Applications of machine learning in induction cooking

Bono-Nuez, Antonio; Bernal-Ruíz, Carlos; Martin-del-Brio, B.; Pérez-Cebolla, Francisco Jose; Martínez, A.

doi:10.1109/iecon.2016.7793141

Cited by 3 publications

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Machine learning has also been applied to implement a detector of sizes and types of vessels [15]. The algorithm is based on the spectral analysis of the output voltage and the load current and on the power factor of the load.…”

Section: Introductionmentioning

confidence: 99%

SoC-Based In-Cycle Load Identification of Induction Heating Appliances

Villa

Barragán

Artigas

et al. 2021

IEEE Trans. Ind. Electron.

View full text Add to dashboard Cite

The equivalent load of an induction hob is strongly dependent on many parameters such as the switching frequency, the excitation level, the size, type and material of the vessel, etc. However, real-time methods with the ability to capture the variation of the load with the excitation level have not been proposed in the literature. This is an essential issue as most of the commercial induction hobs are based on an ac bus voltage arrangement. This paper proposes a method based on a phase sensitive detector that offers an online tracking of the equivalent impedance for this type of arrangements. This algorithm enables advanced control functionalities such as clustering of vessels, material recognition and premature detection of ferromagnetic saturation, among others. After simulation and experimental validation, the method is implemented into a prototype with a system-on-chip (SoC) to verify its real-time behavior. The proposed approach is applied to different real-life situations which prove its great performance and applicability.

show abstract

Section: Introductionmentioning

confidence: 99%