传感器增益衰减情况下电力系统动态同步的鲁棒估计方法。

ISA transactions Pub Date : 2025-01-01 Epub Date: 2024-11-14 DOI:10.1016/j.isatra.2024.10.031
Yi Wang, Jiawei Zhang, Yaoqiang Wang, Zhongwen Li, Kewen Wang, Jun Liang
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引用次数: 0

摘要

高效、准确的电力系统同步实时估算对于电力系统的安全控制和运行相当重要。然而,信号传感故障、电磁干扰、系统延迟等都会导致传感器增益衰减。为了在传感器增益衰减的情况下提供可靠的电网同步动态估算方法,本研究提出了一种能够监测和跟踪频率、电压相位角和幅值的鲁棒估算系统。首先,测量数据的随机衰减以 [0,1] 范围内的离散分布为特征。其次,建立传感器增益衰减的状态空间模型。随后,在递归估计器框架下开发了一种新颖的改进型容错扩展卡尔曼滤波器(MFTEKF)。最后,大量实验结果明确表明,所提出的 MFTEKF 能够准确监测电网的动态特性。
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Robust estimation method for power system dynamic synchronization with sensor gain degradation.

Efficient and accurate real-time estimation of power system synchronization is quite important for its safety control and operation. However, signal sensing failure, electromagnetic interference, system delay, etc., will cause the sensor gain degradation. To furnish a dependable method for dynamic estimation in power grid synchronization amid sensor gain degradation, this research presents a robust estimation system capable of monitoring and tracking the frequency, voltage phase angles, and magnitudes. Firstly, the random degradation of measurement data is characterized by a discrete distribution within the range [0,1]. Secondly, the state space model of sensor gain degradation is established. Subsequently, a novel modified fault-tolerant extended Kalman filter (MFTEKF) is developed under the recursive estimator framework. Finally, extensive experimental results definitively demonstrate that the proposed MFTEKF can accurately monitor the dynamic characteristics of the power grid.

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