CN111293693A - Doubly-fed wind turbine converter control parameter identification method based on extended Kalman filtering - Google Patents

Doubly-fed wind turbine converter control parameter identification method based on extended Kalman filtering Download PDF

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CN111293693A
CN111293693A CN202010236258.7A CN202010236258A CN111293693A CN 111293693 A CN111293693 A CN 111293693A CN 202010236258 A CN202010236258 A CN 202010236258A CN 111293693 A CN111293693 A CN 111293693A
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extended kalman
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王彤
高明阳
王增平
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North China Electric Power University
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • 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
    • 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
    • H02P21/13—Observer control, e.g. using Luenberger observers or Kalman filters
    • 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
    • H02P2101/00—Special adaptation of control arrangements for generators
    • H02P2101/15—Special adaptation of control arrangements for generators for wind-driven turbines

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Abstract

The invention discloses a doubly-fed wind generator converter control parameter identification method based on extended Kalman filtering. The method comprises the following steps: constructing a complete double-fed fan converter control system model; determining parameters to be identified including all integral coefficients and proportional coefficients in a PI link of a controller; the identifiability and the identification difficulty degree of the control parameters of the current transformer are analyzed by combining the track sensitivity; establishing a parameter identification model of a control system based on an iterative process of Extended Kalman Filtering (EKF) by utilizing phasors which can be directly measured in an actual operation process and voltages and currents at a rotor side, a stator side and a power grid side; and obtaining a final parameter identification result by repeatedly identifying and analyzing the experimental result. The invention can effectively solve the problem that the parameters of the controller cannot be directly identified because the input and output terminals of the PI controller are difficult to directly test, and has more accurate parameter identification result, simple whole process and high calculation speed.

