CN107732940B - Power system stabilizer parameter optimization test method based on ADPSS - Google Patents

Power system stabilizer parameter optimization test method based on ADPSS Download PDF

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CN107732940B
CN107732940B CN201710980428.0A CN201710980428A CN107732940B CN 107732940 B CN107732940 B CN 107732940B CN 201710980428 A CN201710980428 A CN 201710980428A CN 107732940 B CN107732940 B CN 107732940B
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丘浩
周柯
张翌晖
王晓明
李明珀
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Electric Power Research Institute of Guangxi Power Grid Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]

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Abstract

The invention discloses an ADPSS-based power system stabilizer parameter optimization test method, and relates to the technical field of power system stabilizer parameter setting. According to the parameter optimization test method for the power system stabilizer based on the ADPSS, EEMD-PRONY algorithm identification is carried out on the sampling signals, filtering and denoising processing is carried out on the noise signals in the sampling signals by introducing the EEMD algorithm, the problems that in a traditional signal analysis method, the identification result is inaccurate and modal aliasing is caused by noise influence are solved, the identification stability and accuracy are improved, and a good basis is provided for optimal setting of PSS parameters. Based on the identified relevant parameters of the sampling signals, the residue number under the corresponding mode is calculated by using a residue number method, the relevant parameters of the PSS are optimally set, and the influence of the error of the measurement of the hysteresis characteristic on the setting of the parameters of the PSS in the actual engineering is avoided.

