CN109753754B - Generator dynamic parameter online identification method based on Hi-Honow regularization - Google Patents
Generator dynamic parameter online identification method based on Hi-Honow regularization Download PDFInfo
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Abstract
The invention discloses a synchronous generator dynamic parameter online identification method based on Highronov regularization. Aiming at the operating characteristics of a synchronous generator in an actual system, the method collects the generator terminal current, the generator terminal voltage, the excitation voltage, the active power and the reactive power of the synchronous generator during the real disturbance of a power grid, and constructs a dynamic parameter online identification database; meanwhile, a Hi-Honow regularization method is used to avoid the over-fitting of the system noise of the identified parameters, and finally an interior point algorithm is used for solving. The invention provides an online identification method which can accurately obtain dynamic parameters of a synchronous generator by using actually measured online data aiming at the actual condition that the actual disturbance of a power grid is generally weak.
Description
Technical Field
The invention belongs to the field of identification of dynamic parameters of synchronous power generation systems of power systems, and particularly relates to an on-line identification method of dynamic parameters of a generator based on Hi-Honowov regularization.
Background
Power system dynamic analysis is one of the most important tools for power system control and operation. And the power grid dispatching personnel determine a dispatching strategy according to a dynamic simulation result, so that the accuracy of the dispatching strategy directly influences the safety and the economy of the whole power grid operation control. The accurate generator model is the premise of power grid dynamic characteristic simulation, and with the wide application of devices such as an online measuring device and the like, the dynamic response signal of the synchronous generator can be accurately acquired and sent to a power grid dispatching department, so that the online identification of the dynamic parameters of the synchronous generator becomes possible.
However, the power grid operation data at any time interval can not be used for online parameter identification, and the dynamic behavior of the synchronous generator can be excited only by acquiring a data set under disturbance through an online measuring device, so that response parameters can be identified. In addition, the traditional online identification algorithm uses a method which considers that random binary signals are applied, but the applied signal size lacks a theoretical basis and can be judged only through historical experience; moreover, most of the existing synchronous generator identification algorithms stay in theoretical research, are not applied to an actual running unit, and do not consider the saturation characteristic of the synchronous generator.
Despite the results of many theoretical researches, there is no convenient and accurate online identification and calculation method in the online identification field considering the actual operation characteristics of the synchronous generator.
Disclosure of Invention
The invention aims to overcome the defects of the existing theoretical results and provides a generator dynamic parameter online identification method based on the HiHonowov regularization.
Therefore, the invention adopts the following technical scheme: a generator dynamic parameter online identification method based on Hi-Honow regularization is characterized by comprising the following steps:
the first step is as follows: constructing a standard six-order model of the synchronous generator, and considering the saturation characteristic of the model;
the second step is that: collecting signals of generator terminal current, generator terminal voltage, excitation voltage, generator terminal active power and reactive power before and after power grid disturbance, and establishing a database;
the third step: taking an excitation voltage signal and a terminal voltage signal in a database as input signals u, and taking a terminal current signal, an active power signal and a reactive power signal as output signals y;
the fourth step: constructing a corresponding differential algebraic equation set F (u, theta) to represent the dynamic process and the output state of the corresponding disturbance generator according to the input signal u and the parameter variable theta of the synchronous generator in the database;
the fifth step: using HipinoA method of regularization, which introduces a generator nameplate parameter theta ref As a priori knowledge, and in combination with the output signal y, a parameter identification objective function is constructed
And a sixth step: using implicit trapezoidal integral formula to differentiate algebraic equation set F (u, theta) and identify target function
Discretizing, and using the discretized nonlinear equation set>
And Ψ description,' holds>
Representing the discretized input signal and initializing a data set variable s and a parameter variable θ for a non-linear equation group +>
If true;
the seventh step: calculating a data set variable s and a parameter variable theta to an objective function Ψ and a nonlinear system of equations
Jacobian matrices L and J, and hessian matrix H;
eighth step: and solving by using an interior point algorithm to obtain the dynamic parameters of the generator.
