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A scalable distribution network risk evaluation framework via symbolic dynamics.

Yuan K, Liu J, Liu K, Tan T - PLoS ONE (2015)

Bottom Line: Finally, an example from the Sanya Power Company is given to demonstrate the feasibility of the proposed method.Distribution networks are exposed and can be affected by many things.The topology and the operating mode of a distribution network are dynamic, so the faults and their consequences are probabilistic.

View Article: PubMed Central - PubMed

Affiliation: Reliability Evaluation of Power Systems Group, School of Electrical Engineering, Wuhan University, Hubei, China.

ABSTRACT

Background: Evaluations of electric power distribution network risks must address the problems of incomplete information and changing dynamics. A risk evaluation framework should be adaptable to a specific situation and an evolving understanding of risk.

Methods: This study investigates the use of symbolic dynamics to abstract raw data. After introducing symbolic dynamics operators, Kolmogorov-Sinai entropy and Kullback-Leibler relative entropy are used to quantitatively evaluate relationships between risk sub-factors and main factors. For layered risk indicators, where the factors are categorized into four main factors - device, structure, load and special operation - a merging algorithm using operators to calculate the risk factors is discussed. Finally, an example from the Sanya Power Company is given to demonstrate the feasibility of the proposed method.

Conclusion: Distribution networks are exposed and can be affected by many things. The topology and the operating mode of a distribution network are dynamic, so the faults and their consequences are probabilistic.

No MeSH data available.


High-voltage distribution network.
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pone.0112940.g004: High-voltage distribution network.

Mentions: Fig. 4 shows the high-voltage distribution network in Sanya. Certain substations or lines that are not under the SPC’s administration are included to simplify the calculations.


A scalable distribution network risk evaluation framework via symbolic dynamics.

Yuan K, Liu J, Liu K, Tan T - PLoS ONE (2015)

High-voltage distribution network.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4366100&req=5

pone.0112940.g004: High-voltage distribution network.
Mentions: Fig. 4 shows the high-voltage distribution network in Sanya. Certain substations or lines that are not under the SPC’s administration are included to simplify the calculations.

Bottom Line: Finally, an example from the Sanya Power Company is given to demonstrate the feasibility of the proposed method.Distribution networks are exposed and can be affected by many things.The topology and the operating mode of a distribution network are dynamic, so the faults and their consequences are probabilistic.

View Article: PubMed Central - PubMed

Affiliation: Reliability Evaluation of Power Systems Group, School of Electrical Engineering, Wuhan University, Hubei, China.

ABSTRACT

Background: Evaluations of electric power distribution network risks must address the problems of incomplete information and changing dynamics. A risk evaluation framework should be adaptable to a specific situation and an evolving understanding of risk.

Methods: This study investigates the use of symbolic dynamics to abstract raw data. After introducing symbolic dynamics operators, Kolmogorov-Sinai entropy and Kullback-Leibler relative entropy are used to quantitatively evaluate relationships between risk sub-factors and main factors. For layered risk indicators, where the factors are categorized into four main factors - device, structure, load and special operation - a merging algorithm using operators to calculate the risk factors is discussed. Finally, an example from the Sanya Power Company is given to demonstrate the feasibility of the proposed method.

Conclusion: Distribution networks are exposed and can be affected by many things. The topology and the operating mode of a distribution network are dynamic, so the faults and their consequences are probabilistic.

No MeSH data available.