Abrupt Change Detection of Home Electrical Appliance Signals Using Mathematical Morphology Gradient
| Autor: | Zhaoxia Jing, François Auger, Huu Kien Bui, Lu Zhang, Xinglei Han, Sarra Houidi |
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| Přispěvatelé: | South China University of Technology [Guangzhou] (SCUT), Institut de Recherche en Electrotechnique et Electronique de Nantes Atlantique EA4642 (IREENA), Institut Universitaire de Technologie Saint-Nazaire (IUT Saint-Nazaire), Université de Nantes (UN)-Université de Nantes (UN)-Ecole Polytechnique de l'Université de Nantes (EPUN), Université de Nantes (UN)-Institut Universitaire de Technologie - La Roche-sur-Yon (IUT La Roche-sur-Yon), Université de Nantes (UN)-Université de Nantes (UN) |
| Rok vydání: | 2019 |
| Předmět: |
Computer science
[SPI.NRJ]Engineering Sciences [physics]/Electric power 05 social sciences 050209 industrial relations CUSUM Mathematical morphology Data set Position (vector) Bayesian information criterion 0502 economics and business Generalized likelihood ratio Algorithm ComputingMilieux_MISCELLANEOUS 050203 business & management Change detection Energy (signal processing) |
| Zdroj: | 2019 IEEE Innovative Smart Grid Technologies-Asia (ISGT Asia) 2019 IEEE Innovative Smart Grid Technologies-Asia (ISGT Asia), May 2019, Chengdu, France. pp.726-730, ⟨10.1109/ISGT-Asia.2019.8881110⟩ |
| DOI: | 10.1109/isgt-asia.2019.8881110 |
| Popis: | This paper presents an Abrupt Change Detection (ACD) algorithm based on Mathematical Morphology Gradient (MMG) for Non-Intrusive Load Monitoring (NILM). In the present research of MMG, it is focused on filtering the original signals rather than locating change points. Therefore, this paper considers this approach to detect abrupt changes. For the purpose of evaluating the performance of different algorithms, the True Positive Rate (TPR) and the F-score are employed in this paper. The simulation studies have been carried out on low-frequency signals and high-frequency signals from the Reference Energy Disaggregation Data Set (REDD) respectively. The MMG algorithm, which has the advantages of high accuracy and fast calculation speed, is superior for both two types of signals. In addition, it can provide not only the information of the position of a change but also the direction of a change. The results demonstrate that the MMG algorithm shows better performance than the CUmulative SUM (CUSUM), the Bayesian Information Criterion (BIC) and the Generalized Likelihood Ratio (GLR) algorithms. |
| Databáze: | OpenAIRE |
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