CN112653158B - Frequency response characteristic identification method for doubly-fed wind power plant with additional frequency control - Google Patents
Frequency response characteristic identification method for doubly-fed wind power plant with additional frequency control Download PDFInfo
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Abstract
The embodiment of the invention discloses a method for identifying frequency response characteristics of a doubly-fed wind power plant with additional frequency control, relates to the technical field of wind power, and can improve the frequency analysis efficiency of a wind power system. The invention comprises the following steps: and preprocessing the collected wind field data to obtain parameters related to the average wind speed of the wind power plant, the frequency of a grid-connected point and grid-connected power. And according to whether the subsequences are stable or not, dividing the subsequences related to the average wind speed of the wind power plant and the frequency of the grid-connected point into different categories. And acquiring the characteristic parameters of the low-frequency dominant component by utilizing the sub-sequence related to the grid-connected power. And performing reconstruction processing by using the low-frequency dominant component characteristic parameters and different types of subsequences to obtain a reconstructed power subsequence. And firstly, acquiring the frequency response characteristic under the stable wind speed working condition by using the reconstructed power subsequence. And secondly, determining a corresponding relation between the wind speed and the frequency response characteristic, and analyzing the frequency response characteristic of the doubly-fed wind power plant by utilizing the corresponding relation.
Description
Technical Field
The invention relates to the technical field of wind power, in particular to a frequency response characteristic identification method for a double-fed wind power plant with additional frequency control.
Background
With the large-scale construction of wind generating sets, wind power gradually constitutes one of the main sources of electric energy production in China and becomes an important characteristic of new energy electric power systems. However, for a wind power plant comprising a plurality of wind driven generators with frequency modulation function, the dynamic characteristics of each unit in the wind power plant are obviously different. If the single unit and the additional frequency controller thereof are used as basic units to carry out the integration of the whole frequency response characteristics of the wind field, the problem of dimension disaster exists. And the parameters of the wind farm are difficult to obtain due to the existence of the current collection line. Therefore, the method becomes an effective alternative scheme for simplifying the wind power field analysis process by identifying the parameters of the frequency response characteristics of the double-fed wind power field participating in frequency modulation.
The existing common method is to analyze the output response of the wind field participating in the primary frequency modulation through the dynamic equivalence of the wind power plant, but the accuracy of the analysis depends on the rationality of the equivalent fan frequency modulation representation. In addition, the idea of adding frequency modulation control to the equivalence machine has the problems of complex equivalence process, high algorithm order, poor engineering practicability and the like. Therefore, in practical application, for the frequency response analysis process of the wind power plant with the additional frequency control, the problems of complex analysis process and difficult parameter acquisition exist.
Therefore, the whole response analysis process needs to be further optimized, and the key point is how to optimize the effect of parameter identification, so that the frequency response characteristic analysis process of the wind farm can be further simplified, and the frequency response analysis efficiency of the wind power-containing power system is improved.
Disclosure of Invention
The embodiment of the invention provides a method for identifying the frequency response characteristic of a doubly-fed wind power plant with additional frequency control, which can simplify the analysis process of the frequency response characteristic of the wind power plant and improve the frequency response analysis efficiency of a wind power system with wind power.
In order to achieve the above purpose, the embodiment of the invention adopts the following technical scheme:
s1, collecting wind field data to obtain sequences of average wind speed, grid-connected point frequency and grid-connected power of the wind power field, and dividing the sequences into subsequences with the same length;
s2, normalizing the subsequence of the average wind speed of the wind power plant and the frequency of the grid-connected point, judging the state of the subsequence, and dividing the subsequence into different categories according to whether the subsequence is stable or not;
s3, preprocessing the grid-connected power subsequence, and estimating parameters of low-frequency components of the grid-connected power subsequence;
s4, reconstructing the low-frequency component of the power subsequence in the form of the sum of the sequence attenuation indexes, and obtaining the expression of the low-frequency component of the power subsequence in the S2 under different subsequence states;
s5, under the stable wind speed working condition, estimating the dynamic response characteristic between grid-connected power and frequency by using the frequency disturbance and the corresponding reconstructed power subsequence;
and S6, removing disturbance of grid-connected power caused by frequency disturbance under wind speed disturbance by using the transfer characteristics determined in S5, estimating the frequency response characteristics of the wind farm by using the wind speed, the frequency sequence and the power corresponding to the wind speed and the frequency sequence, and displaying the result of estimating the frequency response characteristics of the wind farm on an employee terminal.
