.png){kind=icon}
返回
Fault detection for DC-DC converters using adaptive parameter identification
.png){kind=icon}
PRE
AI
DOI：10.1016/j.jfranklin.2022.05.036.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
As a clean and environmentally friendly renewable energy source, photovoltaic (PV) energy has attracted widespread attention. DC-DC converters are an essential component of all PV systems and it is vital for them to maintain normal function. Therefore, research on fault detection and identification in DC-DC converters is significant. This paper designs a new type of adaptive sliding mode observer, which realizes parameter estimation and fault detection of components by ensuring the consistent boundedness of parameter estimation errors. When the system fails, the parameter estimated value and actual value of the observer will generate a residual signal which can be analyzed and compared with the designed threshold value. If the threshold value is exceeded, the component will be diagnosed as malfunctioning. Finally, the effectiveness of the method is verified by a digital online simulation and hardware-in-the-loop simulation.(c) 2022 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
Keyword:
DIAGNOSIS
期刊
J
IF:
3.7
论文数:
6.4K
被引数:
1.5W
机构
引用论文
A Comprehensive Monitoring System for Online Fault Diagnosis and Aging Detection of Non-Isolated DC-DC Converters' Components非隔离直流-直流变换器组件在线故障诊断与老化检测综合监测系统
PREAI
分享收藏Relationships Between Chemical Characteristics and Root Growth Promotion of Humic Acids Isolated From Brazilian Oxisols.png)
Soil Science
Soil ScienceIF0
PREAI
分享收藏
Integrated sensor fault estimation and control for continuous-time switched systems: A new separation principleISA TRANSACTIONS
OAAI
ISA TRANSACTIONSIF6.5
OAAI分享收藏
查看更多内容