Power System Coherency and Model Reduction

doi:10.1007/978-1-4614-1803-0

Cited by 192 publications

(1 citation statement)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Consequently, they are nontrivial to analyze in real-time. Therefore, model reduction (also called equivalencing [16,26]) has long been used to reduce both static and dynamical models of power systems [9,36,50].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear model reduction of dynamical power grid models using quadratization and balanced truncation

Ritschel¹,

Weiß²,

Baumann³

et al. 2020

Preprint

View full text Add to dashboard Cite

In this work, we present a nonlinear model reduction approach for reducing two commonly used nonlinear dynamical models of power grids: the effective network (EN) model and the synchronous motor (SM) model. Such models are essential in real-time security assessments of power grids. However, as power grids are often large-scale, it is necessary to reduce the models in order to utilize them in real-time. We reformulate the nonlinear power grid models as quadratic systems and reduce them using balanced truncation based on approximations of the reachability and observability Gramians. Finally, we present examples involving numerical simulation of reduced EN and SM models of the IEEE 57 bus and IEEE 118 bus systems.

show abstract

Section: Introductionmentioning

confidence: 99%

Nonlinear model reduction of dynamical power grid models using quadratization and balanced truncation

Ritschel¹,

Weiß²,

Baumann³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

A tutorial on data‐driven eigenvalue identification: Prony analysis, matrix pencil, and eigensystem realization algorithm

Almunif

Fan

Miao

2020

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Summary To identify power system eigenvalues from measurement data, Prony analysis, matrix pencil (MP), and eigensystem realization algorithm (ERA) are three major methods. This paper reviews the three methods and sheds insight on the principles of the three methods: eigenvalue identification through various Hankel matrix formulations. In addition, multiple channel data handling and noise‐resilience techniques are investigated. In the literature, singular value decomposition (SVD)‐based rank reduction technique has been applied to MP and resulted in a reduced‐order system eigenvalue estimation and an excellent noise resilient feature. In this paper, ERA is refined using the SVD‐based rank reduction to achieve superior performance. Further, a reduced‐order Prony analysis method is proposed. With this technique, Prony analysis can not only give reduced‐order system eigenvalues, but also become noise resilient. Four case studies are conducted to demonstrate the effectiveness of the eigenvalue identification methods, including a tutorial example of an RLC circuit resonance, a power grid oscillation case study for a 16‐machine 68‐bus system, an example of subsynchronous resonance (SSR) of a type‐3 wind grid integration system, and real‐world oscillation events captured by Independent System Operator‐New England (ISO‐NE).

show abstract

Dynamic Security Assessment

Wang

Lin

Howell

et al. 2016

Smart Grid Handbook

View full text Add to dashboard Cite

This chapter covers the subject of dynamic security assessment (DSA) of power systems. Development of advanced DSA technologies is considered as part of the trend in building smart control centers. After a short discussion on the concept of power system security analysis, three basic elements of DSA are described, namely, security criteria and contingencies to be applied, models to be used, and analysis methods to perform the studies. This is followed by DSA applications using the traditional off‐line approach and more advantageous online approach. Further details for the deployment of the online DSA systems are provided to illustrate the applications of this innovative technology.

show abstract

Power System Coherency and Model Reduction

Cited by 192 publications

References 4 publications

Nonlinear model reduction of dynamical power grid models using quadratization and balanced truncation

Nonlinear model reduction of dynamical power grid models using quadratization and balanced truncation

A tutorial on data‐driven eigenvalue identification: Prony analysis, matrix pencil, and eigensystem realization algorithm

Dynamic Security Assessment

Contact Info

Product

Resources

About