Second order blind identification algorithm with exact model order estimation for harmonic and interharmonic decomposition with reduced complexity

Oliveira, Daniel Ramalho de; Lima, Marcelo A. A.; Silva, Leandro Rodrigues Manso; Ferreira, Danton Diego; Duque, Carlos A.

doi:10.1016/j.ijepes.2020.106415

Cited by 14 publications

(6 citation statements)

References 21 publications

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“…31 Oliveira et al presented a blind identification algorithm with second order through the harmonic and interharmonic decomposition to reduce the computational complexity for the power distribution network. 32 Saxena et al employed the parameterization decomposition strategy to derive an estimation algorithm for the multivariate model systems. 33 Ji et al used the decomposition principle to present a stochastic gradient identification framework by separating the system parameters into three parameter sets for the nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

“…Wang et al proposed a parameter identification method based on the tensor decomposition technique to estimate the parameters of the nonlinear Volterra systems 31 . Oliveira et al presented a blind identification algorithm with second order through the harmonic and interharmonic decomposition to reduce the computational complexity for the power distribution network 32 . Saxena et al employed the parameterization decomposition strategy to derive an estimation algorithm for the multivariate model systems 33 .…”

Section: Introductionmentioning

confidence: 99%

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Decomposition and composition modeling algorithms for control systems with colored noises

Xu,

Ding

2023

Adaptive Control & Signal

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SummaryThis article proposes a novel identification framework for estimating the parameters of the controlled autoregressive autoregressive moving average (CARARMA) models with colored noise. By means of building an auxiliary model and using the hierarchical identification principle, this article investigates a highly‐efficient parameter estimation algorithm. In order to meet the need for identifying the systems with large‐scale parameters, the whole parameters of the CARARMA system is separated into two parameter sets and a decomposition and composition recursive identification algorithm (i.e., hierarchical generalized extended least squares algorithm or decomposition‐based recursive generalized extended least squares algorithm) is presented. Moreover, this article analyzes the convergence of the proposed decomposition and composition recursive identification algorithm. The performance analysis shows that the proposed decomposition and composition identification algorithm can reduce the complexity of the identification algorithm compared with the algorithm without decomposition.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Decomposition and composition modeling algorithms for control systems with colored noises

Xu,

Ding

2023

Adaptive Control & Signal

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“…Moreover, multiple signal classification, Prony's method, estimation based on rotational invariance technique (ESPRIT), adaptive linear element, matching pursuit, and discrete wavelet transform [11][12][13][14][15][16] were considered. The genetic algorithm [17], least-squares optimisation [18], machine learning [19], deep learning [20], and independent component analysis [21] methods were also considered. Some of the above-mentioned methods present good performance; however, it is attained by demanding high computational complexity.…”

Section: Introductionmentioning

confidence: 99%

A DFT‐based method for estimating interharmonics in wind power generation

et al. 2022

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This paper introduces a discrete Fourier transform (DFT)-based method for estimating the amplitudes and frequencies of interharmonics, which arise from the interaction between wind power generators and electric power systems. This method exploits the synergy of choosing signal enhancement, mains frequency estimation, and re-sampling techniques. Consequently, it accomplishes a cost-effective estimation process. The performance results show that signal enhancement and re-sampling techniques are necessary to establish suitable conditions for performing amplitude and frequency estimation. Furthermore, numerical results show that the proposed method, relying on low-cost digital signal processing techniques, offers competitive performance and significant computational complexity reduction compared to a previous DFT-based method.

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“…Literatürde kör kaynak ayrıştırması alanında yapılan çalışmalarda (Çelik et al, 2019;Çelik & Karaboğa, 2020) tarafından karışmış ses işaretleri ayrıştırılmıştır. Güç elektroniğinde harmoniklerin analizi için kör kaynak ayrıştırma algoritmaları ile bir çalışma yapılmıştır (De Oliveira et al, 2021). Ayrıca haberleşme alanında gürültü içinde bulunan haberleşme işaretlerinin belirlenmesi için de yapılan çalışmalar literatürde mevcuttur (Çiflikli & Ilgin, 2020;Ilgin, 2020).…”

Section: Introductionunclassified

Negatif Olmayan Matris Ayrıştırma Yöntemlerinde Fetal Elektrokardiyogram İşaretin Ayrıştırılması

Çelik¹,

Karaboğa²

2021

European Journal of Science and Technology

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ÖzElektrokardiyogram (EKG) işareti, kalp atımları esnasında kalp kasları tarafından üretilen ve kalbin elektriksel aktivitesini öğrenmek için vücudun yüzeyine yerleştirilen elektrotlar yardımıyla alınan işaretlerdir. Elde edilen işaretler kuvvetlendirildikten sonra sayısal işaret işleme yöntemleri ile analiz edilebilirler. Analiz sonucunda elde edilen işaretler özellikle kalp hastalıklarının teşhis ve tedavisinde veya kişinin sağlığı açısından belirleyici etkiye sahip olacaktır. Ölçüm sonucunda insan vücudundan elde edilen işaretlerde en az iki işaretin karışmış şekli elde edilecektir. Bu çalışmada anne karnındaki bebeğin EKG' sinin elde edilmesi için işaret ayrıştırmada kör kaynak ayrıştırma algoritmalarından negatif olmayan matris ayrıştırma yöntemi kullanılmıştır. Kaynak işareti olarak anne EKG, Fetal EKG (bebeğin) ve gürültü işaretlerinin karışımı olan veride işaret ayrıştırma işlemi yapılmıştır. Negatif olmayan matris ayrıştırma algoritmalarından Çarpımsal Güncelleme yöntemi ve Hiyerarşik Değişen En Küçük Kareler yöntemi kullanılarak performans analizleri ile birlikte işlem maliyetleri karşılaştırılmıştır. Uygun olan yöntemi belirlemek için işaret/gürültü oranı ölçütü kullanılmıştır.

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Second order blind identification algorithm with exact model order estimation for harmonic and interharmonic decomposition with reduced complexity

Cited by 14 publications

References 21 publications

Decomposition and composition modeling algorithms for control systems with colored noises

Decomposition and composition modeling algorithms for control systems with colored noises

A DFT‐based method for estimating interharmonics in wind power generation

Negatif Olmayan Matris Ayrıştırma Yöntemlerinde Fetal Elektrokardiyogram İşaretin Ayrıştırılması

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