The invention uses historical multi-disturbance data to realize the on-line parameter identification of the synchronous generator with multiple data sets, and expands the existing synchronous generator parameter identification method.
Further, in the first step, the standard sixth-order model of the synchronous generator considering the saturation characteristics is as follows:
wherein, delta represents the power angle of the synchronous generator, omega represents the rotating speed of the synchronous generator, omega s Indicating the synchronous speed of the grid, T j Representing the inertia time constant, P, of the synchronous generator m Representing mechanical power of synchronous generators, P e Represents the electrical power of a synchronous generator;
T 0 ' represents the transient open-circuit time constant, T, of the shafting winding 0 "represents the sub-transient open-circuit time constant of the shafting winding, E' represents the transient potential of the generator, E" represents the sub-transient potential of the generator, V f Representing the voltage of a generator no-load stator, I representing the current of the generator stator, X representing the synchronous reactance of the generator, X 'representing the transient reactance of the generator, X' representing the sub-transient reactance of the generator, subscripts d and q representing the corresponding physical quantities of the d-axis and q-axis of the synchronous generator, respectively, X l Representing the leakage reactance of the generator, sat representing the flux linkage saturation of the shafting, A s And B s Is a parameter, V, determined by fitting the saturation characteristics of the synchronous generator qt Representing the q-axis voltage amplitude, V dt Representing the d-axis voltage magnitude.
Further, in the fifth step, the parameter identifies the target
Has the following form:
wherein the content of the first and second substances,
Ω(θ)＝(θ-θ ref ) T W θ T W θ (θ-θ ref )，
where a represents the regularization parameter, y represents the generator output state m Representing the measured output signal, W representing the corresponding sampling error when the corresponding signal is acquired by the on-line monitoring data, T f Denotes the termination time of parameter identification, omega (theta) denotes the regularization term, W θ Representing the weights of the parameters of the synchronous generator and T representing the matrix transposition.
Further, in the sixth step, the non-linear equation system
Has the following form:
wherein the subscript t represents the t-th moment, the subscript 0 represents the initial value of the variable,
and &>
Respectively representing discretized state variables, input signals and output states, n t Indicates the total number of time instants after all discretization, and>
represents a discretized non-linear function representing the dynamic process of the synchronous generator, and->
Represents a discretized non-linear function defining the output characteristic of the synchronous generator, C represents a function determined by +>
And &>
Discretized non-line of compositionThe linear function, s, represents a dataset variable of the form:
further, in the seventh step, the jacobian matrices L and J have the following form:
where the index t denotes the t-th instant,
representing a partial derivative operator, s representing a data set variable, theta representing a parameter variable, Ψ representing a discretized objective function, and C representing a discretized nonlinear function characterizing generator dynamics and output characteristics.
Further, in the seventh step, the hessian matrix is expanded
Has the following form:
wherein H θθ Representing a discretized non-linear function C characterizing the dynamic and output characteristics of the generator, for the hessian matrix of parameter variables, H, only θ H represents that the discretization nonlinear function C only sums the hessian matrix of the data set variable with the discretization objective function psi only sums the hessian matrix of the data set variable, and T represents the matrix transposition.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention uses the measured data of the on-line measuring device to carry out the on-line identification of the generator parameters, the method is easy to implement, and the generator equipment is protected.
2. The method can be used in the field of synchronous generator on-line parameter identification, can identify all parameters, and has high parameter identification precision.
3. The method considers the saturation characteristic of the synchronous generator and can be applied to the identification of the actual unit.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a graph of the saturation curve of the test unit of the present invention;
FIG. 3 is a comparison graph of the output active power fitting curve obtained by applying the identification parameters, the output active power fitting curve obtained by nameplate parameters and the actually measured active power.
Detailed Description
The invention is described in detail below with reference to the drawings and the detailed description.