Description

Power system stabilizer parameter optimization test method based on ADPSS
Technical Field
The invention belongs to the technical field of parameter setting of power system stabilizers, and particularly relates to an ADPSS-based power system stabilizer parameter optimization test method.
Background
With the development of modern power systems, a weak-connection power grid, a long-distance power transmission line, a heavy system load and a large number of rapid excitation systems in the power grid become main characteristics. These factors cause the damping of the power system to decrease, so that the possibility of low frequency oscillation of the power system is greatly increased, and the stable operation of the power system is seriously affected, and a Power System Stabilizer (PSS) is the most effective measure for suppressing the low frequency oscillation at present.
In the research of low-frequency oscillation analysis and suppression of an electric power system, how to effectively filter noise in a sampled fault signal, quickly and accurately identify relevant parameters in the sampled signal and optimally set parameters of a damping controller is a research hotspot problem of domestic and foreign scholars.
At present, parameters such as amplitude, initial phase, frequency, damping ratio and the like of a sampling fault signal are generally identified and analyzed by a signal analysis method. The wavelet analysis method in the signal analysis method is an analysis method combining a frequency domain and a time domain, and can convert back and forth in a frequency window and a time domain window, so that different frequency components in a signal can be transformed and highlighted, the transient component is very suitable for being proposed, but the problem of difficulty in wavelet base selection exists due to the localized property, and the fitting precision cannot be guaranteed. The Prony algorithm is proposed by french mathematician Prony at the end of the 18 th century, and is to sample signals at equal intervals, then express the signals by linear combination of exponential functions, and obtain the information content contained in the signals through derivation and conversion of linearization so as to obtain the amplitude, frequency, phase angle, damping ratio and the like in oscillation signals. The EMD algorithm obtains a plurality of intrinsic mode functions of the signal after decomposition, the decomposed signal has the characteristics of linearization and stabilization at the same time, but the mode aliasing problem occurs in the signal processing process.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an ADPSS-based power system stabilizer parameter optimization test method, which aims to overcome the problems that the existing identification algorithm is sensitive to noise signals and is easy to generate modal aliasing phenomenon, and errors of hysteresis characteristic measurement in practical engineering, so that the set PSS parameters can not effectively compensate the phase hysteresis of a generator excitation system.
The invention solves the technical problems through the following technical scheme: an ADPSS-based power system stabilizer parameter optimization test method comprises the following steps:
(1) based on a PSS-AVR series signal connection model of the on-site actual measurement parameters, applying an excitation signal at a PSS signal superposition point under the condition of withdrawing the PSS, and recording and sampling an output signal;
(2) carrying out EEMD-PRONY algorithm decomposition on the sampling signal in the step (1) to obtain parameters such as amplitude, initial phase, frequency and damping in the sampling signal;
(3) obtaining the remaining number in the leading mode and the parameters of the PSS lead-lag link by a remaining number method for the parameters of the amplitude, the initial phase, the frequency, the damping and the like of the sampling signal obtained in the step (2);
(4) and (4) substituting the parameters of the PSS lead-lag link obtained in the step (3) into a simulation model, and comparing the PSS before and after optimization to inhibit the oscillation effect of the system so as to verify the correctness of the optimized parameters.
Further, a PSS parameter optimization setting system based on a GUI platform comprises an acquisition module, a modeling module, a decomposition module and an optimization module; the acquisition module is respectively connected with the modeling module and the decomposition module; the modeling module and the decomposition module are also respectively connected with the optimization module;
the acquisition module is used for sampling fault signals in the power system; the modeling module is used for constructing a generator mathematical model containing a PSS-AVR series signal connection model based on actually measured relevant data of a certain hydroelectric power plant generator set; the decomposition module is used for decomposing the sampling signal of the PSS-AVR serial signal connection model by using an EEMD-PRONY algorithm and solving parameters such as amplitude, initial phase, frequency and damping in the sampling signal; and the optimization module is used for obtaining the residue in the dominant mode and the parameters of the PSS lead-lag link by a residue method by utilizing the parameters such as the amplitude, the initial phase, the frequency, the damping and the like in the dominant mode in the sampling signal.
Compared with the prior art, the parameter optimization test method for the power system stabilizer based on the ADPSS provided by the invention has the advantages that EEMD-PRONY algorithm identification is carried out on the sampling signals, and filtering and denoising processing is carried out on the noise signals in the sampling signals by introducing the EEMD algorithm, so that the problems of inaccurate identification result and modal aliasing caused by noise influence in the traditional signal analysis method are solved, the identification stability and accuracy are improved, and a good basis is provided for the optimal setting of PSS parameters. Based on the identified relevant parameters of the sampling signals, the residue number under the corresponding mode is calculated by using a residue number method, the relevant parameters of the PSS are optimally set, and the influence of the error of the measurement of the hysteresis characteristic on the setting of the parameters of the PSS in the actual engineering is avoided.
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In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only one embodiment of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
FIG. 1 is a schematic flow chart of an ADPSS-based power system stabilizer parameter optimization test method according to the present invention;
FIG. 2 is a diagram of a PSS-AVR series signal connection model according to the present invention;
FIG. 3 is an EEMD-PRONY recognition fitting sampling signal effect diagram of the present invention;
FIG. 4 is a graph of the suppression of oscillation by the EEMD-PRONY optimized setting PSS of the present invention;
FIG. 5 is a block diagram of the PSS parameter optimization setting system based on the GUI platform according to the present invention.
Detailed Description
The technical solutions in the present invention are 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 only a part of the embodiments of the present invention, and not all of the embodiments. 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.
As shown in fig. 1, the method for testing parameter optimization of the power system stabilizer based on the ADPSS provided by the present invention comprises the following steps:
(1) based on a PSS-AVR series signal connection model of the on-site actual measurement parameters, applying an excitation signal at a PSS signal superposition point under the condition of withdrawing the PSS, and recording and sampling an output signal;
establishing a PSS-AVR series signal connection model as shown in FIG. 2, in case of the PSS exiting the operation, Δ U of the PSS-AVR series signal connection modelPSSOutputting a superposition point, adding an excitation signal I (0.1 s in duration)t) And then recording and sampling the output rotation speed deviation signal delta omega.
(2) And (3) carrying out EEMD-PRONY algorithm decomposition on the sampling signal in the step (1) to obtain parameters such as amplitude, initial phase, frequency, damping and the like in the sampling signal.
The step (2) adopts EEMD-PRONY algorithm decomposition, and the specific process of solving the amplitude, the initial phase, the frequency and the damping in the sampling signal is as follows:
