The MMC structure, known for its high efficiency, superior availability, and low harmonic distortion, is hailed as a next-generation converter topology suitable for high-voltage and high-power applications. However, this structure also has many drawbacks. The traditional MMC structure has poor ability to traverse system DC bus faults (such as short circuits or open circuits). It requires multiple sensors to maintain normal operation, and when these sensors fail, the control system of the converter is prone to internal disorder, leading to poor overall stability. Additionally, it requires a large number of power electronic components, resulting in higher costs. Therefore, research on the stability and fault-tolerance capability of MMC, as well as the application of new MMC structures, has become a hot topic in the field of power electronics.During the experimental validation stage of MMC research, there is a need for repeated adjustments and testing of the grid and MMC topology. Such experiments are time-consuming and labor-intensive to implement on physical systems, while pure software simulations may struggle to reflect the delays and limited precision in real controllers. Therefore, in order to improve the efficiency of scientific research experiments and reliably simulate experimental results, real-time simulation can be employed to validate controller algorithms.
Associate Professor Yang Xingwu's research team at Shanghai University of Electric Power has proposed a new two-stage model predictive control method to reduce computation and increase the output voltage level to 2N+1. The discrete-time mathematical model of modular multilevel controller was first derived, and two circulating factors were introduced to directly calculate the optimal voltage difference and sum of the bridge arm, so as to determine the optimal number of submodules for upper and lower bridge arms in the first-stage control. In the second-stage control, based on the optimal number of submodules determined in the first-stage control, upper and lower bridge arms were adjusted by adding or subtracting one submodule, forming an array of optimization options that were then put into the objective function to select the submodule with the lowest objective function value for investment, thereby determining the final investment in MMC. Both first-stage control and second-stage control avoided the selection of weight factors. Finally, the RSF algorithm was applied to control the submodule capacitor voltage and reduce the switching frequency. The research used the StarSim real-time simulator for power electronics with small time steps provided by ModelingTech to experimentally verify the effectiveness of the proposed method, and the results were summarized and published in
StarSim real-time simulator, provided by ModelingTech, enables real-time simulation of power electronic systems with a step size of 1 microsecond, arbitrary topology, and arbitrary operating conditions. The simulator is widely used in real-time simulation for validating new topologies and control strategies. Using the latest FPGA fiber-coupled parallel simulation and CPU multi-core simulation technology introduced by ModelingTech, multiple simulators can be connected in parallel to simulate different parts of large-scale MMC systems. Utilizing fiber-optic signals with ns-level synchronization accuracy to coordinate multiple simulators solves the challenges posed by high simulation resource and interface requirements for MMC system simulation.
1us step simulation meets the requirements of high-precision simulation, can be freely configured with different structures of MMC systems, and accurately simulate the operating characteristics of MMC.
Support up to 8 sfp fibre-optic signal modules, can easily achieve physical IO expansion or multi-device parallel simulation, to meet the requirements of MMC large system testing.
Provide professional automation test Python API, convenient for industrial users to develop automation test project; support “HIL Scope” high-speed recording function, can achieve 500k sampling rate for multi-channel waveform observation.
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