CN111181455A - Self-adaptive tracking control method and system for maximum power point of doubly-fed wind generator - Google Patents

Self-adaptive tracking control method and system for maximum power point of doubly-fed wind generator Download PDF

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CN111181455A
CN111181455A CN202010092944.1A CN202010092944A CN111181455A CN 111181455 A CN111181455 A CN 111181455A CN 202010092944 A CN202010092944 A CN 202010092944A CN 111181455 A CN111181455 A CN 111181455A
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maximum power
power point
adaptive
tracking control
self
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孙玉亮
徐艳霞
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Shandong Water Conservancy Vocational College
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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
    • H02J1/00—Circuit arrangements for dc mains or dc distribution networks
    • H02J1/10—Parallel operation of dc sources
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03D—WIND MOTORS
    • F03D7/00—Controlling wind motors 
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/0003—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control
    • H02P21/0017—Model reference adaptation, e.g. MRAS or MRAC, useful for control or parameter estimation
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/0003—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control
    • H02P21/0021—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control using different modes of control depending on a parameter, e.g. the speed
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/14—Estimation or adaptation of machine parameters, e.g. flux, current or voltage
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/24—Vector control not involving the use of rotor position or rotor speed sensors
    • H02P21/28—Stator flux based control
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P9/00—Arrangements for controlling electric generators for the purpose of obtaining a desired output
    • H02P9/14—Arrangements for controlling electric generators for the purpose of obtaining a desired output by variation of field
    • 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
    • Y02E10/72—Wind turbines with rotation axis in wind direction

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  • Power Engineering (AREA)
  • Life Sciences & Earth Sciences (AREA)
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  • Combustion & Propulsion (AREA)
  • Mechanical Engineering (AREA)
  • General Engineering & Computer Science (AREA)
  • Control Of Eletrric Generators (AREA)

Abstract

The invention provides a self-adaptive tracking control method and a self-adaptive tracking control system for a maximum power point of a double-fed wind generator.A formula of the relation between the maximum power point of captured wind energy and the angular frequency of a DFIG rotor is deduced according to a power formula of the wind energy captured by a wind turbine, so that the optimal angular frequency of the rotor at different wind speeds is obtained and is used as a maximum power point tracking object of the DFIG system; and constructing a DFIG mathematical model under a d/q synchronous coordinate system, and performing maximum power point tracking control on the uncertain resistance system after single-loop feedback linear decoupling by adopting a self-adaptive control method.

