CN112636366B - Wind power plant dynamic frequency control method based on control process data fitting - Google Patents

Wind power plant dynamic frequency control method based on control process data fitting Download PDF

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CN112636366B
CN112636366B CN202011398967.1A CN202011398967A CN112636366B CN 112636366 B CN112636366 B CN 112636366B CN 202011398967 A CN202011398967 A CN 202011398967A CN 112636366 B CN112636366 B CN 112636366B
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fan
moment
matrix
dynamic
control
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CN112636366A (en
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吴文传
郭子榛
柯贤波
霍超
任冲
王衡
杨桂兴
亢朋朋
樊国伟
印欣
宋朋飞
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Tsinghua University
State Grid Corp of China SGCC
State Grid Xinjiang Electric Power Co Ltd
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State Grid Corp of China SGCC
State Grid Xinjiang Electric Power Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
    • H02J3/241—The oscillation concerning frequency
    • 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
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • 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
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • 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 provides a data-driven dynamic optimization control method for the frequency stability control of a high-proportion wind power independent power system, so that the wind power plant is stabilized and optimized in the internal transient process while participating in system frequency modulation. According to the wind field frequency optimization control method, an initial data set is constructed, on-line dynamic modeling and centralized wind field control modeling are carried out, and wind field frequency optimization control is carried out according to a fan on-line dynamic modeling result. According to the invention, the online equivalent dynamic model of the fan is obtained by fitting the historical data generated in the operation process of the fan, and the online equivalent dynamic model has the characteristic of pure data driving, so that the online equivalent dynamic model can be suitable for operation control of fans of different manufacturers under different working conditions, meanwhile, the algorithm has a pure linear form, the solving process is simple, the calculation load is small, the complex nonlinear optimization control problem is not required to be solved, the real-time operation requirement is met, and the balance of dynamic response precision and speed can be considered.

Description

Wind power plant dynamic frequency control method based on control process data fitting
Technical Field
The invention belongs to the technical field of electric power, and particularly relates to a wind power plant dynamic frequency control method based on control process data fitting.
Background
In recent decades, with the increasing energy crisis and environmental problems caused by the rapid consumption of global fossil energy, renewable energy sources such as wind power and photovoltaics keep a rapid increase trend in the installed capacity around the world. Taking wind power as an example, by the end of 2019, the total installed amount of wind power worldwide reaches 594GW, and the average annual growth from 2010 is 46GW. Because the variable speed fan can adapt to a wider wind speed working range, most of newly-added fans adopt variable speed fans. However, while the wind energy utilization rate is improved, the active output and the frequency of the variable speed fan are decoupled, and as the permeability of the power grid of the variable speed fan is continuously improved, the power grid has the trend and the characteristics of power electronization, so that the problem of the frequency stability of the whole power grid becomes a great challenge for large-scale access of the fan to the power grid.
The existing fan participating in the frequency modulation technical route comprises two types of local control and coordinated control, wherein the local control introduces frequency response characteristics by modifying a local control loop of the fan, but because the in-field fan lacks coordinated interaction, the overall response is difficult to ensure optimality under the condition of uneven in-field wind speed distribution, and meanwhile, the control parameters of a plurality of devices in the field are required to be set one by one at regular intervals, so that the flexibility of the overall response is greatly reduced. And the coordination control is performed by relying on a dynamic time domain model of the fan to perform the coordination optimization control of the whole wind field. At present, a dynamic modeling method for a fan mainly depends on experience cognition and parameter measurement of a physical model of the fan, on one hand, the method is difficult to fully consider actual processes such as each time lag of a complete control closed loop in the process of describing the physical model, and on the other hand, the accuracy and even the feasibility of parameter measurement are often limited by actual working conditions, so that the actual approximation effect of a final dynamic model is difficult to ensure, and the final optimal control result is greatly influenced.