The embodiment of the invention provides wind for a double-fed wind driven generatorFirstly, establishing a doubly-fed wind power plant frequency response model with balanced complexity and precision and containing additional frequency control; secondly, acquiring dynamic processes of wind speed, frequency and grid-connected power, and identifying parameters of the frequency response model by combining a matrix beam method and a least square method. Finally, considering the coupling among wind speed, power and frequency, it is necessary to estimate the G under steady wind speed pω (z) then identifying G by linear approximation and superposition cancellation pv (z), the specific process is embodied at steps 7 and 8. Therefore, the problem that the frequency response model parameters of the doubly-fed wind power plant with the additional frequency control are difficult to obtain is effectively solved, the modeling process of the wind power plant is simplified, and the frequency response analysis efficiency of the wind power system is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flowchart of a specific example of a frequency response characteristic identification process of a doubly-fed wind farm with additional frequency control.
Fig. 2 is a schematic flow chart of a method according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the present invention will be described in further detail with reference to the accompanying drawings and specific embodiments. Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention. As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or coupled. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items. It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The embodiment belongs to a scheme in the field of frequency analysis of a wind power system, and particularly relates to a method for identifying frequency response analysis characteristic parameters of a doubly-fed wind power plant with additional frequency control. The general design idea of the embodiment is as follows: firstly, establishing a frequency response model of the doubly-fed wind generator, and obtaining a 2-order discrete model G of the frequency response of the doubly-fed wind generator by using singular perturbation and Rous approximation pω (z) and G pv And (z) taking the frequency response model as a double-fed wind farm frequency response model mathematical form. Setting sampling parameters and collecting average wind speed u of wind field vi Frequency u of grid-connected points ωi And power y i And (i is 1,2 and …), normalizing the wind speed and frequency sequence and dividing the sequence state combination. For any grid-connected power sequence y i Construct the Hankel matrix Y (N-1)×(L+1) And performing SVD (singular value decomposition), and reserving the right unitary matrix V corresponding to the first M singular values 1 、V 2 Calculating
The generalized eigenvalues of (1). And then by the generalized eigenvalue z h H 1,2, … M to calculate the low frequency dominant feature parameter p h Reconstructing the low frequency component y i . Then, considering the coupling of wind speed, frequency and power, the power sequence and the corresponding frequency sequence are used to perform a single frequency response characteristic
And (6) estimating. Finally, adopting a linear approximation and superposition cancellation mode to carry out uniform identification on wind speed and frequency response characteristics, and identifying G pω (z) and G pv And (z) determining a numerical expression of the frequency response model of the doubly-fed wind farm with the additional frequency control. The simulation or actual measurement data are utilized to carry out parameter identification on the wind power plant frequency response model participating in frequency modulation, so that the modeling process of the wind power plant is simplified, and the frequency response analysis efficiency of the wind power system is improved. The method has the advantages that the measurement data are fully utilized to carry out parameter identification on the frequency response model, the difficulty in modeling and parameter acquisition caused by inconsistent running states of the wind power plant and the power collection line is avoided, the modeling process of the wind power plant is simplified, and the analysis efficiency of the wind power system is improved.
The embodiment of the invention provides a method for identifying frequency response characteristic parameters of a doubly-fed wind power plant with additional frequency control, which comprises the following steps:
and S1, collecting wind field data to obtain a sequence of the average wind speed of the wind power plant, the frequency of a grid-connected point and grid-connected power, and dividing the sequence into subsequences with the same length.
The data sequence of the average wind speed of the wind power plant, the frequency of the grid-connected point and the grid-connected power comprises the following steps: wind farm average wind speed sequence u v Frequency sequence u of grid-connected points v And grid-connected power sequence y and subsequent deformation of various parameters, wherein the parameters are parameters for reflecting performances of average wind speed of the wind power plant, frequency of grid-connected points, grid-connected power and the like.
S2, normalizing the subsequence of the average wind speed of the wind power plant and the frequency of the grid-connected point, judging the state of the subsequence, and dividing the subsequence into different categories according to whether the subsequence is stable or not.
And S3, preprocessing the grid-connected power subsequence, and estimating parameters of low-frequency components of the grid-connected power subsequence.
S4, reconstructing the low-frequency component of the power subsequence in the form of the sum of the sequence attenuation indexes, and obtaining the expression of the low-frequency component of the power subsequence in the S2 under different subsequence states.
