TY - CONF
T1 - System identification techniques for power systems analysis using distorted data
AU - Papadopoulos, Theofilos A.
AU - Kontis, Eleftherios O.
AU - Papadopoulos, Panagiotis
AU - Papagiannis, Grigoris K.
PY - 2014/11/5
Y1 - 2014/11/5
N2 - In this paper the performance of four system identification methods is evaluated for the analysis of different power system configurations. The methods considered are: nonlinear least squares (NLS), Prony method, sub-space state space system identification (N4SID) and the prediction error method (PEM). Artificially created data distorted by noise are used to represent real-world conditions. The analysis verifies the practical value of system identification methods for power system dynamic analysis and also illustrates practical issues and solutions encountered in their application.
AB - In this paper the performance of four system identification methods is evaluated for the analysis of different power system configurations. The methods considered are: nonlinear least squares (NLS), Prony method, sub-space state space system identification (N4SID) and the prediction error method (PEM). Artificially created data distorted by noise are used to represent real-world conditions. The analysis verifies the practical value of system identification methods for power system dynamic analysis and also illustrates practical issues and solutions encountered in their application.
KW - noise
KW - nonliner least-squares
KW - phasor measurements
KW - system identification
U2 - 10.1049/cp.2014.1653
DO - 10.1049/cp.2014.1653
M3 - Paper
ER -