Recipient size estimation for induction heating home appliances based on artificial neural networks

In induction home appliances, recipient size estimation is very important for the adjustment of the power that the hob must supply to the cooking recipient. Conventional techniques first calculate a simple R–L equivalent circuit from voltage and current waveforms in the heating inductor and then estimate physical parameters (such as recipient size) by regression. In this paper, a new technique is proposed for recipient size estimation, based on spectral analysis and artificial neural networks (ANN), which, for two reasons, is more accurate than current procedures: (i) The new technique performs a direct estimation of recipient size from voltage and current, without needing to compute an intermediate electrical equivalent circuit (which in fact only represents a rough approximation), and (ii) due to their nonlinear modeling capabilities, ANNs are more appropriate than regression for this problem. By using a database of cooking recipients, our procedure provides an accuracy of 85–90 %, outperforming the 58 % of conventional techniques (70 % including an additional sensor). The new technique has been implemented and verified by using a commercial embedded processor, similar to those included in current domestic induction home appliances. It could be built in by manufacturers at no extra cost as it requires no additional sensors and makes use of computational resources integrated into the microcontroller or the digital signal processor that controls the home appliance.