A Digital Pre-Distortion Based on Nonlinear Autoregressive With Exogenous Inputs

Outdoor insulators may experience stress due to severe environmental conditions, such as pollution and contamination. Through the identification of partial discharges by ultrasonic noise, it is possible to assess the possibility of a power grid failure occurring. In this paper, ensemble models are used to analyze an ultrasonic signal from an ultrasonic microphone Pettersson M500. As the insulators are susceptible to developing irreversible failures, it will be evaluated whether the ultrasonic signal will remain over time, so that it is possible to assess whether the discharges being captured can result in a failure in contaminated polymeric insulators, evaluated in a high voltage laboratory under controlled conditions. The ensemble models were used in this paper because they typically require less computational effort than techniques based on deep learning and have acceptable performance for the problem at hand. The bagging, boosting, random subspace, bagging plus random subspace, and stacked generalization ensemble models are evaluated, and the best result of each model is used to compare the differences between the models. The bagging ensemble learning model proved to be faster and have lower error than other ensemble models, long short-term memory (LSTM), and nonlinear autoregressive (NAR).

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“…where y(t) is the predicted output of the model given d past values and another series of x(t) [80].…”

Section: B Nonlinear Autoregressivementioning

confidence: 99%

Analysis of the Ultrasonic Signal in Polymeric Contaminated Insulators Through Ensemble Learning Methods

et al. 2022

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“…In [8], a nonlinear autoregressive moving average (NARMA) DPD based on multi-lookup table functions was proposed. In [11], a new adjacent channel leakage ratio DPD is proposed. However, a comprehensive analysis was not presented for the approach and a comparison with other methods was not given.…”

Section: Introductionmentioning

confidence: 99%

“…However, the main drawback of the proposed approach is in its high complexity and low convergence speed which causes performance degradation in high data rate applications. In [11], a new adjacent channel leakage ratio DPD is proposed. Moreover, the NARX DPD model requires more coefficients compared with the one proposed in this paper, which gives lower complexity by using only linear terms of the output.…”

Section: Introductionmentioning

confidence: 99%

A low complexity pre‐distortion scheme for power amplifier linearization in wideband applications

Varahram

Dooley

Finnerty

et al. 2016

Int J Communication

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In this paper, a nonlinear autoregressive with exogenous inputs (NARX) digital pre-distortion scheme to linearize power amplifiers is proposed. The proposed NARX digital pre-distortion gives better accuracy and spectral leakage suppression compared with other commonly used Volterra-based techniques. The stability criterion of the NARX digital pre-distortion is derived from the frequency domain analysis. Simulation is carried out with a 20 MHz single carrier long-term evolution signal and two-carrier long-term evolution signal and instantaneous to average ratio of 6.2 dB at 0.01% complementary cumulative distribution function (CCDF). The results of simulation analysis show a slight improvement in adjacent channel leakage ratio performance with 30% reduction in the number of floating point operations compared with conventional predistortion techniques.NMSE, normalized mean square error; MP, memory polynomial; GMP, generalized memory polynomial; NARX DPD, nonlinear autoregressive exogenous digital pre-distortion; LTE, long-term evolution. 8 of 12 P. VARAHRAM ET AL.

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Reduced-Complexity Polynomials with Memory Applied to the Linearization of Power Amplifiers with Real-Time Discrete Gain Control

Schuartz

Santos

Leite

et al. 2019

Circuits Syst Signal Process

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A Digital Pre-Distortion Based on Nonlinear Autoregressive With Exogenous Inputs

Cited by 8 publications

References 9 publications

Analysis of the Ultrasonic Signal in Polymeric Contaminated Insulators Through Ensemble Learning Methods

Analysis of the Ultrasonic Signal in Polymeric Contaminated Insulators Through Ensemble Learning Methods

A low complexity pre‐distortion scheme for power amplifier linearization in wideband applications

Reduced-Complexity Polynomials with Memory Applied to the Linearization of Power Amplifiers with Real-Time Discrete Gain Control

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