CN111293706A - Method and device for identifying low-frequency oscillation parameters of power system - Google Patents

Method and device for identifying low-frequency oscillation parameters of power system Download PDF

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CN111293706A
CN111293706A CN201811489869.1A CN201811489869A CN111293706A CN 111293706 A CN111293706 A CN 111293706A CN 201811489869 A CN201811489869 A CN 201811489869A CN 111293706 A CN111293706 A CN 111293706A
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鲍万昌
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China Mobile Group Shandong Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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Abstract

The embodiment of the invention provides a method and a device for identifying low-frequency oscillation parameters of an electric power system, wherein the dominant mode of low oscillation of the system is identified by using the actual measurement data of the system or the output data of an electromechanical transient simulation program by using an Esprit algorithm to obtain the frequency, the attenuation coefficient and the damping ratio of the oscillation mode of the electric power system; when the power system generates low-frequency oscillation, oscillation parameter identification can be carried out by utilizing measured data of a power grid; in the planning stage of the power system, the oscillation parameters can be identified by using simulation data, and the identification method is not influenced by the system scale and noise. The problem of low-frequency oscillation of the power system is analyzed by using the measured data or the time domain simulation data of the power system, the method is not limited by the scale of a power grid, and the limitation of the traditional characteristic value analysis method is overcome. The anti-noise performance is high, the operation speed is higher, and the result is more accurate.

Description

Method and device for identifying low-frequency oscillation parameters of power system
Technical Field
The embodiment of the invention relates to the technical field of stable operation of an electric power system, in particular to a method and a device for identifying low-frequency oscillation parameters of the electric power system.
Background
Power system low frequency oscillations occur with grid interconnection. Low frequency oscillations were first generated in China during the operation of the United systems of Guangdong and hong Kong in 1984. However, in the initial stage of networking of the power system, the generator groups are closely connected electrically, the damping is sufficient, and low-frequency oscillation rarely occurs. With the expansion of the interconnection scale of the power grid, the wide application of the high-amplification-factor quick excitation system and the influence of factors such as economy, environmental protection and the like, the operation of the power grid is closer to the stable limit, so that low-frequency oscillation is observed in many power grids all over the world including China.
The phenomena of low-frequency oscillation of the power system are increasing, and in order to obtain a method for effectively inhibiting the low-frequency oscillation, the characteristic parameters of the low-frequency oscillation need to be researched. At present, the low-frequency oscillation identification method of the power system mainly comprises two types of eigenvalue analysis and Prony algorithm; the eigenvalue analysis method is a method for studying the stability of small interference of a power system, and is a traditional method for analyzing low-frequency oscillation, and the eigenvalue is calculated by using a linearization method, and information such as oscillation mode sensitivity is analyzed. The characteristic value analysis method can be established on the basis of the model and carries out off-line analysis on the system. The Prony algorithm analyzes the problem by using a curve fitting method, analyzes information such as the frequency, attenuation factors and the like of a signal through a curve, belongs to a time domain simulation method, can be used on line, avoids calculating a characteristic solution of a large-scale matrix, and solves the non-linear engineering problem by using a linear mathematical method.
The accuracy of the characteristic value analysis method is based on the accuracy of the model, and if the accuracy of the model is not enough, the accuracy of the analysis result is not mentioned. With the expansion of the scale of the power system and the development of the smart grid, the scale of the current power system cannot be adapted, and when the power scale is too large, dimension disaster may be caused, and the method is generally only suitable for medium and small-scale power systems. The Prony algorithm is sensitive to noise because Prony is a polynomial-based algorithm, and firstly obtains a polynomial satisfied by observation data through various methods, and then identifies information such as frequency, damping and the like of low-frequency oscillation by solving the root of the polynomial. When the polynomial coefficient is calculated, the noise has a great influence on the accuracy of parameter identification. In order to suppress the influence of noise, links such as auxiliary filtering and denoising are usually adopted or the order of the model is increased, so that the complexity and the calculation cost of the algorithm are increased.
Disclosure of Invention
Embodiments of the present invention provide a method and an apparatus for identifying low frequency oscillation parameters of a power system, which overcome the above problems or at least partially solve the above problems.
