Development of an Improved Time–Frequency Analysis-Based Nonintrusive Load Monitor for Load Demand Identification

Lin, Yu‐Hsiu; Tsai, Men‐Shen

doi:10.1109/tim.2013.2289700

Cited by 138 publications

(55 citation statements)

References 34 publications

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“…Various research papers studying NILM using transient features have been published in order to overcome the shortcomings of the steady-state feature [11][12][13][14][15]. Starting current or switching transient waveform are typical transient features.…”

Section: Introductionmentioning

confidence: 99%

“…In [13], the energizing and de-energizing transient feature are adaptively adjusted by an artificial immune algorithm (AIA) and Fisher criterion. The energizing events are also processed by the S-transform to obtain the feature vectors in the complex domain [14]. Generally, the use of transient features requires the implementation of signal processing such as DWT or S-transform at the high sampling rate to capture the transient effects.…”

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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“…Khal et al [59] have identified 36 such features, including wavelet analysis, voltage-current (V-I) trajectory, inrush current ratio, waveform approximation, and log attack time, along with other spectral and temporal features. When considering load disaggregation, it is always better to incorporate more parameters, as certain parameters work better for particular load types [33,60].…”

Section: Appliance Identification Parametersmentioning

confidence: 99%

Prospects of Appliance-Level Load Monitoring in Off-the-Shelf Energy Monitors: A Technical Review

Haq

Jacobsen

2018

Energies

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Abstract:The smart grid initiative has encouraged utility companies worldwide to roll-out new and smarter versions of energy meters. Before an extensive roll-out, which is both labor-intensive and incurs high capital costs, consumers need to be incentivised to reap the long-term benefits of such smart meters. Off-the-shelf energy monitors (e-monitors) can provide consumers with an insight into such potential benefits. As e-monitors are owned by the consumer, the consumer has greater control over the data, which significantly reduces the privacy and data confidentiality concerns. Because only limited online technical information is available about e-monitors, we evaluate several existing e-monitors using an online technical survey directly from the vendors. Besides automated e-monitoring, the use of different off-the-shelf e-monitors can also help to demonstrate state-of-the-art techniques such as non-intrusive load monitoring (NILM), data analytics, and the predictive maintenance of appliances. Our survey indicates a trend towards the incorporation of such state-of-the-art capabilities, particularly the appliance-level e-monitoring and load disaggregation. We have also discussed some essential requirements to implement load disaggregation in the next generation e-monitors. In future, these intelligent e-monitoring techniques will encourage effective consumer participation in the demand-side management (DSM) programs.

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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 an Improved Time–Frequency Analysis-Based Nonintrusive Load Monitor for Load Demand Identification

Cited by 138 publications

References 34 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

Prospects of Appliance-Level Load Monitoring in Off-the-Shelf Energy Monitors: A Technical Review

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

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