Extraction method of low frequency oscillation characteristic parameters of power system based on meemd-prony joint algorithm
A technology of low frequency oscillation and characteristic parameters, applied in the measurement of electricity, electrical components, circuit devices, etc., it can solve the problems of difficult threshold estimation, difficulty in obtaining input signal characteristics, slow convergence process, etc., and achieve the effect of suppressing modal confusion.
Active Publication Date: 2022-07-12
FUJIAN UNIV OF TECH
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However, Prony analysis is very sensitive to noise, and there must be noise interference in the actual power system. Some studies have shown that only when the signal-to-noise ratio is not lower than 50-60dB, the Prony algorithm can be used to obtain ideal identification results.
At present, there are Kalman filtering, adaptive filtering and filtering methods based on wavelet transform that are widely used by scholars to filter out noise. However, Kalman filtering needs to determine the system model in advance, and it is difficult to obtain the actual input signal characteristics; , but the convergence process is slow, and there is a contradiction between the step size, convergence speed, and misalignment; although the wavelet denoising method has a small amount of calculation, it has the limitation of difficult threshold estimation
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[0040] In order to make the purposes, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present application.
[0041] In recent years, an improved ensemble empirical mode decomposition (modifies ensemble empirical modedecomposition, MEEMD) has been proposed, which is to add pairs of white noise to make the signal extreme point distribution more uniform, and then use the empirical mode decomposition method (EMD) and The randomness of the signal is detected by permutation entropy, which can effectively improve the phenomenon that EMD is prone to modal aliasing. The MEEMD method has been effectively applied in the fields of transportation and medicine, but this emerging method has not been applied to the low frequency oscillation of the power system.
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The invention discloses a method for extracting low-frequency oscillation characteristic parameters of a power system based on the MEEMD-Prony joint algorithm. First, the measurement signal is decomposed by MEEMD to obtain a series of eigenmode functions (IMFs), and then the IMF components except the residual residual are processed The reconstruction achieves the purpose of noise reduction. Finally, the reconstructed signal is used as a new input signal to perform Prony analysis to extract the modal characteristics of the low frequency oscillation. It is verified by simulation that the joint method proposed by the present invention can effectively suppress modal confusion and completely and accurately identify the oscillation mode, and has certain anti-noise and superiority compared with other methods.
Description
technical field [0001] The invention relates to the technical field of power electronics, in particular to a method for extracting low-frequency oscillation characteristic parameters of a power system based on a MEEMD-Prony joint algorithm. Background technique [0002] Oscillation is one of the main characteristics of power system operation. Small disturbances such as load changes may trigger system oscillations. In this case, if a large power failure such as short circuit and disconnection occurs, an increased oscillation accident is more likely to occur. With further deterioration, the system will eventually break down, leading to larger accidents and even life-safety issues. The frequent occurrence of low-frequency oscillations in power systems has become one of the problems affecting the safe and stable operation of power grids. Therefore, it is of great significance to timely and accurately extract the characteristic parameters of low-frequency oscillation signals. ...
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IPC IPC(8): G06F17/40H02J3/00G01R31/00
CPCY02E60/00
Inventor 张程刘佳静林谷青匡宇邱炳林
Owner FUJIAN UNIV OF TECH