对同步冷凝器故障预测进行LLM优化波束转换
Dongqing Zhang1, Chaofeng Zhang2, Michel Kadoch3
1DC Technical Center of State Grid Corporation of China, Beijing, China.
这项研究引入了一种用于预测超高压直流 (UHVDC) 同步电容器故障的新方法. 这种方法提高了故障检测的准确性和效率,确保了系统的可靠性.
科学领域:
- 电气工程
- 电力系统
- 人工智能
背景情况:
- 同步冷凝器是超高压直流传输系统 (UHVDC) 的关键组件.
- 早期故障预测对于防止灾难性故障和确保电网稳定性至关重要.
- 现有的故障检测方法往往缺乏复杂电力系统所需的准确性和效率.
研究的目的:
- 开发一种用于预测超高频电流同步电容器故障的创新框架.
- 提高这些关键部件的故障检测的准确性,效率和可靠性.
- 能够及时进行维护,防止系统故障.
主要方法:
- 使用波段包转换 (WPT) 来从故障信号中智能提取特征.
- 采用大型语言模型 (LLM) 进行智能功能选择,增强WPT功能.
- 采用多头注意力机制 (MHA-GRU) 来捕捉时间依赖性.
主要成果:
- 拟议的框架在分类准确性,检测时间和错误报警率方面显著超过了最先进的方法.
- 在不同的负载条件下表现出强大的稳定性.
- 在检测空气间隙异常故障方面取得了特别显著的改进.
结论:
- 开发的基于WPT和MHA-GRU的框架为UHVDC同步电容器的早期故障预测提供了可靠的解决方案.
- 智能特征提取和选择机制提高了检测性能.
- 这种方法有助于主动维护,防止小故障升级为重大故障.
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