This paper proposes a family of spread-spectrum schemes, several of which are new, for conducted-noise reduction in dc-dc converters. The schemes use three randomized parameters to generate the switching signals: carrier frequency, duty ratio, and pulse position. The increasing performance and cost reduction of field-programmable gate array (FPGA) technology have made the application of these schemes possible in this field. A theoretical framework for a general representative scheme is provided. Then, the proposed schemes are designed and implemented using an FPGA-based controller. Furthermore, the effect of using the proposed controller on common-mode, differential-mode, and total conducted-noise characteristics of the dc-dc converter is experimentally investigated. In addition, the three randomization parameters are swept to determine the values that best achieve the conducted-noise spectrum spread. All studied cases are designed, implemented, and experimentally investigated. Then, the conducted-noise spectra are compared. The experimental results show that the use of the proposed controller with the determined randomization values significantly improves the conducted-noise spectrum and effectively reduces the noise peaks at both high- and low-frequency ranges.

This paper introduces a comprehensive analysis of the dynamic performance of an electric vehicle system using different control algorithms. The complete mathematical models of the electric vehicle and its motor drive system are described in a systematic manner. Furthermore, the vehicle dynamics are tested with several control topologies to investigate the most appropriate one. The adopted control topologies were the direct torque control (DTC), the model predictive direct torque control (MP DTC), and the predictive voltage control (PVC). The obtained results showed that the dynamics of the electric drive system of the vehicle under the PVC system were enhanced compared to the other control schemes.

To satisfy the ever-increasing requirement from load-side and improve the power quality, the paralleled converters structure is seen to be an interesting solution for such purpose. However, paralleled converters suffer from a major challenge related to the excessive circulating currents due to the difference in the induced CMV in each converter. This voltage difference is due to the instantaneous potential difference between paralleled phase-legs. Consequently, ZSCC as well as DMCC shall appear between the paralleled converters. In this context, this paper utilizes the interleaved PWM concept for the ZSCC and DMCC reduction in paralleled SSIs, where this SSI is a single-stage dc-ac converter and the prior mentioned operation and associated issues have never been investigated yet. This paper describes the three-phase SSI structure and its basic operation, and shows the corresponding CMV waveform. Then, based on the shown analysis, an interleaved DPWM strategy is proposed to restrain the CMV amplitude and reduce the induced circulating currents. Furthermore, the effect of changing the modulation index and interleaving angle on the overall converter behavior has also been evaluated in case of using SSI. The analysis and simulations revealed the effectiveness of the proposed modulation strategy compared to counterparts. Finally, the shown simulation results are validated experimentally.

The exponential growth of the size of seismic data recorded in seismic surveys and real time data monitoring makes seismic data compression strongly demanded. Furthermore, compression will lead to an efficient use of the bandwidth assigned for the communication link between the seismic stations and the main center. In this paper, two convolutional autoencoders (CAEs) are proposed for seismic data compression. The two algorithms are mainly based on the convolutional neural network (CNN), which has the capability to compress the seismic data into feature representations, thereby allowing the decoder to perfectly reconstruct the input seismic data. The results show that the first model is efficient at low compression ratios (CRs), while the second model improves the signal-to-noise ratio (SNR) from about 3 dB to 12 dB compared to the other benchmark algorithms at moderate and high CRs.