Topology identification for dynamic equivalent models of large power system networks

Nabavi, Seyedbehzad; Chakrabortty, Aranya

doi:10.1109/acc.2013.6579989

Cited by 41 publications

(22 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…the IEEE New England 39-bus system [26]). The topology of the power network is represented in Figure 2 are provided in Table II, where a base power of 1000 MW is assumed.…”

Section: Simulation Resultsmentioning

confidence: 99%

Distributed Second Order Sliding Modes for Optimal Load Frequency Control

Cucuzzella

Trip

Persis

et al. 2017

2017 American Control Conference (ACC)

View full text Add to dashboard Cite

Abstract-This paper proposes a Distributed Second Order Sliding Mode (D-SOSM) control strategy for Optimal Load Frequency Control (OLFC) in power networks, where besides frequency regulation also minimization of generation costs is achieved. Because of unknown load dynamics and possible network parameters uncertainties, the sliding mode control methodology is particularly appropriate for the considered control problem. This paper considers a power network partitioned into control areas, where each area is modelled by an equivalent generator including second-order turbine-governor dynamics. On a suitable designed sliding manifold, the controlled system exhibits an incremental passivity property that allows us to infer convergence to a zero steady state frequency deviation minimizing the generation costs.

show abstract

“…the IEEE New England 39-bus system [26]). The topology of the power network is represented in Figure 2 are provided in Table II, where a base power of 1000 MW is assumed.…”

Section: Simulation Resultsmentioning

confidence: 99%

Distributed Second Order Sliding Modes for Optimal Load Frequency Control

Cucuzzella

Trip

Persis

et al. 2017

2017 American Control Conference (ACC)

View full text Add to dashboard Cite

show abstract

“…If the weight of any edge is estimated to be less than a small tolerance (0 < ϵ << 1) then it may be assumed to be zero indicating that the edge does not exist in the equivalent graph. For details of this approach, please see [9].…”

Section: Wave Models Defined Over Networkmentioning

confidence: 99%

“…i.e., the adjacency matrix E is known. If, however, the exact structure of E is not known, it can be estimated conveniently by running a least squares estimation algorithm based on filtered PMU measurements of phase angles and frequency at selected generator buses in different areas, as shown in out recent work [9]. For this, E is first assumed to be a complete graph, and then its edgeweights (or the line reactances) and its node weights (or machine inertias) are estimated from PMU data.…”

Section: Wave Models Defined Over Networkmentioning

confidence: 99%

Spatio-temporal oscillation monitoring in spatially distributed power system networks using energy functions

Chakrabortty

Khan

2014

2014 American Control Conference

Self Cite

View full text Add to dashboard Cite

In this paper we consider continuum models of power system networks divided into clusters, and present a model reduction method to derive the analytical expressions for frequency waves propagating from one cluster to another depending on the network topology. Previous results have modeled such electromechanical waves mostly for two-area radial transfer paths, i.e., for two-node line graphs. Our results extend this approach to a network of areas connected by equivalent transfer paths by defining an equivalent SturmLiouville eigenvalue equation for the clustered network. We derive analytical expressions for the equivalent damping and power spectrum of this eigenvalue problem, as well for the spatio-temporal kinetic and potential energy functions that may used for transient stability assessment of the clustered network model following a disturbance.

show abstract

“…He first runs an eigenvalue decomposition on L E m in (16) to determine the λ 2 nodal domains. There are always exactly two nodal domains corresponding to λ 2 -one positive and one negative-hence one of the aggregate areas comprises the positive nodal domain while the other comprises the negative nodal domain.…”

Section: ) Topology Identificationmentioning

confidence: 99%

“…In contrast, in this paper, we develop a completely real-time algorithm by dividing the localization problem into two parts. First, we utilize PMU data transmitted from the different clusters, or utility companies, to the control center of an independent system operator (ISO), and run a model reduction algorithm by which a dynamic equivalent of the clustered network is identified in real-time [16]. Second, in parallel to the model reduction step, we run a system identification routine in every cluster using local PMU data to identify a transfer matrix model for the full-order power system.…”

Section: Introductionmentioning

confidence: 99%

A Real-Time Attack Localization Algorithm for Large Power System Networks Using Graph-Theoretic Techniques

Nudell

Nabavi

Chakrabortty

2015

IEEE Trans. Smart Grid

Self Cite

View full text Add to dashboard Cite

We develop a graph-theoretic algorithm for localizing the physical manifestation of attacks or disturbances in large power system networks using real-time synchrophasor measurements. We assume the attack enters through the electromechanical swing dynamics of the synchronous generators in the grid as an unknown additive disturbance. Considering the grid to be divided into coherent areas, we pose the problem as to localize which area the attack may have entered using relevant information extracted from the phasor measurement data. Our approach to solve this problem consists of three main steps. We first run a phasor-based model reduction algorithm by which a dynamic equivalent of the clustered network can be identified in real-time. Second, in parallel, we run a system identification in each area to identify a transfer matrix model for the full-order power system. Thereafter, we exploit the underlying graph-theoretic properties of the identified reduced-order topology, create a set of localization keys, and compare these keys with a selected set of transfer function residues. We validate our results using a detailed case study of the two-area Kundur model and the IEEE 39-bus power system.

show abstract

Topology identification for dynamic equivalent models of large power system networks

Cited by 41 publications

References 13 publications

Distributed Second Order Sliding Modes for Optimal Load Frequency Control

Distributed Second Order Sliding Modes for Optimal Load Frequency Control

Spatio-temporal oscillation monitoring in spatially distributed power system networks using energy functions

A Real-Time Attack Localization Algorithm for Large Power System Networks Using Graph-Theoretic Techniques

Contact Info

Product

Resources

About