A Novel Voltage Sensorless Estimation Method for Modular Multilevel Converters with a Model Predictive Control Strategy

• Published in: Energies (Basel) Vol. 17; no. 1; p. 61
• Main Authors: Liao, Yantao, Jin, Long, You, Jun, Xu, Zhike, Liu, Kaiyuan, Zhang, Hongbin, Shen, Zhan, Deng, Fujin
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2024
• Subjects: adaptive neural network (ANN); Algorithms; Capacitors; Communication; disturbance observer (DOB); Methods; model predictive control (MPC); modular multilevel converter (MMC); Neural networks; Sensors; voltage estimation
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en17010061

This paper proposes a novel voltage estimation scheme for the modular multilevel converter (MMC) based on model predictive control (MPC). The developed strategy is presented by combining a disturbance observer (DOB) with an adaptive neural network (ANN) for voltage estimation in the MMC. Firstly, the ac-side and dc bus voltages are estimated as the disturbance items of the DOB which acts as the cost function during each control cycle and ensures the minimal computational cost. Then, the submodule (SM) capacitor voltage estimation is achieved based on the ANN with the estimated ac-side and dc bus voltages. The proposed method requires only one current sensor per arm and has a simple structure with three weights to be adjusted. Comprehensive simulation studies and experiments are presented to demonstrate its effectiveness and feasibility. The results indicate that the proposed method has a high accuracy, a fast dynamic response, and no effects on the original MPC performance.