Parameter estimation of superimposed damped sinusoids using exponential windows

Ali, Muhammad; Abed-Meraim, Karim

doi:10.1016/j.sigpro.2013.12.025

Cited by 1 publication

(2 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…, t n k T , and s(t k ), s s (t k ), e(t k ) are defined in (1), (2), and (3), respectively. Finally, the CRB is equal to F −1 (θ ) obtained after inserting expressions (19) in (18) and inverting F (θ ).…”

Section: Parameter Estimationmentioning

confidence: 99%

“…For example, it was used for modeling electric disturbances [15], transient audio signals [16], and the free induction decay observed in nuclear resonance spectroscopy [17]. Along with the model, different algorithms were proposed for its parameter estimation [18]. The most known methods are Prony's [19], Pisarenko's [20], matrix pencil [21], Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) [22], and MUltiple SIgnal Classification (MUSIC) [23].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Electrical transient modeling for appliance characterization

Meziane

Ravier

Abed-Meraim

et al. 2019

EURASIP J. Adv. Signal Process.

Self Cite

View full text Add to dashboard Cite

Transient signals are characteristic of the underlying phenomenon generating them, which makes their analysis useful in many fields. Transients occur as a sudden change between two steady state regimes, subsist for a short period, and tend to decay over time. Hence, superimposed damped sinusoids (SDS) were extensively used for transients modeling as they are adequate for describing decaying phenomena. However, SDS are not adapted for modeling the turn-on transient current of electrical appliances as it tends to decay to a steady state that is different from the one preceding it. In this paper, we propose a new and more suitable model for these signals for the purpose of characterizing appliances. We also propose an algorithm for the model parameter estimation and validate its performance on simulated and real data. Moreover, we give an example on the use of the model parameters as features for the classification of appliances using the Controlled On/Off Loads Library (COOLL) dataset. The results show that the proposed algorithm is efficient and that for real data the network fundamental frequency must be estimated to account for its variations around the nominal value. Finally, real data experiments showed that the model parameters used as features yielded a classification accuracy of 98%.

show abstract

Section: Parameter Estimationmentioning

confidence: 99%