EEMD-PRONY algorithm adds uniformly distributed noise signal n to original sampling signal by adding uniformly distributed noise signal in time domain spacei(t) entering the original signal x (t) to obtain independent signals x of different scalesi(t) is:
xi(t)＝x(t)+ni(t)，i＝1,2,…,N (1)
wherein, N is the data recording time length.
For independent signal x after adding noisei(t) performing EMD decomposition, the decomposition result comprises a plurality of intrinsic signal functions cij(t) (IMF) and a residual signal function ri(t) is represented by the formula (2)
Figure BDA0001439297800000041
And finally, averaging and evaluating each obtained IMF component, and then:
Figure BDA0001439297800000042
Figure BDA0001439297800000051
the decomposed signal x (t), t 0,1,2.. N-1 is transformed into a linear combination of the corresponding exponential functions:
Figure BDA0001439297800000052
Figure BDA0001439297800000053
Figure BDA0001439297800000054
in the formula: a. theiIs the amplitude; thetaiIs the phase; f. ofiIs the frequency; alpha is alphaiIs an attenuation factor; Δ t is the sampling time interval.
Defining the damping ratio xi of the sampled signali：
Figure BDA0001439297800000055
And carrying out differential change on the function expression, and sequentially deducing corresponding differential equations as follows:
Figure BDA0001439297800000056
parameter a in equation (9)iPerforming least square estimation to obtain error (deviation between original signal and difference signal)
Figure BDA0001439297800000057
Sum of squares of minimumWherein p is the order of the linear prediction model, and a group of linear matrix equations is obtained as follows:
Figure BDA0001439297800000058
definition of
Figure BDA0001439297800000059
Equation for the Prony algorithm:
Figure BDA00014392978000000510
order to
Figure BDA00014392978000000511
Solving the equation of the formula (11) to obtain the coefficient a1,a2,…,apThen the characteristic root z of the matrix R can be obtainediAnd satisfy
Figure BDA0001439297800000061
The characteristic equation expressions are developed according to the recursion formula (5) and are shown as (13), and z is respectively expressedi，
Figure BDA0001439297800000062
Substituting in formula (13) to obtain characteristic equation parameter bi,i＝1,2,…,p。
Figure BDA0001439297800000063
The amplitude A is obtained byiPhase thetaiFrequency fiAttenuation factor alphaiDamping ratio xii：
Figure BDA0001439297800000064
According to the identified amplitude, initial phase, frequency and damping, the EEMD-PRONY algorithm is used for carrying out reduction fitting on the sampling signal, and the effect graph is shown in FIG. 3. Therefore, the method can carry out filtering and denoising processing on the sampling signals, can better carry out reduction fitting on the sampling signals, and improves the stability and the precision of identification.
(3) And (3) obtaining the residue under the leading mode and the parameters of the PSS lead-lag link by a residue method for the parameters of the amplitude, the initial phase, the frequency, the damping and the like of the sampling signal obtained in the step (2).
Performing a laplace transform on a linear time invariant system to obtain: y(s) ═ g(s) i(s), where y(s), g(s), i(s) are laplace changes of the output function, transfer function, and input function of the PSS-AVR series signal connection model, respectively.
The transfer function G(s) of the PSS-AVR series signal connection model is as follows:
Figure BDA0001439297800000071
wherein R isiIs the residue of the transfer function G(s), lambda in the denominatoriIs the corresponding feature root.
Input function I(s) is transformed by delay factor ci(i-0, 1, …, k) and its corresponding characteristic value djThe denominator of the transformation formula is a characteristic root lambda corresponding to the input signaln+1。
Figure BDA0001439297800000072
Thus, the expression of the output function Y(s) is:
Figure BDA0001439297800000073
wherein the content of the first and second substances,
Figure BDA0001439297800000074
when d iskT is less than or equal to t and let tau be t-dkWhen, expression (14) becomes by inverse Lass transformation:
Figure BDA0001439297800000075
the residue expression of the ith mode in the transfer function g(s) is derived from equation (18):
Figure BDA0001439297800000076
in the formula:
Figure BDA0001439297800000077
and
Figure BDA0001439297800000078
wherein A isi、θi、fi、αiAnd delta t are respectively the amplitude, phase, frequency, attenuation factor and sampling time interval of the output signal y (tau), the parameters can be obtained by parameter fitting estimation of the output signal y (tau) through EEMD-PRONY algorithm, and the delay factor ciAnd its corresponding feature root diCharacteristic root lambda corresponding to input signaln+1It can be derived from the known input signal.
According to the residue RjThe angle to be compensated in the j mode is obtained as follows:
Figure BDA0001439297800000079
for the determined compensation angle, the following equation is used:
Figure BDA0001439297800000081
the parameters for determining the lead-lag link are as follows:
Figure BDA0001439297800000082
wherein f isiTo identify the frequency of the signal, T1、T2、T3、T4The compensation parameters of the PSS lead-lag link are obtained.
And performing reduction fitting on the sampling signal by using an EEMD-PRONY algorithm according to the identified amplitude, initial phase, frequency and damping, and then obtaining the residue under the dominant mode and the parameters of the PSS lead-lag link by a residue method.
(4) Substituting the parameters of the PSS lead-lag link obtained in the step (3) into an ADPSS simulation model, and comparing the effects of the PSS on inhibiting the system oscillation before and after optimization, wherein the inhibition and comparison effects are shown in FIG. 4. It can be seen that the optimized PSS parameter has better oscillation suppression effect than the parameter before optimization, the oscillation time and times are reduced, and the oscillation amplitude is reduced.
In the embodiment, EEMD-PRONY is adopted to identify the amplitude, initial phase, frequency and damping of the decomposed sampling signal, and the residue R in the dominant mode is obtained by the residue methodjAnd then, the angle needing to be compensated in the j mode is obtained, and the parameters of the PSS lead-lag link are determined for the determined compensation angle.
According to the method, EEMD-PRONY algorithm identification is carried out on the sampling signal, filtering and denoising processing is carried out on the noise signal in the sampling signal by introducing the EEMD algorithm, the problems that an identification result is inaccurate and mode aliasing is caused by noise influence in the traditional signal analysis method are solved, the identification stability and accuracy are improved, and a good basis is provided for optimally setting PSS parameters. Based on the identified relevant parameters of the sampling signals, the residue number under the corresponding mode is calculated by using a residue number method, the relevant parameters of the PSS are optimally set, and the influence of the error of the measurement of the hysteresis characteristic on the setting of the parameters of the PSS in the actual engineering is avoided.
As shown in fig. 5, a PSS parameter optimization setting system based on a GUI platform includes an acquisition module, a modeling module, a decomposition module, and an optimization module; the acquisition module is respectively connected with the modeling module and the decomposition module; the modeling module and the decomposition module are also respectively connected with the optimization module;
the acquisition module is used for sampling fault signals in the power system;
the modeling module is used for constructing different generator mathematical models containing a PSS-AVR series signal connection model based on actually measured relevant data of a certain hydroelectric power plant generator set;
the decomposition module is used for decomposing the sampling signal of the PSS-AVR serial signal connection model by using an EEMD-PRONY algorithm and solving parameters such as amplitude, initial phase, frequency and damping in the sampling signal;
and the optimization module is used for obtaining the residue in the dominant mode and the parameters of the PSS lead-lag link by a residue method by utilizing the parameters such as the amplitude, the initial phase, the frequency, the damping and the like in the dominant mode in the sampling signal.
The above disclosure is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of changes or modifications within the technical scope of the present invention, and shall be covered by the scope of the present invention.

