Title :
Parameter identification of a wind generator unit RMS model using sparse grid optimization algorithm
Author :
Qing Fang ; Zivanovic, Rastko
Author_Institution :
Electr. & Electron. Eng. Sch., Univ. of Adelaide, Adelaide, SA, Australia
Abstract :
This paper presents a global sparse grid optimization algorithm applied in parameter identification of a wind generation Root Mean Square (RMS) phasor model. The details of vendor specific RMS models used in dynamic simulation software (e.g. PSSE) are not provided by manufacturers. Therefore, there is a need to develop a procedure which can convert vendor specific models to standardized generic models (e.g. International Electrotechnical Commission model, IEC model). The procedure we propose, identifies the parameters of the IEC generic model using dynamic response of a given vendor specific model as input. The IEC model parameters can be found and dynamic response of the vendor model can be approximated with sufficient accuracy. In the simulation example we show that the parameter identification based on the global sparse grid optimization algorithm is effective in converting a vendor specific model to a standardized generic model.
Keywords :
asynchronous generators; least mean squares methods; optimisation; parameter estimation; power grids; wind power plants; IEC generic model; RMS model; RMS phasor model; dynamic response; dynamic simulation software; parameter identification; sparse grid optimization algorithm; vendor specific RMS models; vendor specific models; wind generation root mean square phasor model; wind generator unit; Cost function; Generators; Mathematical model; Parameter estimation; Power generation; Regulators; Doubly Fed Induction Generators (DFIGs); RMS phasor model; global optimization; parameter identification; sparse grid; wind generation;
Conference_Titel :
Modelling, Identification & Control (ICMIC), 2014 Proceedings of the 6th International Conference on
Conference_Location :
Melbourne, VIC
DOI :
10.1109/ICMIC.2014.7020766