In a first aspect, an embodiment of the present invention provides a method for identifying a low-frequency oscillation parameter of an electric power system, including:
converting an oscillation signal of the power system into an oscillation signal model of a combination of a sinusoidal signal and white noise which change according to an exponential law based on an Esprit algorithm; and converting the signal oscillation model into a vector form
Figure BDA0001895425670000021
n is the sampling time;
converting the actual measurement oscillation signal of the power system into a Hankel data matrix Y; performing singular value decomposition on the Hankel data matrix Y to obtain a signal subspace VsSum noise subspace Vn；
Based on the signal subspace VsSum noise subspace VnConstructing the invertible matrix Ψ such that V2＝V1Ψ, wherein,
Figure BDA0001895425670000022
↓and ↓ respectively represent new matrixes obtained after the first row and the last row of the matrixes are deleted; and acquiring the characteristic value of the reversible matrix psi, and acquiring the frequency, the attenuation coefficient and the damping ratio of each component in the oscillation signal based on the characteristic value.
Preferably, the oscillation signal model is:
Figure BDA0001895425670000023
in the above formula, △ t is the sampling period, p is the order of the signal model, Am、θm、fmAnd αmThe amplitude, the initial phase, the frequency and the attenuation coefficient of the mth attenuation are respectively divided; w (n) is white gaussian noise with an average value of 0;
Figure BDA0001895425670000024
zmis a signal pole; j is an imaginary numberA bit.
Preferably, the signal oscillation model is converted into a vector form
Figure BDA0001895425670000031
The method specifically comprises the following steps:
defining a vector
Figure BDA0001895425670000032
Wherein M > p;
writing the oscillation signal model as vector form:
Figure BDA0001895425670000033
wherein the content of the first and second substances,
Figure BDA0001895425670000034
Φ＝diag(z1,z2,…,zp)
Figure BDA0001895425670000035
Figure BDA0001895425670000036
Figure BDA0001895425670000037
in the above formula, zm(m-1, 2, … p) determines the frequency and damping of each component in the oscillating signal, and determines the rotation factor Φ based on which the signal pole is found.
Preferably, the method for converting the oscillation signal actually measured by the power system into the Hankel data matrix Y specifically includes:
according to the actually measured oscillation signal data sequence Y (0), Y (1),.. Y (N-1), a Hankel data matrix Y is constructed:
Figure BDA0001895425670000038
wherein L > p; m is more than p; l + M-1 ═ N.
Preferably, the singular value decomposition of the Hankel data matrix Y specifically includes:
singular value decomposition is carried out on the Hankel data matrix Y to obtain:
Figure BDA0001895425670000039
in the above equation, svd represents singular value decomposition; superscript H denotes conjugate transpose; u shapeHU＝I；VHV is I; i is an identity matrix; u is belonged to CL×L；V∈CM×M；Σ∈RL×MFor diagonal matrices, the diagonal elements being the singular values σ of the matrix Y1,σ2,…,σp,σp+1,…,σmax(L,M)In descending order; vsAnd VnRepresenting the signal subspace and the noise subspace, respectively.
Preferably, based on the signal subspace VsSum noise subspace VnAfter constructing the invertible matrix Ψ, the method further comprises:
based on V1And V2Constructing a matrix [ V ]1V2]And performing singular value decomposition
Figure BDA0001895425670000041
Will be provided with
Figure BDA0001895425670000042
Split into 4 p × p matrices:
Figure BDA0001895425670000043
to obtain
Figure BDA0001895425670000044
Preferably, the obtaining a characteristic value of the reversible matrix Ψ and obtaining the frequency, the attenuation coefficient, and the damping ratio of each component in the oscillation signal based on the characteristic value specifically include:
to obtain the characteristic value lambda of psii(i ═ 1,2, …, p); acquiring the frequency, the attenuation coefficient and the damping ratio of each component in the oscillation signal based on the characteristic values:
Figure BDA0001895425670000045
Figure BDA0001895425670000046
Figure BDA0001895425670000047
in the above formula, fiFrequency of each component in the oscillating signal αiξ being the attenuation coefficient of each component in the oscillating signaliIs the damping ratio; ln (z)i)＝λi＝αi+jωi。
In a second aspect, an embodiment of the present invention provides an apparatus for identifying low-frequency oscillation parameters of a power system, including:
the system comprises a first module, a second module and a third module, wherein the first module is used for converting an oscillating signal of the power system into an oscillating signal model of a combination of a sinusoidal signal and white noise which change according to an exponential law based on an Esprit algorithm; and converting the signal oscillation model into a vector form
Figure BDA0001895425670000048
n is the sampling time;
the second module is used for converting the actual measurement oscillation signal of the power system into a Hankel data matrix Y; performing singular value decomposition on the Hankel data matrix Y to obtain a signal subspace VsSum noise subspace Vn；
A third module for determining a signal subspace V based on the signal subspace VsSum noise subspace VnConstructing the invertible matrix Ψ such that V2＝V1Ψ ofIn (1),
Figure BDA0001895425670000051
↓and ↓ respectively represent new matrixes obtained after the first row and the last row of the matrixes are deleted; and acquiring the characteristic value of the reversible matrix psi, and acquiring the frequency, the attenuation coefficient and the damping ratio of each component in the oscillation signal based on the characteristic value.
