Appliances identification method of non-intrusive load monitoring based on load signature of V-I trajectory

Iksan, Nur; Sembiring, Jaka; Haryanto, Nanang; Supangkat, Suhono Harso

doi:10.1109/icitsi.2015.7437744

Cited by 19 publications

(9 citation statements)

References 13 publications

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“…After event detection, the next stage is the extraction of features that will be used to characterize the loads. These extraction methods usually are divided into steady-state, transient, or a combination of these two approaches [17].…”

Section: Review Of Techniques For Load Event Detection and Power Signature Recognitionmentioning

confidence: 99%

A Multi-Agent NILM Architecture for Event Detection and Load Classification

et al. 2020

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A multi-agent architecture for a Non-Intrusive Load Monitoring (NILM) solution is presented and evaluated. The underlying rationale for such an architecture is that each agent (load event detection, feature extraction, and classification) outperforms others of the same type in particular scenarios; hence, by combining the expertise of these agents, the system presents an improved performance. Known NILM algorithms, as well as new algorithms, proposed by the authors, were individually evaluated and compared. The proposed architecture considers a NILM system composed of Load Monitoring Modules (LMM) that report to a Center of Operations, required in larger facilities. For the purposed of evaluating and comparing performance, five load event detect agents, five feature extraction agents, and five classification agents were studied so that the best combinations of agents could be implemented in LMMs. To evaluate the proposed system, the COOLL and the LIT-Dataset were used. Performance improvements were detected in all scenarios, with power-ON and power-OFF detection improving up to 13%, while classification accuracy improved up to 9.4%.

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Section: Review Of Techniques For Load Event Detection and Power Signature Recognitionmentioning

confidence: 99%

A Multi-Agent NILM Architecture for Event Detection and Load Classification

et al. 2020

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“…The identification of electrical appliances is possible through their electrical behavior (active power (P) reactive power (Q), voltage (V), current (I), harmonics, power factor (pf), phase angle, etc.). Moreover, PQ and VI trajectories were used as described in [5][6][7]. These parameters can be at a steady state or transient (turned on).…”

Section: Identification Of Load Featuresmentioning

confidence: 99%

Non-Intrusive Identification of Load Patterns in Smart Homes Using Percentage Total Harmonic Distortion

2020

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Demand Response (DR) plays a vital role in a smart grid, helping consumers plan their usage patterns and optimize electricity consumption and also reduce harmonic pollution in a distribution grid without compromising on their needs. The first step of DR is the disaggregation of loads and identifying them individually. The literature suggests that this is accomplished through electric features. Present-day households are using modern power electronic-based nonlinear loads such as LED (Light Emitting Diode) lamps, electronic regulators and digital controllers to reduce the electricity consumption. Furthermore, usage of SMPS (Switched-Mode Power Supply) for computing and mobile phone chargers is increasing in every home. These nonlinear loads, while reducing electricity consumption, also introduce harmonic pollution into the distribution grid. This article presents a deterministic approach to the non-intrusive identification of load patterns using percentage Total Harmonic Distortion (THD) for DR management from a Power Quality perspective. The percentage THD of various combinations of loads is estimated by enhanced dual-spectrum line interpolated FFT (Fast Fourier Transform) with a four-term minimal side-lobe window using a LabVIEW-based hardware setup in real time. The results demonstrate that percentage THD identifies a different combination of loads effectively and advocates alternate load combinations for recommending to the consumer to reduce harmonic pollution in the distribution grid.

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“…Ultimately, the obtained results have shown that the selected WS outperform the traditional features in all four problems, further suggesting the high-discrimination capability of such features. Iksan et al [15] have evaluated the potential of including two WS features (enclosed area-EA, and curvature of the mean line-CML) in a hybrid signature along with active power-P, reactive power-Q, power factor-PF, and total harmonic distortion-THD. Their approach was tested against the REDD dataset using a Naïve Bayes algorithm, and the results have shown an increase from 55% to 91% in the overall classification accuracy when EA and CML were added to the feature space.…”

Section: V-i Shapes and Nilmmentioning

confidence: 99%

“…Transmission usign the proposed method (15) Now, considering the sampling frequency, f s , of 30 kHz, the window size period of 20 to develop the classifier and the grid frequency, f g , of 60 Hz in USA (50 Hz in EU). If the online identification method is used, 10,000 samples needed to be sent through the internet.…”

Section: Conclusion and Future Work Directionsmentioning

confidence: 99%

Implementation Strategy of Convolution Neural Networks on Field Programmable Gate Arrays for Appliance Classification Using the Voltage and Current (V-I) Trajectory

Baptista

Mostafa

Pereira³

et al. 2018

Energies

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Specific information about types of appliances and their use in a specific time window could help determining in details the electrical energy consumption information. However, conventional main power meters fail to provide any specific information. One of the best ways to solve these problems is through non-intrusive load monitoring, which is cheaper and easier to implement than other methods. However, developing a classifier for deducing what kind of appliances are used at home is a difficult assignment, because the system should identify the appliance as fast as possible with a higher degree of certainty. To achieve all these requirements, a convolution neural network implemented on hardware was used to identify the appliance through the voltage and current (V-I) trajectory. For the implementation on hardware, a field programmable gate array (FPGA) was used to exploit processing parallelism in order to achieve optimal performance. To validate the design, a publicly available Plug Load Appliance Identification Dataset (PLAID), constituted by 11 different appliances, has been used. The overall average F-score achieved using this classifier is 78.16% for the PLAID 1 dataset. The convolution neural network implemented on hardware has a processing time of approximately 5.7 ms and a power consumption of 1.868 W.

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Appliances identification method of non-intrusive load monitoring based on load signature of V-I trajectory

Cited by 19 publications

References 13 publications

A Multi-Agent NILM Architecture for Event Detection and Load Classification

A Multi-Agent NILM Architecture for Event Detection and Load Classification

Non-Intrusive Identification of Load Patterns in Smart Homes Using Percentage Total Harmonic Distortion

Implementation Strategy of Convolution Neural Networks on Field Programmable Gate Arrays for Appliance Classification Using the Voltage and Current (V-I) Trajectory

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