Description

Self-adaptive tracking control method and system for maximum power point of doubly-fed wind generator
Technical Field
The disclosure belongs to the technical field of maximum power point tracking control of wind driven generators, and particularly relates to a double-fed wind driven generator maximum power point self-adaptive tracking control method based on uncertainty of direct-current grid-connected resistance.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
Doubly-fed induction generators (DFIGs) are the most commonly used wind generators. The wind power plant grid connection has two topology schemes of alternating current and direct current. When the distance between the offshore wind power plant and the land exceeds 80km, flexible direct current grid connection is required. However, conventional DFIG systems require additional equipment to connect to the dc power grid. Therefore, various DFIG converter systems are proposed which are directly connected to the dc grid. One approach is to employ an IGBT-based Stator Side Converter (SSC) and Rotor Side Converter (RSC) that connect the stator and rotor of the DFIG to the dc grid, respectively, as shown in fig. 1. The novel structure has the advantages that the influence of current harmonic waves on a power grid in the power generation process is effectively reduced, the stator magnetic flux and the stator current are not limited by an alternating current power grid any more, and the stator magnetic flux and the stator current can be flexibly adjusted according to the system requirements.
However, the new configuration adds a stator side converter, so the input and state variables are 2 times larger than the conventional configuration. At the same time, it also brings about more complex coupling, and the control method in the conventional structure cannot be used continuously. In recent years, many scholars have been devoted to research on control algorithms of new structures, and a common algorithm is to realize high-power point tracking by using a system linearization model. However, to the inventors' knowledge, most linearization methods require precise motor parameters, such as stator and rotor resistance. Generally, the resistance of the motor is changed by the temperature and the skin effect in the running process of the motor, so that the performance of the motor is greatly influenced, the reliability of a system is reduced, and particularly, when a wind turbine generator frequently encounters random working conditions in the running process. Therefore, when the nominal parameters of the fan deviate from the actual values, the linear control algorithm depending on the precise parameters cannot ensure the operation performance of the system.
In order to avoid the negative effects caused by the linearization method, researchers research nonlinear control and intelligent control methods and apply the nonlinear control and intelligent control methods to the traditional DFIG system based on alternating current grid connection, but the strategies cannot be directly applied to the DFIG based on direct current grid connection.
Disclosure of Invention
The invention provides a maximum power point self-adaptive tracking control method and system based on a direct-current grid-connected doubly-fed wind generator, aiming at solving the problems.
According to some embodiments, the following technical scheme is adopted in the disclosure:
a self-adaptive tracking control method for the maximum power point of a direct-current grid-connected doubly-fed wind driven generator comprises the following steps:
deducing a formula of the relation between the maximum power point of the captured wind energy and the angular frequency of the DFIG rotor according to a power formula of the wind energy captured by the wind turbine to obtain the optimal angular frequency of the rotor at different wind speeds, and using the optimal angular frequency as a maximum power point tracking object of the DFIG system;
and constructing a DFIG mathematical model under a d/q synchronous coordinate system, and performing maximum power point tracking control on the uncertain resistance system after single-loop feedback linear decoupling by adopting a self-adaptive control method.
As an alternative embodiment, a DFIG mathematical model under a d/q synchronous coordinate system is constructed, the DFIG mathematical model is sorted, the output is derived, and the input-output linearization from the output to the input is realized.
As an alternative embodiment, a model reference adaptive tracking control algorithm is adopted to realize the tracking of a given target, and the output is stabilized in a preset reference range according to the control requirement of the maximum power point.
As an alternative embodiment, varying resistance values are represented using unknown bounded constants.
