Data assimilation(DA)and uncertainty quantification(UQ)are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics.Typical applications span from computational fluid ...Data assimilation(DA)and uncertainty quantification(UQ)are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics.Typical applications span from computational fluid dynamics(CFD)to geoscience and climate systems.Recently,much effort has been given in combining DA,UQ and machine learning(ML)techniques.These research efforts seek to address some critical challenges in high-dimensional dynamical systems,including but not limited to dynamical system identification,reduced order surrogate modelling,error covariance specification and model error correction.A large number of developed techniques and methodologies exhibit a broad applicability across numerous domains,resulting in the necessity for a comprehensive guide.This paper provides the first overview of state-of-the-art researches in this interdisciplinary field,covering a wide range of applications.This review is aimed at ML scientists who attempt to apply DA and UQ techniques to improve the accuracy and the interpretability of their models,but also at DA and UQ experts who intend to integrate cutting-edge ML approaches to their systems.Therefore,this article has a special focus on how ML methods can overcome the existing limits of DA and UQ,and vice versa.Some exciting perspectives of this rapidly developing research field are also discussed.Index Terms-Data assimilation(DA),deep learning,machine learning(ML),reduced-order-modelling,uncertainty quantification(UQ).展开更多
Fault diagnostics is important for safe operation of nuclear power plants(NPPs). In recent years, data-driven approaches have been proposed and implemented to tackle the problem, e.g., neural networks, fuzzy and neuro...Fault diagnostics is important for safe operation of nuclear power plants(NPPs). In recent years, data-driven approaches have been proposed and implemented to tackle the problem, e.g., neural networks, fuzzy and neurofuzzy approaches, support vector machine, K-nearest neighbor classifiers and inference methodologies. Among these methods, dynamic uncertain causality graph(DUCG)has been proved effective in many practical cases. However, the causal graph construction behind the DUCG is complicate and, in many cases, results redundant on the symptoms needed to correctly classify the fault. In this paper, we propose a method to simplify causal graph construction in an automatic way. The method consists in transforming the expert knowledge-based DCUG into a fuzzy decision tree(FDT) by extracting from the DUCG a fuzzy rule base that resumes the used symptoms at the basis of the FDT. Genetic algorithm(GA) is, then, used for the optimization of the FDT, by performing a wrapper search around the FDT: the set of symptoms selected during the iterative search are taken as the best set of symptoms for the diagnosis of the faults that can occur in the system. The effectiveness of the approach is shown with respect to a DUCG model initially built to diagnose 23 faults originally using 262 symptoms of Unit-1 in the Ningde NPP of the China Guangdong Nuclear Power Corporation. The results show that the FDT, with GA-optimized symptoms and diagnosis strategy, can drive the construction of DUCG and lower the computational burden without loss of accuracy in diagnosis.展开更多
With the significant improvement of microgrid technology, microgrid has gained large-scale application.However, the existence of intermittent distributed generations, nonlinear loads and various electrical and electro...With the significant improvement of microgrid technology, microgrid has gained large-scale application.However, the existence of intermittent distributed generations, nonlinear loads and various electrical and electronic devices causes power quality problem in microgrid, especially in islanding mode. An accurate and fast disturbance detection method which is the premise of power quality control is necessary. Aiming at the end effect and the mode mixing of original Hilbert-Huang transform(HHT), an improved HHT with adaptive waveform matching extension is proposed in this paper. The innovative waveform matching extension method considers not only the depth of waveform, but also the rise time and fall time. Both simulations and field experiments have verified the correctness and validity of the improved HHT for power quality disturbance detection in microgrid.展开更多
Existing Physics-of-Failure-based （PoF-based） system reliability prediction methods are grounded on the independence assumption, which overlooks the dependency among the compo- nents. In this paper, a new type of de...Existing Physics-of-Failure-based （PoF-based） system reliability prediction methods are grounded on the independence assumption, which overlooks the dependency among the compo- nents. In this paper, a new type of dependency, referred to as failure collaboration, is introduced and considered in reliability predictions. A PoF-based model is developed to describe the failure behavior of systems subject to failure collaboration. Based on the developed model, the Bisection-based Reliability Analysis Method （BRAM） is exploited to calculate the system reliability. The developed methods are applied to predicting the reliability of a Hydraulic Servo Actuator （HSA）. The results demonstrate that the developed methods outperform the traditional PoF-based reliability prediction methods when applied to systems subject to failure collaboration.展开更多
