Robust recursive AR speech analysis

Kovačevíc, Branko; Milosavljević, Milan; Veinović, Mladen

doi:10.1016/0165-1684(95)00019-a

Cited by 31 publications

(12 citation statements)

References 10 publications

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“…As stated in [10] and [20], there is no closed-form solution for the robust objective function defined by (7)-(9) when . A Newton-Raphson-type method is adopted to find the numerical solution for the robust functions.…”

Section: E Robust Algorithmmentioning

confidence: 97%

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Electromechanical Mode Online Estimation Using Regularized Robust RLS Methods

Zhou

Trudnowski

Pierre³

et al. 2008

IEEE Trans. Power Syst.

208

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This paper proposes a regularized robust recursive least squares (R3LS) method for online estimation of power-system electromechanical modes based on synchronized phasor measurement unit (PMU) data. The proposed method utilizes an autoregressive moving average exogenous (ARMAX) model to account for typical measurement data, which includes low-level pseudorandom probing, ambient, and ringdown data. A robust objective function is utilized to reduce the negative influence from nontypical data, which include outliers and missing data. A dynamic regularization method is introduced to help include a priori knowledge about the system and reduce the influence of under-determined problems. Based on a 17-machine simulation model, it is shown through the Monte Carlo method that the proposed R3LS method can estimate and track electromechanical modes by effectively using combined typical and nontypical measurement data.Index Terms-Autoregressive moving average processes, least squares methods, power system identification, power system measurements, power system monitoring, power system parameter estimation, power system stability, recursive estimation, robustness.

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Section: E Robust Algorithmmentioning

confidence: 97%

“…The robust processing only starts when exceeds . The standard deviation can be estimated using [20] (48) for , .…”

Section: E Robust Algorithmmentioning

confidence: 99%

Electromechanical Mode Online Estimation Using Regularized Robust RLS Methods

Zhou

Trudnowski

Pierre³

et al. 2008

IEEE Trans. Power Syst.

208

View full text Add to dashboard Cite

show abstract

“…There are many results which theoretically treat the change detection problem in different uncertainty conditions, assuming different stochastic models of a signal [11][12][13]. Particularly, the classical auto-recursive (AR) modeling with variable forgetting factors (VFF) is commonly used [14][15][16]. Furthermore, in classical AR analysis the linear prediction (LP) parameters of the signal model are determined by RLS method.…”

Section: Introductionmentioning

confidence: 99%

“…Generally, we should estimate the degree of nonstationarity of signal to calculate the next value of FF. A different adaptation schemes have been proposed by varying the memory length of signal [11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Adaptive estimation of time-varying parameters in AR models with variable forgetting factor

Kvaščev

Eurovic

Kovačevíc

et al. 2014

MELECON 2014 - 2014 17th IEEE Mediterranean Electrotechnical Conference

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A new method for estimating time-varying parameters of nonstationary AR signal models, based on adaptive recursive least squares with variable forgetting factors, is described. The adaptive estimator differs from the conventional one by the simultaneously estimation of AR model parameters and scale factor of prediction residuals, while the variable forgetting factor values are adapted to the nonstationary signal via a new extended prediction error detection scheme. The method has good adaptability in the non-stationary situations, and gives low bias and low variance at the stationary situations. The feasibility of the approach is demonstrated with simulations.

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“…Next, outliers need to be considered; they are data that have significantly deviated from normal measures that can result in artificial transients which degrade mode estimation results. Outliers can be detected through model prediction errors [], residuals [] or robust objective functions []; in this article, a moving median algorithm is used to detect samples deviating largely from the median within a parcel.…”

Section: Introductionmentioning

confidence: 99%

Preprocessing synchronized phasor measurement data for spectral analysis of electromechanical oscillations in the Nordic Grid

Vanfretti

Bengtsson

Gjerde

2013

Int. Trans. Electr. Energ. Syst.

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Summary Spectral analysis techniques have successfully been applied for near real‐time monitoring of power system small‐signal electromechanical oscillations using synchronized phasor data from PMUs. The methods used for this purpose commonly assume that random load variations adopt the distribution function of Gaussian noise. Hence, careful attention has to be paid so that the preprocessing of synchronized phasor measurements is capable of providing data with the characteristics expected for these methods to work properly. This can be viewed as a “conditioning” step in the data handling process that has an impact on the results from spectral analysis which consume this data. This article aims to revisit the crucial step of preprocessing of PMU data in a tutorial fashion. The goal is to share the authors' experience when dealing with real PMU data originating from the Nordic Grid. This article offers a systematic and detailed methodology developed by the authors which has been successfully used in studies on estimation of electromechanical modes in the Nordic Grid. Copyright © 2013 John Wiley & Sons, Ltd.

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Robust recursive AR speech analysis

Cited by 31 publications

References 10 publications

Electromechanical Mode Online Estimation Using Regularized Robust RLS Methods

Electromechanical Mode Online Estimation Using Regularized Robust RLS Methods

Adaptive estimation of time-varying parameters in AR models with variable forgetting factor

Preprocessing synchronized phasor measurement data for spectral analysis of electromechanical oscillations in the Nordic Grid

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