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In high-power-density power electronics applications, it is important to predict the power losses of semiconductor devices in order to maximize global system efficiency and avoid thermal damages of the components. When different effects influence the power losses, some of which difficult to be physically modeled, it is worthwhile to use empirical laws obtained starting from experimental data, like the Steinmetz's equation widely used for inductors' magnetic core losses prediction. This paper discusses a method to find empirical power loss models by using Genetic Programming (GP). In particular, the GP approach has been applied to identify power losses in Insulated Gate Bipolar Transistors for Induction Cooking application. A loss model has been obtained using an experimental training set, and the result has been successively validated

In high-power-density power electronics application, it's important to be able to predict the power losses of semiconductor devices in order to maximize global system efficiency and to avoid thermal damages of the components. In this paper a novel approach to model the power losses of Insulate Gate Bipolar Transistors (IGBT) in Induction Cooking (IC) application is proposed. The inherent lack of precise physical IGBT loss model and the uncertainty of load in IC application has stimulated the idea to identify system-level behavioral power loss models that allow to cover a variety of devices and load conditions. For this goal, a Genetic Programming approach has been adopted, that starts from measured electrical quantities and returns a set of models, each one with the same structure but with different parameters relevant to the device under test. The models generated by the proposed method based on a training set of case studies have been merged into a generalized model and verified through a validation set

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This paper discusses an adaptive Maximum Power Point Tracking (MPPT) control involving minimum computing effort, suitable for implementation with low cost microcontrollers. The runtime multiple optimal setup of the Perturb&Observe MPPT algorithm, is achieved by exploiting the correlation existing among the MPPT efficiency and the onset of a permanent 3 level quantized oscillation around the MPP. As no operations on measured voltage, current and power are required, the adaptive MPPT control is achievable with very cheap digital devices. Experimental test results are presented in the paper, regarding a 70W LED lighting system fed by a photovoltaic source, including a energy storage device