As shown in fig. 1, a method for on-line identification of generator dynamic parameters based on schinnov regularization includes the following steps:
the first step is as follows: constructing a standard six-order model of the synchronous generator, and considering the saturation characteristic of the model;
the second step: collecting signals of generator terminal current, generator terminal voltage, excitation voltage, generator terminal active power and reactive power before and after power grid disturbance by using an online monitoring system of a power plant, and establishing a database;
the third step: taking an excitation voltage signal and a terminal voltage signal in a database as an input signal u, and taking a terminal current signal, an active power signal and a reactive power signal as an output signal y;
the fourth step: constructing a corresponding differential algebraic equation set F (u, theta) to represent the dynamic process and the output state of the corresponding disturbance generator according to the input signal u and the parameter variable theta of the synchronous generator in the database;
the fifth step: introducing a generator nameplate parameter theta by using a HiHonow regularization method ref As a priori knowledge, and in combination with the output signal y, a parameter identification target is constructedFunction(s)
And a sixth step: using implicit trapezoidal integral formula to differentiate algebraic equation set F (u, theta) and identify target function
Discretizing, and using the discretized nonlinear equation set>
And Ψ description,' holds>
Representing the discretized input signal and initializing a data set variable s and a parameter variable θ for a non-linear equation group +>
If true;
the seventh step: calculating a data set variable s and a parameter variable theta to an objective function Ψ and a nonlinear system of equations
Jacobian matrices L and J, and Hessian matrix H;
eighth step: and solving by using an interior point algorithm to obtain dynamic parameters of the generator.
In the first step, a standard sixth-order model of the synchronous generator considering the saturation characteristic is as follows:
wherein, delta represents the power angle of the synchronous generator, omega represents the rotating speed of the synchronous generator, omega s Indicating the synchronous speed of the grid, T j Representing the inertial time constant, P, of the synchronous generator m Representing mechanical power of synchronous generators, P e Represents the electrical power of the synchronous generator;
T 0 ' represents the transient open-circuit time constant, T, of the shafting winding 0 "represents the sub-transient open-circuit time constant of the shafting winding, E' represents the transient potential of the generator, E" represents the sub-transient potential of the generator, V f Representing the voltage of a generator no-load stator, I representing the current of the generator stator, X representing the synchronous reactance of the generator, X 'representing the transient reactance of the generator, X' representing the sub-transient reactance of the generator, subscripts d and q representing the corresponding physical quantities of the d-axis and q-axis of the synchronous generator, respectively, X l Representing the leakage reactance of the generator, sat representing the flux linkage saturation of the shafting, A s And B s Is a parameter, V, determined by fitting the saturation characteristics of the synchronous generator qt Representing the q-axis voltage amplitude, V dt Representing the d-axis voltage magnitude.
In the fifth step, the parameter identifies the target
Has the following form:
wherein the content of the first and second substances,
Ω(θ)＝(θ-θ ref ) T W θ T W θ (θ-θ ref )，
where a represents the regularization parameter, y represents the generator output state m Representing the measured output signal, W representing the corresponding sampling error when the corresponding signal is acquired by the on-line monitoring data, T f Denotes the termination time of parameter identification, omega (theta) denotes the regularization term, W θ Weights, T-tables, representing parameters of synchronous generatorsShowing a matrix transpose.
In the sixth step, a nonlinear equation system
Has the following form:
wherein the subscript t represents the t-th moment, the subscript 0 represents the initial value of the variable,
and &>
Respectively representing discretized state variables, input signals and output states, n t Indicates the total number of time instants after all discretization, and>
represents a discretized non-linear function representing the dynamic process of the synchronous generator, and->
Represents a discretized non-linear function defining the output characteristic of the synchronous generator, C represents a function represented by {/H } {/H { (R } {)>
And &>
A discretized non-linear function is constructed, s represents a dataset variable of the form:
in the seventh step, the Jacobian matrices L and J have the following form:
where the index t denotes the t-th instant,
representing a partial derivative operator, s representing a data set variable, theta representing a parameter variable, Ψ representing a discretized objective function, and C representing a discretized nonlinear function characterizing generator dynamics and output characteristics.
