Control‐oriented fault detection of solid oxide fuel cell system unknown input on fuel supply

Wu, Xiaolong; Xu, Yuanwu; Xue, Tao; Junchao, Shuai; Jiang, Jianhua; Deng, Zongquan; Fu, Xiaowei; Li, Xi

doi:10.1002/asjc.2014

Cited by 11 publications

(4 citation statements)

References 31 publications

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“…If the error exceeds a designed threshold, then it can be judged that the SOFC system condition has changed. The threshold design method can be seen in the literature …”

Section: Resultsmentioning

confidence: 99%

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Novel Hybrid Modeling and Analysis Method for Steam Reforming Solid Oxide Fuel Cell System Multifault Degradation Fusion Assessment

Wu,

Xu,

Peng

et al. 2023

ACS Omega

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Steam reforming solid oxide fuel cell (SOFC) systems are important devices to promote carbon neutralization and clean energy conversion. It is difficult to monitor system working conditions in real time due to the possible fusion fault degradation under high temperatures and the seal environment, so it is necessary to design an effective system multifault degradation assessment strategy for solid oxide fuel cell systems. Therefore, in this paper, a novel hybrid model is developed. The hybrid model is built to look for the system fault reason based on first principles, machine learning (radial basis function neural network), and a multimodal classification algorithm. Then, stack, key balance of plant components (afterburner, heat exchanger, and reformer), thermoelectric performance, and system efficiency are studied during the progress of the system experiment. The results show that the novel hybrid model can track well the system operation trend, and solid oxide fuel cell system working dynamic performance can be obtained. Furthermore, four fault types of solid oxide fuel cell systems are analyzed with thermoelectric parameters and energy conversion efficiency based on transition and fault stages, and two cases are also successful by using the built model to decouple the multifault degradation fusion. In addition, the solid oxide fuel cell multifault degradation fusion assessment method proposed in this paper can also be used in other fuel cell systems.

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“…If the error exceeds a designed threshold, then it can be judged that the SOFC system condition has changed. The threshold design method can be seen in the literature …”

Section: Resultsmentioning

confidence: 99%

“…The threshold design method can be seen in the literature. 44 It should be noted that the threshold design needs to be completed based on a large number of experimental data. At present, the SOFC system's working conditions can be judged by using the SOFC stack voltage degradation trend.…”

Section: System Fault Analysis With the Hybrid Model And Threshold De...mentioning

confidence: 99%

Novel Hybrid Modeling and Analysis Method for Steam Reforming Solid Oxide Fuel Cell System Multifault Degradation Fusion Assessment

Wu,

Xu,

Peng

et al. 2023

ACS Omega

Self Cite

View full text Add to dashboard Cite

show abstract

“…Installing a sufficient number of sensors is difficult for SOFC systems [5,6]. SOFC system belongs to the high temperature fuel cell system, and its key components are often concentrated in a highly sealed hotbox [7].…”

Section: Introductionmentioning

confidence: 99%

Modeling Analysis of SOFC System Oriented to Working Condition Identification

Zhang

Liu

et al. 2022

Energies

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Solid oxide fuel cell (SOFC) generation system is an important equipment to realize “carbon neutralization”. In SOFC system, a fault will cause changes in working conditions, which is difficult to detect early and find the reason due to the high temperature and seal environment. Therefore, the mechanistic model is a feasible way to find the reasons for the change of system working conditions. In this paper, based on the first law of thermodynamics, the system model of SOFC is built under multiple working conditions, and the influence of stack, afterburner, heat exchanger, and reformer fault is studied on the thermoelectric characteristics and efficiency of the system. The results show that with the introduction of these fault mechanistic models, the dynamic response characteristics of SOFC system under multiple working conditions can be obtained by tracking the key performance parameters qualitatively. The work of this paper is helpful for the guidance of the fault diagnosis of SOFC system in the future.

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“…Human beings are striving to make low-carbonization energy systems and gradually decarbonize them to cope with energy problems and ecological degradation. With the ability to effectively convert chemical energy into electrical energy [1] and the advantages of near-zero emissions, low noise, superior energy efficiency and high power density [2][3][4][5], solid oxide fuel cell (SOFC) technology has huge potential for application in new energy vehicles, distributed power generation and military equipment [6]. Due to the complex coupling relationships in SOFC systems, performance degradation or even failure can easily occur during actual operation, which makes the durability and long-term performance of SOFC system poor, and hinders its large-scale commercialization [7].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven State Prediction and Analysis of SOFC System Based on Deep Learning Method

Rao¹,

Wang

Chen³

et al. 2022

Energies

Self Cite

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A solid oxide fuel cell (SOFC) system is a kind of green chemical-energy–electric-energy conversion equipment with broad application prospects. In order to ensure the long-term stable operation of the SOFC power-generation system, prediction and evaluation of the system’s operating state are required. The mechanism of the SOFC system has not been fully revealed, and data-driven single-step prediction is of little value for practical applications. The state-prediction problem can be regarded as a time series prediction problem. Therefore, an innovative deep learning model for SOFC system state prediction is proposed in this study. The model uses a two-layer LSTM network structure that supports multiple sequence feature inputs and flexible multi-step prediction outputs, which allows multi-step prediction of system states using SOFC system experimental data. Comparing the proposed model with the traditional ARIMA model and LSTM recursive prediction model, it is shown that the multi-step LSTM prediction model performs better than the ARIMA and LSTM recursive prediction models in terms of two evaluation criteria: root mean square error and mean absolute error. Thus, the proposed multi-step LSTM prediction model can effectively and accurately predict and evaluate the SOFC system’s state.

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Control‐oriented fault detection of solid oxide fuel cell system unknown input on fuel supply

Cited by 11 publications

References 31 publications

Novel Hybrid Modeling and Analysis Method for Steam Reforming Solid Oxide Fuel Cell System Multifault Degradation Fusion Assessment

Novel Hybrid Modeling and Analysis Method for Steam Reforming Solid Oxide Fuel Cell System Multifault Degradation Fusion Assessment

Modeling Analysis of SOFC System Oriented to Working Condition Identification

Data-Driven State Prediction and Analysis of SOFC System Based on Deep Learning Method

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