TY - GEN
T1 - A Working Pattern Recognition Method for Satellite Power System Based on Uncertain Data Clustering Strategy
AU - Yan, Xiaozhen
AU - Luo, Qinghua
AU - Yang, Yipeng
AU - Yang, Zhuo
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - The power system plays a major role in the maintenance of working properly for satellite. As there are many working loads and different working attitudes, the power system has many diverse working patterns. So it is very critical to recognize the working patterns accurately. However, due to the measurement error, environmental interference, and other uncertainty factors, the output voltage of the satellite power system has remarkable uncertainty. If we did not consider the uncertainty and various working patterns, poor recognized result will be generated. For this issue, we proposed a working patterns recognition method for satellite power system based on uncertainty data clustering strategy. In this method, we firstly utilize uncertainty data clustering strategy to modeling working patterns. Then during pattern recognition stage, we calculate the distances between uncertain cluster centers and the measurement data. The experimental results of actual power system data illustrate the validation and feasibility of our proposed method.
AB - The power system plays a major role in the maintenance of working properly for satellite. As there are many working loads and different working attitudes, the power system has many diverse working patterns. So it is very critical to recognize the working patterns accurately. However, due to the measurement error, environmental interference, and other uncertainty factors, the output voltage of the satellite power system has remarkable uncertainty. If we did not consider the uncertainty and various working patterns, poor recognized result will be generated. For this issue, we proposed a working patterns recognition method for satellite power system based on uncertainty data clustering strategy. In this method, we firstly utilize uncertainty data clustering strategy to modeling working patterns. Then during pattern recognition stage, we calculate the distances between uncertain cluster centers and the measurement data. The experimental results of actual power system data illustrate the validation and feasibility of our proposed method.
KW - Satellite Power
KW - Uncertainty data
KW - Working pattern recognition
KW - clustering
UR - https://www.scopus.com/pages/publications/85078052491
U2 - 10.1109/PHM-Qingdao46334.2019.8942921
DO - 10.1109/PHM-Qingdao46334.2019.8942921
M3 - 会议稿件
AN - SCOPUS:85078052491
T3 - 2019 Prognostics and System Health Management Conference, PHM-Qingdao 2019
BT - 2019 Prognostics and System Health Management Conference, PHAI-Qingdao 2019
A2 - Guo, Wei
A2 - Li, Steven
A2 - Miao, Qiang
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th Prognostics and System Health Management Conference, PHM-Qingdao 2019
Y2 - 25 October 2019 through 27 October 2019
ER -