The switched reluctance machine (SRM) has certain advantages such as robustness and the absence of permanent magnets which have encouraged its use in recent years. One of its applications is as a self-excited generator. This behavior is produced when a capacitor is series or parallel connected to each single phase forming a nonlinear pseudoresonant circuit. This application can be used in isolated sites with a wind turbine for energy generation. This paper describes a novel method used for the specific design of a 1-kW self-excited SRM for wind power applications based on torque considerations. The machine volume, dimensions, finite-element analysis, model verification, simulations, and experimental results are all presented.

Predicting core losses is a difficult task for designing magnetic components. Under triangular current waveforms the classical approaches work with duty cycles close to 0.5 but are inaccurate for high and low duty cycles. In this work the composite waveform hypothesis is tested, using data from the MagNet database for N87 material at 25 ºC (3312 data points). Limitations of the composite waveform hypothesis are discussed and a technique to expand the loss space is presented. The new approach based in an extended loss space is validated against the classical improved Generalized Steinmetz Equation, reducing the root mean square and 95 percentile errors by 5.85 and 6.96 times, allowing to predict core losses accurately even at high and low duty cycles. A direct correlation between the expanded loss space and the Steinmetz parameters from the improved Generalized Steinmetz Equation is proven, allowing to represent the core losses as functions of these parameters and generating the composite improved Generalized Steinmetz Equation. The proposed methodology for core loss prediction is validated making use of all data available from the MagNet database (10 materials, 4 temperatures, 59423 data points), achieving better results than the commonly used improved Generalized Steinmetz Equation.

A new nonlinear lumped parameter model is proposed for a switched reluctance motor (SRM) with core losses. The components of the model and their origin are justified by finite-element simulations and experimental tests. The winding resistance and the leakage inductance are analyzed by different methods taken from the literature, and a new algorithm is proposed to overcome their shortcomings such as the nonbiunivocal flux linkage versus current relationship. In contrast to previous works, the nonlinear elements for modeling the magnetic core, such as the incremental magnetizing inductance and the core-loss resistance, are determined together and simultaneously for each value of the phase current and the rotor position. The performance of the new model and the methodology proposed for estimating its components are analyzed for different excitation types and rotor positions. The core losses are evaluated by the proposed model and by two well-known predictive models, and the results are compared to illustrate the suitability of the former. Finally, the dynamic behavior of the model, acting as an ac self-excited generator, is considered.

In induction home appliances, the characterization of the physical properties of the vessel allows for better control strategies and new cooking functionalities. Current modelling techniques calculate a simple R-L equivalent circuit that models the inductor impedance taking into account all the physical coupling parameters, but these techniques do not allow to directly extract the physical parameters, such as vessel material. In this paper, a new methodology for online detection of the pot material is presented. On this approach, the inductor included in the hob simultaneously serves as heating element and sensor. First, harmonic impedances are calculated by spectral estimation from the currents and voltages waveforms recorded at the inductor. Then, a feature set, including four harmonic impedances and the power factor, is processed by machine learning algorithms for the identification of the pot material. The material identification system is implemented onto an embedded processor similar to those included in current induction hobs; thus, smart sensing features are incorporated onto a domestic cooker without using additional sensors, at no additional cost.

We analyzed an AC self-excited switched reluctance generator used as a battery charger, using a second- order nonlinear oscillator model. Generator capacity can be maintained under variable rotor speed by adapting the external capacitance. The AC voltage generated is rectified and adapted with a DC-DC converter to battery voltage. To keep the flux machine under control, the rectified bus voltage should be changed according to oscillating frequency, following a constant voltage/frequency ratio. The simplicity of the system makes it an adequate wind generator for battery charging in isolated locations.