LAUSR

Large offshore wind power plants (OWPPs) installed far from the coastline are emerging to benefit from the strong and steady wind resources available at these locations. The high-voltage direct-current (HVDC) transmission system based on the modular multilevel converter (MMC) is the most appropriate solution to transmit the produced energy to the onshore grid, in a way that a complex power-electronic-based electrical system is formed at the OWPP. Undesired interactions can occur between the MMC-based HVDC station, the several wind-turbine (WT) converters, and the passive elements of the offshore grid. To guarantee a safe and reliable operation of the OWPP, a small-signal analysis must be performed in advance to predict possible unstable situations and their root causes, in a way as to take corrective measures to avoid them. In this paper, a state-space model of an OWPP is developed adopting a recently proposed model in multiple $dq$ frames of the MMC, which considers the internal dynamics of the converter. With the developed model, an eigenvalue-based stability assessment is carried out, where participation factors are calculated to identify the main contributors to the unstable modes that appear as a consequence of undesired low-frequency interactions at the offshore grid. As far as the authors know, an eigenvalue-based stability assessment of an OWPP with an MMC-HVDC connection is a topic rarely or never explored in the literature before since a detailed time-invariant state-space model of the MMC was not available until recently. Thus, the eigenvalue-based stability study is the main contribution of this paper. The developed model can easily be extended to more complex OWPPs with different configurations in future works. Another contribution of this paper is the proposal of an adaptation to the MMC’s grid-following state-space model so that the converter’s dynamics in grid-forming mode can be represented. Finally, various studies are presented to prove that the adopted MMC’s approximated model is accurate enough for small-signal stability analyses.