CN113193572B - Method for realizing fan control by modeling wind power frequency modulation parameter probability distribution - Google Patents

Method for realizing fan control by modeling wind power frequency modulation parameter probability distribution Download PDF

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CN113193572B
CN113193572B CN202110481274.7A CN202110481274A CN113193572B CN 113193572 B CN113193572 B CN 113193572B CN 202110481274 A CN202110481274 A CN 202110481274A CN 113193572 B CN113193572 B CN 113193572B
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frequency modulation
fan
wind power
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probability distribution
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CN113193572A (en
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李海峰
阎诚
金涛
胥国毅
毕天姝
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State Grid Jiangsu Electric Power Co Ltd
North China Electric Power University
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State Grid Jiangsu Electric Power Co Ltd
North China Electric Power University
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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
    • 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
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • 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]
    • 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y02E10/76—Power conversion electric or electronic aspects

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Abstract

The invention discloses a method for realizing fan control by modeling wind power frequency modulation parameter probability distribution, which fully considers information interaction time delay among devices in wind power frequency modulation control during modeling, realizes self-adaptive adjustment of wind power frequency modulation parameters, reflects and evaluates frequency change capability of a wind power response system by fine modeling of the wind power frequency modulation control parameter probability distribution, and is beneficial to improving the stability of system operation.

Description

Method for realizing fan control by modeling wind power frequency modulation parameter probability distribution
Technical Field
The invention relates to the field of frequency stability control of a power system, in particular to a method for realizing fan control by modeling wind power frequency modulation parameter probability distribution.
Background
Along with the improvement of the permeability of wind power in an electric power system, the capability of the electric power system for independently carrying out frequency modulation by depending on a traditional power supply is continuously weakened, and the wind power is required to have the potential of being matched with a traditional thermal power generating unit to participate in system frequency response. The wind turbine generator set has the characteristics of small single machine capacity, high active power regulation speed and the like, and the inertia response and primary frequency modulation processes of a conventional thermal power generating set can be simulated through an additional active power control link, so that the fan actively and quickly responds to frequency changes, and the frequency is prevented from being quickly reduced.
The traditional control strategy generally adopts preset fixed frequency modulation parameters, and has the problems of difficult parameter setting and the like. The wind power frequency modulation potential cannot be fully excavated in a high wind speed section due to the excessively conservative frequency modulation parameter value, and the excessive response of wind power to the system frequency is caused by an aggressive wind power frequency modulation parameter setting strategy, so that the frequency modulation burden of the system is increased. A large number of researches show that minute-level and even second-level wind speed turbulence exists in a wind power plant, so that the running state of a fan is full of uncertainty, and the wind power frequency response capability changes frequently. In the face of large-scale wind power grid-connected operation, a fixed parameter value-taking strategy cannot enable wind power to bear more frequency modulation tasks. In a research on stable operation of a power system, the dynamic characteristics of a wind turbine are generally ignored. How to consider the frequency control of the uncertainty of the wind power running state is a difficult problem to solve urgently.
In the existing research, the time delay of data sampling, instruction transmission and operation execution of system operation is not considered, and for the time-varying characteristic of the fan frequency response capability in the wind power plant, it is necessary to predict the control parameters related to fan frequency control in advance so as to accurately mine the frequency modulation value of the wind power. Meanwhile, uncertainty of wind power operation is considered, and distribution characteristics of wind power frequency modulation parameters need to be further researched for realizing fine scheduling of system operation. Therefore, the existing research method cannot accurately depict the prediction of the fan frequency modulation parameters and the distribution characteristics of the fan frequency modulation parameters, so that a fan control scheme needs to be improved.
Disclosure of Invention
The invention aims to provide a method for realizing fan control by modeling wind power frequency modulation parameter probability distribution, which is beneficial to fully exploiting wind power frequency modulation potential and improving the stability of system operation.
