EMF Signature for Appliance Classification

Kulkarni, Anand S.; Harnett, C. K.; Welch, Karla Conn

doi:10.1109/jsen.2014.2379113

Cited by 19 publications

(9 citation statements)

References 20 publications

(13 reference statements)

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“…The second step focuses on implementing a fuzzy rule-based technique to identify the individual appliances at the consumer-end. In [12], Kulkarni et al collect electromagnetic field characteristics generated by various domestic devices in order to construct a distinct fingerprint for every electrical appliance. Afterward, a decision tree classifier is used to automatically manage the identification task.…”

Section: Related Workmentioning

confidence: 99%

Appliance identification using a histogram post-processing of 2D local binary patterns for smart grid applications

Himeur¹,

Alsalemi²,

Bensaali³

et al. 2020

Preprint

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Identifying domestic appliances in the smart grid leads to a better power usage management and further helps in detecting appliance-level abnormalities. An efficient identification can be achieved only if a robust feature extraction scheme is developed with a high ability to discriminate between different appliances on the smart grid. Accordingly, we propose in this paper a novel method to extract electrical power signatures after transforming the power signal to 2D space, which has more encoding possibilities. Following, an improved local binary patterns (LBP) is proposed that relies on improving the discriminative ability of conventional LBP using a post-processing stage. A binarized eigenvalue map (BEVM) is extracted from the 2D power matrix and then used to post-process the generated LBP representation. Next, two histograms are constructed, namely up and down histograms, and are then concatenated to form the global histogram. A comprehensive performance evaluation is performed on two different datasets, namely the GREEND and WITHED, in which power data were collected at 1 Hz and 44000 Hz sampling rates, respectively. The obtained results revealed the superiority of the proposed LBP-BEVM based system in terms of the identification performance versus other 2D descriptors and existing identification frameworks.

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Section: Related Workmentioning

confidence: 99%

Appliance identification using a histogram post-processing of 2D local binary patterns for smart grid applications

Himeur¹,

Alsalemi²,

Bensaali³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…This method takes advantage of the fact that electrical appliances during operation generate electromagnetic fields around their enclosures. A suitable broadband antenna sensor and data acquisition system allows this disturbance to be recorded and characterized [42]. Similarly, in Reference [43], the acoustic noise produced during operation was used to identify the type of device.…”

Section: Extra-high-frequency (Ehf) Measurementsmentioning

confidence: 99%

Nonintrusive Appliance Load Monitoring: An Overview, Laboratory Test Results and Research Directions

Wójcik

Łukaszewski

Kowalik

et al. 2019

Sensors

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Nonintrusive appliance load monitoring (NIALM) allows disaggregation of total electricity consumption into particular appliances in domestic or industrial environments. NIALM systems operation is based on processing of electrical signals acquired at one point of a monitored area. The main objective of this paper was to present the state-of-the-art in NIALM technologies for the smart home. This paper focuses on sensors and measurement methods. Different intelligent algorithms for processing signals have been presented. Identification accuracy for an actual set of appliances has been compared. This article depicts the architecture of a unique NIALM laboratory, presented in detail. Results of developed NIALM methods exploiting different measurement data are discussed and compared to known methods. New directions of NIALM research are proposed.

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“…Prior work in RFI can be classified into five subcategories as (1) Detection or sensing of RFI [12,13,14,15], (2) Feature extraction and characterization of RFI [11,16], (3) Modeling of RFI [11], (4) Techniques for mitigating RFI [11,17] and (5) implications of RFI in a particular field of study [9,[18][19][20][21].…”

Section: Related Workmentioning

confidence: 99%

“…Some prior work on conducted EMI has shown that these noise peaks can be used as a signature to detect appliances running on power line [23,28]. Recently [13,23] have also explored the possibility of using RFI to identify appliances operational in the vicinity. Our current work augments this existing work significantly by characterizing and modeling RFI generated from electrical and electronic appliances emitting both low-frequency and highfrequency RF emissions.…”

Section: Related Workmentioning

confidence: 99%

Appliance Activity Recognition Using Radio Frequency Interference Emissions

2016

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Over the past few decades, with rapid growth in infrastructure, there has been tremendous growth in energy consumption. Along with this, more and more electronic appliances are added to the existing infrastructure every day. Furthermore, the existing energy bills just provide an aggregate number of units consumed but fail to provide any actionable details of appliance level usage. With the quest for long-term energy sustainability and to reduce this ever-growing energy consumption, research groups across the globe have started looking into energy disaggregation as a means of providing feedback. Some promising techniques like Non-intrusive appliance load monitoring (NIALM) have been adopted to provide detailed energy breakdown to the end consumer. Despite all these efforts, energy attribution to the electrical activities still seems to be a farfetched goal, especially in shared spaces. In this work, we have analyzed the possibility of using RF emissions from electronic appliances to detect electrical activity. Besides their known operation, these appliances are known to radiate high-frequency noise in the ambient environment, also called radio frequency interference (or RFI). Hence by utilizing these RFI emissions from electronic appliances, electrical activity from the appliance can be detected in multiple frequency bands and at varying distances. An 8-fit Gaussian mixture model (GMM) and k-peak finder are used for feature extraction from RFI data, followed by appliance activity recognition using k-nearest neighbor based classification. Appliance detection is performed with a mean accuracy of 71.9% across 7-class classification problem. Finally, characteristic features of RFI observed from these appliances, are discussed.Index Terms-RFI (Radio frequency interference), kNN (knearest neighbor), NIALM (Non-intrusive appliance load monitoring), SMPS (Switched mode power supply), HF (High frequency), LF (Low frequency), UPS (Uninterruptable power supply), CFL (Compact fluorescent lamp). A 1530-437X (c)

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EMF Signature for Appliance Classification

Cited by 19 publications

References 20 publications

Appliance identification using a histogram post-processing of 2D local binary patterns for smart grid applications

Appliance identification using a histogram post-processing of 2D local binary patterns for smart grid applications

Nonintrusive Appliance Load Monitoring: An Overview, Laboratory Test Results and Research Directions

Appliance Activity Recognition Using Radio Frequency Interference Emissions

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