Development of distinct load signatures for higher efficiency of NILM algorithms

Bouhouras, Aggelos S.; Milioudis, A. N.; Labridis, Dimitris P.

doi:10.1016/j.epsr.2014.08.015

Cited by 34 publications

(13 citation statements)

References 26 publications

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“…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%

“…Those HOS features can retrieve the important information of electrical signals, so they possess the capability of discrimination in the feature space. Bouhouras et al [22] described the concept of spectra distribution analysis for individual load activation. In that work, the raw signals are transformed to the corresponding fast Fourier Transform (FFT) spectra.…”

Section: Introductionmentioning

confidence: 99%

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

Chang

Linh

2017

Energies

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Chang

Linh

2017

Energies

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“…This requires high sampling rates, however. Several features can be extracted from frequency information, such as harmonics [64] obtained with Fourier transform and multiple frequency bands using information entropy [65]. Due to its multi-resolution and time-frequency localization property, Discrete Wavelet Transform (DWT), is also employed [4,66].…”

Section: Feature Setsmentioning

confidence: 99%

NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review

et al. 2019

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The ongoing deployment of smart meters and different commercial devices has made electricity disaggregation feasible in buildings and households, based on a single measure of the current and, sometimes, of the voltage. Energy disaggregation is intended to separate the total power consumption into specific appliance loads, which can be achieved by applying Non-Intrusive Load Monitoring (NILM) techniques with a minimum invasion of privacy. NILM techniques are becoming more and more widespread in recent years, as a consequence of the interest companies and consumers have in efficient energy consumption and management. This work presents a detailed review of NILM methods, focusing particularly on recent proposals and their applications, particularly in the areas of Home Energy Management Systems (HEMS) and Ambient Assisted Living (AAL), where the ability to determine the on/off status of certain devices can provide key information for making further decisions. As well as complementing previous reviews on the NILM field and providing a discussion of the applications of NILM in HEMS and AAL, this paper provides guidelines for future research in these topics.

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“…The load classification is performed from the data collected from smart meters installed outdoors; [25] Fridge, furnace, microwave, stove, oven, kettle, cloth dryer and washer Liang et al [26] LED, LCD and plasma TV, LCD monitor, set-top box, heater, portable fan, microwave oven, desktop and laptop computer, DVD player and cellphone He et al [27] Electric heat, furnace, heat pump, lighting, TV, monitor, projector, fan, desktop computer and printer Bouhouras et al [28] Air conditioner, coffee machine, hair dryer, heater, home theatre, electric iron, laptop, refrigerator, washing machine, halogen lights and led lights Wang and Zheng et al [29] Washing machine, fan, mixer, personal computer, TV, stereo, air conditioner, heater, refrigerator, cooker, microwave ovens and hair dryer Lin et al [30] Vacuum cleaner, electric boiler, microwave oven and hair dryer…”

Section: Smart Meters and Identification Of Nonlinear Loadsmentioning

confidence: 99%

A framework for classification of non-linear loads in smart grids using Artificial Neural Networks and Multi-Agent Systems

2015

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Development of distinct load signatures for higher efficiency of NILM algorithms

Cited by 34 publications

References 26 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

NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review

A framework for classification of non-linear loads in smart grids using Artificial Neural Networks and Multi-Agent Systems

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