Dynamic performance enhancement of a three-phase interleaved resonant boost MMC using adaptive disturbance compensation

Offshore wind energy is emerging as a critical renewable energy source. For the three-phase interleaved resonant boost MMC considered in this study, load variations, input-voltage fluctuations, and modeling uncertainties may degrade output-voltage regulation performance. To improve the transient response and disturbance-rejection capability of the converter, a backstepping dynamic surface control strategy assisted by a Zernike neural-network-based adaptive compensator is proposed. First, a state-space model of the three-phase interleaved parallel MMC boost converter is established. Then, the Zernike neural network is employed to adaptively approximate the unknown load-dependent term in the output-voltage dynamics and generate an online compensation signal. with a compact basis-function structure compared with the conventional RBF neural network used for comparison. Meanwhile, DSC is incorporated into the backstepping framework to avoid the derivative explosion problem and reduce implementation complexity. Lyapunov analysis proves that all closed-loop signals are bounded and the tracking error is semi-globally uniformly ultimately bounded. The inherent low-ripple and self-balancing characteristics of the topology are validated before the dynamic control performance is investigated. The proposed method achieves smaller overshoot and requires less execution time than the RBF-NN-assisted ABDSC under the adopted implementation settings. Therefore, the proposed strategy provides a preliminary methodological reference for improving the dynamic performance of MMC-based converters, with potential relevance to future offshore wind power HVDC applications.