Therefore, a data-driven wind field frequency optimization control method needs to be researched, and the method is suitable for the actual requirements of wind fields for rapidly and flexibly participating in dynamic response of the power grid frequency.
Disclosure of Invention
Aiming at the problems, the wind farm dynamic frequency control method based on the control process data fitting is suitable for the actual requirements of the wind farm for rapidly and flexibly participating in the power grid frequency dynamic response.
A wind farm dynamic frequency control method based on control process data fitting, the control method comprising:
on-line dynamic modeling of the fan;
and optimally controlling the wind field frequency according to the online dynamic modeling result of the fan.
Further, on-line dynamic modeling of fans is performed by:
constructing an initial data set;
on-line dynamic modeling;
and (5) modeling the concentrated wind field control.
Further, the initial dataset is constructed by:
establishing a state equation of a single fan or a power generation unit:
ω k+1 ＝f(ω k ，u k ) (1.1)
f is a state transition relation function, w k The fan rotation speed at the moment k is u k Is the control system input variable at time k;
Figure BDA0002811599110000021
P k is an active instruction at time k, v k The wind speed at time k;
Figure BDA0002811599110000022
is the generalized state variable at time k, +.>
Figure BDA0002811599110000023
Is a generalized state variable at time k+1, and N pairs of data (x k ，y k ) The initial data set is obtained for the matrix arrangement:
X＝[x 1 x 2 … x N ]，Y＝[y 1 y 2 … y N ] (1.3)。
further, online dynamic modeling is performed by:
the linear model characterizes the current dynamic characteristics of the fan:
y k ＝Ax k (1.4)
solving an optimization problem by algebraic operation and obtaining a fan dynamic model:
Figure BDA0002811599110000031
Figure BDA0002811599110000032
is the generalized state variable at time k, +.>
Figure BDA0002811599110000033
Is a generalized state variable at time k+1, and N pairs of data (x k ，y k ) The initial data set is obtained for the matrix arrangement:
X＝[x 1 x 2 … x N ]，Y＝[y 1 y 2 … y N ] (1.3)
matrix a is the state transition matrix corresponding to the generalized state variable,
Figure BDA00028115991100000310
is a matrix pseudo-inverse operation.
Further, a concentrated wind field control model is established by:
dynamic model of each power generation monomer in wind field:
Figure BDA0002811599110000034
Figure BDA0002811599110000035
is the fan rotating speed of the ith fan at the moment k+1, A i Is the state transition matrix corresponding to the generalized state variable of the ith fan,/the system is provided with a plurality of control modules>
Figure BDA0002811599110000036
Is the fan rotating speed of the ith fan at the moment k, B i Is the input matrix of the ith fan, +.>
Figure BDA0002811599110000037
Is the input variable of the control system of the ith fan at the moment k, and M is the total number of fans;
state variable χ of concentrated wind field control model at k moment k ：
Figure BDA0002811599110000038
Input vector u of current wind speed and active command of each power generation unit at k moment k ：
Figure BDA0002811599110000039
Obtaining a control model of the concentrated state vector from equation (1.6):
χ k+1 ＝Aχ k +Bu k (1.9)
constructing a state transition matrix of each power generation unit according to a diagonal form:
Figure BDA0002811599110000041
Figure BDA0002811599110000042
A 1 is a state transition matrix corresponding to the generalized state variable of the 1 st fan, A M Is a state transition matrix corresponding to the generalized state variable of the Mth fan, B 1 Is the input matrix of the 1 st fan, B M An input matrix of the Mth fan.