We consider reliability engineering in modern civil aviation industry, and the related engineering activities and methods. We consider reliability in a broad sense, referring to other system characteristics that are r...We consider reliability engineering in modern civil aviation industry, and the related engineering activities and methods. We consider reliability in a broad sense, referring to other system characteristics that are related to it, like availability, maintainability, safety and durability. We covered the entire lifecycle of the equipment, including reliability requirement identification, reliability analysis and design, verification and validation of reliability requirements(typically involved in the equipment design and development phase), quality assurance(which typically enters in the manufacturing phase), and fault diagnosis and prognosis and maintenance(which are connected to the operation phase). Lessons learnt from reliability engineering practices in civil aviation industry are given, which might serve as reference for reliability managers and engineers, also from other industries with high reliability requirements.展开更多
The exchange of information between transmission system operators(TSOs)and distribution system operators(DSOs)is a common practice.However,the evolution of the regulatory frameworks in Europe has increased the need fo...The exchange of information between transmission system operators(TSOs)and distribution system operators(DSOs)is a common practice.However,the evolution of the regulatory frameworks in Europe has increased the need for enhancing TSO-DSO data exchange and interoperability.This paper provides an overview of the TSO-DSO data exchanges and demonstrates the best practices using International Electrotechnical Commission(IEC)common information model(CIM),including the implementation of IEC common grid model exchange standard(CGMES),and discussion of the corresponding advantages,disadvantages,and challenges.Furthermore,this paper evaluates and reports the activities already carried out within European projects,with particular focus on TSO-DSO interoperability.Finally,this paper concludes the need for TSOs and DSOs to rely on standard-based solutions when performing TSO-DSO data exchange,which enables the efficient operation and development of the future power systems.展开更多
基金the support of the Leverhulme Centre for Wildfires,Environment and Society through the Leverhulme Trust(RC-2018-023)Sibo Cheng,César Quilodran-Casas,and Rossella Arcucci acknowledge the support of the PREMIERE project(EP/T000414/1)+5 种基金the support of EPSRC grant:PURIFY(EP/V000756/1)the Fundamental Research Funds for the Central Universitiesthe support of the SASIP project(353)funded by Schmidt Futures–a philanthropic initiative that seeks to improve societal outcomes through the development of emerging science and technologiesDFG for the Heisenberg Programm Award(JA 1077/4-1)the National Natural Science Foundation of China(61976120)the Natural Science Key Foundat ion of Jiangsu Education Department(21KJA510004)。
文摘Data assimilation(DA)and uncertainty quantification(UQ)are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics.Typical applications span from computational fluid dynamics(CFD)to geoscience and climate systems.Recently,much effort has been given in combining DA,UQ and machine learning(ML)techniques.These research efforts seek to address some critical challenges in high-dimensional dynamical systems,including but not limited to dynamical system identification,reduced order surrogate modelling,error covariance specification and model error correction.A large number of developed techniques and methodologies exhibit a broad applicability across numerous domains,resulting in the necessity for a comprehensive guide.This paper provides the first overview of state-of-the-art researches in this interdisciplinary field,covering a wide range of applications.This review is aimed at ML scientists who attempt to apply DA and UQ techniques to improve the accuracy and the interpretability of their models,but also at DA and UQ experts who intend to integrate cutting-edge ML approaches to their systems.Therefore,this article has a special focus on how ML methods can overcome the existing limits of DA and UQ,and vice versa.Some exciting perspectives of this rapidly developing research field are also discussed.Index Terms-Data assimilation(DA),deep learning,machine learning(ML),reduced-order-modelling,uncertainty quantification(UQ).
