Fault detection for DC-DC converters using adaptive parameter identification. Issue 11 (July 2022)

• Record Type: Journal Article
• Authors: Su, Qingyu; Wang, Ze; Xu, Jing; Li, Chenglong; Li, Jian
• Is Part Of: Journal of the Franklin Institute. Volume 359:Issue 11(2022)
• Page Start: 5778
• Page End: 5797
• Publication Date: 2022-07
• Subjects: Science -- Periodicals; Technology -- Periodicals; Patents -- United States -- Periodicals; 505
• DOI: 10.1016/j.jfranklin.2022.05.036
• Languages: English
• ISSNs: 0016-0032

Abstract

Abstract: 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.