Method for acquiring parameters of dynamic signal

Patent metadata

Field	Value
Publication number	US-2016274155-A1
Application number	US-201414412675-A
Country	US
Kind code	A1
Filing date	Mar 4, 2014
Priority date	Dec 16, 2013
Publication date	Sep 22, 2016
Grant date	—

Abstract

The application discloses a method for acquiring parameters of a dynamic signal, including: selecting a dynamic sample signal sequence of a power grid to constitute an autocorrelation matrix; determining an effective rank of the autocorrelation matrix and the number of frequency components of the dynamic sample signal sequence; establishing an AR model, and solving a model parameter of the AR model; determining an expression and a complex sequence of the dynamic sample signal sequence by using a Prony algorithm, wherein the dynamic sample signal sequence is represented by the complex sequence with a minimum square error; and substituting a root of a characteristic polynomial corresponding to the model parameter into the complex sequence and solving various parameters of the dynamic sample signal sequence. In the application, with the idea of AR parameter model, a current signal is considered to be a linear combination of signals at previous time points.

First claim

1 . A method for acquiring parameters of a dynamic signal, comprising:
selecting a dynamic sample signal sequence of a power grid, and constituting an autocorrelation matrix by the dynamic sample signal sequence; determining an effective rank of the autocorrelation matrix, and determining the number of frequency components of the dynamic sample signal sequence based on the effective rank; establishing an AR model, and solving a model parameter of the AR model; representing the dynamic sample signal sequence as a set of sinusoidal components of a damping oscillation by using a Prony algorithm; determining a complex sequence of the dynamic sample signal sequence, wherein the dynamic sample signal sequence is represented by the complex sequence with a minimum square error; and substituting a root of a characteristic polynomial corresponding to the model parameter into the complex sequence, and solving various parameters of the dynamic sample signal sequence, wherein the various parameters comprises amplitude, phase, attenuation and frequency.

2 . The method according to claim 1 , wherein an order P e of the autocorrelation matrix satisfies the following formula: N/4<p e <N/3, wherein N is the number of sampling points.

3 . The method according to claim 2 , wherein the process of determining the effective rank of the autocorrelation matrix and determining the number of frequency components of the dynamic sample signal sequence based on the effective rank comprises:
decomposing the autocorrelation matrix by using a SVD method: decomposing the autocorrelation matrix into: R e =USV T , wherein R e is representative of the autocorrelation matrix, U is a p e ×p e -dimensional orthogonal matrix, V is a (p e +1)×(p e +1)-dimensional orthogonal matrix, and S is a p e ×(p e +1)-dimensional non-negative diagonal matrix; taking a diagonal matrix Σ p constituted by the first p singular values of the diagonal matrix S as the optimal approximation {circumflex over (R)} e of R e ,

R
^

e

=

U


∑
p



V
T

=

U


[

S
p

0

0

0

]



V
T

,

wherein S p =diag(σ 1 , σ 2 , . . . , σ p );
determining whether the dynamic sample signal sequence contains noise;
calculating β i =σ i+1 /σ i , 1≦i≦p e −1, determining i corresponding to a maximum β i as an effective rank P, and determining the integer part of P/2 as the number P′ of frequency components, in the case that the dynamic sample signal sequence does not contain noise; and
determining the effective rank P based on a signal-to-noise ratio SNR and a local maximum value of β i , and determining the integer part P/2 as the number P′ of the frequency components, in the case that the dynamic sample signal sequence contains noise.

4 . The method according to claim 3 , wherein the process of establishing the AR model comprises:
representing the dynamic sample signal sequence as:

x


(
n
)

=

-

∑

k
=
1

C





a
k



x


(

n
-
k

)

+

w


(
n
)

,

wherein C is an order of the AR model, w(n) is a zero mean white noise sequence, a k is a model parameter of a C-order AR model.

5 . The method according to claim 4 , wherein the process of solving the model parameter of the AR model comprises:
determining whether the dynamic sample signal sequence contains noise; taking the order C of the AR model as the effective rank P in the case that the dynamic sample signal sequence does not contain noise; taking the order C of the AR model as the order P e of the autocorrelation matrix in the case that the dynamic sample signal sequence contains noise; and solving the model parameter a k by using a covariance algorithm.

6 . The method according to claim 5 , wherein the process of representing the dynamic sample signal sequence as a set of sinusoidal components of a damping oscillation by using the Prony algorithm comprises:
representing the dynamic sample signal sequence as:

Assignees

• State Grid Corp Of China (Sgcc)
• State Grid Chongqing Electric Power Co Electric Power Res Inst
• Univ Chongqing

Inventors

• Ou Xiyang
• Liu Ran
• Hou Xingzhe
• Zheng Ke
• Fu Zhihong
• Hu Xiaorui
• Zhang Xiaoyong
• Zhang Huaiqing
• Ji Jing
• Wu Hua
• Sun Hongliang

Classifications

• G01R23/16 Primary

  Spectrum analysis; Fourier analysis · CPC title