文摘Fault diagnostics is important for safe operation of nuclear power plants(NPPs). In recent years, data-driven approaches have been proposed and implemented to tackle the problem, e.g., neural networks, fuzzy and neurofuzzy approaches, support vector machine, K-nearest neighbor classifiers and inference methodologies. Among these methods, dynamic uncertain causality graph(DUCG)has been proved effective in many practical cases. However, the causal graph construction behind the DUCG is complicate and, in many cases, results redundant on the symptoms needed to correctly classify the fault. In this paper, we propose a method to simplify causal graph construction in an automatic way. The method consists in transforming the expert knowledge-based DCUG into a fuzzy decision tree(FDT) by extracting from the DUCG a fuzzy rule base that resumes the used symptoms at the basis of the FDT. Genetic algorithm(GA) is, then, used for the optimization of the FDT, by performing a wrapper search around the FDT: the set of symptoms selected during the iterative search are taken as the best set of symptoms for the diagnosis of the faults that can occur in the system. The effectiveness of the approach is shown with respect to a DUCG model initially built to diagnose 23 faults originally using 262 symptoms of Unit-1 in the Ningde NPP of the China Guangdong Nuclear Power Corporation. The results show that the FDT, with GA-optimized symptoms and diagnosis strategy, can drive the construction of DUCG and lower the computational burden without loss of accuracy in diagnosis.
基金supported by National High Technology Research and Development Program of China(863 Program)(No.2015AA050104)National Natural Science Foundation of China(No.51577068)
文摘With the significant improvement of microgrid technology, microgrid has gained large-scale application.However, the existence of intermittent distributed generations, nonlinear loads and various electrical and electronic devices causes power quality problem in microgrid, especially in islanding mode. An accurate and fast disturbance detection method which is the premise of power quality control is necessary. Aiming at the end effect and the mode mixing of original Hilbert-Huang transform(HHT), an improved HHT with adaptive waveform matching extension is proposed in this paper. The innovative waveform matching extension method considers not only the depth of waveform, but also the rise time and fall time. Both simulations and field experiments have verified the correctness and validity of the improved HHT for power quality disturbance detection in microgrid.
基金supported by the National Natural Science Foundation of China (No.61573043)supported by National Natural Science Foundation of China (No.51675025)
文摘Existing Physics-of-Failure-based （PoF-based） system reliability prediction methods are grounded on the independence assumption, which overlooks the dependency among the compo- nents. In this paper, a new type of dependency, referred to as failure collaboration, is introduced and considered in reliability predictions. A PoF-based model is developed to describe the failure behavior of systems subject to failure collaboration. Based on the developed model, the Bisection-based Reliability Analysis Method （BRAM） is exploited to calculate the system reliability. The developed methods are applied to predicting the reliability of a Hydraulic Servo Actuator （HSA）. The results demonstrate that the developed methods outperform the traditional PoF-based reliability prediction methods when applied to systems subject to failure collaboration.
基金supported by the National Natural Science Foundation of China (Nos. 61573043, 71671009 and 71601010)
文摘We consider reliability engineering in modern civil aviation industry, and the related engineering activities and methods. We consider reliability in a broad sense, referring to other system characteristics that are related to it, like availability, maintainability, safety and durability. We covered the entire lifecycle of the equipment, including reliability requirement identification, reliability analysis and design, verification and validation of reliability requirements(typically involved in the equipment design and development phase), quality assurance(which typically enters in the manufacturing phase), and fault diagnosis and prognosis and maintenance(which are connected to the operation phase). Lessons learnt from reliability engineering practices in civil aviation industry are given, which might serve as reference for reliability managers and engineers, also from other industries with high reliability requirements.
基金the OneNet,TDX-ASSIST,EU-SysFlex,and INTER-RFACE projects funded by the European Union's Horizon 2020 Research and Innovation Programme(especially under Grants No.957739,No.774500,No.773505,and No.824330).
文摘The exchange of information between transmission system operators(TSOs)and distribution system operators(DSOs)is a common practice.However,the evolution of the regulatory frameworks in Europe has increased the need for enhancing TSO-DSO data exchange and interoperability.This paper provides an overview of the TSO-DSO data exchanges and demonstrates the best practices using International Electrotechnical Commission(IEC)common information model(CIM),including the implementation of IEC common grid model exchange standard(CGMES),and discussion of the corresponding advantages,disadvantages,and challenges.Furthermore,this paper evaluates and reports the activities already carried out within European projects,with particular focus on TSO-DSO interoperability.Finally,this paper concludes the need for TSOs and DSOs to rely on standard-based solutions when performing TSO-DSO data exchange,which enables the efficient operation and development of the future power systems.