As an alternative embodiment, an error model based on the changed resistance value is constructed, a cooperative adaptive feedback linearization controller is designed for the error model, and a final controller is obtained by considering the adaptive rate.
A self-adaptive tracking control system for the maximum power point of a direct-current grid-connected doubly-fed wind generator comprises the following components:
the calculation module is configured to deduce a formula of a relation between the maximum power point of the captured wind energy and the angular frequency of the DFIG rotor according to a power formula of the wind energy captured by the wind turbine to obtain the optimal angular frequency of the rotor at different wind speeds, and the optimal angular frequency is used as a maximum power point tracking object of the DFIG system;
and the tracking module is configured to construct a DFIG mathematical model under a d/q synchronous coordinate system, and perform maximum power point tracking control on the system with uncertain resistance after single-loop feedback linear decoupling by adopting an adaptive control method.
A computer readable storage medium, wherein a plurality of instructions are stored, the instructions are suitable for being loaded by a processor of a terminal device and executing the maximum power point adaptive tracking control method of the doubly-fed wind generator.
A terminal device comprising a processor and a computer readable storage medium, the processor being configured to implement instructions; the computer readable storage medium is used for storing a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the maximum power point adaptive tracking control method of the doubly-fed wind generator.
Compared with the prior art, the beneficial effect of this disclosure is:
the invention provides a resistance uncertain estimation method based on a self-adaptive feedback linearization control strategy aiming at the maximum power point tracking control of a direct-current grid-connected doubly-fed generator, and can realize stable and efficient maximum power point tracking control under the disturbance of uncertain stator and rotor resistance values and rapid change of wind speed.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and are not to limit the disclosure.
FIG. 1 is a DFIG using a DC converter based system in a DC grid connection;
FIG. 2 is a graph of the relationship between output mechanical power, optimum power, and rotor speed of the present disclosure;
FIG. 3 is a schematic diagram of the control logic of the present disclosure;
FIG. 4 is a wind speed performance graph;
FIG. 5 is a plot of rotor angular frequency performance for three control algorithms (feedback linearization FLC, adaptive feedback linearization control FLC-A, vector control VC);
FIG. 6 is a graph of rotor angular frequency tracking error performance for three control algorithms;
FIG. 7 is a graph of maximum power point tracking performance for three control algorithms;
FIGS. 8-9 are graphs of the tracking performance of FLC and FLC-A in systems with indeterminate resistance.
The specific implementation mode is as follows:
the present disclosure is further described with reference to the following drawings and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
First, the meaning of the formula parameters in the examples is explained:
usd、usq、isd、isq、ψsd、ψsqthe voltage, the current and the magnetic flux of a d axis and a q axis of the DFIG stator are respectively;
urd、urq、ird、irq、ψrd、ψrqvoltage, current and magnetic flux omega of d-axis and q-axis of DFIG rotor1Synchronizing the angular frequency for the DFIG;
ωris the DFIG rotor angular frequency;
Rs,Rrrespectively a stator resistor and a rotor resistor;
Lm,Lr,Lsrespectively including DFIG winding mutual inductance, rotor winding self-inductance and stator winding self-inductance;
Te,Tmrespectively a DFIG electromagnetic torque and a load torque;
npand J is the DFIG pole pair number and the moment of inertia respectively.
The main purpose of the present disclosure is to perform maximum power point tracking control when the resistance parameters of the DFIG based on the direct current grid connection are influenced and changed by objective factors, so as to achieve the specified system performance. The specific method comprises the following steps: on the basis of feedback linear decoupling, a cooperative adaptive controller is selected to realize Maximum Power Point Tracking (MPPT) control, the change value of a resistor is used as the interference of a system, the adaptive rate is designed to estimate an interference item, and when the interference occurs, the system can still accurately track the maximum power point; by MATLAB/SimPowerSystems simulation, the adaptive feedback linearization control (FLC-A) of the control method of the embodiment is compared with Vector Control (VC) and Feedback Linearization Control (FLC), and the result shows that the controller has better MPPT performance under the condition of uncertain resistance.
