CN114039367A - Wind power energy storage device virtual inertia control system and control method based on data driving - Google Patents

Wind power energy storage device virtual inertia control system and control method based on data driving Download PDF

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CN114039367A
CN114039367A CN202111430077.9A CN202111430077A CN114039367A CN 114039367 A CN114039367 A CN 114039367A CN 202111430077 A CN202111430077 A CN 202111430077A CN 114039367 A CN114039367 A CN 114039367A
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storage device
wind power
data
frequency
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李泰�
李梦婕
王乐秋
纪志成
赵黎
朱志宇
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Jiangsu University of Science and Technology
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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
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • 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
    • 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
    • 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
    • Y02E70/00—Other energy conversion or management systems reducing GHG emissions
    • Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin

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Abstract

The invention discloses a data-driven virtual inertia control system for a wind power energy storage device, which comprises the wind power energy storage device and also comprises: the data acquisition module is used for acquiring the frequency of the historical wind power system and the power of the energy storage device; the data drive controller is used for obtaining Markov parameter MiObtaining the optimal energy storage device power u (k) by a data-driven optimal control method; the energy storage device control module is used for controlling the virtual inertia of the wind power energy storage device; the data acquisition module is also used for acquiring the frequency of the wind power system and the power of the energy storage device which are controlled by the data driving controller. The virtual inertia closed-loop control of the energy storage device is carried out by collecting history and adjusted wind power system frequency, the real-time regulation and control of the wind power system are realized, and a system model does not need to be established by adopting data drive controlAnd only online input and output data are utilized, the problem of difficulty in modeling of a wind power system is solved, online optimization parameter adjustment is realized, and the control performance of DFIG frequency modulation is improved.

Description

Wind power energy storage device virtual inertia control system and control method based on data driving
Technical Field
The invention relates to offshore wind power frequency modulation control, in particular to a wind power energy storage device virtual inertia control system and a wind power energy storage device virtual inertia control method based on data driving.
Background
In order to solve the increasingly serious energy crisis and the problem of climate change caused by fossil energy, wind energy has the advantages of wide distribution area, large generated energy, no pollution and the like, and becomes one of the most reliable technologies and power generation modes with wide commercial application prospect. In order to improve the efficiency of a wind power generation system to the maximum, most of wind turbine generators connected to a power grid adopt maximum power point tracking control (MPPT), at the moment, a transmission system of the wind turbine generator is completely decoupled from the power grid, which means that the rotation speed and the power of the wind turbine generator do not respond to the frequency change of the power grid any more, so that the relative inertia of the power system is greatly reduced, and once the load suddenly changes, the frequency stability of the power system is threatened. With the increase of the permeability of wind power generation, the influence of a wind power generation system on a power system is more and more obvious. However, in order to meet the power grid requirement, the grid-connected wind power plant needs to provide auxiliary functions such as inertia response and primary frequency modulation which are the same as those of a conventional power plant.
In past research work, strategies to support transient frequency response have often been divided into rotor kinetic energy-based inertia compensation and stored energy-based inertia compensation. Although the virtual inertia control based on the kinetic energy of the rotor has a fast response speed and can reduce the maximum frequency deviation, the rotor speed cannot be maintained in a speed-down state or a speed-up state for a long time, and as the rotor speed is recovered, a secondary drop or increase of the system frequency may be caused, and when the system is stabilized again, the frequency support cannot be provided. Compared with inertia compensation based on rotor kinetic energy, the inertia compensation based on energy storage is combined to a wind power generation system through an external energy storage device, the advantages of mature storage battery energy storage technology, high response speed and high energy density are utilized, and more flexible frequency support can be provided for a wind power system.
Due to the characteristics of high order, nonlinearity, strong coupling, time variation and the like of the wind power system and uncertain factors of aerodynamics, it is very difficult to establish an accurate system mathematical model. The traditional control theory requires the accuracy of a mathematical model of a control object, and the traditional control method of virtual inertia frequency modulation control is limited by the accuracy of the model and cannot obtain good control effect.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the defects, the invention provides a data-drive-based virtual inertia control system for a wind power energy storage device, which is used for improving the frequency stability of a wind power plant system.
The invention further provides a data-driven wind power energy storage device virtual inertia control method.