Further, online dynamic optimization is performed by:
wind field dynamic optimization control algorithm:
Figure BDA0002811599110000043
t is the length of a prediction interval of a model prediction control algorithm, and J is an objective function of the control algorithm:
Figure BDA0002811599110000044
Q k a semi-positive target coefficient matrix for the state variable at time k, R k A semi-positive target coefficient matrix for the input variable at time k,
Figure BDA0002811599110000047
target coefficient vector for state variable at time k, < ->
Figure BDA0002811599110000048
A target coefficient vector for the input variable at time k, E k Constraint coefficient matrix for state variable boundary at k moment, F k For the input variable at time kBoundary constraint coefficient matrix, b k The design of the coefficient matrix and the coefficient vector depends on a wind field dynamic optimization control target for the boundary constraint coefficient vector at the moment k;
wind power frequency modulation optimization target:
Figure BDA0002811599110000045
active adjustment quantity of ith fan at k moment
Figure BDA0002811599110000046
Figure BDA0002811599110000051
Active command of ith fan at k moment
Figure BDA0002811599110000052
Control command at time k in maximum power tracking mode with respect to local controller of ith blower>
Figure BDA0002811599110000053
If the wind farm adopts a load shedding mode, the active adjustment amount +.>
Figure BDA0002811599110000054
Figure BDA0002811599110000055
Figure BDA0002811599110000056
For the active instruction of the ith fan at the moment k under the load shedding working mode, R d To subtract the amplitude coefficient, the frequency offset is:
Δf＝f meas -f ref (1.17)
the frequency f of the parallel point measurement meas Relative to the reference frequency f ref Deviation amount, K between df For the sag coefficient of the wind farm to the external power frequency characteristic curve, Q x Weight coefficients for balancing two optimization objectives;
time sequence constraint, input constraint and initial state constraint of concentrated state vector:
χ k+1 ＝Aχ k +Bu k ，k＝0，1，…，T-1 (1.18)
Figure BDA0002811599110000057
Figure BDA0002811599110000058
Figure BDA0002811599110000059
χ k for the state variable of the concentrated wind field control model at the moment k, u k For the input variables of the concentrated wind field control model at time k, the matrices A and B are the state transition matrix and the input matrix of the concentrated wind field control model,
Figure BDA00028115991100000510
for the fan rotating speed omega of the ith fan at the moment k min ，ω max Respectively a lower limit and an upper limit of the rotating speed of the fan, < ->
Figure BDA00028115991100000511
For the active instruction of the ith fan at the moment k, P min ，P max Is the lower and upper bounds of the active instruction.
The invention provides a data-driven dynamic optimization control method for the frequency stability control of a high-proportion wind power independent power system, so that the wind power plant is stabilized and optimized in the internal transient process while participating in system frequency modulation. According to the wind field frequency optimization control method, an initial data set is constructed, on-line dynamic modeling and centralized wind field control modeling are carried out, and wind field frequency optimization control is carried out according to a fan on-line dynamic modeling result. According to the invention, the online equivalent dynamic model of the fan is obtained by fitting the historical data generated in the operation process of the fan, and the online equivalent dynamic model has the characteristic of pure data driving, so that the online equivalent dynamic model can be suitable for operation control of fans of different manufacturers under different working conditions, meanwhile, the algorithm has a pure linear form, the solving process is simple, the calculation load is small, the complex nonlinear optimization control problem is not required to be solved, the real-time operation requirement is met, and the balance of dynamic response precision and speed can be considered.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention may be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a wind farm frequency optimization control module according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of a wind farm frequency optimization control implementation procedure according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
A wind power plant dynamic frequency control method based on control process data fitting is shown in figure 1, and is based on a data-driven wind power plant frequency optimization control module relationship, and mainly comprises two modules, namely a fan online dynamic modeling module and a wind power plant frequency optimization control module; as shown in fig. 2, the wind field frequency optimization control implementation step is based on data driving.
The object of the method is a wind driven generator in a large wind farm group, and because the doubly-fed asynchronous fans in the wind power market at present occupy a larger market share, the fans are assumed to be doubly-fed asynchronous fans. On the other hand, because fans with similar in-site operation conditions and equipment parameters can be polymerized into one power generation monomer, the control strategy provided by the system aims at the active power output of each power generation monomer in the wind field.
