Document Type
Article
Version Deposited
Published Version
Open Access Funding Source
Other
Publication Date
11-10-2023
Publication Title
IEEE Open Journal of Power Electronics
DOI
10.1109/OJPEL.2023.3331731
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. © 2020 IEEE.
Recommended Citation
S. Zhao, Y. Wang, R. Kheirollahi, Z. Zheng, F. Lu and H. Zhang, "Machine Learning Enabled Cluster Grouping of Varistors in Parallel-Structured DC Circuit Breakers," in IEEE Open Journal of Power Electronics, vol. 4, pp. 1003-1010, 2023, doi: 10.1109/OJPEL.2023.3331731.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
