Contingency analysis using synchrophasor measurements

Makram, Elham B.; Vutsinas, Megan; Girgis, A.A.; Zhao, Zheng

doi:10.1016/j.epsr.2012.01.002

Cited by 7 publications

(4 citation statements)

References 36 publications

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“…Further, suppose , then we obtain the following overdetermined system: (12) For ease of notation, let represent the matrix . Then, the system in (12) is of the form (13) In (13), we hypothesize that the relationship between and is approximately linear. Let denote the combination of inherent deterministic error arising from the linearization assumption and stochastic measurement error arising from faulty PMU data.…”

Section: A Isf Computationmentioning

confidence: 99%

“…Such proposed approaches still largely rely on a model of the system and utilize the so-called dc approximation. In [13], phasor measurements were used in online contingency analysis by monitoring buses that had been classified as high-risk by an offline study. This paper builds on preliminary work reported in [14] and provides extensions in several directions.…”

Section: Measurement-based Estimation Of Linear Sensitivitymentioning

confidence: 99%

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Measurement-Based Estimation of Linear Sensitivity Distribution Factors and Applications

Chen

Domínguez-García

Sauer

2014

IEEE Trans. Power Syst.

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In this paper, we propose a method to compute linear sensitivity distribution factors (DFs) in near real-time. The method does not rely on the system power flow model. Instead, it uses only high-frequency synchronized data collected from phasor measurement units to estimate the injection shift factors through linear least-squares estimation, after which other DFs can be easily computed. Such a measurement-based approach is desirable since it is adaptive to changes in system operating point and topology. We further improve the adaptability of the proposed approach to such changes by using weighted and recursive least-squares estimation.Through numerical examples, we illustrate the advantages of our proposed DF estimation approach over the conventional modelbased one in the context of contingency analysis and generation re-dispatch.

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Section: A Isf Computationmentioning

confidence: 99%

Section: Measurement-based Estimation Of Linear Sensitivitymentioning

confidence: 99%

Measurement-Based Estimation of Linear Sensitivity Distribution Factors and Applications

Chen

Domínguez-García

Sauer

2014

IEEE Trans. Power Syst.

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“…Table 4 and Table 5 indicate that the massive samples production as well as DT-dependent features definition for generators according to (5) results in a huge volume of data. However, a standalone BPNN inputs instances one by one leading to both sizable IO overhead and considerable computational overhead.…”

Section: Test Systems and Training Datamentioning

confidence: 99%

“…As WAMS are now being deployed in quite a few power systems, PMU is playing an ever increasingly vital role in transient stability awareness [5]. A number of researches have been carried out to evaluate the transient stability by using PMU data.…”

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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Algorithmic Attack Synthesis Using Hybrid Dynamics of Power Grid Critical Infrastructures

Huang

Etigowni²,

García³

et al. 2018

2018 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)

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Contingency analysis using synchrophasor measurements

Cited by 7 publications

References 36 publications

Measurement-Based Estimation of Linear Sensitivity Distribution Factors and Applications

Measurement-Based Estimation of Linear Sensitivity Distribution Factors and Applications

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

Algorithmic Attack Synthesis Using Hybrid Dynamics of Power Grid Critical Infrastructures

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