A non-intrusive approach to classify electrical appliances based on higher-order statistics and genetic algorithm: a smart grid perspective

Guedes, Juan Diego Silva; Ferreira, Danton Diego; Barbosa, Bruno Henrique Groenner

doi:10.1016/j.epsr.2016.06.042

Cited by 21 publications

(15 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The spectral band energy and entropies are finally calculated for feature extraction. Both [21] and [22] reported the high performance in load disaggregation. Nevertheless, those outcomes were achieved at high sampling frequency, 15.4 kHz and 10.24 kHz, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…In [23], the authors used ANN to deal with harmonic issues, but the results do not involve the various operational modes of loads under different voltage sources. In NILM systems, most studies mainly focus on ANN with back-propagation (BP-ANN) for load recognition algorithms [2,6,7,11,13,21,23,24]. With simple implementation, k-Nearest Neighbors (k-NN) is also a commonly used recognizer.…”

Section: Introductionmentioning

confidence: 99%

“…Guedes et al [21] used the second and fourth-order cumulants of high-order statistics (HOS) to extract the information from the current waveform. Genetic algorithms (GAs) are then applied for feature selection and dimension reduction.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Statistical Feature Extraction for Fault Locations in Nonintrusive Fault Detection of Low Voltage Distribution Systems

Chang

Linh

2017

Energies

View full text Add to dashboard Cite

This paper proposes statistical feature extraction methods combined with artificial intelligence (AI) approaches for fault locations in non-intrusive single-line-to-ground fault (SLGF) detection of low voltage distribution systems. The input features of the AI algorithms are extracted using statistical moment transformation for reducing the dimensions of the power signature inputs measured by using non-intrusive fault monitoring (NIFM) techniques. The data required to develop the network are generated by simulating SLGF using the Electromagnetic Transient Program (EMTP) in a test system. To enhance the identification accuracy, these features after normalization are given to AI algorithms for presenting and evaluating in this paper. Different AI techniques are then utilized to compare which identification algorithms are suitable to diagnose the SLGF for various power signatures in a NIFM system. The simulation results show that the proposed method is effective and can identify the fault locations by using non-intrusive monitoring techniques for low voltage distribution systems.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Statistical Feature Extraction for Fault Locations in Nonintrusive Fault Detection of Low Voltage Distribution Systems

Chang

Linh

2017

Energies

View full text Add to dashboard Cite

show abstract

“…For example, we can classify appliances according to different factors such as method of load control (e.g., dimmed electrical loads, shed electrical loads, shifted electrical loads) [12], [13] or amount of power usage (low-power, medium-power, high-power) [14]- [16]. In this widely studied category which initiated by the work of Hart (1992) [17], several works have been done recently by using latent variable models and taking advantage of appliance feature sets [18], [19]. In [18], a nonintrusive approach to classify electrical appliances based on higher-order statistics (HOS) is proposed.…”

Section: A Related Workmentioning

confidence: 99%

“…In this widely studied category which initiated by the work of Hart (1992) [17], several works have been done recently by using latent variable models and taking advantage of appliance feature sets [18], [19]. In [18], a nonintrusive approach to classify electrical appliances based on higher-order statistics (HOS) is proposed. Aiming at reducing the computational cost of the proposed method, Fisher's Discriminant Ration and Genetic Algorithms (GA) were used for selecting a finite set of representative features among those obtained by HOS.…”

Section: A Related Workmentioning

confidence: 99%

Non-Intrusive Energy Disaggregation Using Non-Negative Matrix Factorization With Sum-to-k Constraint

Rahimpour

Fugate

et al. 2017

IEEE Trans. Power Syst.

105

View full text Add to dashboard Cite

Abstract-Energy disaggregation or Non-Intrusive Load Monitoring (NILM) addresses the issue of extracting device-level energy consumption information by monitoring the aggregated signal at one single measurement point without installing meters on each individual device. Energy disaggregation can be formulated as a source separation problem where the aggregated signal is expressed as linear combination of basis vectors in a matrix factorization framework. In this paper an approach based on Sum-to-k constrained Non-negative Matrix Factorization (S2K-NMF) is proposed. By imposing the sum-to-k constraint and the non-negative constraint, S2K-NMF is able to effectively extract perceptually meaningful sources from complex mixtures. The strength of the proposed algorithm is demonstrated through two sets of experiments: Energy disaggregation in a residential smart home; and HVAC components energy monitoring in an industrial building testbed maintained at the Oak Ridge National Laboratory (ORNL). Extensive experimental results demonstrate the superior performance of S2K-NMF as compared to state-ofthe-art decomposition-based disaggregation algorithms.

show abstract

A NILM Dataset for Cognitive Meters Based on Conservative Power Theory and Pattern Recognition Techniques

Souza

Marafão

Liberado

et al. 2018

J Control Autom Electr Syst

View full text Add to dashboard Cite

A non-intrusive approach to classify electrical appliances based on higher-order statistics and genetic algorithm: a smart grid perspective

Cited by 21 publications

References 15 publications

Statistical Feature Extraction for Fault Locations in Nonintrusive Fault Detection of Low Voltage Distribution Systems

Statistical Feature Extraction for Fault Locations in Nonintrusive Fault Detection of Low Voltage Distribution Systems

Non-Intrusive Energy Disaggregation Using Non-Negative Matrix Factorization With Sum-to-k Constraint

A NILM Dataset for Cognitive Meters Based on Conservative Power Theory and Pattern Recognition Techniques

Contact Info

Product

Resources

About