The purpose of the invention is realized by the following technical scheme:
a method for realizing fan control by modeling wind power frequency modulation parameter probability distribution comprises the following steps:
acquiring historical rotor rotating speed data recorded by a fan data sampling device and preprocessing the historical rotor rotating speed data;
setting fixed parameters of a fan frequency modulation controller in a current system scheduling period;
calculating the time sequence of ideal values of additional variable parameters of the fan frequency modulation controller on historical time points by combining the set fixed parameters of the fan frequency modulation controller and the preprocessed historical rotor rotating speed data with the wind power maximum response system frequency variation capacity;
classifying time sequences of ideal values at historical time points, predicting additional variable parameters of the fan frequency modulation controller through conditional kernel density estimation, generating probability density functions of different time points, calculating corresponding confidence intervals, and realizing fan control.
According to the technical scheme provided by the invention, the information interaction time delay among the devices in the wind power frequency modulation control is fully considered during modeling, the self-adaptive adjustment of the wind power frequency modulation parameters is realized, and the wind power frequency modulation control parameter probability distribution is subjected to refined modeling to reflect and evaluate the frequency change capability of the wind power response system, so that the stability of the system operation is favorably improved.
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In order to more clearly illustrate the technical solutions of the embodiments 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 some embodiments 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 flowchart of a method for implementing fan control by modeling wind power frequency modulation parameter probability distribution according to an embodiment of the present invention;
fig. 2 is a graph of a historical value distribution of an additional variable coefficient parameter of a fan frequency modulation controller obtained by modeling according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of the predicted probability density of the additional variable coefficient parameter of the fan frequency modulation controller obtained by modeling according to the embodiment of the present invention
Fig. 4 is a schematic diagram of a confidence interval of prediction of an additional variable coefficient parameter of a fan frequency modulation controller obtained by modeling according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of 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 embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention provides a method for realizing fan control by modeling wind power frequency modulation parameter probability distribution, which mainly comprises the following steps of:
step 1, obtaining historical rotor rotating speed data recorded by a fan data sampling device and preprocessing the historical rotor rotating speed data.
In the embodiment of the invention, the mode of preprocessing the historical rotor speed data comprises the following steps: judging whether the rotor speed is less than the lower limit of the maximum power tracking state speed according to the data sample rotor speed at each moment; and if the rotor speed of the data sample at the current moment is less than the lower limit of the maximum power tracking state speed or data loss exists, discarding the data sample at the current moment, otherwise, reserving the data sample.
And 2, setting fixed parameters of the fan frequency modulation controller in the current system scheduling period.
The preferred embodiment of this step is as follows:
for inertia response, the frequency change is responded by introducing an additional active control link, and the active output variable quantity of the fan is expressed as follows:
Figure BDA0003049356880000031
wherein, delta P is the frequency modulation auxiliary power of the fan frequency modulation controller responding to the system frequency change, k d And k p The parameters are fixed for a fan frequency modulation controller, the parameters are respectively a fan frequency modulation controller differential coefficient and a proportional coefficient, f is system frequency, delta f is system frequency variation, and d/dt represents derivation operation related to time t;
acquiring frequency modulation related parameters and load demand response potential of a system synchronous generator set in a current system scheduling period; differential coefficient k for fan frequency modulation controller d Which affects the wind power frequency response dynamic characteristics, expressed as:
Figure BDA0003049356880000032
in the formula, H j The inertia time constant of the jth synchronous generator set is obtained, and M is the total number of the synchronous generator sets;
proportional coefficient k for fan frequency modulation controller p The energy which influences the frequency modulation release of wind power is expressed as:
Figure BDA0003049356880000033
in the formula,. DELTA.P L For system unbalanced power,. DELTA.f s Maximum deviation of the allowable steady-state frequency of the system, K G Regulating power, Δ P, per unit for conventional synchronous generator sets T Responding to power for load demand.
For example, a local 1.5MW fan SCADA system (data acquisition and monitoring control system) is used for acquiring data as a data sample, and the sampling interval is 1min. For the fan, the inherent inertia time constant is 5.04s, the wind speed of the fan participating in frequency modulation operation is 7m/s, the rated wind speed of the fan is 12m/s, the maximum rotor rotating speed of the fan is 1.2p.u., and the minimum rotor rotating speed of the fan is 0.7p.u.. For a SCADA system, the equivalent inertia time constant of a conventional synchronous generator set is 10, and the unit regulating power is 20.
