| 引用本文: | 张 虹,孙道兴,王 超,等.基于Voronoi-Softmax概率分配的T型逆变器EMPC全局最优策略[J].电力系统保护与控制,2026,54(04):154-164.[点击复制] |
| ZHANG Hong,SUN Daoxing,WANG Chao,et al.Global optimal EMPC strategy for T-type inverters based on Voronoi-Softmax probability distribution[J].Power System Protection and Control,2026,54(04):154-164[点击复制] |
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| 摘要: |
| T型三电平逆变器在传统有限集模型预测并网控制过程中，因相邻控制周期优化过程缺少关联，进而产生多周期局部最优化问题。提出一种基于Voronoi-Softmax概率分配的T型逆变器EMPC控制(Voronoi-Softmax explicit model predictive control, VS-EMPC)策略。首先，采用死区线性化补偿策略修正并网模型。基于Voronoi图的思想，把开关序列的在线计算转化为对离线计算的显式模型预测控制状态空间进行Voronoi单元划分。在线查表得到3个候选矢量并结合在线Softmax概率探索机制和自适应动态系数计算，通过优化的概率引导进行多周期关联下最优候选矢量的选择。最后通过搭建半实物仿真实验平台，验证了所提策略良好的动稳态性能和在降低存储负担及实现全局最优控制方面的有效性。 |
| 关键词: Voronoi-Softmax 三电平逆变器 概率分配 显式模型预测控制 全局最优 |
| DOI：10.19783/j.cnki.pspc.250650 |
| 投稿时间：2025-06-16修订日期：2025-08-14 |
| 基金项目:国家自然科学基金项目资助(52277170) |
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| Global optimal EMPC strategy for T-type inverters based on Voronoi-Softmax probability distribution |
| ZHANG Hong1,SUN Daoxing1,WANG Chao2,MA Wanji1,YANG Jialin3 |
| (1. Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education
(Northeast Electric Power University), Jilin 132012, China; 2. China Aviation Development Harbin Dong’an Engine
Co., Ltd., Harbin 150066, China; 3. Tongyu CGN Wind Power Co., Ltd., Baicheng 137200, China) |
| Abstract: |
| In traditional finite set model predictive grid-connected control of T-type three-level inverters, the lack of correlation between optimization processes in adjacent control cycles often leads to multi-cycle local optimality issues. To address this problem, this paper proposes a T-type inverter EMPC control strategy based on Voronoi-Softmax probability distribution, referred to as Voronoi-Softmax explicit model predictive control (VS-EMPC). First, a dead-zone linearization compensation strategy is adopted to correct the grid-connected model. Based on the concept of Voronoi diagrams, the online calculation of switching sequences is transformed into a Voronoi cell partitioning of the state space in the offline explicit model predictive control framework. During online operation, three candidate voltage vectors are obtained through a lookup table, and combined with an online Softmax probability exploration mechanism and adaptive dynamic coefficient calculation, the optimal candidate vector is selected through the guidance of the optimized probability distribution under multi-cycle correlation. Finally, a hardware-in-the-loop experimental platform is built to verify the proposed strategy, demonstrating favorable dynamic and steady-state performance, as well as its effectiveness in reducing storage requirements and achieving global optimal control. |
| Key words: Voronoi-Softmax three-level inverter probability distribution explicit model predictive control global optimal |
