Optimizing The Performance of A New Current Source Type Grid-Forming Modular Multilevel Converter Using Genetic Algorithms

Modular Multilevel Converters (MMC) have found extensive application in connecting Renewable Energy Sources to the power grid. Recently, MMCs operated in the Grid Forming (GFM) mode have garnered significant interest due to their superior performance in weak AC networks. The Virtual Synchronous Generator (VSG) is a GFM technology designed to emulate the behavior of a real synchronous generator (SG). There is flexibility in selecting the VSG’s parameters as they are not constrained by the physical attributes of an actual SG. This paper proposes employing the Genetic Algorithm (GA) to select these parameters, underscoring its simplicity and effectiveness in meeting diverse constraints and objectives. Small Signal (SS) models are constructed at potential operational points, with GA leveraging the SS model's eigenvalues to select parameter values that enhance dynamic performance, ensuring resilience across a wide range of operating conditions. The effectiveness of these designed parameters is validated through Electromagnetic Transients (EMT) simulations.