And 3, calculating the time sequence of ideal values of the additional variable parameters of the fan frequency modulation controller on historical time points by combining the set fixed parameters of the fan frequency modulation controller and the preprocessed historical rotor rotating speed data with the wind power maximum response system frequency variation capacity.
The preferred embodiment of this step is as follows:
after an additional variable parameter alpha is introduced, an active control link of the fan frequency modulation controller is expressed as follows:
Figure BDA0003049356880000041
wherein, K max For holding the fanMaximum value of control parameter, k, for stable operation d And k p Respectively are a fan frequency modulation controller differential coefficient and a proportionality coefficient;
according to the frequency variation capacity of the wind power maximum response system, the additional variable parameters are expressed as follows:
Figure BDA0003049356880000042
in the formula, omega is the running speed of the fan, omega min For the lower limit of the maximum power tracking state speed, omega max Is rated speed of fan, gamma is k d And k p A medium to minimum value;
and acquiring the preprocessed rotor rotating speed data at the corresponding time point according to the preset fan frequency modulation controller correction interval, and calculating a time sequence [ x (1),.. Multidot.x (n) ] of ideal values of additional variable parameters of the fan frequency modulation controller at the corresponding time point on historical time points.
And 4, classifying the time sequence of the ideal value on the historical moment point, predicting additional variable parameters of the fan frequency modulation controller through conditional kernel density estimation, generating probability density functions of different moment points, calculating corresponding confidence intervals, and realizing fan control.
The preferred embodiment of this step is as follows:
1) Classifying a time series [ x (1),.. Multidot.x (n) ] of ideal values of the additional variable parameters at historical time points according to the predicted input dimension d to obtain an input set and an output set, wherein the input set is represented as:
Figure BDA0003049356880000051
the output set is represented as:
Figure BDA0003049356880000052
wherein x (l) represents an ideal value of the additional variable parameter of the fan frequency modulation controller at a historical time point l, and l = 1.
2) Splicing the input set and the output set to form a matrix Z = [ X, Y =]＝[x 1 ,x 2 ,..,x d+1 ]Constructing a joint distributed multivariate nuclear density estimation model; the multivariate nuclear density estimation model is represented as:
Figure BDA0003049356880000053
wherein N is the total number of samples, N = N-d, z is an independent variable of the multivariate nuclear density estimation model, and z is i Is the value of the ith row sample of the matrix Z, K d+1 (. Cndot.) is a multivariate kernel function, H z The model bandwidth is estimated for the kernel density, expressed as:
Figure BDA0003049356880000054
the elements in the bandwidth matrix are defined as:
Figure BDA0003049356880000055
wherein σ q Represents the standard deviation of the q-th column of matrix Z;
K d+1 (. Cndot.) is expressed as:
Figure BDA0003049356880000056
as will be appreciated by those skilled in the art, K d+1 (. Cndot.) is an operation on a one-dimensional vector, and θ represents an argument
Figure BDA0003049356880000057
Its length is d +1.
3) Additional possibilities are calculated by conditional kernel density (i.e. conditional probability representation of multivariate kernel density estimation) estimationPredicting probability distribution by variable parameters; for the current time point n, extracting a data set x from an additional variable parameter historical sample data set of system frequency modulation p ＝[x(n-d+1),x(n-d+2),…x(n)]An additional variable parametric prediction probability distribution of the system frequency modulation at the next time point (i.e., the n +1 time point) can be calculated, and can be expressed as:
Figure BDA0003049356880000061
in the formula, f p (y) is a function of y, the meaning expressed being in terms of x p The function f can be obtained by calculating the input set X and the output set Y p The correlation parameter in (g), y is for function f p Independent variable of (a), i.e. additional variable parameter value, f p (y) represents the corresponding probability density when the value of the additional variable parameter takes the value y.
Figure BDA0003049356880000062
Is calculated from the foregoing
Figure BDA0003049356880000063
After the matrix Z is replaced by the input set X, the result obtained by adopting the multivariate kernel density estimation model introduced in the foregoing is the result
Figure BDA0003049356880000064
K 1 (. Is a one-dimensional multivariate kernel density estimation model, y t All elements of row t, H, of the output set y For the bandwidth of output set Y, g t (x p ) Is defined as:
Figure BDA0003049356880000065
in the formula, K d (. Is a d-dimensional multivariate kernel density estimation model, x) t For all elements of row t, H, of the input set X x Is the bandwidth of input set X.
