Czasopismo | PRZEGLĄD ELEKTROTECHNICZNY | Rocznik 2016 - zeszyt 5

Event-based S-transform approach for nonintrusive load monitoring

10.15199/48.2016.05.38

nr katalogowy: 98202
10.15199/48.2016.05.38

In this study, a nonintrusive load monitoring system is developed by analyzing the power signal obtained from a single point of power meter installation to detect ON/OFF load activities. A mathematically designed model with backpropagation neural network is utilized in load pattern recognition to decompose the load operation. Leveraging its unique load signature profile, the S-transform approach is employed to extract the features from the aggregate power signal and analyze the detection of load start-up transient from signal processing. To improve the accuracy of load identification for unknown data, the power factor is used as an additive feature with 99.32% load recognition accuracy. Streszczenie. W artykule analizowany jest system monitorowania obciążenia sieci. Wykorzystano sieć neuronową do rozpoznawania rodzaju Transformata S jest użyta do ekstrakcji danych z sygnału mocy. Dodatkowo do identyfikacji obciążenia użyto współczynnik mocy. Metoda monitorowania obciążenia systemu energetycznego wykorzystująca transformatę S. Keywords: load recognition, feature extraction, artificial neural network, S-transform Słowa kluczowe: rozpoznawania obciążenia, sieć neuronowa, transformata S Introduction The power system has become a complex network with the emergence of distributed generation. Smart grids provide a new set of technology-based tools by integrating both load and generator sections to allow a grid to be fully optimized [1]. The smart meter is one of the components in a smart grid system; it can be used to monitor the load performance by conducting a detailed analysis of the energy demand signal from the consumer site. By monitoring the load activity, users are provided with valuable information, which facilitates the identification of inefficient appliances, inefficient energy usage, and load shifting according to the time of day when energy is cheap. In the low-voltage level, load is monitored by measuring the energy demand, [...]

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