The technical scheme is as follows: in order to solve the problems, the invention adopts a data-drive-based virtual inertia control system of a wind power energy storage device, which comprises the wind power energy storage device, a data acquisition module, a data drive controller and an energy storage device control module; the data acquisition module is used for acquiring the frequency of the historical wind power system and the power of the energy storage device;
the data driving controller is used for obtaining Markov parameters M through historical wind power system frequency and power of the energy storage devicei(ii) a According to the obtained Markov parameter MiObtaining the optimal energy storage device power u (k) by a data-driven optimal control method;
the energy storage device control module is used for controlling the virtual inertia of the wind power energy storage device through the optimal energy storage device power u (k);
the data acquisition module is also used for acquiring the frequency of the wind power system and the power of the energy storage device which are controlled by the data driving controller.
Further, the data driving controller adopts a DSPF2812 chip, a digital input port of the DSPF2812 chip is connected with the data acquisition module, and a digital output port of the DSPF2812 chip is connected with the energy storage device control module.
Further, the data driving controller controls the data driving by setting a Markov parameter MiSubstituting into Riccati equationTo obtain the optimal control gain G of the data driveW(k) (ii) a And the frequency of the wind power system, the power of the energy storage device and P1And P2Estimating a data-driven optimal controller state vector xc(k) (ii) a According to GW(k) And xc(k) Calculating the optimal energy storage device power u (k), u (k) GW(k)xc(k)。
The invention also discloses a data-driven virtual inertia control method for the wind power energy storage device, which comprises the following steps of:
(1) collecting the frequency of a historical wind power system and the power of an energy storage device;
(2) markov parameter M is obtained through frequency of historical wind power system and power of energy storage devicei；
(3) According to the obtained Markov parameter MiObtaining the optimal energy storage device power u (k) by a data-driven optimal control method;
(4) controlling the virtual inertia of the energy storage device through the optimal energy storage device power u (k), and obtaining the controlled frequency y (k) of the wind power system;
(5) and (5) repeating the step (2) through the optimal energy storage device power u (k) obtained in the step (3) and the wind power system frequency y (k) obtained in the step (4), obtaining a new Markov parameter, and performing the next iteration.
Further, the step (2) specifically includes the following steps:
(2.1) forming a matrix Y by using the collected wind power system frequency data at different moments and different iteration times, and forming a matrix V by using the collected energy storage device power data at different moments and different iteration times and the collected wind power system frequency data at different moments;
(2.2) solving by using Y, V matrix to obtain parameter matrix P1、P2、TpThe concrete solving formula is as follows:
[P1 Tp P2]＝YVT(VVT)-1
wherein T is the transposition of the matrix;
(2.3) matching the parameter matrix TpThe first column in (1) is used as the Markov parameter of the wind power systemMiAnd i is 1,2,3, … N, and N is the number of Markov parameters obtained by the wind power system.
Further, the step (3) specifically includes the following steps:
step (3.1): the Markov parameter MiSubstituting Riccati equation to obtain optimal control gain G driven by data through closed solution of Riccati equationW(k)；
Step (3.2): by the frequency of the wind power system, the power of the energy storage device, P1And P2Estimating a data-driven optimal controller state vector xc(k)；
Step (3.3): according to GW(k) And xc(k) Calculating the optimal energy storage device power u (k), u (k) GW(k)xc(k)。
Further, the matrix Y and the matrix V in the step (2.1) are:
Y＝[yp(k+p) yp(k+p+1) … yp(k+p+L)]
Figure BDA0003379938710000031
wherein, yp(k) A column vector formed by the frequency data of the wind power system in the p steps; u. ofp(k) A column vector formed by power data of the energy storage devices in the p steps; k is the time and p is the number of iterations.
Further, the gain G is controlled in the step (3.1)W(k) The calculation formula of (2) is as follows:
GW(k)＝-(R+θ(k+1)TΩ(k+1)·θ(k+1))-1θ(k+1)TΩ(k+1)；
wherein θ (k +1) ═ M1 M2 … MN-k]T；
Ω(k+1)＝Q(k+1)-Q(k+1)S(k+1)·(R(k+1)+S(k+1)TQ(k+1)S(k+1))-1·S(k+1)TQ(k+1)；
Figure BDA0003379938710000032
R(k+1)＝diag(R,R,…,R)；
Q(k+1)＝diag(Q,Q,…,Q)；
When k +1 is equal to N, S (k +1) is equal to 0; r (k +1) and Q (k +1) are both N-k dimensional diagonal matrixes; r is a positive definite symmetric weight matrix, and Q is a positive semi-definite symmetric weight matrix.