And S5, under the stable wind speed working condition, estimating the dynamic response characteristic between grid-connected power and frequency by using the frequency disturbance and the corresponding reconstructed power subsequence.
And S6, removing disturbance of grid-connected power caused by frequency disturbance under wind speed disturbance by using the transfer characteristics determined in S5 to obtain a final analysis result, and displaying the result obtained by estimating the frequency response characteristics of the wind power plant on an employee terminal.
The employee terminal may specifically be a work computer, a notebook computer, a smart phone, and the like of an operation and maintenance technician of the wind turbine generator system, and the operation and maintenance technician usually connects the work computer thereof with a computer module of the wind turbine generator system through a data interface or a wireless network, so as to implement digital operation and maintenance.
Specifically, the wind speed and the frequency can be used as input, the grid-connected power corresponding to the wind speed and the frequency can be used as output, and the transfer function of the wind speed and the frequency response characteristic can be identified by using a least square method. The obtained result is displayed on the staff terminal, and related personnel can analyze the frequency response characteristic of the wind power plant in the time period after the wind power plant is connected to the power system by using the frequency response characteristic, so that the analysis process of the frequency response characteristic of the wind power plant is simplified, and the analysis efficiency of the frequency response characteristic of the wind power system is improved.
Specifically, the S1 includes: collecting wind field data according to the set sampling time interval delta T and the total sampling time length T to obtain the average wind speed sequence u of the wind power plant v Grid connectionFrequency sequence u of points ω And grid-connected power sequence y. Respectively dividing the three sequences into m subsequences to obtain u vi ,u ωi ,y i I is 1,2, …, m is the number of segments into which the sequence is divided, and the subsequence length is N is T/(m Δ T).
Specifically, the S2 includes: for u is paired vi And u ωi Performing a normalization process, wherein the normalization process is based on the sequence
u i To require a normalized sequence, max (u) i ) To require normalization of the maximum value in the sequence, x i Denotes the sub-sequence that needs to be normalized, is substituted into x i Is composed of u vi Or u ωi . Determining the stationarity of the subsequence, wherein if
Satisfy the requirement of
Then it is judged as a stationary subsequence
Otherwise, it is a non-stationary subsequence
Wherein the content of the first and second substances,
denotes the mean value of the sequence, d max For stationary decision thresholds, superscripts "-" or "-" are used to identify stationary and non-stationary subsequences, respectively, N represents the length of the subsequence,
the representation represents any one element in the sequence. According to the judgment result of the stationarity of the subsequence, u is subjected to vi And u ωi Three types of combinations are divided, including:
and
specifically, the S3 includes: to any one of the belonged grid-connected power subsequences y i Constructing the Hankel matrix Y (N-L)×(L+1) Where L is a beam parameter. For Y (N-L)×(L+1) Performing SVD decomposition, and reserving a right unitary matrix V corresponding to M singular values of a low frequency division number, wherein M is Y (N-L)×(L+1) Singular value of σ h Determine if
Then M is h, where γ is the screening threshold and the ratio is below 10 γ Sigma of h As noise singular values, σ max Is the largest singular value. And performing low-frequency component parameter estimation according to the SVD decomposition result to obtain low-frequency dominant component characteristic parameters. And L is N/2 to N/3.
Wherein the low frequency component parameter estimation comprises: in the result of SVD, the last row and the first row of the right unitary matrix V are respectively removed to obtain V 1 And V 2 . Then calculate
Characteristic value z of h ,h＝1,2,…,M，。
According to the generalized eigenvalue z h Characteristic parameter p of the low-frequency dominant component h Can be composed of
Is obtained by the least squares solution of (a).