Firstly, constructing a direct current grid-connected DFIG mathematical model:
the DFIG mathematical model under the d/q synchronous coordinate system is as follows:
Figure BDA0002384318420000071
wherein: ca＝(LrLs-Lm 2)/Ls,Cb＝Lm/Ls。
Wind turbine mathematical model
When the tip speed ratio lambda is equal to the optimum tip speed ratio lambdaoptOutput mechanical power P of wind turbinewtEqual to the optimum power Pwtmax. At this time, the power coefficient is maintained at the maximum value CpmaxThus, therefore, it isOptimum angular velocity value omega of wind turbine* rThe formula is as follows:
Figure BDA0002384318420000072
wherein R iswtIs the radius of the wind turbine, VwindIs the wind speed. Their relationship is shown in fig. 2.
In the controller design, the method comprises the following steps:
1. coordinated adaptive feedback linearization control
After equation (1) is collated, it becomes the following form:
Figure BDA0002384318420000073
wherein:
x＝[x1x2x3x4x5]T＝[ψsdψsqirdirqωr]T
u＝[u1u2u3u4]T＝[usdusqurdurq]T
y＝[h1(x) h2(x) h3(x) h4(x)]T＝[x1x2x4x5]T＝[ψsdψsqirqωr]T
Figure BDA0002384318420000081
Figure BDA0002384318420000082
Figure BDA0002384318420000083
in the formula (3), x is a 5-dimensional state phasor，fi(x) (i ═ 1,2,. 5) and gi(x) (i ═ 1,2,3,4) are 5d and 4d smooth vector fields, respectively. Each output yiCorresponding to a degree of relation riThe values by calculation are 1, 1, 1 and 2. The system relation r is 1+1+1+2, 5 is n, and the feedback linearization condition is satisfied. Output y is paired according to equation (4)iMaking a derivation until the input ujAnd occurs.
Figure BDA0002384318420000084
Calculating equation (3) according to formula (4) to obtain a new system:
Figure BDA0002384318420000085
wherein:
Figure BDA0002384318420000091
Figure BDA0002384318420000092
Figure BDA0002384318420000093
thereby realizing that y is output fromiTo ujThe MPPT of the system is solved by utilizing a linear control theory. Considering that the resistances of the rotor and the stator are affected by objective factors and the resistances are not constant, the embodiment adopts a model reference adaptive tracking control algorithm to track a given target. The new control input is designed as follows:
Figure BDA0002384318420000094
here, the number of the first and second electrodes,
Figure BDA0002384318420000095
psi is known by equation (17)sqNot equal to 0, so det (E (x)) ≠ 0, there is an inverse matrix E (x)-1，u＝E(x)-1And U is adopted. The system (5) is arranged as follows:
Figure BDA0002384318420000101
stabilizing the output at the MPPT control requirement
Figure BDA0002384318420000102
Wherein psi* sd，ψ* sq，i* rq，ω* rIs the MPP reference value of the system. The system tracking error is defined as:
Figure BDA0002384318420000103
when t → ∞ is reached, eyi→ 0. The error model (8) is derived:
Figure BDA0002384318420000104
the finishing method comprises the following steps:
Figure BDA0002384318420000105
simultaneously, the method comprises the following steps:
Figure BDA0002384318420000106
2. FLC-A based on disturbance observer
Due to the system resistance RsAnd RrThe resistance value of the resistor is sensitive to the change of objective environment, and can be changed along with the change of temperature and the like. We assume Qi(i ═ 1,2.. 4) is an unknown bounded constant, representing a changing resistance value. Then, Δ RsAnd Δ RrThe values of variation of the stator and rotor resistances, respectively. Thus, the error model (9) can be rewritten as:
Figure BDA0002384318420000111
the assembly is as follows:
Figure BDA0002384318420000112
Figure BDA0002384318420000113
Figure BDA0002384318420000114
Figure BDA0002384318420000115
is QiThe estimation of (2) is carried out by substituting equation (10):
Figure BDA0002384318420000116
obviously, the controller U is in a dynamic model
Figure BDA0002384318420000117
Are decoupled. The cooperative adaptive feedback linearization control is designed according to equation (11) as:
Figure BDA0002384318420000121
wherein k isi(i ═ 1,2.. 4) is the normal coefficient feedback gain, ki>0. Adaptive rate
Figure BDA0002384318420000122
Comprises the following steps:
Figure BDA0002384318420000123
wherein, γi(i ═ 1,2.. 4) is adaptiveShould gain, γi>0。
From equation (6), the controller of the system is:
Figure BDA0002384318420000124
for verification, the Lyapanov function (15) is used for proving that the controller (11) can enable the direct-current grid-connected DFIG to operate on MPPT, and robustness is strong to uncertain mismatch caused by parameter change.
Figure BDA0002384318420000125
The derivation of equation (15) is followed by the substitution of equations (11), (12), (13):
Figure BDA0002384318420000126
then define equation M1(t) is
Figure BDA0002384318420000131
Since M (t) is not less than 0, V1(t) may be defined as:
Figure BDA0002384318420000132
wherein
Figure BDA0002384318420000133
and
Figure BDA0002384318420000134
Because of V1(t) ≧ 0, the following can be concluded by equation (16):
