Search: onr:"swepub:oai:DiVA.org:kth-36365" >
System identificati...
-
Eriksson, RobertKTH,Elektriska energisystem
(author)
System identification techniques for obtaining linear models of large power systems with controllable devices from noisy measurements
- Article/chapterEnglish2010
Publisher, publication year, extent ...
Numbers
-
LIBRIS-ID:oai:DiVA.org:kth-36365
-
https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-36365URI
-
https://doi.org/10.1109/IREP.2010.5563293DOI
Supplementary language notes
-
Language:English
-
Summary in:English
Part of subdatabase
Classification
-
Subject category:vet swepub-contenttype
-
Subject category:kon swepub-publicationtype
Notes
-
QC 20110712
-
This paper deals with the use of system identification techniques for estimating low-order black-box state-space models formodal analysis. It presents a method to estimate state-space models for large power systems with many controllable devices from noisy measurements. To excite the system low-energy pulses generated by the controllable devices are used. The input signals are the controllable set-points of the devices, the output signals are the speed signals of some generators obtained from Phasor Measurement Units (PMU). The Subspace State-Space System Identification (N4SID) and Prediction Error Method (PEM) are compared in sense of robustness using measurement with different signal to noise ratios noisy measurements. The method is applied in an extended version of the Cigré Nordic 32-bus test system. This model approach can be used directly for the design of a centralized controller coordinating the controllable devices with the aim to increase the damping in the system.
Subject headings and genre
Added entries (persons, corporate bodies, meetings, titles ...)
-
Söder, LennartKTH,Elektriska energisystem(Swepub:kth)u1fjok0u
(author)
-
KTHElektriska energisystem
(creator_code:org_t)
Related titles
-
In:2010 IREP Symposium - Bulk Power System Dynamics and Control - VIII, IREP20109781424474677
Internet link
Find in a library
To the university's database