进化分数顺序扩展卡尔曼过器的网络物理动力系统的进化分数顺序扩展卡尔曼过器
IEEE transactions on cybernetics
|March 3, 2025
概括
本研究介绍了用于网络物理电力系统的增强分数顺序扩展卡尔曼波器 (EFOEKF). 这种新的方法提高了状态估计的准确性,在模拟中表现优于传统过器.
科学领域:
- 电气工程 电气工程
- 控制系统 控制系统
- 应用数学 应用数学 应用数学
背景情况:
- 状态估计对于网络物理电力系统 (CPPS) 的优化,控制和安全至关重要.
- 分数微积分计算提供了比传统的整数微积分更准确的物理现象建模.
- 现有的方法在确定分数顺序和处理分数顺序系统中的估计困难方面面临挑战.
研究的目的:
- 为CPPS状态估计提出一种新的分数顺序扩展卡尔曼波器 (FOEKF).
- 通过整合遗传算法和深度集合学习来进行参数优化,开发一种进化的FOEKF (EFOEKF).
- 加强EFOEKF以在网络攻击等不利条件下提高性能.
主要方法:
- 使用微分微积分计算建模电力系统,以增强物理现象描述.
- 采用深度集体学习来设计健身函数和优化分数顺序的遗传算法.
- 介绍EFOEKF作为分数顺序电力系统的估计器.
- 开发一个增强的EFOEKF,以适应指数加权函数来解决糟糕的数据场景.
主要成果:
- 与标准的扩展卡尔曼波器 (EKF) 和基本的FOEKF相比,拟的EFOEKF显示出更高的性能.
- 增强的EFOEKF显示状态估计的准确性有所提高,特别是在有坏数据的场景下.
- 对四个不同的IEEE总线系统的评估证实了拟议方法的有效性.
结论:
- 使用进化和深度学习技术优化的新型EFOEKF,为分数顺序CPPSs的状态估计提供了强大的解决方案.
- 增强的EFOEKF有效地减轻了不良数据的影响,提高了系统可靠性.
- 拟议的方法为CPPS状态估计的平均绝对误差提供了显著的改进.
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