Further, the state vector x in said step (3.2)c(k) Is estimated value of
Figure BDA0003379938710000033
The calculation formula of (2) is as follows:
Figure BDA0003379938710000041
wherein, I(N-k)lThe method is characterized in that the method is an N-k order identity matrix, p is N-k +1, l is the dimension of the frequency y (k) of the wind power system, and the value is 1; x is the number ofc(k) Is that
Figure BDA0003379938710000042
The column vectors of line l +1 to line l (N-k + 1).
Further, the method also comprises the step of collecting noise data, and the step (3.2): input noise data omega (k), measured noise data v (k), wind power system frequency, power of energy storage device, P1And P2Estimating a state vector x for a data-driven optimal controllerc(k) The concrete formula is as follows:
Figure BDA0003379938710000043
wherein J ═ Γ Sr-1S is an identity matrix, r is the variance of the measured noise, gamma is an input noise data matrix, CpTo measure the noisy data matrix.
Has the advantages that: compared with the prior art, the method has the obvious advantages that the historical wind power system frequency is collected as the input of the data driving controller, the power of the energy storage device is obtained, the virtual inertia of the energy storage device is controlled, the frequency of the wind power system is regulated and controlled, the regulated wind power system frequency is collected to perform the virtual inertia control of the energy storage device again, the real-time regulation and control of the wind power system are realized, a system model is not required to be established by adopting the data driving control, the data driving controller is designed by only utilizing the online input and output data of the wind power system, the actual control problem caused by the difficult modeling of the wind power system can be solved, the online optimization parameter adjustment is realized, and the control performance of the DFIG frequency modulation is improved. The active power output of the energy storage system is directly and adaptively adjusted according to the real-time input and output data of the wind power system, so that the problems of structure selection, modeling parameter errors and the like in modeling are avoided, and the calculated amount is reduced. Meanwhile, the obtained data driving controller has good robustness and can resist the influence of measurement noise.
Drawings
FIG. 1 is a schematic diagram of a data-driven inertia controller according to the present invention;
FIG. 2 is a schematic structural diagram of a wind power and storage combined system according to the present invention;
FIG. 3 is a schematic diagram of an energy storage device control according to the present invention;
FIG. 4 is a signal structure diagram of the DSP data signal processor of the present invention;
FIG. 5 is a flow chart of a data driving control method according to the present invention.
Detailed Description
Example 1
As shown in fig. 1, the virtual inertia control system of a wind power energy storage device based on data driving in this embodiment includes a wind power energy storage device, and further includes a data acquisition module, a data driving controller, and an energy storage device control module; the data acquisition module acquires the frequency of a historical wind power system and the power of the energy storage device and also acquires the frequency of the wind power system and the power of the energy storage device which are controlled by the data driving controller; the data driving controller obtains Markov parameter M through historical wind power system frequency and power of the energy storage devicei(ii) a According to the obtained Markov parameter MiAnd obtaining the optimal energy storage device power u (k) by a data-driven optimal control method. The energy storage device control module carries out virtual inertia advance on the wind power energy storage device through the optimal energy storage device power u (k)And (5) controlling.
