基于KSQDC-ADEAD在复杂操作条件下的水力发电机组异常检测方法
Tongqiang Yi1,2, Xiaowu Zhao3, Yongjie Shi1,2
1Key Laboratory of Hydraulic Machinery Transients, Ministry of Education, Wuhan University, Wuhan 430072, China.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
本研究引入了一种用于检测水力发电机组异常的新方法,提高运行安全和电力系统稳定性. KSQDC-ADEAD算法提高了识别复杂操作条件和预测维护需求的准确性.
科学领域:
- 工程 工程师 工程师 工程师
- 计算机科学 计算机科学
- 能源系统 能源系统
背景情况:
- 水力发电机组对于清洁能源和电力系统安全至关重要.
- 在复杂的操作条件下检测异常是一个重大的技术挑战.
- 现有的方法与水电机组运行固有的非线性作斗争.
研究的目的:
- 为水力发电机组开发一种先进的异常检测方法.
- 改进复杂运行条件的识别.
- 提高水电基础设施预测性维护的准确性和可靠性.
主要方法:
- K-意味着为操作条件识别进行种子二次区分分类集群 (KSQDC).
- 适应密度感知集体异常检测 (ADEAD) 算法用于改进检测.
- 整合K-平均分区与二次差别分析用于非线性边界检测.
- 在ADEAD内结合学习和密度适应策略,以获得稳健性.
主要成果:
- 在条件识别方面,KSQDC实现了0.64的轮系数,超过了传统方法.
- 在关键监测点 (分数为0.30,0.34,0.23) 上,KSQDC-ADEAD在异常检测方面表现强.
- 提出的方法显著提高了使用真实世界的操作数据检测异常的准确性和可靠性.
结论:
- KSQDC-ADEAD方法为水力发电机组状态监测提供了一个系统的解决方案.
- 这种方法提高了清洁能源系统的安全性和稳定性.
- 这些发现支持了水电设施预测性维护方面的进展.
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