Specifically, the S4 includes:
characteristic parameter p according to low-frequency dominant component h And a generalized eigenvalue z h Pair subsequence y i Performing a reconstruction, wherein the sequence is reconstructed
ObtainingReconstructing the time domain of a sequence
To y i (T) sampling at time intervals Δ T and sampling times T, and correlating
Reconstructed subsequence is denoted y pi To correspond to
Is recorded as y qi Wherein the amplitude A of the component h h Initial phase θ h Attenuation factor alpha h And a frequency parameter f h Respectively as follows:
s5 may be understood as a process of calculating a single frequency response characteristic estimate. Under the stable working condition of wind speed, determining single frequency response characteristic by using frequency disturbance and corresponding power disturbance sequence thereof
In particular to all affiliations
A sub-sequence combination of types to
And y pi -y pi0 As input and output, respectively, where u ωi0 ,y pi0 The initial state of the frequency and grid-connected power subsequence. The accuracy of the parameter vector theta to be identified can be measured by the sum of squared errors
In the formula:
m p is a sub-sequence combination
The total number of (c). According to the function extreme value theorem, when the sum of squared errors has a minimum value, a least square estimation solution of the parameter vector theta to be identified can be obtained, namely theta (phi) is obtained ι T Φ ι ) -1 Φ ι T (y pi -y pi0 ) Estimating a least squares solution of θ, wherein
Finally, a single frequency response characteristic is preliminarily determined from the parameter vector theta
Specifically, S6 includes: preliminary modeling may be performed in advance: establishing a frequency response model of the doubly-fed wind generator, and deducing a 2-order discrete expression G of the frequency response of the doubly-fed wind generator by using a singular perturbation and a Rous approximation method pω (z) and G pv (z) and taking it as a form of a wind farm frequency response model. And then, uniformly identifying the wind speed and frequency response characteristics. The input is disturbance of frequency and wind speed sequence, the output is disturbance of power sequence, and the discrete transfer function G of wind speed and frequency response characteristic with consistent pole distribution can be obtained by utilizing the least square method pω (z) and G pv (z). For example: let the input be u ωi -u ωi0 And u vi -u vi0 Let the output be y ci Where θ is (Φ) ι T Φ ι ) -1 Φ ι T y ci Estimating the least square solution of theta to obtain the discrete transfer function G of the wind speed and frequency response characteristic with consistent pole distribution pω (z) and G pv (z)。
Specifically, the input sequence is u ωi -u ωi0 And u vi -u vi0 Wherein u is vi0 Is the initial state of the wind speed sequence, and when u ωi Attribution
When type is u ωi -u ωi0 Replaced with 0. Let the output sequence be y ci The concrete expression is
The sum of the squares of the errors at this time can be expressed as
According to the formula θ ═ phi ι T Φ ι ) -1 Φ ι T y ci Estimating the least square solution of theta, and obtaining the least square solution of the vector theta to be identified (phi iota) according to the sum of the square errors and the minimum value T Φ ι ) -1 Φ ι T y ci In the formula:
wherein:
obtaining the wind speed and frequency response characteristic discrete transfer function G with consistent pole distribution by the parameter vector theta pω (z) and G pv (z)。
In the embodiment, the response characteristic G of the grid-connected power of the doubly-fed wind generator to the grid frequency disturbance is established on the basis of the doubly-fed wind generator model pω (z) response characteristics G of grid-connected power to wind speed disturbance pv And (z) further determining a DFIG low complexity form containing additional frequency control, and taking the DFIG low complexity form as a mathematical expression of a frequency response model of the doubly-fed wind farm. Setting sampling parameters and collecting the average wind speed u of the wind field vi Frequency u of grid-connected point ωi And power y i And (i is 1,2, …), normalizing the wind speed and frequency sequence and dividing the sequence state combination. For any grid-connected power sequence y i Constructing a Hankel matrixY (N-1)×(L+1) And performing SVD (singular value decomposition), and reserving the right unitary matrix V corresponding to the first M singular values 1 、V 2 Calculating
The generalized eigenvalues of (1). And then by the generalized eigenvalue z h Calculating the low-frequency dominant characteristic parameter p by h-1, 2, … M h Reconstructing the low frequency component y i . Then, considering the coupling of wind speed, frequency and power, the power sequence and the corresponding frequency sequence are used to perform a single frequency response characteristic
And (6) estimating. Finally, the wind speed and frequency response characteristics are uniformly identified by adopting a linear approximation and superposition offset mode, and G is identified pω (z) and G pv And (z) determining a numerical expression of the frequency response characteristic of the doubly-fed wind farm with the additional frequency control. The method provides an effective way for the difficulty in obtaining the frequency response model parameters of the doubly-fed wind power plant, and improves the efficiency of frequency response analysis of the wind power-containing power system.
In the practical application of this embodiment, a person skilled in the art may also implement the scheme of this embodiment according to the operation sequence of steps 1 to 8.
