Machine‐learning‐based reliability evaluation framework for power distribution networks

Li, Gengfeng; Huang, Yidong; Bie, Zhaohong; Ding, Tao

doi:10.1049/iet-gtd.2019.1520

Cited by 21 publications

(8 citation statements)

References 23 publications

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“…In order to compare the evaluation accuracy of four methods, the success failure type, exponential type system and hybrid system are selected as the research objects to compare and analyze the evaluation accuracy of the second-order moment equivalent method, RLS method, ME method and Chebyshev expansion method. In the calculation example, Chebyshev expansion method and RLS method calculate the first eight moments, and ME method selects the first four order moments of the system for calculation [12].…”

Section: Accuracy Comparison Of Various Bayes Methodsmentioning

confidence: 99%

A study on Bayesian method for reliability evaluation of small sample size aviation support systems

Wang¹,

Cheng²

2022

J. Phys.: Conf. Ser.

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In order to realize the reliability evaluation of small sample size aviation support system with known prior information, the Bayes methods of system reliability evaluation are given by using the second-order moment equivalent method, relative least squares method (RLS method), maximum entropy method (ME method) and Chebyshev polynomial method respectively, and the accuracies of the above Bayes synthesis methods are compared and analysed through an example. The results show that the ME method and RLS method have higher evaluation accuracy than the second-order moment equivalent method and Chebyshev expansion method. Finally, ME method and RLS method are applied to the reliability evaluation of aviation support system, and good evaluation results are obtained. At the same time, it provides a valuable reference for the reliability evaluation of complex system with small sample size in engineering practice.

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Section: Accuracy Comparison Of Various Bayes Methodsmentioning

confidence: 99%

A study on Bayesian method for reliability evaluation of small sample size aviation support systems

Wang¹,

Cheng²

2022

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

show abstract

“…Then, the chronological state transition process for all components can be obtained. On this basis, the system state consists of the state of all components that can be determined according to the sequence of component state changes [32]. The scenario generation algorithm is shown in Algorithm 1.…”

Section: Scenario Generation Algorithmmentioning

confidence: 99%

“…normal operation and fault state) model is used to depict the state transition of distribution system components, for example, transformers, overhead lines, cables, isolating switches, fuses etc. [32], as shown in Figure 3, where 𝜆 represents the component failure rate and 𝜇 represents the component failure repaired rate. The component state is represented by a binary value (0 for fault state and 1 for normal operation state).…”

Section: Component State and Supply-demand Modellingmentioning

confidence: 99%

Resilience assessment of active distribution systems considering microgrid formation based on grid‐edge DERs

Huang

Rong

et al. 2023

IET Generation Trans & Dist

Self Cite

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Distributed energy resources (DERs) provide flexible load restoration strategies, which can effectively enhance the resilience of active distribution systems (ADSs). Whereas, the widespread DERs in ADSs complicates the supply‐demand relationship and make the system resilience difficult to access. Therefore, this paper proposes a simulation‐based resilience assessment algorithm of ADSs considering the microgrid formation based on grid‐edge DERs. Microgrid formation is used to depict the resilience gain of grid‐edge DERs on ADSs. Specifically, a resilience assessment framework for ADSs is firstly proposed, where the uncertainty of component state and supply‐demand is modelled based on probability statistics. Then the mixed integer linear programming is used to search for optimal load restoration strategies with microgrid formation. On this basis, a set of resilience indices are defined to quantitatively analyse the resilience of ADSs, and a resilience assessment algorithm with uncertainty scenario generation is proposed to obtain these indices. Furthermore, extensive numerical results based on a modified IEEE 123‐bus feeder validate the effectiveness of the proposed method.

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“…transformers, overhead lines, cables, isolating switches, fuses etc. [26], as shown in Fig. 3, where λ represents the component failure rate and µ represents the component failure repaired rate.…”

Section: Component State and Supply-demand Modellingmentioning

confidence: 99%

“…Then, the chronological state transition process for all components can be obtained. On this basis, the system state consists of the state of all components that can be determined according to the sequence of component state changes [26]. The scenario generation algorithm is shown in Algorithm 1.…”

Section: Scenario Generation Algorithmmentioning

confidence: 99%

Resilience Assessment of Active Distribution Systems Considering Microgrid Formation based on Grid-Edge DERs

Huang

Rong

et al. 2023

Preprint

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Distributed energy resources (DERs) provide flexible load restoration strategies, which can effectively enhance the resilience of active distribution systems (ADSs). Whereas, the widespread DERs in ADSs complicate the supply-demand relationship and make the system resilience difficult to access. Therefore, this paper proposes a simulation-based resilience assessment algorithm of ADSs considering the microgrid formation based on grid-edge DERs. Microgrid formation is used to depict the resilience gain of grid-edge DERs on ADSs. Specifically, a resilience assessment framework for ADSs is firstly proposed, where the uncertainty of component state and supply-demand is modelled based on probability statistics. Then the mixed integer linear programming is used to search for optimal load restoration strategies with microgrid formation. On this basis, a set of resilience indices are defined to quantitatively analyse the resilience of ADSs, and a resilience assessment algorithm with uncertainty scenario generation is proposed to obtain these indices. Furthermore, extensive numerical results based on a modified IEEE 123-bus feeder validate the effectiveness of our proposed method.

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Machine‐learning‐based reliability evaluation framework for power distribution networks

Cited by 21 publications

References 23 publications

A study on Bayesian method for reliability evaluation of small sample size aviation support systems

A study on Bayesian method for reliability evaluation of small sample size aviation support systems

Resilience assessment of active distribution systems considering microgrid formation based on grid‐edge DERs

Resilience Assessment of Active Distribution Systems Considering Microgrid Formation based on Grid-Edge DERs

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