As shown in fig. 2, a topological structure in which the energy storage system and the wind farm run in parallel adopts a distributed structural form, and when the maximum wind power of the wind turbine is tracked, the total control process of the wind power system is as follows: the phase-locked loop PLL collects the power grid frequency signal of the wind power system in real time, and then according to the power grid frequency change condition, the energy storage virtual inertia control system based on data driving is designed to control the energy storage equipment to compensate the active frequency shortage of the power grid caused by load sudden change and the like, so that the frequency stability of the wind power system is improved. The maximum wind power tracking control can enable the wind power generation system to operate at the maximum efficiency by adjusting the pitch angle and the blade tip speed ratio; the rotor-side converter controls the rotor exciting current phase to realize the capability of the unit for providing reactive power, the active power and the reactive power of the unit are decoupled by adopting a stator voltage directional vector control method, and the variable-speed operation of the unit is realized by controlling the rotor exciting voltage frequency; the grid-side converter controls the terminal voltage of the converter, so that the constant voltage of a direct current capacitor of the wind turbine converter and the active power control of the grid-side converter are realized; the DC/DC converter controls the operation of the energy storage system through the data driving controller according to the power grid frequency signal detected by the phase-locked loop PLL in real time so as to provide active support for the power grid frequency
As shown in FIG. 3, the charge and discharge of the battery in the energy storage device is controlled by adjusting the current IESSThe energy storage device control module comprises a power outer ring and a current inner ring, and absorbs (releases) power PESS-refIs divided by the voltage UESSWill expect a current value IESS-refAnd the actual value of the battery current IESSAnd differentiating to obtain a control signal through a PI link, and driving the DC/DC converter to operate.
As shown in fig. 4, the grid-connected double-fed wind power system is controlled by a DSPF2812 chip of TI, and includes a wind power control system, an active power controller, and a data-driven inertial control system, so that the whole system has better stability and coordination performance.
Example 2
As shown in fig. 5, in this embodiment, a method for controlling virtual inertia of a wind power energy storage device based on data driving includes the following steps:
(1) the control method is initially in an open-loop state, the frequency signal of the wind power system is detected in real time according to a phase-locked loop PLL, historical wind power system frequency data are collected in the state, the length L of an obtained data set is enough, so that correlation lines in a matrix V are ensured to be linearly independent of input vectors of the system, accurate and available Markov parameters can be calculated, sampling time is set to be 2e-7s, and sufficient sample data can be obtained only by the 4e-4s system; obtaining the variable quantity delta f of the frequency of the wind power system according to the collected frequency data f of the wind power system
(2) Obtaining Markov parameter M through variable quantity delta f of frequency of wind power system and power of energy storage devicei(ii) a The method comprises the following specific steps:
(2.1) forming variable quantity delta f data of the frequency of the wind power system at different moments into a matrix Y, and forming power data of the energy storage device at different moments and frequency data of the wind power system at different moments into a matrix V;
matrix Y and matrix V are:
Y＝[yp(k+p) yp(k+p+1) … yp(k+p+L)]
Figure BDA0003379938710000061
wherein, yp(k) A column vector formed by the frequency data of the wind power system in the p steps; u. ofp(k) A column vector formed by power data of the energy storage devices in the p steps; k is the time, p is the number of iterations, p is 1 in the initial open loop state,
(2.2) solving by using Y, V matrix to obtain parameter matrix P1、P2、TpThe concrete solving formula is as follows:
[P1 Tp P2]＝YVT(VVT)-1
wherein T is the transposition of the matrix;
(2.3) matching the parameter matrix TpThe first column in (1) is used as Markov parameter M of wind power systemiAnd i is 1,2,3, … N, and N is the number of Markov parameters obtained by the wind power system.
(3) According to the obtained Markov parameter MiObtaining the optimal energy storage device power u (k) by a data-driven optimal control method; the method comprises the following specific steps:
step (3.1): the Markov parameter MiSubstituting Riccati equation to obtain optimal control gain G driven by data through closed solution of Riccati equationW(k) (ii) a Controlling gain GW(k) The calculation formula of (2) is as follows:
GW(k)＝-(R+θ(k+1)TΩ(k+1)·θ(k+1))-1θ(k+1)TΩ(k+1)；
wherein θ (k +1) ═ M1 M2 … MN-k]T；
Ω(k+1)＝Q(k+1)-Q(k+1)S(k+1)·(R(k+1)+S(k+1)TQ(k+1)S(k+1))-1·S(k+1)TQ(k+1)；
Figure BDA0003379938710000062
R(k+1)＝diag(R,R,…,R)；
Q(k+1)＝diag(Q,Q,…,Q)；
When k +1 is equal to N, S (k +1) is equal to 0; r (k +1) and Q (k +1) are both N-k dimensional diagonal matrixes; r is a positive definite symmetric weight matrix, and Q is a positive semi-definite symmetric weight matrix;
step (3.2): by the frequency of the wind power system, the power of the energy storage device, P1And P2Estimating a data-driven optimal controller state vector xc(k) (ii) a State vector xc(k) Is estimated value of
Figure BDA0003379938710000071
The calculation formula of (2) is as follows:
Figure BDA0003379938710000072
wherein, I(N-k)lIs an N-k order identity matrix, p ═ N-k +1, l is the dimension of the frequency y (k) of the wind power system, and the value is 1; x is the number ofc(k) Is that
Figure BDA0003379938710000073
Column vectors from line l +1 to line l (N-k + 1);
step (3.3): according to GW(k) And
Figure BDA0003379938710000074
calculating the optimal energy storage device power u (k), u (k) GW(k)xc(k)
(4) Controlling the virtual inertia of the energy storage device through the optimal energy storage device power u (k), and obtaining the controlled frequency y (k) of the wind power system;
(5) and (5) repeating the step (2) through the optimal energy storage device power u (k) obtained in the step (3) and the wind power system frequency y (k) obtained in the step (4), obtaining a new Markov parameter, and estimating a state vector x at the next momentc(k +1), calculating the next control input u (k +1), and circulating in sequence.