In the examples of the present invention, K 1 (·)、K d (. To) the calculation formula and the preamble K d+1 (. Cndot.) is similar, except that the dimension is changed from d +1 to 1, d.
As shown in fig. 2, a distribution diagram of the historical values of the additional variable coefficient parameters of the frequency modulation controller of the rear fan according to the modeling method of the invention; fig. 3 is a schematic diagram of the probability density prediction of the additional variable coefficient parameter of the rear fan frequency modulation controller according to the modeling method of the present invention, specifically, a probability density curve generated at the first 30 prediction points.
4) Computing additional variable parameter predictors by conditional kernel density estimation: for data set x p The additional variable parameter prediction value x (n + 1) of the system frequency modulation is expressed by the mathematical expectation of the discrete distribution as:
Figure BDA0003049356880000066
here, g is i (x p )、y i And the foregoing g t (x p )、y t The meaning of (1) is the same, only the difference is that the form of the corner mark is adjusted.
And the additional variable parameter predicted value reflects the capacity of the fan participating in system frequency modulation. In engineering, communication delay exists between the wind power plant dispatcher issuing the instruction to each fan, real-time correction of fan frequency modulation controller parameters cannot be achieved, and the characteristic can be well compensated by adopting an extra variable parameter predicted value. In addition, the additional variable parameter prediction result is beneficial to making a better control scheme for the wind turbine and even the wind power plant. For example, under the condition that the extra variable parameters of the fan are predicted to be small in the future, the fan can actively adopt load reduction operation and other control strategies in advance, and the capacity of the fan participating in frequency modulation is improved.
In the embodiment of the invention, the conditional probability distribution and the predicted value calculated in the step 3) and the step 4) reflect the capacity of the fan participating in the frequency modulation of the system and the possible risk information, namely the fluctuation range.
5) For the additional variable parameter prediction probability distribution of the system frequency modulation, confidence intervals under different confidence degrees are calculated, and the method mainly comprises the following steps:
for conditional probability distribution f p (y) the cumulative probability density of which is expressed as:
Figure BDA0003049356880000071
confidence interval [ l ] at confidence level τ τ ,u τ ]Calculating by solving an optimization problem:
Figure BDA0003049356880000072
solving the optimization problem to obtain an upper interval bound u of the shortest interval under the confidence level tau τ Corresponding cumulative probability density r, confidence interval [ l ] τ ,u τ ]Expressed as:
Figure BDA0003049356880000073
FIG. 4 is a schematic diagram of a fan frequency modulation controller additional variable coefficient parameter prediction confidence interval after the modeling method is adopted. Fig. 4 reflects boundary values of additional variable coefficients of the fan frequency modulation controller at different confidence levels, and actual values of the additional variable coefficients can be enveloped in prediction confidence intervals at different confidence levels, so that the reliability is high, and compared with a single prediction value result of the additional variable coefficients, the risk information of the prediction result is reflected in the form of the confidence interval, and the adjustment of the fan control strategy is guided more favorably.
Specifically, the method comprises the following steps: selecting a proper confidence level according to the risk level born by the system, and selecting an additional variable coefficient predicted value x (n + 1) of the fan frequency modulation controller as a control instruction in a normal operation state; when the frequency modulation capability of the system exceeds a set upper limit (namely, when the frequency modulation capability is sufficient), selecting a selected confidence level fan frequency modulation controller additional variable coefficient lower boundary value (namely u) τ ) As a control instruction, the frequency modulation stability of the fan is ensured, and excessive response is avoided; when the frequency modulation capability of the system is lower than the set lower limit (namely, the frequency modulation capability is insufficient), for example, the output of a large number of traditional units approaches the upper limit and the output of a wind farm approaches the lower limitSelecting the boundary value (i.e. |) on the additional variable coefficient of the selected confidence level fan frequency modulation controller when the residual fan can not participate in the frequency modulation and other scenes τ ) As a control instruction, the potential of wind power frequency modulation is fully exerted.