In a third aspect, an embodiment of the present invention provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the method provided in the first aspect when executing the program.
In a fourth aspect, an embodiment of the present invention provides a non-transitory computer readable storage medium, on which a computer program is stored, which when executed by a processor, implements the steps of the method as provided in the first aspect.
The embodiment of the invention provides a method and a device for identifying low-frequency oscillation parameters of an electric power system, which utilize actual measurement data of the system or output data of an electromechanical transient simulation program to identify a dominant mode of low oscillation of the system by using an Esprit algorithm so as to obtain the frequency, the attenuation coefficient and the damping ratio of an oscillation mode of the electric power system; when the power system generates low-frequency oscillation, oscillation parameter identification can be carried out by utilizing measured data of a power grid; in the planning stage of the power system, the oscillation parameters can be identified by using simulation data, and the identification method is not influenced by the system scale and noise. The problem of low-frequency oscillation of the power system is analyzed by using the measured data or the time domain simulation data of the power system, the method is not limited by the scale of a power grid, and the limitation of the traditional characteristic value analysis method is overcome. The anti-noise performance is high, the operation speed is higher, and the result is more accurate.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a schematic diagram of a method for identifying low-frequency oscillation parameters of an electric power system according to an embodiment of the invention;
FIG. 2 is a schematic diagram of an apparatus for identifying low frequency oscillation parameters of a power system according to an embodiment of the present invention;
fig. 3 is a schematic physical structure diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The phenomena of low-frequency oscillation of the power system are increasing, and in order to obtain a method for effectively inhibiting the low-frequency oscillation, the characteristic parameters of the low-frequency oscillation need to be researched. Since the accuracy of the eigenvalue analysis method in the prior art is based on the accuracy of the model, if the accuracy of the model is not enough, the accuracy of the analysis result cannot be mentioned. With the expansion of the scale of the power system and the development of the smart grid, the scale of the current power system cannot be adapted, and when the power scale is too large, dimension disaster may be caused, and the method is generally only suitable for medium and small-scale power systems. The Prony algorithm is sensitive to noise because Prony is a polynomial-based algorithm, and firstly obtains a polynomial satisfied by observation data through various methods, and then identifies information such as frequency, damping and the like of low-frequency oscillation by solving the root of the polynomial. When the polynomial coefficient is calculated, the noise has a great influence on the accuracy of parameter identification. In order to suppress the influence of noise, links such as auxiliary filtering and denoising are usually adopted or the order of the model is increased, so that the complexity and the calculation cost of the algorithm are increased.
Therefore, the embodiments of the invention analyze the low-frequency oscillation problem of the power system by using the measurement data or the time domain simulation data of the power system, are not limited by the scale of the power grid, and overcome the limitation of the traditional characteristic value analysis method; when the power system generates low-frequency oscillation, oscillation parameter identification can be carried out by utilizing measured data of a power grid; in the planning stage of the power system, the oscillation parameters can be identified by using simulation data, and the identification method is not influenced by the system scale and noise. The following description and description will proceed with reference being made to various embodiments.
