一种基于证据理论的多模型融合方法,用于分布线的故障诊断
Wanglong Wan1,2, Yuyu Yue3, Wei Liu1
1College of Information Science and Engineering, Hunan University, Changsha, China.
Scientific reports
|December 14, 2025
概括
这项研究引入了一种新的框架,用于使用多维特征和证据理论来诊断配电线故障. 该方法提高了识别各种故障类型的准确性和效率,即使现实数据有限.
科学领域:
- 电气工程 电气工程
- 电力系统 电力系统
- 人工智能的人工智能
背景情况:
- 分配线故障诊断对于电力系统可靠性至关重要.
- 机器学习 (ML) 是有前途的,但在有限的数据和现实世界的噪音下面临着挑战.
- 现有的ML方法通常依赖于单个故障特征,限制准确性.
研究的目的:
- 开发一个系统的框架,用于准确和高效的配电线路故障诊断.
- 解决当前ML方法在处理复杂故障场景和数据稀缺方面的局限性.
- 通过先进的故障识别来提高电力系统的运行卓越性.
主要方法:
- 从故障数据中提取多维时间频域特征.
- 德姆斯特 - 沙弗证据理论用于多模型融合的应用.
- 为复杂的故障场景开发一个系统诊断框架.
主要成果:
- 拟议的方法在识别四种特定故障类型时,平均准确率为74.6%.
- 与传统方法相比,在现实条件下证明了更高的准确性和效率.
- 通过有限的样本和杂的数据,成功地应对了挑战.
结论:
- 基于Dempster-Shafer证据理论的多模型融合方法显著提高了分布线故障识别.
- 系统框架为复杂的故障诊断场景提供了强大的解决方案.
- 这一进步有助于提高电力系统的可靠性和卓越运行.
相关概念视频
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Calculating subtransient fault currents for three-phase faults in an N-bus power system involves using the positive-sequence network. When a three-phase short circuit occurs at a specific bus, the analysis uses the superposition method to evaluate two separate circuits.
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