A method for the identification of low frequency oscillation modes in power systems subjected to noise. (15th November 2017)

• Record Type: Journal Article
• Authors: Jin, Tao; Liu, Siyi; Flesch, Rodolfo C.C.; Su, Wencong
• Is Part Of: Applied energy. Volume 206(2017)
• Page Start: 1379
• Page End: 1392
• Publication Date: 2017-11-15
• Subjects: Low frequency oscillation -- Mode identification -- PMU -- Accuracy of fitting -- Power system; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2017.09.123
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: A unified two-step methodology for the mode characterization is proposed of low frequency oscillations based on WAMS signals. The proposed method can recognize power low frequency oscillation with high accuracy. Noise in distributed energy generation systems has little influence to the proposed identification algorithm. Abstract: The high penetration of renewable energy sources and the consequent integration of such distributed energy generation systems into the grid have significantly increased the interaction between power sources, which can result in low frequency oscillations. Different control techniques are proposed in literature to suppress low frequency oscillations, but the first step to attenuate such oscillations is to characterize them in terms of their dominant modes. This paper combines different results from literature to propose a unified two-step methodology for the mode characterization of low frequency oscillations based on the signals acquired by wide area measurement systems. Since the measured signals typically contain noise, the first stage of the proposed method uses the basis pursuit denoising method combined with a Tunable Q-factor wavelet transform to increase the signal-to-noise ratio and improve the identification results. In a second stage, an improved version of the matrix pencil algorithm, as proposed in this paper, is used to identify the parameters of low frequency oscillation dominant modes. Both simulation and experimental results show that the proposed method has better characterization accuracy than traditional methods, especially when Gaussian noise is considered in the measurements. In addition, the processing time has proven to be reasonable for online identification and characterization of low frequency oscillations.