先进的AI驱动技术用于高压电力系统中故障和短暂分析
Abdul Aziz1,2, Muhammad Zain Yousaf3,4,5, Feng Renhai6
1CECOS University of IT and Emerging Sciences Peshawar Kpk Pakistan, Peshawar, Pakistan.
Scientific reports
|February 15, 2025
概括
本研究模拟了一个500kV变电站,使用ETAP软件分析故障. 人工智能,特别是Catboost,准确地分类正常和故障条件,提高了电力系统的可靠性.
科学领域:
- 电气工程 电气工程
- 电力系统分析 分析 分析
- 在电力系统中的人工智能
背景情况:
- 电站对于电力系统运行至关重要,但面临着热应力和辐射等危险.
- 变电站组件 (电缆,断路器,保险丝) 的故障会降低可靠性,导致传输损失.
- 优化分布式发电 (DG) 是提高电压配置的必要条件.
研究的目的:
- 使用ETAP软件对500kV变电站进行全面的模拟.
- 分析变电站内部的故障和短暂事件及其对系统性能的影响.
- 使用人工智能技术优化变电站参数,以提高可靠性.
主要方法:
- 利用电气过渡分析仪程序 (ETAP) 进行500kV变电站的详细负载流量和短路分析.
- 使用18个月的实时数据进行多次模拟,包括正常和故障条件.
- 应用人工智能 (AI) 技术,包括Catboost,支持矢量机 (SVM) 和后勤回归,用于故障分类.
主要成果:
- 在分类正常与故障条件时,Catboost实现了98%的准确性,超过了SVM (96%) 和后勤回归 (93%).
- 对于识别特定的故障类型 (线对线,线对地面,双线对地面),Catboost达到97%的准确性,其次是SVM (95%) 和后勤回归 (92%).
- 人工智能模型在分析变电站运行状态方面表现出强的表现.
结论:
- 人工智能技术,特别是Catboost,对于在电力变电站中准确检测和分类故障是非常有效的.
- 使用ETAP的模拟提供了对不同条件下的变电站行为有价值的见解.
- 这些发现有助于通过先进的分析和人工智能提高电力系统的可靠性和效率.
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