For convenience of description, it is assumed that the converters of the doubly-fed asynchronous fan are all three-phase grid-connected, and single-phase or two-phase access can be performed according to requirements in actual engineering, so that implementation of a control method is not affected. And it is assumed that the power control loop of the converter has decoupled the active and reactive control by dq decomposition of the rotor current, so that the active output of the fan can be quickly adjusted following an external active command. Assuming that M fans generate electricity in total, the active instruction of the ith generating electricity monomer at the moment k is that
Figure BDA0002811599110000071
The corresponding real-time wind speed is->
Figure BDA0002811599110000072
The active command and the real-time wind speed form an input variable of a state equation, and the rotating speed of the fan is +.>
Figure BDA0002811599110000073
Form the state variable of the fan.
(1) Construction of an initial dataset
The state equation of a single fan or a power generation unit is established from the frequency modulation control point of view as follows:
ω k+1 ＝f(ω k ，u k ) (1.1)
wherein f represents a state transition relation function, ω k The fan rotation speed at the moment k is u k Is the control system input variable at time k:
Figure BDA0002811599110000074
P k is an active instruction at time k, v k The wind speed at time k;
in order to establish a pure data driving method, only the accumulated data pairs (x k ，y k )，
Figure BDA0002811599110000075
Is the generalized state variable at time k, +.>
Figure BDA0002811599110000076
Is a generalized state variable at the time of k+1, comprises a current state and a current input, and forms a data pair which is accumulated to a preset value (when the step length is 0.1s, the preset value is larger than 6000) and comprises a time sequence characteristic of fan state transition, and the accumulated data pairs (x k ，y k ) The initial data set is obtained for the matrix arrangement as follows:
X＝[x 1 x 2 … x N ]，Y＝[y 1 y 2 … y N ] (1.3)
(2) Online dynamic modeling
From the data driving perspective, the dominant dynamic equation can be deduced from an empirical model formula, and then nonlinear fitting is carried out on model parameters according to data formed by state tracks, but the method generally needs to measure a fan to some extent, otherwise, the fitting effect of the situation with more model parameters is often not ideal. Therefore, on-line dynamic modeling of the fan dynamic characteristics is considered through a pure data driving mode, specifically, N data pairs forward at the current moment form a data window, N depends on the size of the control step T, and typically, the product of NT is in a time scale of about one minute. The data set formed in this way not only contains the dynamic characteristics of the current running working point of the fan, but also avoids the nonlinear characteristics of the fan state under various wind speed levels, so that the current dynamic characteristics of the fan can be represented by a linear model:
y k ＝Ax k (1.4)
in order to restore the state transition matrix from a least squares perspective through the dataset, it is necessary to find matrix A such that AX-Y|| 2 At minimum, according to the theory of linear algebra, the optimization problem can be solved by the following algebraic operation and a fan dynamic model is obtained:
Figure BDA0002811599110000081
wherein the method comprises the steps of
Figure BDA0002811599110000082
Representing the pseudo-inverse of the matrix. It should be noted that, the matrix a here is a state transition matrix corresponding to a generalized state quantity, and for a general form required by a control model, sub-block interception is performed according to dimensions of the state quantity and the input quantity.
