Feature extraction for nonintrusive load monitoring based on S-Transform

Jimenez, Yulieth; Duarte, César; Suárez, Johann Farith Petit; Carrillo, Gilberto

doi:10.1109/psc.2014.6808109

Cited by 21 publications

(6 citation statements)

References 9 publications

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“…It is possible to detect variations in the network from the data received using symbolic aggregate approximation (SAX) and pattern recognition, as defined by Alam et al) [210]. Jimenez et al [211] implement an S-transform algorithm for the detection and monitoring of overvoltage.…”

Section: Power System Infrastructurementioning

confidence: 99%

A Comprehensive Review of Operation and Control, Maintenance and Lifespan Management, Grid Planning and Design, and Metering in Smart Grids

Hernández-Callejo

2019

Energies

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New technological advances based on software, hardware and new materials must be implemented in smart grids. In addition, these advanced electrical grids must incorporate elements of artificial intelligence. Advances in software development must be complemented with the development of new hardware (power electronics and active distribution among others). The aforementioned must rely on the development of new materials and sensors, which should be integrated into the smart grid. Therefore, the four areas of research based on the technologies are: Operation and Control, Maintenance and Lifespan Management, Grid Planning and Design, and Metering. The review presents the algorithms, materials, devices and others paradigms applied to smart grids, classifying the works according to the four areas of research. This review has focused on the four fundamental pillars of smart grids, on the one hand, the need for more efficient operation and control, followed by advanced maintenance management, to continue planning and designing the new grids, and for conclude with the advanced measurement. As you will see in the article, new devices, new techniques, and future scenarios will make possible the transition from the current grid to the future smart grid.

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Section: Power System Infrastructurementioning

confidence: 99%

A Comprehensive Review of Operation and Control, Maintenance and Lifespan Management, Grid Planning and Design, and Metering in Smart Grids

Hernández-Callejo

2019

Energies

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“…They reached the best accuracy with 81.75% using random forest algorithm over PLAID dataset. Some transformations of time-series signals were conducted to shed light on feature engineering including Fourier transformation [25], Wavelet Packet transformation [42], Stockwell transformation [43,44] and Hilbert transformation [45]. Fourier transformation of features are well-known for harmonic vectors or harmonic spectrogram.…”

Section: Features and Features Additive Propertymentioning

confidence: 99%

“…Martins et al [43] applied the Stockwell transformation features firstly in NILM system and then performed load identification with an optimization approach in the laboratory showing preliminary prospect of Stockwell transformation features. Jimenez and colleague extended this work by mapping the Stockwell transformation complex matrix into new space with extracted statistical attributes [44]. They compared their work with wavelet transformation based public dataset and Stockwell transformation features were presented with similar or superior performance than wavelet transformation features based NILM method.…”

Section: Features and Features Additive Propertymentioning

confidence: 99%

A Supervised Event-Based Non-Intrusive Load Monitoring for Non-Linear Appliances

2018

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Smart meters generate a massive volume of energy consumption data which can be analyzed to recover some interesting and beneficial information. Non-intrusive load monitoring (NILM) is one important application fostered by the mass deployment of smart meters. This paper presents a supervised event-based NILM approach for non-linear appliance activities identification. Firstly, the additive properties (stating that, when a certain amount of specific appliances' feature is added to their belonging network, an equal amount of change in the network's feature can be observed) of three features (harmonic feature, voltage-current trajectory feature, and active-reactive-distortion (PQD) power curve features) were investigated through experiments. The results verify the good additive property for the harmonic features and Voltage-Current (U-I) trajectory features. In contrast, PQD power curve features have a poor additive property. Secondly, based on the verified additive property of harmonic current features and the representation of waveforms, a harmonic current features based approach is proposed for NILM, which includes two main processes: event detection and event classification. For event detection, a novel model is proposed based on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. Compared to other event detectors, the proposed event detector not only can detect both event timestamp and two adjacent steady states but also shows high detection accuracy over public dataset with F1-score up to 98.99%. Multi-layer perceptron (MLP) classifiers are then built for multi-class event classification using the harmonic current features and are trained using the data collected from the laboratory and the public dataset. The results show that the MLP classifiers have a good performance in classifying non-linear loads. Finally, the proposed harmonic current features based approach is tested in the laboratory through experiments, in which multiple on-off events of multiple appliances occur. The research indicates that clustering-based event detection algorithms are promising for future works in event-based NILM. Harmonic current features have perfect additive property, and MLP classifier using harmonic current features can accurately identify typical non-linear and resistive loads, which could be integrated with other approaches in the future.

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“…In addition, orthogonal wavelet based NILM methodology is discussed in [10], where supervised and semi-supervised classifiers are used to automate the load disaggregation process. In [11] - [14], features extraction for device identification using modified wavelet transform and S-transform methods are reported.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive NILM Framework: Device Type Classification and Device Activity Status Monitoring Using Capsule Network

et al. 2020

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Non-intrusive load monitoring (NILM) discerns the individual electrical appliances of a residential or commercial building by disaggregating the accumulated energy consumption data without accessing to the individual components applying a single-point sensor. The fundamental concept is to decompose the aggregate load into a family of appliances that can explain its characteristics. In the age of smart grid networks and sophisticated energy management infrastructures, NILM can be considered as a significant tool pertaining to smart and inexpensive energy metering technique. In this article, a novel NILM solution based on capsule network is proposed, where convolutional neural network (CNN) is employed to extract potential features from a set of non-overlapping energy measurement data segments and the capsule architecture is designed to predict class probabilities of the individual segments. Then, a decision making algorithm is proposed to compute the final classification based on the predicted class probabilities of the segments. The presented research design comprises two unique NILM applications-device type classification from individual sensor recordings stored in COOLL and PLAID public databases, and device activity status monitoring at any particular time instant from aggregated energy consumption data recorded in UK-DALE database. Additionally, substantial experimental investigations have been carried out for device type classification accounting on various types of train and test set distributions as well as individual instrument and house classifications. The presented framework analyzes different parameters and metrics in depth to corroborate the efficacious performance evaluations for real-time applications. Relevant performance comparisons with existing works in literature validate the sustainability of the proposed solution.

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Feature extraction for nonintrusive load monitoring based on S-Transform

Cited by 21 publications

References 9 publications

A Comprehensive Review of Operation and Control, Maintenance and Lifespan Management, Grid Planning and Design, and Metering in Smart Grids

A Comprehensive Review of Operation and Control, Maintenance and Lifespan Management, Grid Planning and Design, and Metering in Smart Grids

A Supervised Event-Based Non-Intrusive Load Monitoring for Non-Linear Appliances

Comprehensive NILM Framework: Device Type Classification and Device Activity Status Monitoring Using Capsule Network

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