DC-DC (direct current controlled direct current) converter is the core control circuit in the field of power electronics technology. Based on the theory of sensors and MEMS (microelectromechanical systems), this paper constructs a DC-DC converter device integration and packaging model and proposes an enhanced current equalization technology with offset correction function suitable for two-phase DC-DC converters. Aiming at the generation mechanism of the output ripple of the two-phase DC-DC converter, the model adopts the ripple elimination technology based on the interleaved synchronous clock and the self-calibration interleaved time generator, so that each phase of the converter is accurately staggered within the full-load range, and the problem of output ripple amplitude is solved. During the simulation process, a high-performance two-phase DC-DC converter chip is designed and implemented, which includes an adaptive on-time control logic based on ripple feedback, a self-calibrating zero-current turn-off circuit, and a robust power switch transistor drive logic. The experimental results show that the full-load current of the chip reaches 6A, the peak efficiency is 91%, the phase-to-phase current error is <0.6%, and the output ripple is <9 mV. In the 90.265 V AC input, 0-10 W load range, the output voltage error is less than 0.96%, when the load is switched between no-load and full-load, and the system response speed is less than 200 ps, which effectively improves the overall performance of the DC-DC converter.
To enhance the visualization effect of substation high-voltage electrical equipment vulnerability, this study proposes an ISSA-LSTM coupled video overlay algorithm-based substation high-voltage electrical equipment vulnerability visualization and monitoring model. Using the improved α blending algorithm combined with the inverse sampling of video background color, overlaying visible video as well as infrared video, using the improved adaptive weighted two-dimensional principal component analysis (W2DPCA) to fuse the base layer, selecting the detail layer as the final detail layer, obtaining the final fusion frame, and realizing the visualization and monitoring of substation high-voltage electrical equipment vulnerability, and introducing the improved sparrow search algorithm (ISSA) to establish long and short-term memory network prediction model to reduce the prediction error and improve the monitoring accuracy rate. The experimental results show that the monitoring frames obtained by this method can reflect rich details of substation high-voltage electrical equipment, and the texture color and equipment edge contrast are enhanced to facilitate accurate determination of substation high-voltage electrical equipment vulnerability, and the prediction accuracy of ISSA-LSTM model is as high as 99.85%.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.