(3) Centralized wind field control model
According to the online dynamic modeling method, a dynamic model of each power generation monomer in the wind field can be obtained:
Figure BDA0002811599110000083
Figure BDA0002811599110000084
is the fan rotating speed of the ith fan at the moment k+1, A i Is the state transition matrix corresponding to the generalized state variable of the ith fan,/the system is provided with a plurality of control modules>
Figure BDA0002811599110000085
Is the fan rotating speed of the ith fan at the moment k, B i Is the input matrix of the ith fan, +.>
Figure BDA0002811599110000086
Is the input variable of the control system of the ith fan at the moment k, and M is the total number of fans;
on the basis, defining state vector χ in the k-moment concentrated wind field control model k ：
Figure BDA0002811599110000087
Simultaneously defining an input vector u containing the current wind speed and the active command of each power generation unit at k moment k ：
Figure BDA0002811599110000091
From equation (1.6) a control model corresponding to the concentrated state vector can be given:
χ k+1 ＝Aχ k +Bu k (1.9)
wherein the state transition matrix of each power generation unit is constructed in the following diagonal form:
Figure BDA0002811599110000092
Figure BDA0002811599110000093
a1 is a state transition matrix corresponding to a generalized state variable of the 1 st fan, AM is a state transition matrix corresponding to a generalized state variable of the M-th fan, B1 is an input matrix of the 1 st fan, and BM is an input matrix of the M-th fan.
It can be seen that the matrix A and the matrix B constructed in the mode have special sparse structures, and provide more convenient conditions for rapidly solving the online dynamic optimization control problem.
(4) Online dynamic optimization
On the basis of obtaining a linear dynamic model of the fan by a data driving method, according to the general form of a model predictive control architecture, a wind field dynamic optimization control algorithm can be expressed as follows:
Figure BDA0002811599110000094
where T is the prediction interval length of the model predictive control algorithm and the objective function J is of the form:
Figure BDA0002811599110000095
Q k a semi-positive target coefficient matrix for the state variable at time k, R k A semi-positive target coefficient matrix for the input variable at time k,
Figure BDA0002811599110000109
target coefficient vector for state variable at time k, < ->
Figure BDA00028115991100001010
A target coefficient vector for the input variable at time k, E k Constraint coefficient matrix for state variable boundary at k moment, F k Constraint coefficient matrix for boundary of input variable at k moment, b k And (3) for the boundary constraint coefficient vector at the moment k, the design of the coefficient matrix and the coefficient vector depends on a wind field dynamic optimization control target.
In the problem of wind power frequency modulation, in view of the fact that a control target needs to be optimized in two aspects of wind power plant active frequency modulation instruction following effect and rotational speed transient stability of a power generation unit, the transient stability degree of the wind power plant active frequency modulation instruction is measured by the fluctuation degree of the rotational speed of a fan, and the following optimization targets are given:
Figure BDA0002811599110000101
active adjustment quantity of ith fan at k moment
Figure BDA0002811599110000102
Figure BDA0002811599110000103
Active command of ith fan at k moment
Figure BDA0002811599110000104
Control command +_at time k in maximum power tracking Mode (MPPT) with respect to local controller of ith blower>
Figure BDA0002811599110000105
If the wind farm adopts load shedding mode (load), the active regulation is +.>
Figure BDA0002811599110000106
Figure BDA0002811599110000107
Wherein the method comprises the steps of
Figure BDA0002811599110000108
For the active instruction of the ith fan at the moment k under the load shedding working mode, R d To subtract the amplitude coefficient, the frequency offset is defined as:
Δf＝f meas -f ref (1.17)
representing the frequency f of measurement of the grid-connected point meas Relative to the reference frequency f ref Offset between and parameter K df For the sag coefficient of the wind farm to the external power frequency characteristic curve, Q x To balance the weight coefficients of the two optimization objectives.
The first term of the optimization target in the formula (1.14) enables the distribution of the frequency modulation tasks of the wind turbines to be carried out on the basis of considering the respective power generation level by adjusting the active command on the basis of the local control command, so that the overall distribution of the active output force corresponds to the local working condition of the wind turbines, and the wind power plant group provides frequency modulation service and simultaneously guarantees the overall wind energy conversion benefit of the wind power plant group. The second item of the optimization target enables the running state of the fan to be more stable by optimizing the fluctuation degree of the rotating speed of the fan, so that the mechanical fatigue of the fan, which is increased to fragile mechanical parts such as a gearbox and the like due to participation in a frequency modulation response process, is reduced, and the service life of the fan is prolonged.
