Low-complexity model predictive control of a four-level active neutral point clamped inverter without weighting factors

The four-level active neutral point clamped(ANPC)inverter has become increasingly widely used in the renewable energy indus-try since it offers one more voltage level without increasing the total number of active switches compared to the three-level ANPC inverter.The model predictive current control(MPCC)is a promising control method for multi-level inverters.However,the conven-tional MPCC suffers from high computational complexity and tedious weighting factor tuning in multi-level inverter applications.A low-complexity MPCC without weighting factors for a four-level ANPC inverter is proposed in this paper.The computational burden and voltage vector candidate set are reduced according to the relationship between voltage vector and neutral point voltage balance.The proposed MPCC shows excellent steady-state and dynamics performances while ensuring the neutral point voltage balancing.The efficacy of the proposed MPCC is verified by simulation and experimental results.

Model predictive control of three-level active front-end converter with low switching frequency

In medium voltage-high power(MV-HP)applications,the high switching frequency of power converter will result in unnecessary energy losses,which directly affect efficiency.To resolve this issue,a novel finite control set-model predictive control(FCS-MPC)with low switching frequency for three-level neutral point clamped-active front-end converters(NPC-AFEs)is proposed.With this approach,the prediction model of three-level NPC-AFEs is established inα-βreference frame,and the control objective of low average switching frequency is introduced into a cost function.The proposed method not only achieves the desired control performance under low switching frequency,but also performs the efficient operation for the three-level NPC-AFEs.The simulation results are provided to verify the effectiveness of proposed control scheme.