Synchronized Phasor Data Based Energy Function Analysis of Dominant Power Transfer Paths in Large Power Systems

Chow, Joe H.; Chakrabortty, Aranya; Arcak, Murat; Bhargava, B.; Salazar, Armando

doi:10.1109/tpwrs.2007.895162

Cited by 87 publications

(43 citation statements)

References 14 publications

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“…This concept is then extended to obtain the impedance and inertia of a coherent area in a radial power system, by extracting the interarea model oscillations. The results here extend those in [8], where only the impedance between the measured buses and an equivalent inertia were computed. The work here differs from several out-of-step relay studies [9], [10] which rely on known or precomputed machine and system reactances.…”

supporting

confidence: 81%

“…If the resistance is uniform between A and B, such that (12) which includes the case where the resistance is negligible, (8) reduces to . As a result, reduces to…”

Section: A Reactance Extrapolation Algorithmmentioning

confidence: 99%

“…In [8], synchronized phasor measurements from severe oscillation events were used to establish power-angle curves of two power transfer paths in WECC, without performing reactance extrapolation to the equivalent generator internal node. We apply the new techniques in this paper and recompute the IME model of Transfer Path 1 in [8], which is compensated by series capacitors.…”

Section: Application To a Transfer Path In Weccmentioning

confidence: 99%

“…We apply the new techniques in this paper and recompute the IME model of Transfer Path 1 in [8], which is compensated by series capacitors. The resulting pre-disturbance and post-disturbance power-angle curves established from the new IME model are shown in Fig.…”

Section: Application To a Transfer Path In Weccmentioning

confidence: 99%

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

Chow

Chakrabortty

Vanfretti

et al. 2008

IEEE Trans. Power Syst.

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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.

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supporting

confidence: 81%

“…If the resistance is uniform between A and B, such that (12) which includes the case where the resistance is negligible, (8) reduces to . As a result, reduces to…”

Section: A Reactance Extrapolation Algorithmmentioning

confidence: 99%

Section: Application To a Transfer Path In Weccmentioning

confidence: 99%

Section: Application To a Transfer Path In Weccmentioning

confidence: 99%

See 2 more Smart Citations

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

Chow

Chakrabortty

Vanfretti

et al. 2008

IEEE Trans. Power Syst.

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show abstract

“…For example, Alvarez et al proposed seven trajectory based indices which are suitable for fuzzy inference on real-time dynamic vulnerability [6]. A phasor data-based energy function indicator was developed in [7] aiming at monitoring the dynamic status of power transfer paths. A real-time transient stability assessment (TSA) method based on centre-of-inertia estimation from PMU records was reported in [8].…”

Section: Introductionmentioning

confidence: 99%

High-performance predictor for critical unstable generators based on scalable parallelized neural networks

Liu¹,

Yang²,

et al. 2016

J. Mod. Power Syst. Clean Energy

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A high-performance predictor for critical unstable generators (CUGs) of power systems is presented in this paper. The predictor is driven by the MapReduce based parallelized neural networks. Specifically, a group of back propagation neural networks (BPNNs), fed by massive response trajectories data, are efficiently organized and concurrently trained in Hadoop to identify dynamic behavior of individual generator. Rather than simply classifying global stability of power systems, the presented approach is able to distinguish unstable generators accurately with a few cycles of synchronized trajectories after fault clearing, enabling more in-depth emergency awareness based on wide-area implementation. In addition, the technique is of rich scalability due to Hadoop framework, which can be deployed in the control centers as a high-performance computing infrastructure for real-time instability alert. Numerical examples are studied using NPCC 48-machines test system and a realistic power system of China.

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Smart Grid Impact on Operation and Planning of Electric Energy Systems

Wang

Ton

2015

Handbook of Clean Energy Systems

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The next‐generation electric power grid, also known as the Smart Grid, is expected to address the operational challenges of the nation's aging and outmoded electrical grid infrastructure. This article investigates the impacts of the Smart Grid technologies on operations and planning of electric energy systems at both the transmission and the distribution levels. This article presents a comprehensive overview of the emerging technologies (e.g., plug‐in electric vehicles, microgrids, distributed renewable energy sources and storage, demand‐side management and smart metering) and the development of distribution automation in Smart Grid. This article also discusses the technical information regarding the emerging technologies (e.g., wide‐area monitoring and measurements) and developments for transmission system operations in Smart Grid. There is still a significant amount of investigation to be done before the full vision of Smart Grid comes to fruition. Therefore, this article lists the future research needs covering both transmission and distribution systems in a Smart Grid environment.

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Synchronized Phasor Data Based Energy Function Analysis of Dominant Power Transfer Paths in Large Power Systems

Cited by 87 publications

References 14 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

High-performance predictor for critical unstable generators based on scalable parallelized neural networks

Smart Grid Impact on Operation and Planning of Electric Energy Systems

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