Figure BDA0002384318420000135
thus, according to Barbalt's Lemma, M when t → ∞ time1(t) → 0, in other words, e → time t → ∞i(t) → 0. In summary, the proposed controller is stable and robust even in the presence of parameter uncertainties.
4. Controller reference point
In the control target required in the present embodiment, equation (2) determines the reference value ω* r. The stator flux and rotor current references are derived from stator flux orientation, i.e. stator flux vector references
Figure BDA0002384318420000136
In line with the q-axis direction.
Figure BDA0002384318420000137
Wherein VsIs the rated voltage amplitude of the generator, omega1Substituting equation (17) into equation (1) yields the rotor current reference value:
Figure BDA0002384318420000138
in summary, the system control scheme is shown in FIG. 3.
The simulation results are given by MATLAB/SimPowerSystems and 2 different purpose simulations were performed. Firstly, under the slope wind speed, the proposed control strategy is compared with Vector Control (VC) and Feedback Linearization Control (FLC) when the system carries out maximum power point tracking; secondly, when the system parameter resistance changes, the robustness of the proposed control strategy is verified to be better than that of the FLC. The DFIG system parameters are specifically as follows:
DFIG parameters
Prated＝1.5MW,fnom＝50Hz,vs_nom＝1.0pu,ωs＝1.0pu,Rs＝0.023pu,Rr＝0.016pu,Ls＝3.071pu,Lr＝3.056pu,Lm＝2.9pu,np＝3.
Wind turbine parameters
ρ＝1.225kg/m3,Rwt＝40m,H＝5.04s；
Control coefficient
k1＝1500,k2＝1500,k3＝1000,k4＝6000；
γ1＝2000,γ2＝2000,γ3a＝1500,γ3b＝1500,γ4＝4000；
The maximum power point tracking performance of the DFIG system based on direct current grid connection controlled by the FLC-A, FLC and the VC is shown in the figures 4-7. Fig. 4 depicts the ramp wind speed, rising sharply at 1.5 seconds and 4.8 seconds, from 8 m/s to 12 m/s in 0.3 seconds. FIG. 5 shows the angular frequency operation state of the DFIG rotor under the control of VC and FLC-A, FLC, wherein the dotted line shows the optimal rotor speed omega* rIt is clear that VC provides a rotor angular frequency tracking value that lags behind the optimal value. The tracking error is shown in fig. 6, where the maximum FLC tracking error is 0.08pu, the maximum VC tracking error is 0.48pu, and the maximum FLC-a tracking error is 0.017 pu. FIG. 7 shows the situation that the DFIG system captures the maximum power coefficient output by the wind turbine, and when the FLC-A is adopted, the wind speed is increased rapidly compared with the VCpmaxThe trapping performance is improved by 50 percent and is improved by 2 percent compared with FLC. Thus, the proposed FLC-A may improve fast time-varying tracking performance at random wind speeds.
Simulation result of parameter resistance change
FIGS. 8-9 show the tracking performance of systems with indeterminate resistance controlled by FLC and FLC-A under random wind conditions, with wind speeds taken for the first 4 seconds in FIG. 3. Assuming that the resistance increases with temperature, assuming that the maximum value estimated for the resistance is 2 times the original value,
Figure BDA0002384318420000151
FIG. 8 is a plot of rotor frequency tracking for the FLC at two resistances. When the rotor frequency rises rapidly, the maximum tracking error of the normal resistance is 0.08pu, and the maximum tracking error of the double resistance is 0.17 pu. It is clear that the tracking error increases with the change in resistance. The response of FLC-A under different resistances proposed by the embodiment is shown in FIG. 9, and the adaptive rate of the controller can estimate the resistance in real timeAnd compensates for uncertainty in tracking control due to impedance mismatch, the stator flux observer can also identify resistance changes on-line, providing reliable flux states. Therefore, the tracking values of the rotor frequency at the two resistances shown in fig. 9 are not very different, and the maximum tracking error is about 0.02 pu. In summary, the use of FLC-A improves tracking performance of the system under resistance uncertainty.
As will be appreciated by one skilled in the art, embodiments of the present disclosure may be provided as a method, system, or computer program product. Accordingly, the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and so forth) having computer-usable program code embodied therein.
The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the disclosure. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure, and various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
Although the present disclosure has been described with reference to specific embodiments, it should be understood that the scope of the present disclosure is not limited thereto, and those skilled in the art will appreciate that various modifications and changes can be made without departing from the spirit and scope of the present disclosure.