Fig. 1 shows a method for identifying low-frequency oscillation parameters of a power system, which includes:
s1, converting the oscillation signal of the power system into an oscillation signal model of a combination of a sinusoidal signal and white noise which change according to an exponential law based on an Esprit algorithm; and converting the signal oscillation model into a vector form
Figure BDA0001895425670000061
n is the sampling time;
s2, converting the actual measurement oscillation signal of the power system into a Hankel data matrix Y; performing singular value decomposition on the Hankel data matrix Y to obtain a signal subspace VsSum noise subspace Vn；
S3, based on the signal subspace VsSum noise subspace VnConstructing the invertible matrix Ψ such that V2＝V1Ψ, wherein,
Figure BDA0001895425670000071
↓and ↓ respectively represent new matrixes obtained after the first row and the last row of the matrixes are deleted; and acquiring the characteristic value of the reversible matrix psi, and acquiring the frequency, the attenuation coefficient and the damping ratio of each component in the oscillation signal based on the characteristic value.
On the basis of the above embodiment, the oscillation signal model is:
Figure BDA0001895425670000072
in the above formula, △ t is the sampling period, and p is the signal modelThe order of (a); a. them、θm、fmAnd αmThe amplitude, the initial phase, the frequency and the attenuation coefficient of the mth attenuation are respectively divided; w (n) is white gaussian noise with an average value of 0;
Figure BDA0001895425670000073
zmis a signal pole; j is an imaginary unit.
On the basis of the above embodiments, the signal oscillation model is converted into a vector form
Figure BDA0001895425670000074
The method specifically comprises the following steps:
defining a vector
Figure BDA0001895425670000075
Wherein M > p;
writing the oscillation signal model as vector form:
Figure BDA0001895425670000076
wherein the content of the first and second substances,
Figure BDA0001895425670000077
Φ＝diag(z1,z2,…,zp)
Figure BDA0001895425670000078
Figure BDA0001895425670000079
Figure BDA00018954256700000710
in the above formula, zm(m-1, 2, … p) determines the frequency and damping of each component in the oscillating signal and determines completely the rotation factor phiA signal pole is found based on the rotation factor phi. Therefore, the method can seek to indirectly find the signal pole by finding phi, and further obtain the low-frequency oscillation parameter.
On the basis of the above embodiments, the method for converting the oscillation signal actually measured by the power system into the Hankel data matrix Y specifically includes:
according to the actually measured oscillation signal data sequence Y (0), Y (1),.. Y (N-1), a Hankel data matrix Y is constructed:
Figure BDA0001895425670000081
wherein L > p; m is more than p; l + M-1 ═ N.
On the basis of the above embodiments, performing singular value decomposition on the Hankel data matrix Y specifically includes:
singular value decomposition is carried out on the Hankel data matrix Y to obtain:
Figure BDA0001895425670000082
in the above equation, svd represents singular value decomposition; superscript H denotes conjugate transpose; u shapeHU＝I；VHV is I; i is an identity matrix; u is belonged to CL×L；V∈CM×M；Σ∈RL×MFor diagonal matrices, the diagonal elements being the singular values σ of the matrix Y1,σ2,…,σp,σp+1,…,σmax(L,M)In descending order; vsAnd VnRepresenting the signal subspace and the noise subspace, respectively.
The rank of the Hankel data matrix Y is p, assuming that the signal is formed by the superposition of only p complex sinusoidal signal components. At this time, σ1＞σ2＞…＞σp＞σp+1＝σp+2＝…＝σmax(L,M)＝0。
When the signal contains noise, the constructed Hankel data matrix is full rank, all singular values are not 0, and therefore V can be divided into two parts, namely V ═ V [, V-s,Vn]，VsIs corresponding toThe eigenvectors of the p singular values with the largest magnitude in matrix Y.
Based on the signal subspace VsSum noise subspace VnConstructing the invertible matrix Ψ such that V2＝V1Ψ, wherein,
Figure BDA0001895425670000083
and ↓and ↓ respectively represent new matrixes obtained after the first row and the last row of the matrixes are deleted.
