Studies On Methods For State Monitoring And Analysis Of Low-frequency Oscillation In Powersystem

The low frequency oscillation(LFO)is one of the classical problems in power system. With the expanding of power grid scale, the massive application of FACTS(Flexible AC Transmission Systems), series compensators and mass integration of renewable energy integrate, the LFO in power system emerges as a pressing issue that needs to be solve in engineering practice. In this paper new methods are proposed for state monitoring and analysis of LFO based on the measured signals.The first question in LFO state monitoring: whether the sustained LFO occurs or not should be determined in order to achieve the correct start oscillation monitoring and alarming which can not be done by the on-line oscillation analysis algorithms.These algorithms belong to identification after oscillation occurrs.In addition, start oscillation alarming can’t determine the type of oscillation and damping characteristics at the same time.Based on the above, a new method is proposed to monitor LFO based on order stopping oscillation system of Duffing oscillator. The state of order stopping oscillation system is highly sensitive to periodic perturbation while it is quite insensitive to random small perturbation. Firstly, the measured data are changed into small ones. Then small ones used as the excitation(input signal), are put into order stopping oscillation system, and then the phase trajectories of system are obtained. Thus, according to monitored clustering characteristic of trajectory,the change of system mode can be tracked. Processing and characteristics of LFO are showed using the method of graphic visualization. Parameter calculation is not necessary, and the method is immune to noise,and the logarithm distance with equilibrium point provides a method of quantitative analysis. Due to the change of phase trajectory, whether there is a steady-state LFO can be judged, and the mode and damping characteristic can also be determined correctly.LFO attractor exists in both weak damping mode and forced power oscillation’s phase trajectory, so longer oscillation type discrimination time is needed. A new method is proposed to discriminate oscillation type correctly and quickly using cascade second-order tracking-differentiator(TD). By the pretreatment, two oscillation types both become stable more quickly. Furthermore, the treatment obviously accelerate the contraction time of forced power oscillation’s phase trajectory. Time of oscillation type determined is shortened up to 10 seconds.The colored Gaussian noise of the measured LFO signal produced by low-pass filters may have a great impact on the accuracy of oscillation mode identification.This paper proposes a new method based on resonance-based sparse signal decomposition(RSSD). The LFO signal is the output of under-damped system with high-resonance property at a specific frequency, while colored Gaussian noise has not got resonance property. Complex signal can be separated by predictable resonance properties using RSSD. Firstly, the LFO signal is decomposed into a high-resonance component, a low-resonance component and a residual by RSSD. The high-resonance component is extractive LFO signal, and the residual contains most of colored Gaussian noise. Secondly, modal parameter of high-resonance component is identified by other method selected. After that, high-accuracy detection for modal parameter identification is achieved. Examples have proved the effectiveness of the method.Frequency slice wavelet transform(FSWT) is a time-frequency analysis method which is suitable for analysis of LFO signals with high-resonance properties. FSWT can cut time-frequency areas freely, so that any band component feature can be extracted. It can analyze the LFO signal from overall and detailed aspects. Furthermore, the noise in the LFO signal could influence accurate reconstruction of fine FSWT inverse transform, which may affect the accuracy of oscillation mode identification. To solve it, this paper proposes a new method based on RSSD and FSWT for oscillation time-frequency analysis. Firstly, the LFO signal is decomposed into a high-resonance component, a low-resonance component and a residual by RSSD. The high-resonance component is the de-noised LFO signal. Secondly, the high-resonance component is decomposed by FSWT and the full band of its time-frequency distribution is obtained. The 3D map expresion and dominant mode of LFO can be obtained,and the LFO dominant mode can be determined. After that,due to its energy distribution, frequency slices are chosen to get accurate analysis of time-frequency features. Through reconstructing signals in characteristic frequency slices, separation and extraction of LFO mode components are realized. Thirdly, high-accuracy detection for modal parameter identification is achieved by the Hilbert transform.