A Transient and Steady-State Power Signature Feature Extraction Using Different Prony's Methods

Ancelmo, Hellen Cristina; Grando, Flavio Lori; Mulinari, Bruna Machado; Costa, Clayton H. da; Lazzaretti, André E.; Oroski, Elder; Renaux, Douglas P. B.; Pöttker, Fabiana; Lima, Carlos R. Erig; Linhares, Robson R.

doi:10.1109/isap48318.2019.9065959

Cited by 6 publications

(6 citation statements)

References 10 publications

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“…As shown in Table IV, the proposed method presents an improvement of Accuracy of 8.67% in relation to [33], 0.54% to [34], and 2.02% in relation to [35].…”

Section: Comparisons With State-of-the-art Approachesmentioning

confidence: 87%

Performance of Scattering Transform Feature Extraction for Electrical Load Classification

Aguiar¹,

Lazzaretti²,

Pipa³

2021

Anais Do 15. Congresso Brasileiro De Inteligência Computacional

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The Scattering transform (ST) presents itself as an alternative approach to the classic methods that involve neural networks and deep learning techniques for the feature extraction and classification of signals and images. Among its main advantages, one can emphasize that the coefficients of the ST are determined analytically and do not need to be learned, as typically performed in Convolutional Neural Networks (CNNs). Additionally, ST has time-shifting and small time-warping invariance, which reduces the need for precise temporal localization (detection) for subsequent classification. This paper originally proposes the application of ST to extract features and classify Non-intrusive Load Monitoring (NILM) high-frequency signals. We validate the extraction strategy performance varying several parameters for ST calculation, such as signal length, number of examples, and sampling frequency. The results outperform other state-of-the-art feature extraction techniques, reaching up to 99.98% of accuracy and 99.51% of FScore for a publicly available dataset, demonstrating the feasibility and promising aspects of the ST for NILM problems.

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“…As shown in Table IV, the proposed method presents an improvement of Accuracy of 8.67% in relation to [33], 0.54% to [34], and 2.02% in relation to [35].…”

Section: Comparisons With State-of-the-art Approachesmentioning

confidence: 87%

Performance of Scattering Transform Feature Extraction for Electrical Load Classification

Aguiar¹,

Lazzaretti²,

Pipa³

2021

Anais Do 15. Congresso Brasileiro De Inteligência Computacional

View full text Add to dashboard Cite

show abstract

“…The authors demonstrate that the identification of the damping observed during the transients improves the classification performance when combined with Fourier coefficients, achieving an accuracy of 90-99% for the COOLL dataset, even for unsupervised approaches. However, the implementation of this technique may present an instability during the exponential estimation [28] that causes an imprecise estimation of the harmonic contents. An analytic solution, based on the Prony's Method, to the problem of estimating the harmonic content with an exponential damping factor, presented in [28], improves the accuracy of the estimation of frequency, phase, amplitude, and damping factor for current signals.…”

Section: Review Of Techniques For Load Event Detection and Power Signature Recognitionmentioning

confidence: 99%

“…However, the implementation of this technique may present an instability during the exponential estimation [28] that causes an imprecise estimation of the harmonic contents. An analytic solution, based on the Prony's Method, to the problem of estimating the harmonic content with an exponential damping factor, presented in [28], improves the accuracy of the estimation of frequency, phase, amplitude, and damping factor for current signals.…”

Section: Review Of Techniques For Load Event Detection and Power Signature Recognitionmentioning

confidence: 99%

“…Several approaches have been proposed to simplify the solution, apart from the classical Polynomial method, such as Least Squares, Total Least Squares, and Matrix Pencil Method. As presented in [28], the Matrix Pencil approach results in a lower reconstruction error and a higher classification accuracy for different NILM datasets, being the most appropriate method to extract Prony's features of electrical current signals.…”

Section: Pronymentioning

confidence: 99%

“…For V-I trajectories, the transient and steady-state feature parameters were based on the recommendation presented in [27]. Prony's parameters and solving procedures are based on the comparison discussed in [28]. For the power envelope technique, first, the envelope is analyzed, and its type is determined.…”

Section: Parameter Selectionmentioning

confidence: 99%

See 2 more Smart Citations

A Multi-Agent NILM Architecture for Event Detection and Load Classification

et al. 2020

Self Cite

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A multi-agent architecture for a Non-Intrusive Load Monitoring (NILM) solution is presented and evaluated. The underlying rationale for such an architecture is that each agent (load event detection, feature extraction, and classification) outperforms others of the same type in particular scenarios; hence, by combining the expertise of these agents, the system presents an improved performance. Known NILM algorithms, as well as new algorithms, proposed by the authors, were individually evaluated and compared. The proposed architecture considers a NILM system composed of Load Monitoring Modules (LMM) that report to a Center of Operations, required in larger facilities. For the purposed of evaluating and comparing performance, five load event detect agents, five feature extraction agents, and five classification agents were studied so that the best combinations of agents could be implemented in LMMs. To evaluate the proposed system, the COOLL and the LIT-Dataset were used. Performance improvements were detected in all scenarios, with power-ON and power-OFF detection improving up to 13%, while classification accuracy improved up to 9.4%.

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New Appliance Signatures for NILM Based on Mono-Fractal Features and Multi-Fractal Formalism

Mughees,

Kamran,

Mughees

et al. 2024

IEEE Access

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A Transient and Steady-State Power Signature Feature Extraction Using Different Prony's Methods

Cited by 6 publications

References 10 publications

Performance of Scattering Transform Feature Extraction for Electrical Load Classification

Performance of Scattering Transform Feature Extraction for Electrical Load Classification

A Multi-Agent NILM Architecture for Event Detection and Load Classification

New Appliance Signatures for NILM Based on Mono-Fractal Features and Multi-Fractal Formalism

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