Through the above description of the embodiments, it is clear to those skilled in the art that the above embodiments can be implemented by software, and can also be implemented by software plus a necessary general hardware platform. With this understanding, the technical solutions of the embodiments can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.), and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods according to the embodiments of the present invention.
The above description is only for the preferred 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 (6)

1. A method for realizing fan control by modeling wind power frequency modulation parameter probability distribution is characterized by comprising the following steps:
acquiring historical rotor rotating speed data recorded by a fan data sampling device and preprocessing the historical rotor rotating speed data;
setting fixed parameters of a fan frequency modulation controller in a current system scheduling period;
calculating the time sequence of ideal values of additional variable parameters of the fan frequency modulation controller on historical time points by combining the set fixed parameters of the fan frequency modulation controller and the preprocessed historical rotor rotating speed data with the wind power maximum response system frequency variation capacity;
classifying time sequences of ideal values at historical time points, predicting additional variable parameters of the fan frequency modulation controller through conditional kernel density estimation, generating probability density functions of different time points, calculating corresponding confidence intervals, and then realizing fan control;
wherein, setting the fixed parameter of fan frequency modulation controller includes:
for inertia response, by introducing additional active control link to respond to frequency change, the active output variable quantity of the fan is expressed as:
Figure FDA0003746506040000011
wherein, delta P is the frequency modulation auxiliary power of the fan frequency modulation controller responding to the system frequency change, k d And k p The parameters are fixed for a fan frequency modulation controller, the parameters are respectively a fan frequency modulation controller differential coefficient and a proportional coefficient, f is system frequency, delta f is system frequency variation, and t represents time;
acquiring frequency modulation related parameters and load demand response potential of a system synchronous generator set in a current system scheduling period; differential coefficient k for fan frequency modulation controller d Which affects the wind power frequency response dynamic characteristics, expressed as:
Figure FDA0003746506040000012
in the formula, H j The inertia time constant of the jth synchronous generator set is obtained, and M is the total number of the synchronous generator sets;
proportional coefficient k of frequency modulation controller for fan p The energy which influences the frequency modulation release of wind power is expressed as:
Figure FDA0003746506040000013
in the formula,. DELTA.P L For system unbalanced power,. DELTA.f s Maximum deviation of the allowable steady-state frequency of the system, K G Is a conventional synchronous generatorUnit regulated power of group, Δ P T Responding to power for load demand.
2. The method for realizing the fan control by modeling the probability distribution of the wind power frequency modulation parameters according to claim 1, wherein the mode of preprocessing the historical rotor speed data comprises the following steps:
judging whether the rotor speed is less than the lower limit of the maximum power tracking state speed according to the data sample rotor speed at each moment; and if the rotor speed of the data sample at the current moment is less than the lower limit of the maximum power tracking state speed or data loss exists, discarding the data sample at the current moment, otherwise, reserving the data sample.
3. The method for realizing wind turbine control by modeling wind power frequency modulation parameter probability distribution according to claim 1, wherein the calculating the time sequence of ideal values of additional variable parameters of the wind turbine frequency modulation controller at historical time points by combining the wind power maximum response system frequency variation capability and the preprocessed historical rotor speed data comprises:
after an additional variable parameter alpha is introduced, the active control link of the fan frequency modulation controller is expressed as follows:
Figure FDA0003746506040000021
wherein, K max Control parameter maximum value k for keeping stable operation of fan d And k p Fixing parameters for a fan frequency modulation controller, wherein the parameters are a differential coefficient and a proportionality coefficient of the fan frequency modulation controller respectively;
according to the frequency variation capacity of the wind power maximum response system, the additional variable parameters are expressed as follows:
Figure FDA0003746506040000022
wherein, omega is the running speed of the fan, omega is min Is the most excellentLower limit of rotation speed, omega, in high-power tracking state max Is rated speed of fan, gamma is k d And k p A medium to minimum value;
and acquiring the preprocessed rotor rotating speed data at the corresponding moment point according to the preset fan frequency modulation controller correction interval, and calculating the time sequence of the ideal value of the extra variable parameter of the fan frequency modulation controller at the corresponding moment on the historical moment point.
