Particle Swarm Optimization Tuning of Modular Multilevel Converters in a Time-Invariant Framework

Abstract

This work investigates the Particle Swarm Optimization (PSO) algorithm as a tool to tune the control parameters of a Modular Multilevel Converter (MMC) in a single-terminal HVdc configuration. More precisely, due to its inherent capacity of handling system non-linearities, the PSO algorithm is used to tune a nonlinear control structure based on passivity arguments capable of ensuring global asymptotic stability of the converter. This nonlinear control strategy was successfully applied to the MMC in previous efforts, albeit with sub-optimal tuning, and therefore below par performance. Thus, this work aims to contribute to the state of the art by proving that system performance under the nonlinear control structure of interest can be further improved via PSO-tuning. Finally, to reduce the computational burden, we propose to apply the PSO algorithm directly to a recent state-space representation of an MMC with a constant equilibrium point.