As shown in fig. 1, the method for identifying the frequency characteristics of the doubly-fed wind farm with the additional frequency control comprises the following steps:
step 1: establishing a frequency response model of the doubly-fed wind generator, and deriving a 2-order discrete expression G of the frequency response of the doubly-fed wind generator by using a singular perturbation and Laus approximation method pω (z) and G pv (z) and taking it as a form of a wind farm frequency response model.
Step 1.1: and establishing a frequency response model of the doubly-fed wind generator based on the relation between the small signal model of the doubly-fed wind generator and the power flow direction.
Step 1.2: 2-order discrete expression G of frequency response of doubly-fed wind generator is deduced by using singular perturbation and Laus approximation method pω (z) and G pv (z)And the frequency response model is taken as a form of a wind power plant frequency response model.
Wherein, the frequency response of the doubly-fed wind generator is a 2-order reduced discrete model
In the formula: a is 1 ,a 0 ,b 2 ,b 1 ,b 0 ,c 1 ,c 0 Is a parameter to be identified. It is taken as a low complexity form of the wind farm frequency model.
Step 2: and collecting wind field data. Setting sampling parameters and acquiring average wind speed sequence u of wind power plant v Frequency sequence u of grid-connected points v And a grid-connected power sequence y. And dividing the three into m subsequences u vi ,u ωi ,y i ,i＝1,2,…,m。
Step 2.1: setting a sampling time interval delta T and a total sampling time length T, and simultaneously collecting average wind speed (wind speed of each monitoring point of a wind field is averaged), frequency of a grid-connected point and grid-connected power data to form an average wind speed, grid frequency and grid-connected power sequence u v ,u ω And y.
Step 2.2: dividing the three into m segments with equal length, each subsequence being u vi ,u ωi And y i When i is 1,2, … m, the subsequence length is N ═ T/(m Δ T);
and 3, step 3: and judging the state of the subsequence. For average wind speed and grid frequency sub-sequence u vi ,u ωi And (5) carrying out normalization processing so as to judge the stationarity of the sequence and further divide the state of the molecular sequence.
Step 3.1: for average wind speed and grid frequency sub-sequence u vi ,u ωi And (6) carrying out normalization processing. The normalized sequence is
In the formula: u. of i To require a normalized sequence, max (u) i ) The maximum in the sequence needs to be normalized.
Step 3.2: and judging the stationarity of the subsequence. The determination condition is as follows
Satisfy the requirement of
Then it is judged as a stationary subsequence
Otherwise, it is a non-stationary subsequence
Wherein x i Denotes the normalized subsequence u vi Or u ωi ，
Denotes the mean value of the sequence, d max For stationary decision thresholds, superscripts "-" or "-" are used to identify stationary and non-stationary subsequences, respectively.
Step 3.3: according to whether the subsequence is stable or not, the wind speed and frequency subsequence is divided into three categories
And
and 4, step 4: and (4) preprocessing a power sequence. For any one of the belonged grid-connected power subsequences y i Construct the Hankel matrix Y (N-L)×(L+1) And performing SVD decomposition, and reserving a right unitary matrix V corresponding to the first M singular values, wherein the beam parameter L is usually N/3-N/2.
Step 4.1: to any one of the belonged grid-connected power subsequences y i Constructing the Hankel matrix Y (N-L)×(L+1) In the matrix, L is a beam parameter, and is usually selected to be N/2 to N/3 in order to filter out high-frequency noise.
Step 4.2: for Y (N-L)×(L+1) Performing SVD, and reserving a right unitary matrix V corresponding to M singular values of low frequency division number, wherein M can be Y (N-L)×(L+1) Singular value of σ h Determine if σ h When the following formula is satisfied, M ═ h.
In the formula: gamma is the screening threshold, indicating a ratio below 10 γ σ of (a) h Considered as noise singular values, σ max Is the largest singular value.
And 5: and estimating the low-frequency component parameters. Calculating the generalized eigenvalue of the Y matrix beam after SVD decomposition and denoising, i.e. solving
Characteristic value z of h H is 1,2, …, M, wherein V 1 And V 2 The last row and the first row are respectively removed by V. Thus, the low frequency dominant component characteristic parameter p h Obtained by using a least square method.
