Machine Learning Based on Bayes Networks to Predict the Cascading Failure Propagation

Pi, Renjian; Cai, Ye; Li, Yong

doi:10.1109/access.2018.2858838

Cited by 31 publications

(10 citation statements)

References 22 publications

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“…A stochastic cascading failure model based on the MAS approach is proposed in [120] considering interdependencies between physical and cyber networks. In addition to model-based approaches, datadriven machine learning techniques have been also applied to study cascading failures [121,122].…”

Section: Modelling Of Blackouts and Cascading Failuresmentioning

confidence: 99%

Power Grids as Complex Networks: Resilience and Reliability Analysis

Amani

Jalili

2021

IEEE Access

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Power grids are cyber-physical systems and can be modelled as network systems where individual units (generators, busbars and loads) are interconnected through physical and cyber links. Network components (nodes/edges) may undergo intentional and/or random failures. In catastrophic cases, a failure initiating from a small set of these components can quickly propagate through the whole network, leading to a cascade of failures that might force a deep whole-grid blackout. Often network components have different vitality and protecting some is more critical than others. This manuscript aims to provide a focused overview of modelling power grids as complex networks and their resilience and reliability analysis. We also perform a critical review of vitality metrics and their precision in power grid resilience analysis. The review is accompanied by some simulations on benchmark and real power grids to show the applicability of these concepts in studying resilience.

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Section: Modelling Of Blackouts and Cascading Failuresmentioning

confidence: 99%

Power Grids as Complex Networks: Resilience and Reliability Analysis

Amani

Jalili

2021

IEEE Access

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“…The Bayesian algorithm (Bayes' net) is used via WEKA on the post-operative patients' dataset of 50 records [21], [22]. In the present paper, the numbers of correctly and incorrectly classified instances are focused upon.…”

Section: Bayes Networkmentioning

confidence: 99%

Redesigning Post-Operative Processes Using Data Mining Classification Techniques

Alwattar¹

2021

IJSECS

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Data mining classification models are developed and investigated in this paper. These models are adopted to develop and redesign several business processes based on post-operative data. Post-operative data were collected and used via the Waikato Environment for Knowledge Analysis (WEKA), to investigate the factors influencing patients’ admission after surgery and compare the developed DM classification models. The results reveal that each implemented DM technique entails different attributes affecting patients’ post-surgery admission status. The comparison suggests that neural networks outperform other classification techniques. Further, the optimal number of beds required to accommodate post-operative patients is investigated. The simulation was conducted using queuing theory software to compute the expected number of beds required to achieve zero waiting time. The results indicate that the number of beds required to accommodate post-surgery patients waiting in the queue is the length of 1, which means that one bed will be available due to patient discharge.

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“…The system full state space obtained in the foregoing and the state transition probability under different conditions are combined in Eq. (11). At the same time, in order to simplify the system state, let X (x, y) denote the asymptotic probability when the system state returns to the absorption state, Y (x, y) represents the asymptotic probability when the system state is still in the transition state, and the following recursive equation is obtained by derivation:…”

Section: ) State Transition Probability Of Dual Network Interactionmentioning

confidence: 99%

Power Cyber-Physical System Risk Area Prediction Using Dependent Markov Chain and Improved Grey Wolf Optimization

Xie

Liu

et al. 2020

IEEE Access

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Existing power cyber-physical system (CPS) risk prediction results are inaccurate as they fail to reflect the actual physical characteristics of the components and the specific operational status. A new method based on dependent Markov chain for power CPS risk area prediction is proposed in this paper. The load and constraints of the non-uniform power CPS coupling network are first characterized, and can be utilized as a node state judgment standard. Considering the component node isomerism and interdependence between the coupled networks, a power CPS risk regional prediction model based on dependent Markov chain is then constructed. A cross-adaptive gray wolf optimization algorithm improved by adaptive position adjustment strategy and cross-optimal solution strategy is subsequently developed to optimize the prediction model. Simulation results using the IEEE 39-BA 110 test system verify the effectiveness and superiority of the proposed method.INDEX TERMS Cyber-physical system, Markov chain, risk region prediction, cross-adaptive grey wolf optimization.

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Machine Learning Based on Bayes Networks to Predict the Cascading Failure Propagation

Cited by 31 publications

References 22 publications

Power Grids as Complex Networks: Resilience and Reliability Analysis

Power Grids as Complex Networks: Resilience and Reliability Analysis

Redesigning Post-Operative Processes Using Data Mining Classification Techniques

Power Cyber-Physical System Risk Area Prediction Using Dependent Markov Chain and Improved Grey Wolf Optimization

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