On the basis of an optimization target, timing constraints, input constraints and initial state constraints of the concentrated state vector are given:
χ k+1 ＝Aχ k +Bu k ，k＝0，1，…，T-1 (1.18)
Figure BDA0002811599110000111
Figure BDA0002811599110000112
Figure BDA0002811599110000113
χ k for the state variable of the concentrated wind field control model at the moment k, u k For the input variables of the concentrated wind field control model at time k, the matrices A and B are the state transition matrix and the input matrix of the concentrated wind field control model,
Figure BDA0002811599110000114
for the fan rotating speed omega of the ith fan at the moment k min ，ω max Respectively a lower limit and an upper limit of the rotating speed of the fan, < ->
Figure BDA0002811599110000115
For the active instruction of the ith fan at the moment k, P min ，P max Is the lower and upper bounds of the active instruction.
The complete wind field frequency dynamic optimization control method is formed by the formulas (1.14) to (1.21), and the constraint condition and the objective function have convexity, so that the whole model forms a convex optimized QP problem, and the existing optimization solver can accurately and rapidly solve the problem.
The invention provides a data-driven dynamic optimization control method for the frequency stability control of a high-proportion wind power independent power system, so that the wind power plant is stabilized and optimized in the internal transient process while participating in system frequency modulation. According to the wind field frequency optimization control method, an initial data set is constructed, on-line dynamic modeling and centralized wind field control modeling are carried out, and wind field frequency optimization control is carried out according to a fan on-line dynamic modeling result. According to the invention, the online equivalent dynamic model of the fan is obtained by fitting the historical data generated in the operation process of the fan, and the online equivalent dynamic model has the characteristic of pure data driving, so that the online equivalent dynamic model can be suitable for operation control of fans of different manufacturers under different working conditions, meanwhile, the algorithm has a pure linear form, the solving process is simple, the calculation load is small, the complex nonlinear optimization control problem is not required to be solved, the real-time operation requirement is met, and the balance of dynamic response precision and speed can be considered.
Although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (2)

1. The wind farm dynamic frequency control method based on control process data fitting is characterized by comprising the following steps of:
on-line dynamic modeling of the fan, wherein the on-line dynamic modeling of the fan is performed in the following manner: an initial dataset is constructed, wherein the initial dataset is constructed by:
establishing a state equation of a single fan or a power generation unit:
ω k+1 ＝f(ω k ，u k ) (1.1)
f is a state transition relation function, ω k The fan rotation speed at the moment k is u k Is the control system input variable at time k;
Figure FDA0004075070880000011
P k is an active instruction at time k, v k The wind speed at time k;
Figure FDA0004075070880000012
is the generalized state variable at time k, +.>
Figure FDA0004075070880000013
Is a generalized state variable at time k+1, and N pairs of data (x k ，y k ) The initial data set is obtained for the matrix arrangement:
X＝[x 1 x 2 … x N ]，Y＝[y 1 y 2 … y N ] (1.3)；
online dynamic modeling, wherein online dynamic modeling is performed by:
the linear model characterizes the current dynamic characteristics of the fan:
y k ＝Ax k (1.4)
solving an optimization problem by algebraic operation and obtaining a fan dynamic model:
Figure FDA0004075070880000014
Figure FDA0004075070880000015
is the generalized state variable at time k, +.>
Figure FDA0004075070880000016
Is a generalized state variable at time k+1, and N pairs of data (x k ，y k ) The initial data set is obtained for the matrix arrangement:
X＝[x 1 x 2 … x N ]，Y＝[y 1 y 2 … y N ] (1.3)
matrix a is the state transition matrix corresponding to the generalized state variable,
Figure FDA0004075070880000021
is a matrix pseudo-inverse operation;
establishing a concentrated wind field control model, wherein the concentrated wind field control model is established by the following steps:
dynamic model of each power generation monomer in wind field:
Figure FDA0004075070880000022
Figure FDA0004075070880000023
is the fan rotating speed of the ith fan at the moment k+1, A i Is the state transition matrix corresponding to the generalized state variable of the ith fan,/the system is provided with a plurality of control modules>
Figure FDA0004075070880000024
Is the fan rotating speed of the ith fan at the moment k, B i Is the input matrix of the ith fan, +.>
Figure FDA0004075070880000025
Is the input variable of the control system of the ith fan at the moment k, and M is the total number of fans;
state variable χ of concentrated wind field control model at k moment k ：
Figure FDA0004075070880000026
Input vector u of current wind speed and active command of each power generation unit at k moment k ：
Figure FDA0004075070880000027
Obtaining a control model of the concentrated state vector from equation (1.6):
χ k+1 ＝Aχ k +Bu k (1.9)
constructing a state transition matrix of each power generation unit according to a diagonal form:
Figure FDA0004075070880000028
Figure FDA0004075070880000029
A 1 is a state transition matrix corresponding to the generalized state variable of the 1 st fan, A M Is a state transition matrix corresponding to the generalized state variable of the Mth fan, B 1 Is the input matrix of the 1 st fan, B M An input matrix of the M-th fan;
and optimally controlling the wind field frequency according to the online dynamic modeling result of the fan.
2. The control method according to claim 1, characterized in that the on-line dynamic optimization is performed by:
wind field dynamic optimization control algorithm:
Figure FDA0004075070880000031
t is the length of a prediction interval of a model prediction control algorithm, and J is an objective function of the control algorithm:
Figure FDA0004075070880000032
Q k a semi-positive target coefficient matrix for the state variable at time k, R k A semi-positive target coefficient matrix for the input variable at time k,
Figure FDA0004075070880000033
target coefficient vector for state variable at time k, < ->
Figure FDA0004075070880000034
A target coefficient vector for the input variable at time k, E k Constraint coefficient matrix for state variable boundary at k moment, F k Constraint coefficient matrix for boundary of input variable at k moment, b k The design of the coefficient matrix and the coefficient vector depends on a wind field dynamic optimization control target for the boundary constraint coefficient vector at the moment k;
wind power frequency modulation optimization target:
Figure FDA0004075070880000035
active adjustment quantity of ith fan at k moment
Figure FDA0004075070880000036
Figure FDA0004075070880000037
Active command of ith fan at k moment
Figure FDA0004075070880000038
Relative toControl command of local controller of ith fan at k moment in maximum power tracking mode +.>
Figure FDA0004075070880000039
If the wind farm adopts a load shedding mode, the active adjustment amount +.>
Figure FDA0004075070880000041
Figure FDA0004075070880000042
Figure FDA0004075070880000043
For the active instruction of the ith fan at the moment k under the load shedding working mode, R d To subtract the amplitude coefficient, the frequency offset is:
Δf＝f meas -f ref (1.17)
the frequency f of the parallel point measurement meas Relative to the reference frequency f ref Deviation amount, K between df For the sag coefficient of the wind farm to the external power frequency characteristic curve, Q x Weight coefficients for balancing two optimization objectives;
time sequence constraint, input constraint and initial state constraint of concentrated state vector:
χ k+1 ＝Aχ k +Bu k ，k＝0，1，…，T-1 (1.18)
Figure FDA0004075070880000044
Figure FDA0004075070880000045
Figure FDA0004075070880000046
χ k for the state variable of the concentrated wind field control model at the moment k, u k For the input variables of the concentrated wind field control model at time k, the matrices A and B are the state transition matrix and the input matrix of the concentrated wind field control model,
Figure FDA0004075070880000047
for the fan rotating speed omega of the ith fan at the moment k min ，ω max Respectively a lower limit and an upper limit of the rotating speed of the fan, < ->
Figure FDA0004075070880000048
For the active instruction of the ith fan at the moment k, P min ，P max Is the lower and upper bounds of the active instruction. />
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