Step 5.1: respectively removing the last row and the first row of the right unitary matrix V to obtain V 1 And V 2 。
Step 5.2: calculating out
Characteristic value z of h ,h＝1,2,…,M。
Step 5.3: characteristic parameter p of low-frequency dominant component h Can be obtained from the least squares solution of
Step 6: and (5) reconstructing low-frequency components. In sequence decay exponential and form
Reconstruction of y i Further, the amplitude, initial phase, attenuation factor and frequency parameter of the component h can be calculated and will correspond to
Is recorded as y pi To correspond to
Is recorded as y qi 。
Step 6.1: from the characteristic parameter p of step 5 h And z h In decaying exponential and form
Reconstructed sequence y i 。
Step 6.2: amplitude A of component h h Initial phase θ h Attenuation factor alpha h And a frequency parameter f h Is solved as follows
Step 6.3: the time domain expression of the reconstructed sequence is
For y i (T) samples at time intervals Δ T, sample times T, and will correspond to
Reconstructed subsequence is denoted y pi To correspond to
The reconstructed power subsequence is marked as y qi 。
And 7: a single frequency response characteristic estimate. Under the working condition of stable wind speed, determining single frequency by using frequency disturbance and corresponding power disturbance sequence thereofResponse characteristic
The concrete method is as follows: for all attribution
A sub-sequence combination of types to
And y pi -y pi0 As input and output, respectively. By theta ═ phi ι T Φ ι ) -1 Φ ι T (y pi -y pi0 ) Estimating a least squares solution of theta to initially determine a single frequency response characteristic
Step 7.1: for all affiliations
A sub-sequence combination of types to
And y pi -y pi0 As input and output, respectively, where u ωi0 ,y pi0 The initial state of the frequency and grid-connected power sub-sequence. The accuracy of the parameter vector theta to be identified can be measured by the sum of squared errors
In the formula:
m p is a sub-sequence combination
The total number of (c).
And 7.2: according to a functionThe extremum theorem, when the sum of squared errors in step 7.1 has a minimum value, may obtain a least-squares estimation solution of the parameter vector θ to be identified, i.e., θ ═ Φ ι T Φ ι ) -1 Φ ι T (y pi -y pi0 ) Estimating a least squares solution of θ, wherein
Step 7.3: preliminary determination of a single frequency response characteristic from a parameter vector theta
And step 8: and uniformly identifying the wind speed and frequency response characteristics. The input is the disturbance of the frequency and wind speed sequence, the output is the disturbance of the power sequence, and the discrete transfer function G of the wind speed and frequency response characteristics with consistent pole distribution can be obtained by utilizing the least square method pω (z) and G pv (z). For example: let the input be u ωi -u ωi0 And u vi -u vi0 Let the output be y ci According to the formula θ (═ Φ) ι T Φ ι ) -1 Φ ι T y ci Estimating the least square solution of theta to obtain the discrete transfer function G of the wind speed and frequency response characteristics with consistent pole distribution pω (z) and G pv (z)。
Step 8.1: the input sequence is u ωi -u ωi0 And u vi -u vi0 Wherein u is vi0 Is the initial state of the wind speed sequence, and when u ωi Attribution
When type u is ωi -u ωi0 Replaced with 0.
Step 8.2: let the output sequence be y ci The concrete expression is
Step 8.3: the sum of the squares of the errors at this time can be expressed as
Push type
θ＝(Φ ι T Φ ι ) -1 Φ ι T y ci Estimating the least square solution of theta, and obtaining the least square solution of the vector theta to be identified (phi) according to the sum of the squared errors and the minimum value ι T Φ ι ) -1 Φ ι T y ci In the formula:
wherein the content of the first and second substances,
step 8.4: obtaining a wind speed and frequency response characteristic discrete transfer function G with consistent pole distribution by the parameter vector theta pω (z) and G pv (z)。
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, the apparatus embodiments are substantially similar to the method embodiments and therefore are described in a relatively simple manner, and reference may be made to some of the description of the method embodiments for relevant points. The above description is only for the specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.
