Coherency Identification in Multimachine Power Systems Using Dynamic Mode Decomposition

Synchronous generators continue to oscillate in multi-machine power systems in many coherent communities, and each community corresponds to the virtual generator. However, coherent communities can vary in response to different activities under different operating conditions. Hence it is necessary to conduct an online analysis however the resulting control actions are based on the system dynamical model. The system models formed using the first principle fail to represent the processes entirely throughout operating circumstances. Therefore a data-driven approach to system modeling is an effective resource for interpreting real-world activities and analyzing its effects in the future. For that, this paper introduces a novel approach for data-driven modeling based on dynamic mode decomposition (DMD) for the coherency identification in a multimachine power system. In order to model the system more efficiently, both steady-state and transient data are considered. An augmented data matrix in the Hankel structure is passed as an input to the DMD algorithm for capturing the behavior of the system accurately. Incorporating the persistency of excitation and Hankel structure in DMD, a rank condition is derived to identify the coherency of generators in post-fault situations. The proposed coherency identification dynamic mode decomposition algorithm is verified and validated on a 22-bus 6-generator system.