Implementation of a Cascade Fault Tolerant Control and Fault Diagnosis Design for a Modular Power Supply

The main objective of this research work was to develop reliable and intelligent power sources for the future. To achieve this objective, a modular stand-alone solar energy-based direct current (DC) power supply was designed and implemented. The converter topology used is a two-stage interleaved boost converter, which is monitored in closed loop. The diagnosis method is based on analytic redundancy relations (ARRs) deduced from the bond graph (BG) model, which can be used to detect the failures of power switches, sensors, and discrete components such as the output capacitor. The proposed supervision scheme including a passive fault-tolerant cascade proportional integral sliding mode control (PI-SMC) for the two-stage boost converter connected to a solar panel is suitable for real applications. Most model-based diagnosis approaches for power converters typically deal with open circuit and short circuit faults, but the proposed method offers the advantage of detecting the failures of other vital components. Practical experiments on a newly designed and constructed prototype, along with simulations under PSIM software, confirm the efficiency of the control scheme and the successful recovery of a faulty stage by manual isolation. In future work, the automation of this reconfiguration task could be based on the successful simulation results of the diagnosis method.This research was funded by the Tunisian Ministry of Higher Education and Scientific Research

Online Implementation of Inequality Constraints Monitoring in Dynamical Systems

This paper deals with fault detection in dynamical systems where the state variables evolutions are constrained by inequality constraints. The latter corresponds either to physical limitations or to safety specification. Two classical residual generation approaches are studied, namely, parity space and unknown input observer approaches, and are extended to monitor the inequality constraints. A practical implementation on a real process is performed and permits to validate the relevance of the proposed methods

Online Implementation of Inequality Constraints Monitoring in Dynamical Systems

This paper deals with fault detection in dynamical systems where the state variables evolutions are constrained by inequality constraints. The latter corresponds either to physical limitations or to safety specification. Two classical residual generation approaches are studied, namely, parity space and unknown input observer approaches, and are extended to monitor the inequality constraints. A practical implementation on a real process is performed and permits to validate the relevance of the proposed methods

Mode Identification and Fault Detection for Hybrid Dynamical Systems using Luenberger Observers

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Distinguishability and Similarity between modes in Hybrid System Monitoring

International audienceThe general principle of model-based Fault Detection and Isolation (FDI) algorithms aims to compare the expected behavior of the system, given by a model, with its actual behavior, known through on-line observations. Accordingly, this paper considers Hybrid Dynamical Systems (HDS), since the behavior is determined by the interaction between continuous and discrete dynamics. The proposed FDI method is based on Luenberger observers. The resulting faults indicators allow detecting both continuous and discrete faults. The property of distinguishability will be considered to guarantee modes identification and the similarity between modes is given in order to reduce residuals calculation

Behavior graphs for hybrid systems monitoring

International audienceHybrid Dynamical Systems (HDS) constitute a wide class of common industrial applications, where the behavior is determined by the interaction between continuous and discrete dynamics, i.e. behavioral modes succession. The general principle of model-based Fault Detection and Isolation (FDI) algorithms is to compare the expected behavior of the system, given by a model, with its actual behavior, known through on-line observations. Faults in HDS may corrupt the two dynamics. In that paper, we propose to limit the set of possible mode candidates by using a priori information on the discrete evolution under normal and faulty hypothesis. Two kinds of graphs are derived from the initial hybrid model, namely Normal Behavior Graphs (NBG), Faulty Behavior Graphs (FBG). Using these graphs allows us not only to identify efficiently the actual mode but also to directly interpret (diagnose) the discrete faulty evolution in terms of faults. The whole FDI methodology is described and applied to a two tanks system example

A Novel Two Stage Controller for a DC-DC Boost Converter to Harvest Maximum Energy from the PV Power Generation

In this article, an efficient and fast two-stage approach for controlling DC-DC boost converter using non linear sliding mode controller for a PV power plant is proposed. The control approach is based on two online methods instead of using the conventional combination of online and offline methods to harvest maximum energy and deliver an output PV voltage with reduced ripples. The proposed two-stage maximum power point tracking (MPPT) control can be integrated into many applications such as hybrid electric vehicles. Simulation results compared with the standard approaches P&O prove the tracking efficiency of the proposed method under fast changing atmospheric conditions of an average 99.87% and a reduced average ripple of 0.06. The two-stage MPPT control was implemented involving the embedded dSPACE DSP in comparison to the classical P&O to prove the efficiency and the validity of the control scheme. The experimental set-up system was carried out on boost converter and programmable DC electronic resistive load to highlights the robustness of the proposed controller against atmospheric changes and parametric variation. View Full-Tex