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<p>This paper proposes a risk assessment model based on Stacking ensemble learning and Convolutional Neural Network (CNN) for power systems with high-penetration renewable energy integration. By constructing wind farm output scenarios for the IEEE 39-bus system, the impact of renewable energy uncertainty on risk identification is systematically analyzed. Experimental results demonstrate that the model achieves optimal performance under 30% renewable energy penetration (accuracy rate: 98.01%, missed detection rate: 2.04%), significantly outperforming single CNN models. The study provides reliable decision-making support for power systems with diverse generation structures.</p>

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Risk identification model for power enterprises based on convolutional neural network

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月28日

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  • Wei Pan,
  • Fengwei Liu

摘要

This paper proposes a risk assessment model based on Stacking ensemble learning and Convolutional Neural Network (CNN) for power systems with high-penetration renewable energy integration. By constructing wind farm output scenarios for the IEEE 39-bus system, the impact of renewable energy uncertainty on risk identification is systematically analyzed. Experimental results demonstrate that the model achieves optimal performance under 30% renewable energy penetration (accuracy rate: 98.01%, missed detection rate: 2.04%), significantly outperforming single CNN models. The study provides reliable decision-making support for power systems with diverse generation structures.