On the basis of the above embodiments, based on the signal subspace VsSum noise subspace VnAfter constructing the invertible matrix Ψ, the method further comprises:
to find Ψ, based on V1And V2Constructing a matrix [ V ]1V2]And performing singular value decomposition
Figure BDA0001895425670000084
Will be provided with
Figure BDA0001895425670000091
Split into 4 p × p matrices, i.e.:
Figure BDA0001895425670000092
to obtain
Figure BDA0001895425670000093
On the basis of the foregoing embodiments, obtaining a feature value of the reversible matrix Ψ, and obtaining a frequency, an attenuation coefficient, and a damping ratio of each component in an oscillation signal based on the feature value specifically includes:
to obtain the characteristic value lambda of psii(i ═ 1,2, …, p); acquiring the frequency, the attenuation coefficient and the damping ratio of each component in the oscillation signal based on the characteristic values:
Figure BDA0001895425670000094
Figure BDA0001895425670000095
Figure BDA0001895425670000096
in the above formula, fiFrequency of each component in the oscillating signal αiξ being the attenuation coefficient of each component in the oscillating signaliIs the damping ratio; ln (z)i)＝λi＝αi+jωi。
Fig. 2 is a device for identifying low-frequency oscillation parameters of an electric power system according to an embodiment of the present invention, which is based on the method for identifying low-frequency oscillation parameters of an electric power system in the foregoing embodiments, and includes a first module 40, a second module 50, and a third module 60, where:
the first module 40 converts the oscillation signal of the power system into an oscillation signal model of a combination of a sinusoidal signal and white noise that changes according to an exponential law based on Esprit algorithm; and converting the signal oscillation model into a vector form
Figure BDA0001895425670000097
n is the sampling time;
the second module 50 converts the oscillation signal actually measured by the power system into a Hankel data matrix Y; performing singular value decomposition on the Hankel data matrix Y to obtain a signal subspace VsSum noise subspace Vn；
The third module 60 is based on the signal subspace VsSum noise subspace VnConstructing the invertible matrix Ψ such that V2＝V1Ψ, wherein,
Figure BDA0001895425670000098
↓and ↓ respectively represent new matrixes obtained after the first row and the last row of the matrixes are deleted; and acquiring the characteristic value of the reversible matrix psi, and acquiring the frequency, the attenuation coefficient and the damping ratio of each component in the oscillation signal based on the characteristic value.
Fig. 3 is a schematic entity structure diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 3, the electronic device may include: a processor (processor)810, a communication Interface 820, a memory 830 and a communication bus 840, wherein the processor 810, the communication Interface 820 and the memory 830 communicate with each other via the communication bus 840. The processor 810 may call a computer program stored on the memory 830 and executable on the processor 810 to perform the method for identifying low frequency oscillation parameters of the power system provided by the above embodiments, for example, the method includes:
s1, converting the oscillation signal of the power system into an oscillation signal model of a combination of a sinusoidal signal and white noise which change according to an exponential law based on an Esprit algorithm; and converting the signal oscillation model into a vector form
Figure BDA0001895425670000101
n is the sampling time;
s2, converting the actual measurement oscillation signal of the power system into a Hankel data matrix Y; performing singular value decomposition on the Hankel data matrix Y to obtain a signal subspace VsSum noise subspace Vn；
S3, based on the signal subspace VsSum noise subspace VnConstructing the invertible matrix Ψ such that V2＝V1Ψ, wherein,
Figure BDA0001895425670000102
↓and ↓ respectively represent new matrixes obtained after the first row and the last row of the matrixes are deleted; and acquiring the characteristic value of the reversible matrix psi, and acquiring the frequency, the attenuation coefficient and the damping ratio of each component in the oscillation signal based on the characteristic value.
In addition, the logic instructions in the memory 830 may be implemented in software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products. Based on such understanding, the technical solutions of the embodiments of the present invention may be essentially implemented or make a contribution to the prior art, or may be implemented in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
An embodiment of the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program is implemented to perform the method for identifying low-frequency oscillation parameters of an electric power system provided in the foregoing embodiments when executed by a processor, and the method includes:
s1, converting the oscillation signal of the power system into an oscillation signal model of a combination of a sinusoidal signal and white noise which change according to an exponential law based on an Esprit algorithm; and converting the signal oscillation model into a vector form
Figure BDA0001895425670000111
n is the sampling time;
s2, converting the actual measurement oscillation signal of the power system into a Hankel data matrix Y; performing singular value decomposition on the Hankel data matrix Y to obtain a signal subspace VsSum noise subspace Vn；
S3, based on the signal subspace VsSum noise subspace VnConstructing the invertible matrix Ψ such that V2＝V1Ψ, wherein,
Figure BDA0001895425670000112
↓and ↓ respectively represent new matrixes obtained after the first row and the last row of the matrixes are deleted; and acquiring the characteristic value of the reversible matrix psi, and acquiring the frequency, the attenuation coefficient and the damping ratio of each component in the oscillation signal based on the characteristic value.
