直流微网中多永磁同步风电机组间的分布式协调预测控制

The permanent magnet synchronous generators (PMSGs)-powered dc microgrid provides a promising solution for the integration of wind energy. For such PMSGs-powered dc microgrid, its reliable and economical operation strongly depends on the cooperation of multiple PMSGs. However, even in a small microgrid, the wind speed faced by each PMSG are significantly different. Furthermore, the wind speed faced by the downstream PMSGs may be affected by the wake interference of some upstream PMSGs. In the dc microgrid, these two factors make the cooperation of multiple PMSGs a particularly difficult task. To address this issue, this proposal proposes a distributed predictive control scheme, which globally coordinates the operation of multiple PMSGs in a dc microgrid. This scheme takes into account the aforementioned wind speed difference and wake effect. From the perspective of control design, these two factors pose a series of challenges, e.g., how to achieve the optimal load sharing among multiple PMSGs, how to maximize the total wind power production, and how to ensure the stable operation of all PMSGs. Focusing on these challenges, this project will establish the dynamic model of the PMSGs-powered dc microgrid. Based on this model, two semi-cooperative distributed predictive control methods will be developed, the former one allows the stable and optimal load sharing among multiple PMSGs during the islanded mode, while the latter one maximizes the total wind power production during the grid-connected mode. In order to achieve a seamless transition between these two control modes, a bumpless control transfer strategy will also be investigated. Furthermore, low-complexity implementation architecture will be developed for the distributed predictive control scheme, thus ensuring its real-time operation in the dc microgrid. This project will lay the theoretical foundation and develop practical guidelines for improving the integration of wind power generation into dc microgrids.

将永磁同步风电机组（PMSG）和直流微网相结合，构成以多台PMSG为主要微源的风电直流微网，是一种极具前景的微网组织形式。此类微网的高效运行，有赖于其内部各PMSG间的协调配合。然而在微网内，多台PMSG机组并联运行时其风速会存在显著差异，且会因尾流效应而相互影响，这两个固有运行特性给上述协调配合的实现带来了困难。对此，本项目将从如何处理这两个固有特性出发，探索建立一种直流微网内多PMSG间的分布式协调预测控制方案。各PMSG机组间风速差异和相互影响的引入，将在多机组负荷功率分配、多机组整体最大风能捕获、PMSG稳定性等方面带来一系列挑战性科学问题。围绕这些难题，本项目将分层次地研究半协作式的多机组负荷功率分配和最大风能捕获分布式预测控制策略、两种控制策略间的无缝切换方法、及直流微网环境下分布式预测控制问题的高效求解算法。本项目将为提升直流微网内PMSG控制水平提供理论支持和一定技术铺垫。

以直流母线为骨干，以多台永磁同步风电机组为主要微电源的风电直流微网，是各种微电网组织形式中极具前景的一种。对于风电直流微网而言，其独立运行时的发用电平衡，有赖于在多台风速各异的风电机组之间进行高效的负荷功率分配。针对此问题，本项目提出了一种分布式负荷功率分配控制策略，实现了直流微网中多个风电机组的协同控制。该策略基于分布式模型预测控制，并充分考虑了各个风电机组间的风速差异，求解得到各机组的最优功率参考指令，以实现并联机组协同负荷功率分配。该策略的一个显著特点是将转子动能和发电机功率裕量纳入控制器设计，保证了负荷功率分配过程中，所有风电机组的稳定运行。对于并网运行的风电直流微网，各风电机组整体最大风功率捕获的实现，必须充分考虑机组间尾流效应的影响，对此，本项目提出一种分布式的两阶段控制策略，该策略优化了每个风机的控制动作，以最大化多机组整体总功率输出。该控制策略由两个控制阶段组成。在第一个控制阶段，基于 Park 尾流模型构建解耦的最优控制问题，依次解决这些问题，获得单个风力发电机的子最优控制动作，从而提高多机组总发电量。在第二个控制阶段，应用分布式的无模型自适应控制来进一步细化次优控制动作，在这个阶段，通过让每个风机与其相邻风机交换有关其发电量的信息，控制动作被进一步调整到使得总功率提高的方向。针对以上分布式预测控制所对应优化问题的求解难题，本项目提出了一种新的串行式求解算法。该算法基于平均一致共识算法和邻域通信，确定所有风电机组的统一风资源利用水平，在实现风电直流微网全局最优控制的同时，大大减少了分布式控制系统的通信需求。. 本项目共发表SCI论文7篇，EI期刊论文1篇，EI会议论文3篇。培养博士研究生4人、硕士研究生3人。项目成果在国网山东电力公司获得应用。项目组成员赴墨尔本参加IEEE CDC并做特邀报告，赴迈阿密参加IEMDC、赴新加坡参加INTERMAG等国际学术会议。