基于数据驱动的分布式卡尔曼波器传感器故障隔离和大规模互连系统的估计.
IEEE transactions on cybernetics
|December 2, 2025
概括
本研究介绍了一种数据驱动的分布式卡尔曼波器 (DKF),用于在大型系统中检测传感器故障. 它使局部故障隔离和估计成为可能,提高了系统可靠性,而不需要全球信息.
科学领域:
- 控制系统工程 控制系统工程
- 网络系统分析 网络系统分析
- 数据驱动的故障诊断数据驱动的故障诊断
背景情况:
- 由于复杂性和未知的相互作用,大规模互连系统在传感器故障管理方面面临挑战.
- 现有的方法通常需要集中处理,限制可扩展性和稳定性.
研究的目的:
- 提出一种新的数据驱动分布式卡尔曼波器 (DKF) 方案,用于传感器故障隔离和估计.
- 为了使异质子系统的有效故障诊断能够通过定向图进行合.
- 实现完全分布式的传感器故障隔离和估计,而不需要全球系统知识.
主要方法:
- 在局部诊断单元 (LDU) 中开发数据驱动的基于DKF的残留生成器,使用局部和邻近数据.
- 在子系统和元件层面实施分布式传感器故障隔离,包括同时发生的故障.
- 基于DKF的估计器的设计,用于多个子系统传感器故障估计,使用分布式卡尔曼增强计算.
- 在不依赖整体系统信息的情况下执行局部稳定性分析.
主要成果:
- 通过局部化数据利用,成功解未知的相互作用组件.
- 实现完全分布式传感器故障隔离,使得全球隔离与关键的LDUs.
- 在多个子系统中准确估计传感器故障,使用建议的DKF估计器.
- 在电网系统上验证方案的有效性和性能.
结论:
- 拟议的数据驱动的DKF方案为大型互连系统中的传感器故障诊断提供了强大的和可扩展的解决方案.
- 分布式故障隔离和估计提高了系统的可靠性和可维护性.
- 该方法的本地处理方法确保了稳定性,并减少了通信开销.
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