Visible to the public Dynamic-Feature Extraction, Attribution, and Reconstruction (DEAR) Method for Power System Model Reduction

TitleDynamic-Feature Extraction, Attribution, and Reconstruction (DEAR) Method for Power System Model Reduction
Publication TypeJournal Article
Year of Publication2014
AuthorsShaobu Wang, Shuai Lu, Ning Zhou, Guang Lin, Elizondo, M., Pai, M.A.
JournalPower Systems, IEEE Transactions on
Date PublishedSept
Keywordsand reconstruction method, attribution, characteristic generator state variable, computational cost reduction, Computational modeling, cost reduction, DEAR Method, dynamic feature extraction, dynamic response, dynamic response measurement, electric generators, feature extraction, Generators, IEEE standard, IEEE standards, model reduction, orthogonal decomposition, power system dynamic stability, Power system dynamics, power system interconnection, power system model reduction, power system stability, power system transient stability, Power systems, quasi-nonlinear reduced model, reduced order systems, Rotors, transient stability

In interconnected power systems, dynamic model reduction can be applied to generators outside the area of interest (i.e., study area) to reduce the computational cost associated with transient stability studies. This paper presents a method of deriving the reduced dynamic model of the external area based on dynamic response measurements. The method consists of three steps, namely dynamic-feature extraction, attribution, and reconstruction (DEAR). In this method, a feature extraction technique, such as singular value decomposition (SVD), is applied to the measured generator dynamics after a disturbance. Characteristic generators are then identified in the feature attribution step for matching the extracted dynamic features with the highest similarity, forming a suboptimal "basis" of system dynamics. In the reconstruction step, generator state variables such as rotor angles and voltage magnitudes are approximated with a linear combination of the characteristic generators, resulting in a quasi-nonlinear reduced model of the original system. The network model is unchanged in the DEAR method. Tests on several IEEE standard systems show that the proposed method yields better reduction ratio and response errors than the traditional coherency based reduction methods.

Citation Key6730699