Claims (3)

1. A parameter optimization test method for an ADPSS-based power system stabilizer is characterized by comprising the following steps:
(1) based on a PSS-AVR series signal connection model of the on-site actual measurement parameters, applying an excitation signal at a PSS signal superposition point under the condition of withdrawing the PSS, and recording and sampling an output signal;
(2) carrying out EEMD-PRONY algorithm decomposition on the sampling signal in the step (1) to obtain the amplitude, initial phase, frequency and damping in the sampling signal;
(3) obtaining the remaining number in the leading mode and the parameters of the PSS lead-lag link by a remaining number method for the amplitude, the initial phase, the frequency and the damping of the sampling signal obtained in the step (2);
the amplitude, initial phase, frequency and damping of the sampling signal obtained by the EEMD-PRONY algorithm in the step (3) are expressed by the following formula:
Figure FDA0003069324790000011
wherein A isiIs the amplitude, thetaiIs a phase, fiIs the frequency, alphaiIs an attenuation factor, xiiIs the damping ratio, Δ t is the sampling time interval; biCharacteristic equation parameter, z, being an expression of a characteristic equationiIs the characteristic root of the matrix R; the remaining number in the dominant mode in the step (3) is as follows:
Figure FDA0003069324790000012
wherein the content of the first and second substances,
Figure FDA0003069324790000013
and
Figure FDA0003069324790000014
wherein A isj、θj、fj、αjAnd delta t are respectively the amplitude, phase, frequency, attenuation factor and sampling time interval of the output signal y (tau); c. Ci、diAnd k are respectively a delay factor, a corresponding characteristic root and the number of signal samples; lambda [ alpha ]n+1For the corresponding characteristic root, d, of the input signal I(s)kIs a delay factor ci(i ═ 0,1, …, k) corresponding characteristic values;
(4) and (4) substituting the parameters of the PSS lead-lag link obtained in the step (3) into a simulation model, and comparing the PSS before and after optimization to inhibit the oscillation effect of the system so as to verify the correctness of the optimized parameters.
2. The ADPSS-based power system stabilizer parameter optimization test method of claim 1, wherein the parameters of the PSS lead-lag link in the step (4) are
Figure FDA0003069324790000021
Where β is the determined compensation angle, fiTo identify the frequency of the signal, T1、T2、T3、T4The compensation parameters of the PSS lead-lag link are obtained.
3. A PSS parameter optimization setting system based on a GUI platform, wherein the PSS parameter optimization setting system applies the power system stabilizer parameter optimization test method of claim 1, and is characterized in that: the system comprises an acquisition module, a modeling module, a decomposition module and an optimization module; the acquisition module is respectively connected with the modeling module and the decomposition module; the modeling module and the decomposition module are also respectively connected with the optimization module;
the acquisition module is used for sampling fault signals in the power system; the modeling module is used for constructing a generator mathematical model containing a PSS-AVR series signal connection model based on actually measured relevant data of a certain hydroelectric power plant generator set; the decomposition module is used for decomposing the sampling signal of the PSS-AVR serial signal connection model by using an EEMD-PRONY algorithm, and solving the amplitude, the initial phase, the frequency and the damping in the sampling signal; and the optimization module is used for obtaining the residue in the dominant mode and the parameters of the PSS lead-lag link by a residue method by utilizing the amplitude, the initial phase, the frequency and the damping in the dominant mode in the sampling signal.
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