Abstract
The paper presents a multiaspect analysis of multivalues and the broadband nature of system oscillation. By analyzing the ambient signal caused by random small disturbances during the normal operation of interconnected power grids, many system operation characteristics can be obtained. The traditional signal processing method cannot extract the information from ambient signals effectively. Aiming at the problem of broadband oscillation mode superposition and the difficulty of extracting information from ambient signals, an iterative adaptive variational mode decomposition (IA-VMD) method is proposed based on frequency domain analysis and signal energy. Additionally, the IA-VMD method, combined with a bandpass filter and the Prony algorithm, is used to realize the modal identification of broadband oscillation and ambient signals. Simulation experiments show that the IA-VMD method has good adaptability, antinoise characteristics, and a certain significant engineering application value as well.
Highlights
In recent years, with the development of electric power technology and the scale of power grids, power exchanges across regions and over long distances have become more common
The analysis methods for low-frequency oscillation (LFO) of power systems mainly include offline analysis methods based on mathematical models of power systems [3,4] and signal analysis methods based on measured signals
A wide area measurement system (WAMS) has been widely applied to power systems, which provides the foundation for signal analysis methods
Summary
With the development of electric power technology and the scale of power grids, power exchanges across regions and over long distances have become more common. Ambient signals are generally caused by normal electrical operation during the operation of a power grid, such as load switching and line parameter adjustment These signals widely exist in measurement signals, contain rich characteristic information of system operation, and can be used for early warning of oscillation. In [15], the basis pursuit denoising method, combined with a tuneable Q-factor wavelet transform, was proposed to increase the signal-to-noise ratio, and an improved version of the matrix pencil algorithm was used to identify the parameters of LFO. The Prony algorithm, the most widely used method in many oscillation mode parameter extraction algorithms, can fit the actual LFO signals by a linear combination of multiorder exponential functions.
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