Capacitor Voltage Balancing Control of MMC Sub-Module Based on Neural Network Prediction

Abstract

The issue of sub-module (SM) capacitor voltage unbalance is a hot topic in the current research into the modular multilevel converter (MMC). An excellent strategy comprises mitigating the SM capacitor voltage imbalance by adjusting the SM on time. The traditional capacitor voltage balancing control regulates the speed to maintain accuracy. A unique SM capacitor voltage balancing control strategy is presented in this paper and is based on conventional capacitor voltage balance management and neural network prediction. Firstly, the SM capacitor voltage and arm current are speculated by operating the time series forecasting technique in real time, considering the dynamic changes in the SM capacitor voltage and arm current. Secondly, the SM capacitor voltage distinction between the actual and theoretical value is determined, and a deviation’s mixed Gaussian distribution is established to estimate its compensation voltage. Thirdly, the SM triggering sequence is anticipated by using the neural network along with the pilot values of the SM capacitor voltage, arm current, and the offset compensation value, and the control is executed. Finally, a three-phase, six-leg, eight-module, nine-level MMC model is built to verify the feasibility of the suggested approach.