An embodiment of the present invention further provides a computer program product, where the computer program product includes a computer program stored on a non-transitory computer readable storage medium, where the computer program includes program instructions, and when the program instructions are executed by a computer, the computer can execute the method for identifying low-frequency oscillation parameters of a power system as described above, for example, the method includes:
s1, converting the oscillation signal of the power system into an oscillation signal model of a combination of a sinusoidal signal and white noise which change according to an exponential law based on an Esprit algorithm; and converting the signal oscillation model into a vector form
Figure BDA0001895425670000113
n is the sampling time;
s2, converting the actual measurement oscillation signal of the power system into a Hankel data matrix Y; performing singular value decomposition on the Hankel data matrix Y to obtain a signal subspace VsSum noise subspace Vn；
S3, based on the signal subspace VsSum noise subspace VnConstructing the invertible matrix Ψ such that V2＝V1Ψ, wherein,
Figure BDA0001895425670000114
↓and ↓ respectively represent new matrixes obtained after the first row and the last row of the matrixes are deleted; and acquiring the characteristic value of the reversible matrix psi, and acquiring the frequency, the attenuation coefficient and the damping ratio of each component in the oscillation signal based on the characteristic value.
In summary, according to the method and the device for identifying the low-frequency oscillation parameters of the power system provided by the embodiment of the present invention, the dominant mode of the low oscillation of the system is identified by using the Esprit algorithm according to the actual measurement data of the system or the output data of the electromechanical transient simulation program, so as to obtain the frequency, the damping coefficient and the damping ratio of the oscillation mode of the power system; when the power system generates low-frequency oscillation, oscillation parameter identification can be carried out by utilizing measured data of a power grid; in the planning stage of the power system, the oscillation parameters can be identified by using simulation data, and the identification method is not influenced by the system scale and noise. The problem of low-frequency oscillation of the power system is analyzed by using the measured data or the time domain simulation data of the power system, the method is not limited by the scale of a power grid, and the limitation of the traditional characteristic value analysis method is overcome. The anti-noise performance is high, the operation speed is higher, and the result is more accurate.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (10)

1. A method for identifying low-frequency oscillation parameters of a power system is characterized by comprising the following steps:
based on EspritThe algorithm converts an oscillation signal of the power system into an oscillation signal model of a combination of a sinusoidal signal and white noise which change according to an exponential law; and converting the signal oscillation model into a vector form
Figure FDA0001895425660000011
n is the sampling time;
converting the actual measurement oscillation signal of the power system into a Hankel data matrix Y; performing singular value decomposition on the Hankel data matrix Y to obtain a signal subspace VsSum noise subspace Vn；
Based on the signal subspace VsSum noise subspace VnConstructing the invertible matrix Ψ such that V2＝V1Ψ, wherein,
Figure FDA0001895425660000017
V2＝Vs ↑↓and ↓ respectively represent new matrixes obtained after the first row and the last row of the matrixes are deleted; and acquiring the characteristic value of the reversible matrix psi, and acquiring the frequency, the attenuation coefficient and the damping ratio of each component in the oscillation signal based on the characteristic value.
2. The method according to claim 1, wherein the oscillation signal model is:
Figure FDA0001895425660000012
in the above formula, △ t is the sampling period, p is the order of the signal model, Am、θm、fmAnd αmThe amplitude, the initial phase, the frequency and the attenuation coefficient of the mth attenuation are respectively divided; w (n) is white gaussian noise with an average value of 0;
Figure FDA0001895425660000013
zmis a signal pole; j is an imaginary unit.
