A minimal realization approach to reduced-order modelling and modal analysis for power system response signals

Kamwa, Innocent; Grondin, R.; Dickinson, E.J.; Fortin, Simon

doi:10.1109/59.260898

Cited by 84 publications

(29 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We need two pieces of information. First, from the linearized model (10), the inter-area frequency (in Hz) is given as (18) Thus, by measuring the frequency of the swing mode in the voltage measurement, the equivalent inertia constant can be computed as (19) When the line resistance is negligible compared to the reactance, then , and hence, (19) can be reduced to (20) Second, to calculate and separately, we develop a companion equation by exploiting the frequencies measured at Buses 3 and 13. Neglecting losses and machine damping effects, the conservation of the total angular momentum of the two-machine system is given as The expressions in (24) can be readily derived from the dynamic model (2) for the two-machine machine.…”

Section: B Inertia Extrapolation Algorithmmentioning

confidence: 99%

“…8, the interarea mode oscillation is dominant in all three voltages. We apply the Eigensystem Realization Algorithm (ERA) to extract the modes and their mode shapes in the time response [18], [19], using the time period of 1 to 10 s to avoid the initial voltage transients. 4 In addition to the steady-state values, ERA shows that the oscillations can be approximated by two modes, a 0.5732-Hz interarea mode and a 1.0823-Hz local mode.…”

Section: Illustration With Two-area Systemmentioning

confidence: 99%

See 1 more Smart Citation

Estimation of Radial Power System Transfer Path Dynamic Parameters Using Synchronized Phasor Data

Chow

Chakrabortty

Vanfretti

et al. 2008

IEEE Trans. Power Syst.

View full text Add to dashboard Cite

Abstract-This paper develops a measurement-based method for estimating a two-machine reduced model to represent the interarea dynamics of a radial, multimachine power system. The method uses synchronized bus voltage phasor measurements at two buses and the line current on the power transfer path. The innovation is the application of the interarea oscillation components in the voltage variables resulting from disturbances for extrapolating system impedances and inertias beyond the measured buses. Expressions for the amplitudes of the bus voltage and bus frequency oscillations as functions of the location on the transmission path are derived from a small-signal perturbation approach. The reduced model provides approximate response to disturbances on the transfer path and offers an alternative to model reduction techniques based on detailed system models and data.Index Terms-inertia extrapolation, interarea oscillations, phasor measurement, power system model reduction, power transfer interface, reactance extrapolation.

show abstract

Section: B Inertia Extrapolation Algorithmmentioning

confidence: 99%

Section: Illustration With Two-area Systemmentioning

confidence: 99%

Estimation of Radial Power System Transfer Path Dynamic Parameters Using Synchronized Phasor Data

Chow

Chakrabortty

Vanfretti

et al. 2008

IEEE Trans. Power Syst.

View full text Add to dashboard Cite

show abstract

“…(i.e., memory requirement), the generalized Hankel matxAJIhus efficiently incorporates the larger observation windows required by systems with very slow inter-area oscillations. This point was discussed in some depth in [22].…”

Section: A Minimal Realization Of Pulse Datamentioning

confidence: 95%

Low-order black-box models for control system design in large power systems

Kamwa

Trudel

Gérin-Lajoie

1996

IEEE Trans. Power Syst.

Self Cite

View full text Add to dashboard Cite

The paper studies two multi-input multi-output (MIMO) procedures for the identification of low-order state-space models of power systems, by probing the network in open loop with low-energy pulses or random signals. Although such data may result from actual measurements, the development assumes simulated responses from a transient stability program, hence benefiting from the existing large base of stability models. While pulse data is processed using the eigensystem realization algorithm, the analysis of random responses is done by means of subspace identification methods. On a prototype Hydro-Qu6bec power system, including SVCs, DC lines, series compensation, and more than 1100 buses, it is verified that the two approaches are equivalent only when strict requirements are imposed on the pulse length and magnitude. The lmh-order equivalent models derived by randomsignal probing allow for effective tuning of decentralized power system stabilizers (PSSs) able to damp both local and very slow inter-area modes.

show abstract

“…The Eigenvalue Realisation Algorithm was introduced in [38] and involves the singular value decomposition (SVD) of the Hankel matrix of measured outputs to obtain the reduced state-space model of the system. The statetransition matrix is then analysed to obtain the system modes and damping.…”

Section: C) Eramentioning

confidence: 99%

Comparative review of methods for stability monitoring in electrical power systems and vibrating structures

Thambirajah

Barocio

Thornhill

2010

IET Gener. Transm. Distrib.

View full text Add to dashboard Cite

A minimal realization approach to reduced-order modelling and modal analysis for power system response signals

Cited by 84 publications

References 19 publications

Estimation of Radial Power System Transfer Path Dynamic Parameters Using Synchronized Phasor Data

Estimation of Radial Power System Transfer Path Dynamic Parameters Using Synchronized Phasor Data

Low-order black-box models for control system design in large power systems

Comparative review of methods for stability monitoring in electrical power systems and vibrating structures

Contact Info

Product

Resources

About