In consideration of the complex external environment of the actual wind power system, the data driving control needs to have certain anti-interference and noise reduction capabilities. For a wind power system with input noise ω (k) and measurement noise v (k), using an online estimation of the noise, in step (3.2): input noise data omega (k), measured noise data v (k), wind power system frequency, power of energy storage device, P1And P2Substituting the Riccati equation to obtain the Riccati equation with self-correcting capability, and estimating the state vector x of the data-driven optimal controllerc(k) The concrete formula is as follows:
Figure BDA0003379938710000075
wherein J ═ Γ Sr-1S is an identity matrix, r is the variance of the measured noise, gamma is an input noise data matrix, CpFor measuring noisy data matrices, xc(k) Is that
Figure BDA0003379938710000076
The column vectors of line l +1 to line l (N-k + 1).
The control method in the embodiment intelligently controls the DC/DC converter, so that active support is provided for the energy storage device, and the anti-interference capability of the system is improved. The energy generated by the wind turbine is transmitted to the alternating current bus through the transformer after passing through the rectifier and the inverter, the energy generated by the offshore wind farm is transmitted to the VSC-HVDC system after filtering, rectifying and transforming, and the transformed and rectified energy is transmitted to the onshore power grid through the second alternating current bus. The VSC-HVDC system independently controls active power and reactive power to realize electric energy transmission from an offshore wind turbine to a land grid; the converter comprises a VSC1 rectifying converter and a VSC2 inverting converter. When the system encounters a frequency event, a data-driven state observer is established by monitoring the system frequency and the active output of the energy storage system, and the Markov parameter of the system is obtained. And an optimal feedback controller based on system Markov parameters is established by using the closed solution of a differential Riccati equation with self-tuning capability, so that the system can stably run under different working conditions.

Claims (10)

1. A wind power energy storage device virtual inertia control system based on data driving comprises a wind power energy storage device and is characterized by further comprising a data acquisition module, a data driving controller and an energy storage device control module;
the data acquisition module is used for acquiring the frequency of the historical wind power system and the power of the energy storage device;
the data driving controller is used for obtaining Markov parameters M through historical wind power system frequency and power of the energy storage devicei(ii) a According to the obtained Markov parameter MiObtaining the optimal energy storage device power u (k) by a data-driven optimal control method;
the energy storage device control module is used for controlling the virtual inertia of the wind power energy storage device through the optimal energy storage device power u (k);
the data acquisition module is also used for acquiring the frequency of the wind power system and the power of the energy storage device which are controlled by the data driving controller.
2. The control system of claim 1, wherein the data driving controller is a DSPF2812 chip, a digital input port of the DSPF2812 chip is connected with the data acquisition module, and a digital output port of the DSPF2812 chip is connected with the energy storage device control module.
