evaluation strategy is developed to assign different cost evaluations according to particle states, avoiding unnecessary evaluations and improving computational efficiency. Experimental data confirm that, compared with the classical method, the proposed method reduces the computational time while achieving higher control accuracy under practical digital-controller deployment.
Comments10 pages, 9 figures
