Influence of the Forgetting Factor in the Recursive Least Squares RLS Algorithm on the Quality and Precision of the Identified Parameters in a DC Corona Discharge

In this work, we present a contribution on the applicability of the recursive least squares method used for the parametric identification of a corona discharge phenomenon at small distances. Furthermore, we show the influence of the choice of the forgetting factor for a better performance of the ide...

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Published in:IEEE transactions on plasma science Vol. 53; no. 1; pp. 108 - 115
Main Authors: Abdelkarim, Tahtah, Driss, Raouti, Eichwald, Olivier, Vido, Lionel, Kamel, Nassour, Abdelkrim, Bouanane
Format: Journal Article
Language:English
Published: IEEE 01.01.2025
Institute of Electrical and Electronics Engineers
Subjects:
Corona
Corona discharge
Current measurement
dc power supply
Discharges (electric)
Electronic mail
Engineering Sciences
Equivalent circuits
formatting factor
Integrated circuit modeling
Mathematical models
Resistance
RLS method
Vectors
Voltage measurement
ISSN:0093-3813, 1939-9375
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Abstract In this work, we present a contribution on the applicability of the recursive least squares method used for the parametric identification of a corona discharge phenomenon at small distances. Furthermore, we show the influence of the choice of the forgetting factor for a better performance of the identification operation and the quality of estimation of the identified parameters. The identification process is based on experimental input/output measurements. The validation of the parameter results is done by a physical analysis of the behaviors of these parameters and by comparing the output calculated according to these parameters, with the real output obtained experimentally. The results show that with a constant forgetting factor close to 1 (<inline-formula> <tex-math notation="LaTeX">\lambda = 0.99 </tex-math></inline-formula>), parameter quality improves but output accuracy may vary. In contrast, a variable forgetting factor enhances both parameter quality and model output consistently. A good agreement observed between the real and calculated outputs confirms both the good choice of the forgetting factor and the precision of the estimated parameters as well as the validity of the identified model in general.
AbstractList In this work, we present a contribution on the applicability of the recursive least squares method used for the parametric identification of a corona discharge phenomenon at small distances. Furthermore, we show the influence of the choice of the forgetting factor for a better performance of the identification operation and the quality of estimation of the identified parameters. The identification process is based on experimental input/output measurements. The validation of the parameter results is done by a physical analysis of the behaviors of these parameters and by comparing the output calculated according to these parameters, with the real output obtained experimentally. The results show that with a constant forgetting factor close to 1 (<inline-formula> <tex-math notation="LaTeX">\lambda = 0.99 </tex-math></inline-formula>), parameter quality improves but output accuracy may vary. In contrast, a variable forgetting factor enhances both parameter quality and model output consistently. A good agreement observed between the real and calculated outputs confirms both the good choice of the forgetting factor and the precision of the estimated parameters as well as the validity of the identified model in general.
Author Abdelkrim, Bouanane
Vido, Lionel
Eichwald, Olivier
Abdelkarim, Tahtah
Driss, Raouti
Kamel, Nassour
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SubjectTerms Corona
Corona discharge
Current measurement
dc power supply
Discharges (electric)
Electronic mail
Engineering Sciences
Equivalent circuits
formatting factor
Integrated circuit modeling
Mathematical models
Resistance
RLS method
Vectors
Voltage measurement
Title Influence of the Forgetting Factor in the Recursive Least Squares RLS Algorithm on the Quality and Precision of the Identified Parameters in a DC Corona Discharge
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