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August 25, 2025ProcessesOpen Access

A Study on the Key Factors Influencing Power Grid Outage Restoration Times: A Case Study of the Jiexi Area

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Authors

JLJiajun LinRXR. XieHLHung-Yu Lin

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Overview

Case study reveals how machine learning identifies predictors of outage duration in the Jiexi area, suggesting actionable improvements.

Key Points

  • Key structural indicators like network redundancy significantly predict restoration time, influencing investment priorities.
  • Machine learning models, including Random Forest and Lasso Regression, provided positive, quantifiable insights on outages.
  • Data-driven approach leveraged outage data to reveal how regulating line loads can reduce outage duration effectively.
  • Analysis demonstrates the potential of machine learning in energy systems under constraints, supporting infrastructure improvements.

Cite This Study

Lin et al. (2025) studied this question.

synapsesocial.com/papers/68af7f397567bf4f94ff5ed0https://doi.org/10.3390/pr13092708
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