Comparison of Prony and eigenanalysis for power system control design

Measurement‐based analysis of the dynamic performance of microgrids using system identification techniques

“…The applied disturbances are large enough to influence the system This paper is a postprint of a paper submitted to and accepted for publication in IET Generation, Transmission and Distribution, and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at IET Digital Library, or http://dx.doi.org/10.1049/ietgtd.2014.0555 7 dynamics, as well as to avoid round-off errors [22]. Using the above assumptions the MG system can be assumed linear.…”

“…The recorded system responses cover the whole transient period as well as the steady-states prior to and after the disturbance with a sufficient sampling rate (2 ms). Therefore, the dynamic responses contain all system mode frequencies typically present in power systems [22], [34]. The length of the time window selected for the identification of the eigenvalues for both methods starts with the initiation of the disturbance and ends right after the oscillations stop.…”

“…System identification techniques have been traditionally applied in power systems to develop dynamic equivalent models of extended transmission networks using both simulation and measurement data [4], [19]- [22]. For the analysis of the MGs dynamics, black-or grey-box modelling techniques are used in [23]- [27] to develop equivalent This paper is a postprint of a paper submitted to and accepted for publication in IET Generation, Transmission and Distribution, and is subject to Institution of Engineering and Technology Copyright.…”

Black‐box dynamic equivalent model for microgrids using measurement data

“…changes in the load power and the operational conditions of DG units. Therefore, the investigated dynamic disturbances are considered large enough to influence the system dynamics and small enough to ignore nonlinearities in the system [24]. …”

“…1 [ Fig. 1] Next, the model parameters A mn , ω mn , σ mn , and φ mn of (3) are extracted for each of the three datasets, using Prony analysis to get an initial estimation [17], [19], [23], [24], while the initial operating condition y 0m is acquired directly from the corresponding measurement data. The appropriate number of Prony terms for each system variable, and thus the order of each subsystem can be identified using the subsystem identification method N4SID [25], available in MATLAB [26].…”