Adaptive sampling scheme for monitoring oscillations using Prony analysis

Peng, Jimmy Chih-Hsien; Nair, Nirmal-Kumar C.

doi:10.1049/iet-gtd.2009.0174

Cited by 41 publications

(37 citation statements)

References 12 publications

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“…Prony's methods allow for accurate estimation of parameters of the analysed signal [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. They provide new measurement opportunities by identifying actual frequencies of the analysed signal components and extending the signal model with data on damping coefficients of particular components.…”

Section: Introduction To Prony's Methodsmentioning

confidence: 99%

Prony’s Method with Reduced Sampling - Numerical Aspects

Zygarlicki¹,

Mroczka²

2014

Metrology and Measurement Systems

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This paper presents a new modification of the least-squares Prony's method with reduced sampling, which allows for a significant reduction in the number of the analysed signal samples collected per unit time. The specific combination of non-uniform sampling with Prony's method enables sampling of the analysed signals at virtually any average frequency, regardless of the Nyquist frequency, maintaining high accuracy in parameter estimation of sinusoidal signal components. This property allows using the method in measuring devices, such as for electric power quality testing equipped with low power signal processors, which in turn contributes to reducing complexity of these devices. This paper presents research on a method for selecting a sampling frequency and an analysis window length for the presented method, which provide maximum estimation accuracy for Prony's model component parameters. This paper presents simulation tests performed in terms of the proposed method application for analysis of harmonics and interharmonics in electric power signals. Furthermore, the paper provides sensitivity analysis of the method, in terms of common interferences occurring in the actual measurement systems.

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Section: Introduction To Prony's Methodsmentioning

confidence: 99%

Prony’s Method with Reduced Sampling - Numerical Aspects

Zygarlicki¹,

Mroczka²

2014

Metrology and Measurement Systems

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“…In this particular case, the model is (6) The parameter model (6) is obviously highly nonlinear, so to estimate the unknown model parameters, the nonlinear estimation techniques must be used. In Appendix I the derivation of the practical nonlinear model (12) which is used as the starting point for monitoring of inter-area oscillations is given. The vector equation (12) represents a set of nonlinear equations with unknowns.…”

Section: Signal Model Representationmentioning

confidence: 99%

“…In Appendix I the derivation of the practical nonlinear model (12) which is used as the starting point for monitoring of inter-area oscillations is given. The vector equation (12) represents a set of nonlinear equations with unknowns. It can be solved if using different numerical approaches.…”

Section: Signal Model Representationmentioning

confidence: 99%

“…It can be solved if using different numerical approaches. In this paper the authors demonstrate the use of the NTA for solving the problem stated in (12). The solution (the estimates of the unknown model parameters) can be obtained iteratively, or through a sequential update of estimates obtained from a single data window.…”

Section: Signal Model Representationmentioning

confidence: 99%

“…In the case of samples belonging to the data window at , (10) can be expressed in the following vector form: (12) where is an measurement vector, is an vector of nonlinear functions given by (11) and…”

Section: Appendix the Nonlinear Model Of The Signalmentioning

confidence: 99%

See 2 more Smart Citations

Wide Area Inter-Area Oscillation Monitoring Using Fast Nonlinear Estimation Algorithm

Cai

Regulski

Osborne

et al. 2013

IEEE Trans. Smart Grid

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This paper presents the results of the development of Smart Grid transmission network applications in the Great Britain (GB) power system. A new Wide Area Monitoring System (WAMS) application for monitoring inter-area oscillations is developed. The core of this novel application is a fast nonlinear algorithm for the real-time estimation of the dominant inter-area oscillation mode, which processes GPS synchronized information obtained from Phasor Measurement Units (PMUs) installed in the power system. It is based on the Newton-Type Algorithm (NTA), an efficient parameter estimator. The paper focuses on the practical application of the new WAMS application: two data sets were tested, one based on computer simulations and the other based on real-life data records. The computer simulated oscillatory signals were obtained through dynamic simulations of the full GB power system model consisting of over 200 generators. The real-life data records used information collected by the FlexNet Wide Area Monitoring System (FlexNET-WAMS) installed in the GB network. Based on these data records, the features of inter-area oscillations in the GB network are drawn. Index Terms-Inter-area oscillations, Newton type algorithm, nonlinear parameter estimation, wide area monitoring systems. 1949-3053 © 2013 IEEE

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Multi‐objective optimal PMUs placement for online assessment of small‐signal stability

Tarif

Amrane

Chabane

2019

Int Trans Electr Energ Syst

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Summary The current paper presents a multi‐objective approach for the optimal placement of phasor measurement units (PMUs) for small‐signal stability assessment of a power system. The proposed methodology is based on a multi‐signal Prony analysis of the voltage amplitude, voltage angle, and frequency signals measured by the PMUs. A Pareto Archive Differential Evolution (PADE) algorithm is used to determine the best location of PMUs minimizing the PMUs number and maximizing the signal‐to‐noise ratio (SNR) subject to full system constraints. The capability of the proposed approach has been demonstrated through several cases studies, where it gives an accurate estimation of electromechanical modes when confronted with modal analysis under various scenarios, disturbances, and measurement noise. Additionally, the PADE algorithm is compared with nondominated sorting approach (NSGA)‐III, multi‐objective differential evolution (MODE), and multi‐objective particle swarm optimization (MOPSO) algorithms, and the superiority of PADE algorithm for solving the optimal PMUs placement (OPP) problem is demonstrated.

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Adaptive sampling scheme for monitoring oscillations using Prony analysis

Cited by 41 publications

References 12 publications

Prony’s Method with Reduced Sampling - Numerical Aspects

Prony’s Method with Reduced Sampling - Numerical Aspects

Wide Area Inter-Area Oscillation Monitoring Using Fast Nonlinear Estimation Algorithm

Multi‐objective optimal PMUs placement for online assessment of small‐signal stability

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