Switching mode power converters are being extensively applied in different power conversion systems. Parameter identification comprises a set of techniques focused on extracting the relevant parameters of the converters in order to generate accurate discrete simulation models or to design enhanced condition diagnosis schemes. This paper applies a non-invasive optimization approach based on the non-linear least squares algorithm to determine the model parameters of different commercially available DC-DC power converters (buck, boost and buck-boost) from experimental data, including the parameters related to passive, parasitic and control loop elements. The proposed approach is based on a non-invasive on-line acquisition of the input/output voltages and currents under both steady state and transient conditions. The proposed method can also be applied to many other applications requiring precise and efficient parameter identification, including rectifiers, filters, or power supplies among others.

This article presents a novel approach for black-box modeling of 270 V-to-28 V dc–dc step-down converters used in more electric aircrafts. These converters normally feed constant power loads. The proposed deep learning approach, uses offline experimental data of the converter to find an accurate model that reproduces its behavior. It covers a broad range of loading conditions to build a model that replicates the whole behavior of the converter. This article compares the performance of the proposed method, which requires a very low computational burden once the model is trained, with that of a conventional recurrent neural network topology. Results presented in this article show the ability of the obtained solution to accurately emulate the behavior of the real step-down converter when the internal structure is unknown, with no knowledge of the internal parameters, thus preventing disclosure of manufacturer's confidential data. The modeling strategy presented in this article is validated with experimental data by using a step-down converter used in aircrafts. The approach is compared to existing modeling techniques to test its accuracy. This approach can also be applied to many power devices, including diverse types of power converters, power supplies, or filters among others.

This article presents an offline deep learning approach focused to model and identify a 270- to 28-V dc-dc step-down converter used in on-board distribution systems of more electric aircrafts (MEAs). Manufacturers usually do not provide enough information of the converters. Thus, it is difficult to perform design and planning tasks and to check the behavior of the power distribution system without an accurate model. This work considers the converter as a black box and trains a wavelet convolutional neural network (WCNN) that is able to accurately reproduce the behavior of the dc-dc converter from a large set of experimental data. The methodology to design a WCNN based on the characteristics of the input and output signals of the converter is also described. The method is validated with the experimental data obtained from a setup that replicates the 28-V on-board distribution system of an aircraft. The results presented in this article show a high correlation between the measured and estimated data, robustness, and low computational burden. This article also compares the proposed approach against other techniques presented in the literature. It is possible to extend this method to other dc-dc converters, depending on their requirements.

Artificial neural networks allow the identification of black-box models. This paper proposes a method aimed at replicating the static and dynamic behavior of a DC-DC power converter based on a recurrent nonlinear autoregressive exogenous neural network. The method proposed in this work applies an algorithm that trains a neural network based on the inputs and outputs (currents and voltages) of a Buck converter. The approach is validated by means of simulated data of a realistic nonsynchronous Buck converter model programmed in Simulink and by means of experimental results. The predictions made by the neural network are compared to the actual outputs of the system, to determine the accuracy of the method, thus validating the proposed approach. Both simulation and experimental results show the feasibility and accuracy of the proposed black-box approach.

This paper proposes an approach to estimate the state of health of DC-DC converters that feed the electrical system of an electric vehicle. They have an important role in providing a smooth and rectified DC voltage to the electric machine. Thus, it is important to diagnose the actual status and predict the future performance of the converter and specifically of the electrolytic capacitors, in order to avoid malfunctioning and failures, since it is known they have the highest failure rates among power converter components. To this end, accelerated aging tests of the electrolytic capacitors are performed by applying an electrical overstress. The gathered data are used to train a CNN-LSTM model that is capable of predicting the future values of the capacitance and the equivalent series resistance (ESR) of the electrolytic capacitor. This model can be used to estimate the remaining useful life of the device, thus, increasing the reliability of the system and ensuring an adequate operating condition of the electric motor.

This paper proposes an approach to estimate the parameters of an AC–DC boost power factor corrector converter that includes an EMI filter. To this end, once the topology was known, the values of the passive elements were identified from measurements at the input and output terminals of the converter. The parameters of the converter were identified based on the trust region nonlinear least squares algorithm. The steady-state and the transient signals of the converter at the input/output terminals were acquired non-intrusively without any internal modification of the circuitry. The accuracy of the proposed parameter identification approach was determined by comparing the estimated values with those provided by the manufacturer, and by comparing the measured signals with those obtained with a simulation model that included the estimated values of the parameters. The results presented in this paper prove the accuracy of the proposed approach, which can be extended to other power converters and filters.