Open AccessInformation geometry for model identification and parameter estimation in renewable energy – DFIG plant case
Author(s) -
Sarić Andrija T.,
Transtrum Mark K.,
Stanković Aleksandar M.
Publication year - 2018
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2017.0606
Subject(s) - benchmark (surveying) , identification (biology) , renewable energy , electric power system , computer science , wind power , control engineering , virtual power plant , system identification , control theory (sociology) , power (physics) , automotive engineering , engineering , data modeling , electrical engineering , distributed generation , control (management) , botany , database , artificial intelligence , biology , physics , geodesy , quantum mechanics , geography
This study describes a new class of system identification procedures, tailored to electric power systems with renewable resources. The procedure described here builds on computational advances in differential geometry, and offers a new, global, and intrinsic characterisation of challenges in data‐derived identification of electric power systems. The approach benefits from increased availability of high‐quality measurements. The procedure is illustrated on the multi‐machine benchmark example of IEEE 14‐bus system with renewable resources, but it is equally applicable to identification of other components and systems (e.g. dynamic loads). The authors consider doubly‐fed induction generators (DFIG) operating in a wind farm with system level proportional–integral controllers.