Journal article
Machine Learning Enabled Cluster Grouping of Varistors in Parallel-Structured DC Circuit Breakers
IEEE open journal of power electronics, v 4, pp 1003-1010
2023
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
This letter presents the first ever trial of machine learning enabled cluster grouping of varistors for DC circuit breakers (DCCBs). It reveals that the manufacturing discrepancy of varistors is a main challenge in their parallel connection. The proposed cluster grouping concept is introduced to classify varistors according to the interruption characteristic, in which the K -means algorithm is adopted to learn the clamping voltage curves. 70 420 V/50 A V420LA20 varistors are measured in a 120 A transient current interruption platform individually to acquire 70 sets of testing data to train the machine learning engine. Then, 28 new varistors are further tested to verify the trained algorithm, which are classified into 7 clusters using the proposed machine learning method. A 500 V/520 A solid-state circuit breaker (SSCB) is implemented with four parallel varistors in the same cluster. Experiments validate that the current is evenly distributed in varistors, and the difference is limited to 3.1%, which improves parallel varistors lifetime significantly.
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Details
- Title
- Machine Learning Enabled Cluster Grouping of Varistors in Parallel-Structured DC Circuit Breakers
- Creators
- Shuyan Zhao - Drexel UniversityYao Wang - Drexel UniversityReza Kheirollahi - Drexel UniversityZilong Zheng - Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA, USAFei Lu - Drexel UniversityHua Zhang - Rowan University
- Publication Details
- IEEE open journal of power electronics, v 4, pp 1003-1010
- Publisher
- IEEE
- Grant note
- Advanced Research Projects Agency-Energy 2301637 / National Science Foundation (10.13039/501100008982) DE-AR0001114 / U.S. Department of Energy (10.13039/100000015)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:001122755900001
- Scopus ID
- 2-s2.0-85177067264
- Other Identifier
- 991021811636504721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Engineering, Electrical & Electronic