二次操作风险评估方法 集成图形卷积网络和语义嵌入式
Pengyu Zhu1, Youwei Li1, Peidong Xu2
1State Grid Jiangsu Electric Power Co., Ltd., Huaian Power Supply Branch, Huaian 223000, China.
Sensors (Basel, Switzerland)
|April 28, 2025
概括
本研究介绍了一种用于电力行业二次运行风险评估的混合模型,该模型结合了图形卷积网络 (GCN) 和语义嵌入. 这种新的方法提高了确定运营风险的准确性和效率.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 风险管理 风险管理
背景情况:
- 二次运行风险评估对于电力行业的安全至关重要.
- 传统方法难以处理非结构化数据和复杂的设备关系.
- 专家判断的限制妨碍了有效和准确的风险评估.
研究的目的:
- 开发一个混合模型,集成图形卷积网络 (GCN) 和语义嵌入技术,用于电力行业的二级运行风险评估.
- 克服传统方法在处理非结构化数据和复杂设备关系方面的局限性.
- 提高运营场景中风险评估的效率和准确性.
主要方法:
- 为电力行业构建一个特定领域的知识图.
- 使用GCN从知识图中提取结构信息.
- 微调RoBERTa预训练模型以生成文本数据的语义嵌入.
- 实施混合相似度测量,结合K-means集群和多节点加权评估.
主要成果:
- 建议的混合模型在风险评估中明显优于传统方法.
- 关键性能指标,包括准确性,回忆力和F1分数,显示了显著的改善.
- 该模型在电力行业内的二次运行场景中展示了实际应用价值.
结论:
- 混合GCN和语义嵌入模型为电力行业的二级运行风险评估提供了优越的方法.
- 这种方法有效地解决了非结构化数据和复杂设备相互依赖所带来的挑战.
- 经过验证的性能证实了该模型在提高运行安全方面的实用性.
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