Electromechanical oscillations between interconnected generators are considered a major threat to the secure operation of power systems. Therefore, oscillation monitoring systems in real-time are of critical importance to detect the danger of poorly damped oscillations. For the detection and analysis of the oscillations, high-temporal-resolution measurements are required according to the Nyquist theorem. This paper proposes a novel algorithm for the identification of electromechanical oscillations using low-sampled data such as supervisory control and data acquisition (SCADA) measurements. The lack of temporal resolution of the data is compensated by using low-sampled data sets at multiple different locations. At a target location, a high-sampled data-signal can be reconstructed using mode shape information obtained from model-based modal analysis. The variable projection method is then used to detect oscillations and estimate oscillation components including frequency and damping ratio. Case studies based on practical Korean power systems are presented to evaluate the performance of the proposed method. Simulation results show that the proposed method can detect and identify electromechanical oscillations with low-sampled data.

中文翻译：

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使用模态分析方法对低采样数据的电力系统机电振荡进行检测和分析

互连发电机之间的机电振荡被认为是对电力系统安全运行的主要威胁。因此，实时的振荡监测系统对于检测低阻尼振荡的危险至关重要。为了检测和分析振荡，需要根据奈奎斯特定理进行高时间分辨率的测量。本文提出了一种使用低采样数据（如监督控制和数据采集 (SCADA) 测量）识别机电振荡的新算法。通过在多个不同位置使用低采样数据集来补偿数据时间分辨率的缺乏。在目标位置，可以使用从基于模型的模态分析中获得的模态形状信息来重建高采样数据信号。然后使用可变投影方法来检测振荡并估计包括频率和阻尼比在内的振荡分量。介绍了基于实际韩国电力系统的案例研究，以评估所提出方法的性能。仿真结果表明，所提出的方法可以检测和识别低采样数据的机电振荡。