Claims (10)

1. A self-adaptive tracking control method for the maximum power point of a doubly-fed wind generator is characterized by comprising the following steps: the method comprises the following steps:
deducing a formula of the relation between the maximum power point of the captured wind energy and the angular frequency of the DFIG rotor according to a power formula of the wind energy captured by the wind turbine to obtain the optimal angular frequency of the rotor at different wind speeds, and using the optimal angular frequency as a maximum power point tracking object of the DFIG system;
and constructing a DFIG mathematical model under a d/q synchronous coordinate system, and performing maximum power point tracking control on the uncertain resistance system after single-loop feedback linear decoupling by adopting a self-adaptive control method.
2. The self-adaptive maximum power point tracking control method of the doubly-fed wind generator of claim 1, which is characterized in that: and constructing a DFIG mathematical model under a d/q synchronous coordinate system, sorting the DFIG mathematical model, and deriving output, so that input and output linearization from output to input is realized.
3. The self-adaptive maximum power point tracking control method of the doubly-fed wind generator of claim 1, which is characterized in that: during tracking control, tracking of a given target is achieved by adopting a model reference adaptive tracking control algorithm, and output is stabilized within a preset reference range according to the control requirement of the maximum power point.
4. The self-adaptive maximum power point tracking control method of the doubly-fed wind generator of claim 1, which is characterized in that: for a resistance uncertainty system, varying resistance values are represented by unknown bounded constants.
5. The self-adaptive maximum power point tracking control method of the doubly-fed wind generator of claim 4, wherein: and constructing an error model based on the changed resistance value, designing a cooperative adaptive feedback linearization controller aiming at the error model, and considering the adaptive rate to obtain the final controller.
6. A self-adaptive tracking control system for the maximum power point of a direct-current grid-connected doubly-fed wind driven generator is characterized in that: the method comprises the following steps:
the calculation module is configured to deduce a formula of a relation between the maximum power point of the captured wind energy and the angular frequency of the DFIG rotor according to a power formula of the wind energy captured by the wind turbine to obtain the optimal angular frequency of the rotor at different wind speeds, and the optimal angular frequency is used as a maximum power point tracking object of the DFIG system;
and the tracking module is configured to construct a DFIG mathematical model under a d/q synchronous coordinate system, and perform maximum power point tracking control on the system with uncertain resistance after single-loop feedback linear decoupling by adopting an adaptive control method.
7. The self-adaptive maximum power point tracking control system of the direct-current grid-connected doubly-fed wind generator as claimed in claim 6, wherein: the tracking module adopts a model reference self-adaptive tracking control algorithm to realize the tracking of a given target, and stabilizes the output in a preset reference range according to the control requirement of the maximum power point.
8. The self-adaptive maximum power point tracking control system of the direct-current grid-connected doubly-fed wind generator as claimed in claim 6, wherein: the tracking module representing a changing resistance value with an unknown bounded constant; and constructing an error model based on the changed resistance value, designing a cooperative adaptive feedback linearization controller aiming at the error model, and considering the adaptive rate to obtain the final controller.
9. A computer-readable storage medium characterized by: the method comprises the steps of storing a plurality of instructions, wherein the instructions are suitable for being loaded by a processor of a terminal device and executing the maximum power point adaptive tracking control method of the doubly-fed wind generator according to any one of claims 1 to 5.
10. A terminal device is characterized in that: the system comprises a processor and a computer readable storage medium, wherein the processor is used for realizing instructions; the computer readable storage medium is used for storing a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the maximum power point adaptive tracking control method of the doubly-fed wind generator in any one of the claims 1-5.
CN202010092944.1A 2020-02-14 2020-02-14 Self-adaptive tracking control method and system for maximum power point of doubly-fed wind generator Withdrawn CN111181455A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113162035A (en) * 2021-04-22 2021-07-23 云南电网有限责任公司电力科学研究院 Method and system for suppressing low-frequency oscillation of power grid by additional damping of virtual synchronous wind power plant

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113162035A (en) * 2021-04-22 2021-07-23 云南电网有限责任公司电力科学研究院 Method and system for suppressing low-frequency oscillation of power grid by additional damping of virtual synchronous wind power plant
CN113162035B (en) * 2021-04-22 2023-02-24 云南电网有限责任公司电力科学研究院 Method and system for suppressing low-frequency oscillation of power grid by adding damping to virtual synchronous wind power plant

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