针对传统多目标模型预测控制（MPC）权重系数设计复杂的问题,运用分层控制的思想,提出一种无权重系数的多目标优化模型预测控制策略。利用MPC控制算法可直接控制交流量这一优势,在两相静止坐标系下推导双馈感应发电机（DFIG）转子电流、定子电流和定子功率的参考方程和预测方程。最后进行传统多目标MPC控制策略和无权重系数多目标MPC控制策略下的对比仿真实验。仿真结果显示,相比于传统多目标控制策略,所提控制策略的动态响应速度更快,并可改善系统的稳态性能。
Abstract
Aiming at the complex problem of weight factor design of traditional model predictive control（MPC）, a multiobjective optimization model predictive control strategy without weighting factor was proposed by using the idea of hierarchical control. Taking advantage of the direct control of AC variable by MPC control algorithm, the reference equation and prediction equation of rotor current, stator current and stator power were derived inthetwo phase stationary coordinate system. Finally, a comparative simulation experiment was designed under the traditional multi-objective MPC control strategy and the weightless factor multi-objective MPC control strategy. The simulation results show that compared with the traditional multi-objective control strategy, the proposed control strategy has faster dynamic response speed, and improves the steady-state performance of the system.
关键词
双馈感应发电机 /
多目标优化 /
风力发电 /
模型预测控制 /
电压不平衡 /
权重系数
Key words
doubly-fed induction generator（DFIG） /
multiobjective optimization /
wind power /
model predictive control /
unbalance voltage /
weight factor
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基金
山西省自然科学基金（201701D121130）
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