Satheesh Rahul, Rajan Sunitha
Amrita School of Artificial Intelligence, Coimbatore, Amrita Vishwa Vidyapeetham, India.
Department of Electrical Engineering, National Institute of Technology Calicut, Kerala, India.
ISA Trans. 2023 Nov;142:399-408. doi: 10.1016/j.isatra.2023.08.012. Epub 2023 Aug 14.
Estimation of electromechanical mode properties is crucial in modern power systems for providing the operators with an adequate indication of the stress in the system. Measurement-based approaches use signal processing algorithms for mode identification and parameter estimation. This paper presents a novel framework for the assessment of low-frequency oscillation modes using real-world synchrophasor data with minimum computational effort. A nonstationary approach known as Time-Varying Filter based Empirical Mode Decomposition (TVF-EMD) technique is used to identify the dominant low-frequency modes present in the ambient PMU data. The combination of TVF-EMD with Teager Kaiser Energy Operator (TKEO) precisely estimates the instantaneous mode parameters, such as frequency, amplitude, and damping ratio. The efficacy of the proposed approach is demonstrated by applying it in a synthetic signal, simulated data of a standard IEEE test system, and in real-world PMU data of the Indian power grid. The proposed method is compared with the existing methodologies and the observations reveal that the proposed method has robust performance in estimating the instantaneous mode features in the power system with less computational complexities.
在现代电力系统中，机电模式特性的估计对于向运行人员充分指示系统中的应力至关重要。基于测量的方法使用信号处理算法进行模式识别和参数估计。本文提出了一种新颖的框架，用于使用实际同步相量数据以最小的计算量评估低频振荡模式。一种称为基于时变滤波器的经验模态分解（TVF-EMD）技术的非平稳方法用于识别环境相量测量单元（PMU）数据中存在的主导低频模式。TVF-EMD与Teager-Kaiser能量算子（TKEO）的结合精确估计了瞬时模式参数，如频率、幅度和阻尼比。通过将其应用于合成信号、标准IEEE测试系统的模拟数据以及印度电网的实际PMU数据，证明了所提方法的有效性。将所提方法与现有方法进行了比较，观察结果表明，所提方法在估计电力系统中的瞬时模式特征方面具有鲁棒性能，且计算复杂度较低。