In the seventh step, the Hessian matrix is expanded
Has the following form:
wherein H θθ Representing a discretized non-linear function C characterizing the dynamic and output characteristics of the generator, a Hessian matrix, H, of only parametric variables θ H represents that the discretization nonlinear function C only sums the hessian matrix of the data set variable with the discretization objective function psi only sums the hessian matrix of the data set variable, and T represents the matrix transposition.
Application example
A computing program for realizing the on-line identification method (namely the invention) of the dynamic parameters of the generator based on the Hi-Honuofu regularization is developed by using a Matlab programming language, and a generator which is assembled with the method is used
The PC with the internal memory of X5650@2.67HzCPU and 24GB completes the test and verification work of the invention.
In the implementation method, an excitation voltage signal and a generator terminal voltage signal of an actually operated unit in Zhejiang province subjected to certain disturbance are collected by an online measuring device to serve as input data of a synchronous generator, and a generator terminal current signal, an active power signal and a reactive power signal are collected to serve as output data of the generator. The nameplate parameter information and identification parameter information of the synchronous generator used for the test represented in table 1 are used in the synchronous generator modeling process by using a 4-axis 6-order complex synchronous generator model and considering the saturation characteristics (as shown in fig. 2). In this practical example of the present invention, the regularization parameter a is 1.0.
Table 1: parameter identification result of synchronous generator
Fig. 3 shows that the dynamic parameter identification result of the invention is used for fitting the active output curve of the synchronous generator, and the comparison between the nameplate parameter fitting active output curve of the synchronous generator and the actual active curve measured by the online measuring device shows that the parameter identification result obtained by the method of the invention is accurate, can represent the dynamic behavior of the synchronous generator, and can be applied to the actual operation unit.
Claims (6)
1. A generator dynamic parameter online identification method based on Hi-Honow regularization is characterized by comprising the following steps:
the first step is as follows: constructing a standard six-order model of the synchronous generator, and considering the saturation characteristic of the model;
the second step is that: collecting signals of generator terminal current, generator terminal voltage, excitation voltage, generator terminal active power and reactive power before and after power grid disturbance, and establishing a database;
the third step: taking an excitation voltage signal and a terminal voltage signal in a database as input signals u, and taking a terminal current signal, an active power signal and a reactive power signal as output signals y;
the fourth step: constructing a corresponding differential algebraic equation set F (u, theta) to represent the dynamic process and the output state of the corresponding disturbance generator according to the input signal u and the parameter variable theta of the synchronous generator in the database;
the fifth step: introducing a generator nameplate parameter theta by using a HiHonow regularization method ref As a priori knowledge, and in combination with the output signal y, a parameter identification objective function is constructed
And a sixth step: using implicit trapezoidal integral formula to differentiate algebraic equation set F (u, theta) and identify target function
Discretizing, and using the discretized non-linear equation set
And Ψ, the description of,
representing the discretized input signal and initializing the data set variables s and parameter variables θ to make the system of nonlinear equations
If true;
the seventh step: calculating a data set variable s and a parameter variable theta to an objective function psi and a nonlinear system of equations
Jacobian matrices L and J, and Hessian matrix H;
eighth step: and solving by using an interior point algorithm to obtain dynamic parameters of the generator.
2. The on-line identification method for the dynamic parameters of the generator based on the Hi-Honow regularization as recited in claim 1, wherein: in the first step, a standard sixth-order model of the synchronous generator considering the saturation characteristic is as follows:
wherein, delta represents the power angle of the synchronous generator, omega represents the rotating speed of the synchronous generator, omega s Indicating the synchronous speed of the grid, T j Representing the inertial time constant, P, of the synchronous generator m Representing mechanical power of synchronous generators, P e Represents the electrical power of the synchronous generator;
T 0 ' represents the transient open-circuit time constant, T, of the shafting winding 0 "represents the sub-transient open-circuit time constant of the shafting winding, E' represents the transient potential of the generator, E" represents the sub-transient potential of the generator, V f Representing the voltage of a generator no-load stator, I representing the current of the generator stator, X representing the synchronous reactance of the generator, X 'representing the transient reactance of the generator, X' representing the sub-transient reactance of the generator, subscripts d and q representing the corresponding physical quantities of the d-axis and q-axis of the synchronous generator, respectively, X l Representing the leakage reactance of the generator, sat representing the flux linkage saturation of the shafting, A s And B s Is a parameter, V, determined by fitting the saturation characteristics of the synchronous generator qt Representing the q-axis voltage amplitude, V dt Representing the d-axis voltage magnitude.
