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Related Experiment Video

Updated: Apr 16, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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A scalable distribution network risk evaluation framework via symbolic dynamics.

Kai Yuan1, Jian Liu2, Kaipei Liu3

  • 1Reliability Evaluation of Power Systems Group, School of Electrical Engineering, Wuhan University, Hubei, China.

Plos One
|March 20, 2015
PubMed
Summary

This study introduces a novel risk evaluation framework for electric power distribution networks using symbolic dynamics. The method addresses incomplete data and dynamic network changes for more accurate risk assessment.

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Last Updated: Apr 16, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Area of Science:

  • Electrical Engineering
  • Complex Systems Analysis
  • Information Theory

Background:

  • Electric power distribution networks face risks due to incomplete information and dynamic operational changes.
  • Existing risk evaluation frameworks require adaptability to specific contexts and evolving risk understanding.

Purpose of the Study:

  • To develop an adaptable risk evaluation framework for electric power distribution networks.
  • To quantitatively assess relationships between risk sub-factors and main factors using information theory.

Main Methods:

  • Utilized symbolic dynamics to abstract raw data from power distribution networks.
  • Applied Kolmogorov-Sinai entropy and Kullback-Leibler relative entropy for quantitative risk factor evaluation.
  • Developed a merging algorithm for layered risk indicators (device, structure, load, special operation).

Main Results:

  • Demonstrated the feasibility of the symbolic dynamics approach with a case study from Sanya Power Company.
  • Provided a quantitative method to evaluate risk factor relationships in dynamic distribution networks.
  • The proposed framework effectively handles incomplete information and network dynamics.

Conclusions:

  • Distribution networks are inherently exposed to various risks.
  • The dynamic topology and operating modes of distribution networks lead to probabilistic faults and consequences.