Bayesian Networks-Based Approach for Power Systems Fault Diagnosis

Zhu, Yongli; Hou, Limin; Lu, Jianchang

doi:10.1109/tpwrd.2005.858774

Cited by 230 publications

(65 citation statements)

References 6 publications

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“…In (Yongli et al, 2006), an application of BN is presented for the diagnosis of possible transmission faults in power systems. The main motivation presented for the use of this approach is the easiness with which relationships of cause-effect, particularly in domains with a high degree of uncertainty, can be modeled.…”

Section: Wwwintechopencommentioning

confidence: 99%

Optimization Strategies for Improving the Interpretability of Bayesian Networks: an Application in Power Systems

Rocha¹,

Cardoso²,

Santana³

et al. 2010

Bayesian Network

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Section: Wwwintechopencommentioning

confidence: 99%

Optimization Strategies for Improving the Interpretability of Bayesian Networks: an Application in Power Systems

Rocha¹,

Cardoso²,

Santana³

et al. 2010

Bayesian Network

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“…También se han empleado para el diagnóstico de fallas diferentes estructuras neuronales tales como redes Bayesianas [3], redes de funciones de base radial (RBF) [4], [5], perceptrones [4], [6], redes SOM [7], dando buenos resultados pero presentando limitaciones. Una de las limitaciones es la estructura cerrada de tipo monolítico que presentan las redes, de forma tal que cuando se aplican a sistemas eléctricos reales de gran envergadura se empieza a hacer más complicado implementarlas [8], [9], [10].…”

Section: Introductionunclassified

Fault Diagnosis of Electric Transmission Lines using Modular Neural Networks

Novelo¹,

Quiles²,

Moreno³

et al. 2016

IEEE Latin Am. Trans.

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“…T HE problem of diagnosis appears in various applications such as medical diagnosis [1], fault diagnosis in nuclear plants [2], computer networks [3], [4], and power-delivery systems [5], and decoding of messages sent through a noisy channel. In these problems, the goal is to identify the binary states X ¼ ðX 1 ; .…”

Section: Introductionmentioning

confidence: 99%

A Rank-Based Approach to Active Diagnosis

Bellala

Stanley

Bhavnani

et al. 2013

IEEE Trans. Pattern Anal. Mach. Intell.

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Abstract-The problem of active diagnosis arises in several applications such as disease diagnosis and fault diagnosis in computer networks, where the goal is to rapidly identify the binary states of a set of objects (e.g., faulty or working) by sequentially selecting, and observing, potentially noisy responses to binary valued queries. Previous work in this area chooses queries sequentially based on Information gain, and the object states are inferred by maximum a posteriori (MAP) estimation. In this work, rather than MAP estimation, we aim to rank objects according to their posterior fault probability. We propose a greedy algorithm to choose queries sequentially by maximizing the area under the ROC curve associated with the ranked list. The proposed algorithm overcomes limitations of existing work. When multiple faults may be present, the proposed algorithm does not rely on belief propagation, making it feasible for large scale networks with little loss in performance. When a single fault is present, the proposed algorithm can be implemented without knowledge of the underlying query noise distribution, making it robust to any misspecification of these noise parameters. We demonstrate the performance of the proposed algorithm through experiments on computer networks, a toxic chemical database, and synthetic datasets.

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Bayesian Networks-Based Approach for Power Systems Fault Diagnosis

Cited by 230 publications

References 6 publications

Optimization Strategies for Improving the Interpretability of Bayesian Networks: an Application in Power Systems

Optimization Strategies for Improving the Interpretability of Bayesian Networks: an Application in Power Systems

Fault Diagnosis of Electric Transmission Lines using Modular Neural Networks

A Rank-Based Approach to Active Diagnosis

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