3. The on-line identification method of generator dynamic parameters based on the Hi-Honow regularization according to claim 1, characterized in that: in the fifth step, the parameter identifies the target
Has the following form:
wherein the content of the first and second substances,
Ω(θ)＝(θ-θ ref ) T W θ T W θ (θ-θ ref )，
where a represents the regularization parameter, y represents the generator output state m Representing the measured output signal, W representing the corresponding sampling error when the corresponding signal is acquired by the on-line monitoring data, T f Denotes the termination time of parameter identification, omega (theta) denotes the regularization term, W θ Representing the weights of the parameters of the synchronous generator and T representing the matrix transposition.
4. The on-line identification method of generator dynamic parameters based on the Hi-Honow regularization according to claim 1, characterized in that: in the sixth step, a nonlinear equation system
Has the following form:
wherein the subscript t represents the t-th moment, the subscript 0 represents the initial value of the variable,
and
respectively representing discretized state variables, input signals and output states, n t Representing the total number of time instants after all discretization,
representing discretized non-representation of dynamic processes characterizing synchronous generatorsThe function of the linear function is a function of,
a discretized non-linear function defining the output characteristics of the synchronous generator, C being represented by
And
a discretized non-linear function is constructed, s represents a dataset variable of the form:
5. the on-line identification method for the dynamic parameters of the generator based on the Hi-Honow regularization as recited in claim 1, wherein: in the seventh step, the Jacobian matrices L and J have the following form:
where the index t denotes the t-th instant,
representing a partial derivative operator, s representing a data set variable, theta representing a parameter variable, Ψ representing a discretized objective function, and C representing a discretized nonlinear function characterizing generator dynamics and output characteristics.
6. The on-line identification method for the dynamic parameters of the generator based on the Hi-Honow regularization as recited in claim 1, wherein: in the seventh step, the Hessian matrix is expanded
Has the following form:
wherein H θθ Representing a discretized non-linear function C characterizing the dynamic and output characteristics of the generator, a Hessian matrix, H, of only parametric variables θ H represents that the discretization nonlinear function C only sums the hessian matrix of the data set variable with the discretization objective function psi only sums the hessian matrix of the data set variable, and T represents the matrix transposition.
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| RU2003163C1 (en) * | 1990-11-05 | 1993-11-15 | Lashchev Anatolij Ya | System for control of non-stationary non-liner object with reference model |
| CA2301712A1 (en) * | 1997-08-22 | 1999-03-04 | Centro Nacional De Investigaciones Cientificas (Cnic) | System and method for the tomography of the primary electric current of the brain and the heart |
| CN105808833A (en) * | 2016-03-03 | 2016-07-27 | 国网浙江省电力公司电力科学研究院 | Online parameter identification method of parallel synchronous generator based on multi-data sets |
| CN106786567A (en) * | 2017-03-02 | 2017-05-31 | 国家电网公司 | A kind of online load modeling method based on PMU noise like data |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| RU2003163C1 (en) * | 1990-11-05 | 1993-11-15 | Lashchev Anatolij Ya | System for control of non-stationary non-liner object with reference model |
| CA2301712A1 (en) * | 1997-08-22 | 1999-03-04 | Centro Nacional De Investigaciones Cientificas (Cnic) | System and method for the tomography of the primary electric current of the brain and the heart |
| CN105808833A (en) * | 2016-03-03 | 2016-07-27 | 国网浙江省电力公司电力科学研究院 | Online parameter identification method of parallel synchronous generator based on multi-data sets |
| CN106786567A (en) * | 2017-03-02 | 2017-05-31 | 国家电网公司 | A kind of online load modeling method based on PMU noise like data |
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