Fast Performance Evaluation for Switched-Capacitor Converter Based on Convolutional Neural Networks
Source abstract
Abstract
Switched-capacitor converters (SCCs) have been developed for decades and applied successfully in many applications thanks to their merits of high power density, high efficiency, and low cost. Existing SCC evaluation methods do not provide an efficient and effective way to estimate their performance. This paper proposes a new evaluation performance method for SCCs that transforms the topology optimization issue into a more typical non-linear regression problem. The SCC topologies are described by the adjacent matrix. A CNN model is then selected to solve the regression problem. Experimental results confirm that this CNN model greatly shortens the calculation time while performing better than other machine learning algorithms.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Battery Technologies Research · Advanced DC-DC Converters · Advanced Memory and Neural Computing
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Electrical Engineering, Zhejiang University, Hangzhou 310058, China
Economic Research Institute of State Grid Zhejiang Electric Power Co., Ltd., Hangzhou 310063, China
Funding
Planning and preliminary research of conventional hydropower capacity expansion and long-term energy storage pumped storage in Zhejiang Province
References (21)
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