3. The method for identifying low frequency oscillation parameters of power system as claimed in claim 2, wherein the signal oscillation model is converted into vector form
Figure FDA0001895425660000014
The method specifically comprises the following steps:
defining a vector
Figure FDA0001895425660000015
Wherein M > p;
writing the oscillation signal model as vector form:
Figure FDA0001895425660000016
wherein the content of the first and second substances,
Figure FDA0001895425660000021
Φ＝diag(z1,z2,…,zp)
Figure FDA0001895425660000022
Figure FDA0001895425660000023
Figure FDA0001895425660000024
in the above formula, zm(m-1, 2, … p) determines the frequency and damping of each component in the oscillating signal, and determines the rotation factor Φ based on which the signal pole is found.
4. The method for identifying the low-frequency oscillation parameters of the power system as claimed in claim 3, wherein the step of converting the actual measurement oscillation signals of the power system into a Hankel data matrix Y specifically comprises the following steps:
according to the actually measured oscillation signal data sequence Y (0), Y (1),.. Y (N-1), a Hankel data matrix Y is constructed:
Figure FDA0001895425660000025
wherein L > p; m is more than p; l + M-1 ═ N.
5. The method for identifying the low-frequency oscillation parameters of the power system as claimed in claim 4, wherein the singular value decomposition of the Hankel data matrix Y specifically comprises:
singular value decomposition is carried out on the Hankel data matrix Y to obtain:
Figure FDA0001895425660000026
in the above equation, svd represents singular value decomposition; superscript H denotes conjugate transpose; u shapeHU＝I；VHV is I; i is an identity matrix; u is belonged to CL ×L；V∈CM×M；Σ∈RL×MFor diagonal matrices, the diagonal elements being the singular values σ of the matrix Y1,σ2,…,σp,σp+1,…,σmax(L,M)In descending order; vsAnd VnRepresenting the signal subspace and the noise subspace, respectively.
6. The method for identifying low-frequency oscillation parameters of power system according to claim 5, wherein the method is based on the signal subspace VsSum noise subspace VnAfter constructing the invertible matrix Ψ, the method further comprises:
based on V1And V2Constructing a matrix [ V ]1V2]And performing singular value decomposition
Figure FDA0001895425660000031
Will be provided with
Figure FDA0001895425660000032
Split into 4 p × p matrices:
Figure FDA0001895425660000033
to obtain
Figure FDA0001895425660000034
7. The method for identifying the low-frequency oscillation parameters of the power system as claimed in claim 6, wherein the obtaining of the eigenvalue of the reversible matrix Ψ and the obtaining of the frequency, the attenuation coefficient and the damping ratio of each component in the oscillation signal based on the eigenvalue specifically comprises:
to obtain the characteristic value lambda of psii(i ═ 1,2, …, p); acquiring the frequency, the attenuation coefficient and the damping ratio of each component in the oscillation signal based on the characteristic values:
Figure FDA0001895425660000035
Figure FDA0001895425660000036
Figure FDA0001895425660000037
in the above formula, fiFrequency of each component in the oscillating signal αiξ being the attenuation coefficient of each component in the oscillating signaliIs the damping ratio; ln (z)i)＝λi＝αi+jωi。
8. An apparatus for identifying low frequency oscillation parameters of an electric power system, comprising:
a first module for coupling the power system based on an Esprit algorithmThe oscillation signal of (a) is converted into an oscillation signal model of a combination of a sinusoidal signal and white noise which changes according to an exponential law; and converting the signal oscillation model into a vector form
Figure FDA0001895425660000038
n is the sampling time;
the second module is used for converting the actual measurement oscillation signal of the power system into a Hankel data matrix Y; performing singular value decomposition on the Hankel data matrix Y to obtain a signal subspace VsSum noise subspace Vn；
A third module for determining a signal subspace V based on the signal subspace VsSum noise subspace VnConstructing the invertible matrix Ψ such that V2＝V1Ψ, wherein,
Figure FDA0001895425660000041
V2＝Vs ↑↓and ↓ respectively represent new matrixes obtained after the first row and the last row of the matrixes are deleted; and acquiring the characteristic value of the reversible matrix psi, and acquiring the frequency, the attenuation coefficient and the damping ratio of each component in the oscillation signal based on the characteristic value.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method according to any of claims 1 to 7 are implemented when the processor executes the program.
10. A non-transitory computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
CN201811489869.1A 2018-12-06 2018-12-06 Method and device for identifying low-frequency oscillation parameters of power system Pending CN111293706A (en)

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