Decentralized Diagnosis by Petri Nets and Integer Linear Programming

Cong, Xuya; Fanti, Maria Pia; Mangini, Agostino Marcello; Li, Zhiwu

doi:10.1109/tsmc.2017.2726108

Cited by 77 publications

(33 citation statements)

References 29 publications

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“…The fault diagnosis algorithms and methods introduced so far can be categorized as follows [3] • mechanism model based analysis • analysis based on expertise and prior knowledge • wide area monitoring based signal processing The mechanism model is often built on the basis of the physical principles and around some fault scenarios, such as the voltage sag or the power flow. The model is thus established to monitor the consistency between the measured outputs and the model outputs [4]- [8]. However, these methods become increasingly difficult to detect faults occurring in the energy internet, since the network complexity increasing significantly due to the introduction of distributed renewable generations and a variety of new loads such as electric vehicles.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis for Energy Internet Using Correlation Processing-Based Convolutional Neural Networks

Yang

Pang

Zhou

et al. 2019

IEEE Trans. Syst. Man Cybern, Syst.

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Fault feature extraction based on prior knowledge and raw data is increasingly becoming more challenging in energy internet fault diagnosis due to complicated network topology and coupling disturbances introduced into the systems. Deep learning methods that have emerged in recent years, such as the convolutional neural networks (CNNs), have shown a number of advantages and great potentials in the field of feature extraction and image recognition. However, CNNs does not work well in fault diagnosis for industrial systems, due to the totally different data representations between images used in recognition and signals obtained from industrial processes. This paper tackles this problem by introducing a novel and generic fault diagnosis method for complicated system namely the Spearman rank correlation based CNNs (SR-CNN). By imposing the Spearman rank correlation image layer on the typical CNNs, the multiple time series signals measured by the phasor measurement units (PMUs) is converted to appropriate data images, which are then fed to the CNNs. With the aid of this novel design, different fault features can be comprehensively extracted while the fault can be identified more quickly and precisely than other conventional approaches. To validate the efficacy of the proposed approach, an IEEE defined power gird with multi new energy resources are used as the test platform. The experimental results confirm the effectiveness and superiority of the proposed method in energy internet fault diagnosis over conventional methods.

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Section: Introductionmentioning

confidence: 99%

Fault Diagnosis for Energy Internet Using Correlation Processing-Based Convolutional Neural Networks

Yang

Pang

Zhou

et al. 2019

IEEE Trans. Syst. Man Cybern, Syst.

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“…Automata are intuitive models used to achieve the original contributions and most of the subsequent developments in the literature of supervisory control of DESs [20], [50]. Meanwhile, with the development of DESs, Petri nets (PNs) have become a conceptual framework and a fundamental model for DESs since they are widely used to address various classes of problems, including the formal representation and development of systems, along with their supervisory control [9], [10], [17], [49], [55], controller synthesis or design [22], [23], [34], [47], state estimation and observability [2], [14], [36], fault detection, diagnosis, and prognosis [11], [16], [19], [25], [37], [48], [56], [57], diagnosability [3], [8], [40], [41], opacity [12], [13], [46],…”

Section: Introductionmentioning

confidence: 99%

Estimation of Least-Cost Transition Firing Sequences in Labeled Petri Nets by Using Basis Reachability Graph

Hao

Zhou

et al. 2019

IEEE Access

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This paper develops an approach for solving the problem of least-cost transition firing sequences estimation in labeled Petri nets with unobservable transitions. Each transition in the net is labeled as either observable or unobservable, and has a nonnegative cost. Additionally, we assume that the net system is bounded, the unobservable subnet is acyclic, and the cost of each unobservable transition is strictly positive. We propose the method mainly on the basis of the notions of basis marking and basis reachability graph (BRG), which is a compact representation of the reachability graph of the net system. By sacrificing extra storage space to keep the BRG and other necessary information in memory, some time-consuming computations are moved off-line. This makes our proposed approach more feasible for on-line applications. Finally, we present a brief survey and comparison of some representative methods that use labeled Petri nets as a formalism in the literature. INDEX TERMS Basis reachability graph (BRG), discrete event system (DES), Petri net (PN), transition firing sequence.

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“…Petri nets, a formal modeling tool invented by CA Petri, 1 have been applied to many fields, such as control of discrete event systems, [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17] system reliability analysis, [18][19][20][21][22][23] modeling, analysis, control, [24][25][26][27][28][29][30][31][32][33] and the verification of communication protocol. 34 In the analysis of Petri nets, state-space techniques are used in marking reachability, forbidden states including deadlock control, [35][36][37] opacity verification and enforcement, and fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

On state-space compression and state reachability retrieval of Petri nets

Ren

Liang

et al. 2019

Advances in Mechanical Engineering

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The problem of state-space explosion and state reachability decision has been a focus in Petri net analysis. In this article, some algorithms based on data features of state space are proposed for state-space reduction and reachability analysis in Petri nets. A caching arrangement algorithm for the state reachability graph is proposed to alleviate the memory overhead when all states are generated by a traditional reachability graph generation algorithm. For the reduction of the state space, a compression algorithm based on a hybrid coding is formulated. The changing characteristics of state elements are fully utilized to realize a sharp reduction of the whole state space. For the state reachability analysis, a state reachability retrieval algorithm based on the locally sensitive hashing is reported. In this algorithm, all the states in the compressed state space are distributed to different hash buckets according to the distances among the states, which realize the primary state retrieval efficiently. Then, a state retrieval strategy that reverts the primary retrieved state to its original data is adopted. Finally, the proposed compression and retrieval algorithms are validated on several Petri net models, showing high compression efficiency and exact accuracy of state retrieval.

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Decentralized Diagnosis by Petri Nets and Integer Linear Programming

Cited by 77 publications

References 29 publications

Fault Diagnosis for Energy Internet Using Correlation Processing-Based Convolutional Neural Networks

Fault Diagnosis for Energy Internet Using Correlation Processing-Based Convolutional Neural Networks

Estimation of Least-Cost Transition Firing Sequences in Labeled Petri Nets by Using Basis Reachability Graph

On state-space compression and state reachability retrieval of Petri nets

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