Modified Nonintrusive Appliance Load Monitoring For Nonlinear Devices

Akbar, Maryam; Khan, Danial

doi:10.1109/inmic.2007.4557691

Cited by 39 publications

(13 citation statements)

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“…This analysis, however, puts computers in a computationally complicated situation for accurate power signature data. Akbar and Khan [8] deployed the current harmonics as the steady-state features. Nevertheless, several highly resistive loads may not be detected by this approach due to the low level of the current harmonics.…”

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 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%

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

Chang

Linh

2017

Energies

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“…Features based on a high-frequency sampling rate cannot be considered for analyzing linear resistive loads such as ESHs [14,25] because of the similarity between voltage and current waveforms. Therefore, a macroscopic analysis of the active power (and/or reactive power) using a low-frequency sampling rate is more promising to disaggregate these appliances [26,27]. Most of these kinds of loads have on-off behavior, and their operational states can be optimally inferred by probabilistic algorithms in which their observed macroscopic features make them statistically different.…”

Section: Related Workmentioning

confidence: 99%

Power Estimation of Multiple Two-State Loads Using A Probabilistic Non-Intrusive Approach

et al. 2018

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This paper investigates a non-intrusive approach of retrieving electric space heater (ESH) power profiles from a residential aggregated signal. In cold-climate regions with heating appliances controlled by electronic thermostats, an accurate non-intrusive recognition of power profiles is a challenging task. Accordingly, a robust disaggregation approach based on the difference factorial hidden Markov model (DFHMM) and the Kronecker operation is contributed. The proposed method aims to uncover the underlying stochastic tow-state models of ESHs using their common prior knowledge. The major advantage of the developed load-monitoring architecture consists of modeling simplicity and inference as well as load-detection efficacy in the presence of perturbations from other unknown loads. The experimental results prove the effectiveness of the method in manipulating the challenging case of multiple two-state loads with a high event overlapping probability.

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“…Furthermore, as the time interval of data collected by smart meters decreases to fifteen or thirty minutes, various load monitoring techniques [2,3] can be employed to process unencrypted smart meter data to identify what electrical appliances, for example heaters, washing machines, refrigerators, air conditioners etc., are being used based on the electrical signature of those appliances [4,5]. Figure 2 shows a power consumption trace of a customer [6].…”

Section: Access Pointmentioning

confidence: 99%

Design and implementation of a secure cloud-based billing model for smart meters as an Internet of things using homomorphic cryptography

Mai

Khalil

2017

Future Generation Computer Systems

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Modified Nonintrusive Appliance Load Monitoring For Nonlinear Devices

Cited by 39 publications

References 1 publication

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

Power Estimation of Multiple Two-State Loads Using A Probabilistic Non-Intrusive Approach

Design and implementation of a secure cloud-based billing model for smart meters as an Internet of things using homomorphic cryptography

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