Fault detection of a PEMFC system based on delayed LPV observer

Bougatef, Zina; Abdelkrim, Naceur; Aitouche, A.; Abdelkrim, Mohamed Naceur

doi:10.1016/j.ijhydene.2018.11.053

Cited by 27 publications

(11 citation statements)

References 32 publications

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“…where K is the controller gain and should be chosen to (I) assure the closed-loop stability and (II) minimize the outputs (23) and (24). The closed-loop PEMFC system is obtained as follows:…”

Section: Optimal Controller Designmentioning

confidence: 99%

Optimal gain‐scheduling control of proton exchange membrane fuel cell: An LMI approach

Afsharinejad

Asemani

Dehghani

et al. 2021

IET Renewable Power Gen

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Here, an optimal linear parameter varying (LPV) controller is developed for non‐linear proton exchange membrane fuel cell (PEMFC) systems. Two performance criteria are involved in the optimal controller, including the membrane pressure and the power of the PEMFC. The former should be minimized; and, the latter should be maximized. Since the goal is to derive sufficient conditions in terms of linear matrix inequality (LMI) constraints, both performances are re‐formulated and augmented in a unique convex optimization problem. Further, the non‐linear PEMFC with the equilibrium profile dependent on time‐varying parameters, that is, current and temperature, is represented by an LPV model. Using the LMI constraints and the obtained LPV model, the gains of the controller are derived. Comparing with the state‐of‐the‐art methods, the proposed approach offers an optimal control approach with a systematic design method, in which both practical issues are treated, simultaneously. To show the advantages of the developed approach, six typical controllers, including the proposed and existing approaches, are considered and several numerical simulation results are conducted.

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Section: Optimal Controller Designmentioning

confidence: 99%

Optimal gain‐scheduling control of proton exchange membrane fuel cell: An LMI approach

Afsharinejad

Asemani

Dehghani

et al. 2021

IET Renewable Power Gen

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show abstract

“…x * (t) is then iteratively updated by (11) and (12a), which makes the finial estimation x(t) after all iterations. e termination condition of the iteration here is given in (14), which is either a threshold c defined a priori for the error between two iterations or a threshold N max for the number of iterations m:…”

Section: Iterative Algorithm For Solving the Term β T−τ I Tmentioning

confidence: 99%

“…Observer-based method has also been widely used in fault detection and isolation (FDI) of many fields such as PEM fuel cell and heat-exchanger/reactor system [13][14][15]. As examined in [16,17], that model-based diagnosis method will be affected with divergence due to the accumulation of modeling uncertainties.…”

Section: Introductionmentioning

confidence: 99%

A Continuous-Discrete Finite Memory Observer Design for a Class of Nonlinear Systems: Application to Fault Diagnosis

Zhang

Kratz

Hou

et al. 2020

Mathematical Problems in Engineering

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This paper aims to develop a continuous-discrete finite memory observer (CD-FMO) for a class of nonlinear dynamical systems modeled by ordinary differential equations (ODEs) with discrete measurements. The nonlinear systems under consideration are at least locally Lipschitz, which guarantees the existence and uniqueness of solution at each time instant. The proposed nonlinear observer uses a finite number of collected measurements to estimate the system state in the presence of measurement noise. Besides, a one-step prediction algorithm incorporated with an iterative-update scheme is performed to solve the integral problem caused by system nonlinearity, and an analysis of the numerical integration approximation error is given. The properties of estimation performance have been further proved in deterministic case and been analyzed by Monte Carlo simulation in stochastic cases. It is worth noting that the presented method has a finite-time convergence, while most nonlinear observers are usually asymptotically convergent. Another advantage of CD-FMO is that it has no initial value problem. For the application purpose, residuals are generated to implement fault detection cooperated with Cumulative Sum (CUSUM) control charts, while a bank of CD-FMOs is adopted to realize fault isolation for different sensor and actuator faults of the considered nonlinear robotic arm. The robustness and effectiveness of the proposed approach are illustrated via the simulation results.

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“…Liu, [26] proposed a fault diagnosis method which combined an extreme learning machine and the Dempster-Shafer evidence theory to diagnose the faults in a PEM fuel cell system. Bougatef [27] designed the unknown input observer for a delayed LPV model to deal with the fault estimation of actuator fault for a PEM fuel cell. In addition, Wang [28,29] developed a composite support material which possessed intrinsic protonic conductivity and improved electronic conductivity together with the optimization of the microstructure structures.…”

Section: Introductionmentioning

confidence: 99%

Research on the Fault Diagnosis of a Polymer Electrolyte Membrane Fuel Cell System

et al. 2020

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In this work, the possibilistic fuzzy C-means clustering artificial bee colony support vector machine (PFCM-ABC-SVM) method is proposed and applied for the fault diagnosis of a polymer electrolyte membrane (PEM) fuel cell system. The innovation of this method is that it can filter data with Gaussian noise and diagnose faults under dynamic conditions, and the amplitude of characteristic parameters is reduced to ±10%. Under dynamic conditions with Gaussian noise, the faults of the PEM fuel cell system are simulated and the original dataset is established. The possibilistic fuzzy C-means (PFCM) algorithm is used to filter samples with membership and typicality less than 90% and to optimize the original dataset. The artificial bee colony (ABC) algorithm is used to optimize the penalty factor C and kernel function parameter g. Finally, the optimized support vector machine (SVM) model is used to diagnose the faults of the PEM fuel cell system. To illustrate the results of the fault diagnosis, a nonlinear PEM fuel cell simulator model which has been presented in the literature is used. In addition, the PFCM-ABC-SVM method is compared with other methods. The result shows that the method can diagnose faults in a PEM fuel cell system effectively and the accuracy of the testing set sample is up to 98.51%. When solving small-sized, nonlinear, high-dimensional problems, the PFCM-ABC-SVM method can improve the accuracy of fault diagnosis.

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Fault detection of a PEMFC system based on delayed LPV observer

Cited by 27 publications

References 32 publications

Optimal gain‐scheduling control of proton exchange membrane fuel cell: An LMI approach

Optimal gain‐scheduling control of proton exchange membrane fuel cell: An LMI approach

A Continuous-Discrete Finite Memory Observer Design for a Class of Nonlinear Systems: Application to Fault Diagnosis

Research on the Fault Diagnosis of a Polymer Electrolyte Membrane Fuel Cell System

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