Non-equilibrium system frequency estimation method based on improved SmartDFT algorithm

A technology of system frequency and frequency estimation, which is applied in the field of frequency estimation of unbalanced systems of SmartDFT algorithm, and can solve problems such as inaccuracy

Active Publication Date: 2017-08-22
SOUTHEAST UNIV
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Problems solved by technology

[0003] Typically, the standard N-point DFT operation can achieve 2π/N resolution on the spectrum, but is inaccurate under asynchronous sampling
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  • Non-equilibrium system frequency estimation method based on improved SmartDFT algorithm
  • Non-equilibrium system frequency estimation method based on improved SmartDFT algorithm
  • Non-equilibrium system frequency estimation method based on improved SmartDFT algorithm

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specific Embodiment 1

[0048] In this part, we will use the MATLAB platform to show the simulation results of the patented algorithm. We set the sampling frequency to f s =1600, the base frequency is f 0 =50, so, in order to calculate the fundamental frequency of DFT, the sampling point of the system voltage needs to be set as N=f s / f 0 =32. At the same time, we set the window length of the estimation algorithm as L=15.

[0049] First, we evaluate the superior estimation performance of the CRPHD algorithm relative to CLS in noisy environments. The true frequency of unbalanced system voltage f=f 0 +Δf is set at 51Hz, adding noise under different SNR conditions into the system, figure 1 The mean square error of CRPHD and CLS frequency estimation algorithms are shown, as shown in the figure, the proposed CRPHD algorithm is similar to CLS in performance, but under low SNR conditions, its estimation effect will be better than that of CLS algorithm.

specific Embodiment 2

[0051] In this group of simulations, we will study the frequency tracking performance of different estimation algorithms in the case of frequency fluctuations. On the basis of the original unbalanced system, the signal undergoes a complex sinusoidal frequency modulation f(t)=50+2sin(4π(t-0.1))+sin(32π(t-0.1)) at 0.1s to 0.6s, where ω 0 (t) = 2π·f(t), and add 10dB noise to the system at the same time. As shown in Figure 2(a), both algorithms can quickly and accurately track the dynamic changes of the system frequency. In Figure 2(b), the unbalanced system adds 40dB of noise at t=0s, and the system frequency experiences a rise of 1Hz / s, then remains at 50.1Hz for 0.2s, and then experiences from 0.5s to 0.6s A 1Hz / s drop. In all cases, both algorithms accurately tracked frequency changes.

specific Embodiment 3

[0053] In the final set of simulations, we investigate the robustness of the proposed algorithm under real-world power systems. Three-phase voltage signals are recorded in 110 / 20 / 10kV substations. The frequency of the three-phase voltage system to be measured is around 50Hz. After sampling at 1kHz, the voltage amplitude is normalized according to the peak value. In Fig. 3(a), the shown voltage experienced an unbalanced state around 0.27s to 0.54s, as shown in Fig. 3(b), although there was a brief fluctuation, the two algorithms performed well in both balanced and unbalanced voltage situations Accurate frequency estimation results are obtained.

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Abstract

In a non-equilibrium system, estimation of the frequency, the amplitude and the phase of noncircular signals is a quite important nonlinear problem. According to the method, the original Smart DFT technology (SDFT) is extended so that the technology is enabled to be applied to real value sinusoidal signals and is also enabled to process complex value noncircular signals. The mean square error of the model can be reduced by applying the least square framework based on the linear prediction (LP) property between the continuous DFT fundamental components so that the improved complex value least square algorithm (CLS) can be obtained. Meanwhile, the invention also provides a complex value improved Pisarenko harmonic decomposition algorithm (CRPHD). The interference of noise can be removed by the method and accurate frequency estimation can be acquired, and the method can be effectively applied to the non-equilibrium three-phase power system containing the noise.

Description

technical field [0001] The invention relates to the technical field of unbalanced system frequency estimation, in particular to an unbalanced system frequency estimation method based on an improved SmartDFT algorithm. Background technique [0002] Estimating the frequency, amplitude, and phase of a sinusoidal signal or a complex exponential signal under additive white Gaussian noise is a very important nonlinear problem, and it has a wide range of applications in power system analysis, wireless communication, radar signal monitoring, and speech analysis. In different applications, many literatures have also proposed corresponding estimation methods, such as based on DFT algorithm, least squares algorithm, adaptive notch filter, Kalman filter and its extension, maximum a posteriori probability algorithm and sub-based Algorithms for spatial projection. [0003] Typically, the standard N-point DFT operation can obtain a resolution of 2π / N on the spectrum, but it is not accurat...

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Application Information

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IPC IPC(8): G01R23/02
CPCG01R23/02
Inventor 王开柳旭夏亦犁裴文江
Owner SOUTHEAST UNIV
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