LFT Bond Graph for Online Robust Fault Detection and Isolation of Hybrid Multi-Source System

Abstract

Abstract Green hydrogen is undoubtedly the most promising energy vector of the future because it is captured by renewable and inexhaustible sources, such as wind and/or solar energy, and can be stored over the long in high-pressure cylinders, which can be used to feed the fuel cells to produce the electricity without emitting any pollutants. The system incorporated renewable sources and process used to produce the green hydrogen is the hybrid multi-source system (HMS). The production of hydrogen needs a reliable HMS, which always requires online monitoring for real-time Fault Detection and Isolation (FDI) because the risk of accidents in HMS and safety issues increases due to the possibility of faults. However, online monitoring of FDI is challenging due to the multi-physics dynamics of HMS and the inclusion of uncertain parameters and several disturbances. This paper proposes an online robust fault detection algorithm to detect system faults based on the properties of the graphical linear fractional transformation bond graph (LFT-BG) modeling approach. Here, the analytical redundancy relations (ARRs) and their uncertain parts extracted from the LFT-BG model are used to develop an online robust FDI algorithm for HMS. Numerical evaluations of ARRs and their uncertain parts, respectively, generate the residual signals known as “faults indicators” and their uncertain bounds known as “adaptive thresholds.” These thresholds evolve with system variables in the presence of parameter uncertainties for ensuring robust FDI for HMS to minimize false alarms. The validation of this approach is carried out using 20sim software that is familiar with BG modeling.