Level-Increased iSHE Modulation Method for Modular Multilevel Converters Based on DDPG

The output waveform quality of modular multilevel converters (MMCs) may not be good enough when there are only a few submodules (SMs). This article proposes a technique to increase the quantity of equivalent output voltage levels of MMC by changing the capacitor voltage of certain SMs. This method significantly improves the performance of the MMC. In addition, the method combines a level-increased MMC with an improved selected harmonic elimination (iSHE) modulation, aiming to minimize the total harmonic distortion of the MMC output. The primary challenge of the iSHE modulation lies in solving a complex nonlinear mathematical model to determine the optimal switching angles. Deep reinforcement learning has proven to be effective in addressing intricate multidimensional optimization problems within a continuous action space. Therefore, this article utilizes the deep deterministic policy gradient (DDPG) algorithm to calculate the optimal switching angles of the MMC. The integration of DDPG algorithm enhances the operational performance of the MMC, thereby contributing to the progress of power electronics using artificial intelligence.