CAJ | 학술논문

模块化多电平换流器(modular multilevel converter， MMC)具有效率高、谐波小、模块化设计、易级联等优点，在高压大容量电能变换领域得到了日益广泛的应用。作为一种先进的控制策略，模型预测控制(model predictive control， MPC)通过目标函数可同时控制多个系统变量，具有建模直观、动态响应快等优点。传统MMC模型预测控制通过计算所有开关状态组合以实现最优控制目标，但随着桥臂模块数量的增多，计算量将呈几何级数增长，严重制约MPC的工程推广应用。针对N+1电平MMC，提出一种优化的模型预测控制算法，在对子模块电压、交流电流、相间环流、器件开关频率有效控制的同时，将开关状态组合计算量从C2NN降至N+1。针对子模块数高达数百的MMC，进一步提出分组排序优化模型预测控制(grouping-sorting algorithm combined OMPC，GSOMPC)策略，在降低桥臂子模块电压整体排序对硬件资源苛刻需求的同时，将开关状态组合计算量从N+1降至2X+M+3(N=M×X)。基于2.7kV/60kW 23电平MMC背靠背动模实验平台的实验结果证明了所提优化模型预测控制(optimized model predictive control，OMPC)及GSOMPC策略的正确性与有效性。
모괴화다전평환류기(modular multilevel converter， MMC)구유효솔고、해파소、모괴화설계、역급련등우점，재고압대용량전능변환영역득도료일익엄범적응용。작위일충선진적공제책략，모형예측공제(model predictive control， MPC)통과목표함수가동시공제다개계통변량，구유건모직관、동태향응쾌등우점。전통MMC모형예측공제통과계산소유개관상태조합이실현최우공제목표，단수착교비모괴수량적증다，계산량장정궤하급수증장，엄중제약MPC적공정추엄응용。침대N+1전평MMC，제출일충우화적모형예측공제산법，재대자모괴전압、교류전류、상간배류、기건개관빈솔유효공제적동시，장개관상태조합계산량종C2NN강지N+1。침대자모괴수고체수백적MMC，진일보제출분조배서우화모형예측공제(grouping-sorting algorithm combined OMPC，GSOMPC)책략，재강저교비자모괴전압정체배서대경건자원가각수구적동시，장개관상태조합계산량종N+1강지2X+M+3(N=M×X)。기우2.7kV/60kW 23전평MMC배고배동모실험평태적실험결과증명료소제우화모형예측공제(optimized model predictive control，OMPC)급GSOMPC책략적정학성여유효성。
With the attractive features of high efficiency, low harmonic, modularity and scalability, the Modular Multilevel Converter (MMC) is suitable for a wide range of high-voltage large-capacity applications. As an advanced control strategy, the Model Predictive Control (MPC) can control multiple variables through a cost function, which also has advantages of direct modeling and fast dynamic response performances. The conventional Model Predictive Control method for MMC is applied by calculating all the candidate switching states to achieve control objective optimization, which limits the application of MPC along with the submodule increasing. An Optimized Model Predictive Control (OMPC) was proposed to control the submodule voltages, ac currents, circulating currents and the switching frequency, meanwhile reducing the computation load from2NNC toN+1 forN+1 level MMC. In addition, as the number of the submodule increases to hundreds, the proposed Grouping-Sorting algorithm combined OMPC (GSOMPC) can further reduce the computation load fromN+1 to 2X+M+3(N=M×X) to eliminate the strict hardware requirements. Experiment results based on a 2.7kV/60kW of MMC with 23-level back-to-back dynamic test system verify the correctness and effectiveness of the OMPC and GSOMPC strategy.