论文标题
通过双峰高斯混合物测量噪声的分散动态估计
Decentralized Dynamic State Estimation with Bimodal Gaussian Mixture Measurement Noise
论文作者
论文摘要
本文提出了一种分散的动态状态估计（DSE）算法，该算法具有双峰高斯混合物测量噪声。分散的DSE使用集成Kalman滤清器（ENKF）配制，然后与无味的Kalman滤光片（UKF）进行比较。使用实时数字模拟器（RTDS）中模拟的WSCC 9-BUS系统验证了提出的框架的性能。从RTDS运行时环境到MATLAB进行实时可视化和估计，实时流出了相量测量单元（PMU）测量值。为了考虑流中的数据损坏方案，将包含两个具有不同权重和方差的正常分布的双模式分布添加到测量值中作为噪声组件。然后，通过计算实际状态和估计状态之间的均方纠纷（MSE）来比较UKF和ENKF的性能。
This paper proposes a decentralized dynamic state estimation (DSE) algorithm with bimodal Gaussian mixture measurement noise. The decentralized DSE is formulated using the Ensemble Kalman Filter (EnKF) and then compared with the unscented Kalman filter (UKF). The performance of the proposed framework is verified using the WSCC 9-bus system simulated in the Real Time Digital Simulator (RTDS). The phasor measurement unit (PMU) measurements are streamed in real-time from the RTDS runtime environment to MATLAB for real-time visualization and estimation. To consider the data corruption scenario in the streaming process, a bi-modal distribution containing two normal distributions with different weights and variances are added to the measurements as the noise component. The performances of both UKF and EnKF are then compared for by calculating the mean-squared-errors (MSEs) between the actual and estimated states.
