Automatic classification of circuit topologies of appliances  based on higher order statistic

Florencias-Oliveros, Olivia; Blanco, Ana María; Meyer, Jan; Rosa, Juan José González de la; Agüera-Pérez, Agustín

doi:10.24084/repqj17.362

Cited by 3 publications

(11 citation statements)

References 5 publications

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“…Reference [8] proposes an automatic classification method of household devices based on the current waveform characterization in the higher-order statistics (HOS) space. Different waveform shapes exhibit different distributions and in consequence different statistical values.…”

Section: Classification IVmentioning

confidence: 99%

“…The difference between nPFC 1 and nPFC 2 in [8] is due to the current waveform but both are considered to be in the same category since the classification is based on the type of power factor correction.…”

Section: Classification IVmentioning

confidence: 99%

“…As result of the procedure described in [8], different regions are found in the HOS space. The values in Table VI are an example of the HOS values for each topology as given in [8].…”

Section: Classification IVmentioning

confidence: 99%

“…2 shows the results from classification IV method. Three regions can be distinguished and linked to an nPFC (blue circles), pPFC (purple circles) or aPFC (red circles) according to the descriptions of the statistic values linked to each topology given in [8]:…”

Section: B Classification IImentioning

confidence: 99%

“…In the existing literature, authors classify LED lamps and other household devices without opening them based on different criteria, for example, measured flicker [2], true power factor (PF) and THDI [3] or by applying statistics as higher order statistic (HOS) [8]. According to them, a general LED classification is based on the presence of the power factor correction (PFC) block, being active (aPFC), passive (pPFC) or those that simply do not have it (nPFC).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Comparison of Measurement-Based Classification Methods of LED Lamps

Gutierrez-Ballesteros¹,

Rönnberg²,

Gil-de-Castro³

2023

IEEE Open J. Power Energy

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The topology of a device will determine the impact said device has on the grid and how immune that device is for disturbances in the grid. LED lamps are very commonly used devices, with different topologies available in the market, each topology showing different behavior when connected to a grid. For power quality studies, it is important to classify LED lamps, without breaking them to know the topology. Several classification methods are found in the literature with this purpose. In this paper, four methods from different papers for classifying LED lamps have been applied to a group of 21 LED lamps with active power consumption below 25 W. It has been observed that the applicability of the methods may lead to a gap of knowledge needed for classification, leaving space for personal criteria when classifying, that can be afforded using unsupervised Machine Learning. Two unsupervised Machine Learning methods were applied using the electrical parameters and statistics proposed in literature.

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Section: Classification IVmentioning

confidence: 99%

Section: Classification IVmentioning

confidence: 99%

“…As result of the procedure described in [8], different regions are found in the HOS space. The values in Table VI are an example of the HOS values for each topology as given in [8].…”

Section: Classification IVmentioning

confidence: 99%

Section: B Classification IImentioning

confidence: 99%