Claims (2)
1. A frequency response characteristic identification method for a doubly-fed wind farm with additional frequency control is characterized by comprising the following steps:
s1, collecting wind field data to obtain sequences of average wind speed, grid-connected point frequency and grid-connected power of the wind power field, and dividing the sequences into subsequences with the same length;
s2, normalizing the subsequence of the average wind speed of the wind power plant and the frequency of the grid-connected point, judging the state of the subsequence, and dividing the subsequence into different categories according to whether the subsequence is stable or not;
s3, preprocessing the grid-connected power subsequence, and estimating parameters of low-frequency components of the grid-connected power subsequence;
s4, reconstructing the low-frequency component of the power subsequence in the form of the sum of the sequence attenuation indexes, and obtaining the expression of the low-frequency component of the power subsequence in the S2 under different subsequence states;
s5, under the stable wind speed working condition, estimating the dynamic response characteristic between grid-connected power and frequency by using the frequency disturbance and the corresponding reconstructed power subsequence;
s6, removing disturbance of grid-connected power caused by frequency disturbance under wind speed disturbance by using the transfer characteristics determined in S5, estimating the frequency response characteristics of the wind power plant by using the wind speed and the frequency sequence and the power corresponding to the wind speed and the frequency sequence, and displaying the result obtained by estimating the frequency response characteristics of the wind power plant on an employee terminal;
the S1 includes:
setting a sampling time interval delta T and a total sampling time length T, collecting wind field data to obtain a wind power plant average wind speed sequence u v Frequency sequence u of grid-connected points ω And a grid-connected power sequence y;
respectively dividing the three sequences into m subsequences to obtain u vi ,u ωi ,y i I is 1,2, …, m is the number of segments into which the sequence is divided, and the subsequence length is N is T/(m Δ T);
the S2 includes:
for u to u vi And u ωi Performing a normalization process, wherein the normalization process is based on the sequence
u i Max (u) for sequences requiring normalization i ) To normalize the maximum value in the sequence, x i Representing the sub-sequence to be normalized, is brought into x i Is composed of u vi Or u ωi ；
Judging the stationarity of the subsequences if
Satisfy the requirement of
Then it is judged as a stationary subsequence
Otherwise, it is a non-stationary subsequence
Wherein the content of the first and second substances,
denotes the mean value of the sequence, d max For the stationary decision threshold, the superscript "-" or "-" is used to identify stationary and non-stationary subsequences, respectively, N represents the length of the subsequence,
represents any element in the sequence;
according to the judgment result of the stationarity of the subsequence, u is subjected to vi And u ωi Three types of combinations are divided, including:
and
the S3 includes:
for any one of the belonged grid-connected power subsequences y i Construct the Hankel matrix Y (N-L)×(L+1) Wherein L is a beam parameter;
for Y (N-L)×(L+1) Performing SVD decomposition, and reserving a right unitary matrix V corresponding to M singular values of a low frequency division number, wherein M is Y (N-L)×(L+1) Singular value of σ h Determine if
Then M is h, where γ is the screening threshold and the ratio is below 10 -γ σ of (a) h As singular values of noise, σ max Is the maximum singular value;
performing low-frequency component parameter estimation according to the SVD decomposition result to obtain low-frequency dominant component characteristic parameters;
the low frequency component parameter estimation comprises:
in the result of SVD, the last row and the first row of the right unitary matrix V are respectively removed to obtain V 1 And V 2 (ii) a Then calculate
Is a generalized eigenvalue z h ,h＝1,2,…,M；
According to the generalized eigenvalue z h Characteristic parameter p of the low-frequency dominant component h Can be composed of
Obtaining a least square solution;
the S4 includes:
according to p h And z h Pair subsequence y i Performing a reconstruction, wherein the sequence is reconstructed
m is the number of segments into which the sequence is divided;
further obtaining a time domain expression of the reconstruction sequence
To y i (T) samples at intervals of time Δ T, with a sampling time T, will correspond
Reconstructed subsequence is denoted y pi To correspond to
Is recorded as y qi Wherein the amplitude A of the component h h Initial phase θ h Attenuation factor alpha h And a frequency parameter f h Respectively as follows:
the S5 includes:
for all attribution
A sub-sequence combination of types to
And y pi -y pi0 Respectively as input and output, estimating parameter vector theta by using least square method to further determine frequency response characteristic
Wherein θ ═ Φ l T Φ l ) -1 Φ l T (y pi -y pi0 )，u ωi0 ,y pi0 For the initial state of the frequency and grid-connected power sub-sequence,
the S6 includes:
unified identification of wind speed and frequency response, with the input sequence u ωi -u ωi0 ,u vi -u vi0 And when u is ωi Attribution
When type u is ωi -u ωi0 Substitution with 0; output sequence y ci Obtaining the parameter value of the vector theta to be identified by utilizing a least square method, and further determining the frequency response characteristic G of the DFIG pω (z) and G pv (z) wherein, in the above,
2. the method of claim 1, wherein L is N/2 to N/3.
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