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Coherency Identification and Aggregation in Grid-Forming Droop-Controlled Inverter Networks.
- Source :
-
IEEE Transactions on Industry Applications . May/Jun2019, Vol. 55 Issue 3, p2219-2231. 13p. - Publication Year :
- 2019
-
Abstract
- There is an increasing need to apply rigorous model-order-reduction techniques in the analysis of large-scale networks of inverter-based distributed generation resources due to the limitations of existing simulation tools. Various coherency-based aggregation techniques have long been used to construct reduced-order dynamic models of large-scale synchronous machine (SM) networks. Such techniques have the advantage of preserving the nonlinear nature of the dynamic model throughout the order-reduction process, enabling the efficient and accurate analysis of large-scale network dynamics during large disturbances such as fault events. This paper proposes the application of a rigorous coherency-based aggregation technique to the analysis of large-scale networks of grid-forming droop-controlled inverters. A rapid and powerful generalized eigenvalue perturbation technique for coherency identification, previously only applied to SM networks, is adapted to grid-forming droop-controlled inverter networks. The resulting reduced-order models are physically insightful and are capable of accurately reproducing the system response in the aftermath of large disturbances. For some networks, a rigorously-derived condition of coherency can be difficult to achieve, given the expected range of L–C–L filter impedances. To remedy this limitation, the potential for high-bandwidth inverter control to enforce the conditions that allow for coherency of droop-controlled inverters has been investigated and confirmed using a controller hardware-in-the loop testbed. Using this approach, the use of simple nonlinear aggregate inverter models to accurately model large sections of the inverter network can be more rigorously justified. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00939994
- Volume :
- 55
- Issue :
- 3
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Industry Applications
- Publication Type :
- Academic Journal
- Accession number :
- 136101355
- Full Text :
- https://doi.org/10.1109/TIA.2019.2891555