4. The method for realizing the wind turbine control by modeling the probability distribution of the wind power frequency modulation parameters according to claim 1, wherein the time series of ideal values at historical time points are classified, additional variable parameters of the wind turbine frequency modulation controller are predicted by conditional kernel density estimation, probability density functions at different time points are generated, and calculating the corresponding confidence intervals comprises:
classifying the time sequence of ideal values of the additional variable parameters at the historical time points according to the predicted input dimension d to obtain an input set and an output set, wherein the input set is expressed as:
Figure FDA0003746506040000031
the output set is represented as:
Figure FDA0003746506040000032
wherein x (l) represents an ideal value of an additional variable parameter of the fan frequency modulation controller at a historical time point l, l = 1.., n, n represents the total capacity of the historical rotor speed data, namely the total historical time number;
splicing the input set and the output set to form a matrix Z = [ X, Y =]＝[x 1 ,x 2 ,..,x d+1 ]Constructing a joint distributed multivariate nuclear density estimation model; the multivariate nuclear density estimation model is represented as:
Figure FDA0003746506040000033
wherein N is the total number of samples, N = N-d, z is an independent variable of the multivariate nuclear density estimation model, and z is i Is the value of the ith row sample of the matrix Z, K d+1 (. Is a multivariate kernel function, H z The model bandwidth is estimated for the kernel density, expressed as:
Figure FDA0003746506040000034
each element in the bandwidth matrix is defined as:
Figure FDA0003746506040000035
wherein σ q Represents the standard deviation of the q-th column of the matrix Z;
K d+1 (. Cndot.) is expressed as:
Figure FDA0003746506040000036
wherein θ represents an independent variable
Figure FDA0003746506040000037
Its length is d +1; calculating an additional variable parameter prediction probability distribution by conditional kernel density estimation; for the current time point n, extracting x from the historical sample data set of the additional variable parameters of the system frequency modulation p ＝[x(n-d+1),x(n-d+2),…x(n)]And predicting probability distribution of additional variable parameters of system frequency modulation at the next time point, wherein the conditional probability distribution is represented as:
Figure FDA0003746506040000041
in the formula, K 1 (. Is a one-dimensional multivariable kernelDensity estimation model, y t All elements of row t of the output set, H y For the bandwidth of output set Y, g t (x p ) Is defined as:
Figure FDA0003746506040000042
in the formula, K d (. Is a d-dimensional multivariate kernel density estimation model, x) t For all elements of row t of input set X, H x Is the bandwidth of input set X;
computing additional variable parameter predictors by conditional kernel density estimation: for data set x p The additional variable parameter prediction value x (n + 1) of the system frequency modulation is expressed by the mathematical expectation of the discrete distribution as:
Figure FDA0003746506040000043
for the additional variable parameter prediction probability distribution of the system frequency modulation, confidence intervals under different confidence degrees are calculated.
5. The method for realizing wind turbine control by modeling the probability distribution of the wind power frequency modulation parameters according to claim 4, wherein the step of calculating confidence intervals at different confidence degrees for the additional variable parameters of the system frequency modulation to predict the probability distribution comprises:
for conditional probability distribution f p (y) the cumulative probability density of which is expressed as:
Figure FDA0003746506040000044
confidence interval at confidence level τ
Figure FDA0003746506040000045
Calculating by solving an optimization problem:
Figure FDA0003746506040000046
s.t.0≤y≤1
solving the optimization problem to obtain an upper interval bound u of the shortest interval under the confidence level tau τ Corresponding cumulative probability density r, confidence interval [ l ] τ ,u τ ]Expressed as:
Figure FDA0003746506040000047
6. the method for realizing wind turbine control by modeling wind power frequency modulation parameter probability distribution according to claim 1,
under normal conditions, the fan control is realized by using the predicted value of the additional variable parameter of the fan frequency modulation controller;
when the frequency modulation capability of the system exceeds the set upper limit, the fan control is realized by utilizing a boundary value under the confidence interval;
and when the frequency modulation capability of the system is lower than the set lower limit, the fan control is realized by utilizing the boundary value on the confidence interval.
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CN112564132A (en) * 2020-12-15 2021-03-26 深圳供电局有限公司 Wind power primary frequency modulation potential uncertainty modeling method

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