3. A control system according to claim 1 or 2, wherein the data drive controller operates by applying a Markov parameter MiObtaining optimal control gain G of data drive by substituting closed solution of Riccati equationW(k) (ii) a And the frequency of the wind power system, the power of the energy storage device and P1And P2Estimating a data-driven optimal controller state vector xc(k) (ii) a According to GW(k) And xc(k) Calculating the optimal energy storage device power u (k), u (k) GW(k)xc(k)。
4. A wind power energy storage device virtual inertia control method based on data driving is characterized by comprising the following steps:
(1) collecting the frequency of a historical wind power system and the power of an energy storage device;
(2) markov parameter M is obtained through frequency of historical wind power system and power of energy storage devicei；
(3) According to the obtained Markov parameter MiObtaining the optimal energy storage device power u (k) by a data-driven optimal control method;
(4) controlling the virtual inertia of the energy storage device through the optimal energy storage device power u (k), and obtaining the controlled frequency y (k) of the wind power system;
(5) and (5) repeating the step (2) through the optimal energy storage device power u (k) obtained in the step (3) and the wind power system frequency y (k) obtained in the step (4), obtaining a new Markov parameter, and performing the next iteration.
5. The control method according to claim 4, wherein the step (2) specifically includes the steps of:
(2.1) forming a matrix Y by using the collected wind power system frequency data at different moments and different iteration times, and forming a matrix V by using the collected energy storage device power data at different moments and different iteration times and the collected wind power system frequency data at different moments;
(2.2) solving by using Y, V matrix to obtain parameter matrix P1、P2、TpThe concrete solving formula is as follows:
[P1 Tp P2]＝YVT(VVT)-1
wherein T is the transposition of the matrix;
(2.3) matching the parameter matrix TpThe first column in (1) is used as Markov parameter M of wind power systemiAnd i is 1,2,3, … N, and N is the number of Markov parameters obtained by the wind power system.
6. The control method according to claim 5, wherein the step (3) specifically includes the steps of:
step (3.1): the Markov parameter MiSubstituting Riccati equation to obtain optimal control gain G driven by data through closed solution of Riccati equationW(k)；
Step (3.2): by the frequency of the wind power system, the power of the energy storage device, P1And P2Estimating a data-driven optimal controller state vector xc(k)；
Step (3.3): according to GW(k) And xc(k) Calculating the optimal energy storage device power u (k), u (k) GW(k)xc(k)。
7. The control method according to claim 6, characterized in that the matrix Y and the matrix V in step (2.1) are:
Y＝[yp(k+p) yp(k+p+1) … yp(k+p+L)]
Figure FDA0003379938700000021
wherein, yp(k) For p steps of wind power system frequency numberA column vector constructed from; u. ofp(k) A column vector formed by power data of the energy storage devices in the p steps; k is the time and p is the number of iterations.
8. Control method according to claim 7, characterized in that the control gain G in step (3.1)W(k) The calculation formula of (2) is as follows:
GW(k)＝-(R+θ(k+1)TΩ(k+1)·θ(k+1))-1θ(k+1)TΩ(k+1)；
wherein θ (k +1) ═ M1 M2 … MN-k]T；
Ω(k+1)＝Q(k+1)-Q(k+1)S(k+1)·(R(k+1)+S(k+1)TQ(k+1)S(k+1))-1·S(k+1)TQ(k+1)；
Figure FDA0003379938700000031
R(k+1)＝diag(R,R,…,R)；
Q(k+1)＝diag(Q,Q,…,Q)；
When k +1 is equal to N, S (k +1) is equal to 0; r (k +1) and Q (k +1) are both N-k dimensional diagonal matrixes; r is a positive definite symmetric weight matrix, and Q is a positive semi-definite symmetric weight matrix.
9. Control method according to claim 8, characterized in that the state vector x in step (3.2)c(k) Is estimated value of
Figure FDA0003379938700000032
The calculation formula of (2) is as follows:
Figure FDA0003379938700000033
wherein, I(N-k)lThe method is characterized in that the method is an N-k order identity matrix, p is N-k +1, l is the dimension of the frequency y (k) of the wind power system, and the value is 1; x is the number ofc(k) Is that
Figure FDA0003379938700000034
The column vectors of line l +1 to line l (N-k + 1).
10. The control method of claim 9, further comprising collecting noise data, said step (3.2): input noise data omega (k), measured noise data v (k), wind power system frequency, power of energy storage device, P1And P2Estimating a state vector x for a data-driven optimal controllerc(k) The concrete formula is as follows:
Figure FDA0003379938700000035
wherein J ═ Γ Sr-1S is an identity matrix, r is the variance of the measured noise, gamma is an input noise data matrix, CpTo measure the noisy data matrix.
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