Open AccessImproved two‐stage model predictive control method for modular multi‐level converter
Author(s) -
Yang Xingwu,
Liu Haibo,
Mi Yang,
Ji Liang,
Wang Yani,
Fu Yang
Publication year - 2021
Publication title -
iet electric power applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.815
H-Index - 97
eISSN - 1751-8679
pISSN - 1751-8660
DOI - 10.1049/elp2.12089
Subject(s) - weighting , control theory (sociology) , modular design , model predictive control , capacitor , voltage , function (biology) , computer science , a weighting , optimal control , mathematics , control (management) , mathematical optimization , engineering , medicine , artificial intelligence , evolutionary biology , biology , electrical engineering , radiology , operating system
In recent years, the modular multi‐level converter (MMC) has been widely used in high and medium voltage DC transmission systems because of its topological advantages. However, for an MMC with a two‐stage model predictive control (TSMPC) method, it is difficult to precisely and reasonably design the weighting factor in the cost function. Here, an improved TSMPC method is proposed which not only can avoid choosing the weighting factor for both first and second stage control but also can raise the output voltage level (OVL) to 2 N + 1 without increasing computation burden. The discrete‐time mathematical model of the MMC is first derived. Two circulating current factors are introduced to calculate the optimal number of submodules (SMs) of the upper and lower arms in the first stage control. Secondly, the second stage control calculates the optimal number of SMs through the superior control and forms the optimisation array by adding or subtracting one SM. Then the objective function is developed, and the SMs with the minimum value of the objective function are selected for the final input. Finally, the algorithm of reducing switching frequency (RSF) is applied to achieve the balance of the SM capacitor voltage. The simulation and experimental results verify the effectiveness of the proposed method.