Description

Doubly-fed wind turbine converter control parameter identification method based on extended Kalman filtering
Technical Field
The invention relates to the field of power systems, in particular to a doubly-fed wind generator converter control parameter identification method based on extended Kalman filtering.
Background
Chinese wind power is developed rapidly. By the end of 2017, the newly added wind power installed capacity in China reaches 19.66 gigawatts, the installed capacity reaches 168.2 megawatts and is the first in the world. Permanent magnet wind power generators (PMSG) and doubly-fed wind power generators (DFIG) are two wind power generators commonly used in China, and are successfully applied to different fields due to high efficiency and good controllability. In the research of wind power generation systems, accurate wind turbine generator model parameters are required. In fact, there are many brands and types of wind turbines installed in an actual power grid. However, most manufacturers do not provide accurate model parameters due to insufficient protection of detection technology or intellectual property rights. Furthermore, during operation of the wind farm, the actual parameter values for each wind turbine may change from the initial set values after a period of operation, which may result in large deviations between the simulated and actual conditions, sometimes causing damage to the system. Therefore, parameter identification aiming at the actual running state of the fan is always a hot point of research.
At present, the method for identifying parameters mainly includes: extended Kalman Filtering (EKF), neural networks, model reference-based methods, least squares-based methods, adaptive techniques, and improved particle swarm optimization algorithms. These methods are widely used for parameter identification of electrical and drive systems of wind turbines, but there are fewer models for parameter identification of DFIG controllers. The controller is an important component of the DFIG. The control mode and model parameters have great influence on the dynamic characteristics. The researchers have proposed that the input and output ends of each PI controller can be led out to test the parameters of the PI controllers one by one. However, this method can only be implemented in cooperation with manufacturers, and is difficult to popularize. Therefore, the phasor which can be directly measured in the actual operation process is considered to be used for establishing a parameter identification model of the control system.
Because different phasors are influenced by different control parameters in the actual operation process, the measurement quantities are respectively used as input vectors and measurement vectors to be introduced into the algorithm by combining the iterative process of the extended Kalman filtering algorithm, a complete DFIG control parameter identification model can be established, and the calculation precision is high and the speed is high.
Disclosure of Invention
The invention aims to provide a doubly-fed wind generator converter control parameter identification method based on extended Kalman filtering, so as to improve the accuracy of fan model parameters and lay a good foundation for subsequent simulation.
In order to achieve the purpose, the invention provides the following scheme:
constructing a complete double-fed fan converter control system model;
the parameters to be identified comprise all integral coefficients and proportional coefficients K in PI link of converter control systemp1～Kp7，Ki1～Ki7；
Carrying out identifiability and identification difficulty degree analysis on the converter control parameters based on the track sensitivity;
establishing a parameter identification model of the control system based on an iterative process of an Extended Kalman Filter (EKF) algorithm by utilizing phasors which can be directly measured in an actual operation process and voltages and currents at a rotor side, a stator side and a power grid side;
and obtaining a final identification result of the controller parameters by repeatedly identifying and analyzing the experimental result.
Optionally, the constructing a complete doubly-fed wind turbine converter control system model specifically includes:
establishing an algebraic equation and a dynamic equation of a Rotor Side Converter (RSC) control system;
and establishing an algebraic equation and a dynamic equation of a Grid Side Converter (GSC) control system.
Optionally, the determining the parameter to be identified specifically includes an integral coefficient and a ratio in a PI link of the controllerExample coefficient Kp1～Kp7，Ki1～Ki7。
Optionally, the trajectory sensitivity based on the parameters analyzes identifiability and identification difficulty of the control parameters, and specifically includes:
the controller parameter KjCalculating the sensitivity of the dynamic response;
will KjIncrease in value of Δ KjObtaining an observed value Y by simulating Y (k);
will KjDecrease of value of Δ KjObtaining an observed value Y' by simulating Y (k);
according to
Figure BDA0002431072850000031
Calculating KjTrack sensitivity S ofjWherein Y is0Is KjTake the original value Kj0Then, observing a steady state value corresponding to Y; Δ KjTaking 10% Kj；
And summing all sampling values of the track sensitivity curve of the parameters, wherein the absolute value represents the sensitivity of the parameters, the parameter with higher sensitivity is less difficult to identify, and the parameter with lower sensitivity is more difficult to identify.
Optionally, the iterative process based on Extended Kalman Filter (EKF) is performed by using phasors that can be directly measured in an actual operation process, and voltages and currents on a rotor side, a stator side, and a power grid side, to establish a parameter identification model of the control system, and specifically includes:
by adopting step identification, the RSC controller and the GSC controller parameters are respectively divided into two groups according to different control loops and cascade sequences, and the value is [ K ]p1,Ki1,Kp2,Ki2]，[Kp3,Ki3,Kp4,Ki4]And [ K ]p5,Ki5,Kp6,Ki6]，[Kp7,Ki7]；
Selecting different initial state variables X, input variables u and measurement variables z according to different groups;
establishing a discrete state space equation of the controller based on an iterative process of extended Kalman filtering;
setting an initial value of iteration under the principle of ensuring steady-state tracking and iteration not to diverge;
and identifying the parameters of the DFIG controller by using the established EKF parameter identification model.
Optionally, the repeated identification and the analysis of the experimental result are performed to obtain a final parameter identification result, which specifically includes:
changing the iteration initial value of the identification model, and repeatedly identifying the parameters for 50 times;
taking the average value of the identification results of each time as the final identification result of the parameter;
and carrying out error analysis on the final identification result of the parameters.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
on the basis of establishing a complete doubly-fed wind generator converter control system, the identifiability and the identification difficulty degree of converter control parameters are analyzed by adopting the track sensitivity; based on an iterative process of Extended Kalman Filtering (EKF), phasors which can be directly measured in an actual operation process, and voltages and currents on a rotor side, a stator side and a power grid side are utilized to establish a parameter identification model of a control system.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a schematic flow diagram of a doubly-fed wind generator converter control parameter identification method based on extended Kalman filtering according to the present invention;
fig. 2 is a schematic control diagram of a rotor-side converter of a doubly-fed wind turbine in embodiment 1 of the present invention;
fig. 3 is a schematic control diagram of a grid-side converter of a doubly-fed wind turbine in embodiment 1 of the present invention;
FIG. 4 is a system configuration diagram according to embodiment 1 of the present invention;
FIG. 5 is a diagram illustrating the result of identifying the control parameters of the DFIG rotor-side converter in accordance with embodiment 1 of the present invention;
fig. 6 is a diagram illustrating a result of identifying control parameters of a DFIG grid-side converter in accordance with embodiment 1 of the present invention;
FIG. 7 is an error analysis diagram of the DFIG rotor-side converter control parameter identification result according to embodiment 1 of the present invention;
fig. 8 is an error analysis diagram of the DFIG grid-side converter control parameter identification result according to embodiment 1 of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
FIG. 1 is a schematic flow diagram of a doubly-fed wind generator converter control parameter identification method based on extended Kalman filtering. As shown in fig. 1, the method comprises the following steps:
step 1: the method comprises the following steps of constructing a complete double-fed fan converter control system model, obtaining an algebraic equation of the control system based on a control strategy model of the converter control system, and establishing a dynamic equation of the algebraic equation by introducing the output quantity of a PI (proportional integral) controller in an integral link, wherein the specific process comprises the following steps:
(1) rotor Side Converter (RSC) controller
According to the control strategy diagram shown in FIG. 2, the following algebraic equations can be written:
Figure BDA0002431072850000051
in the formula, Kp1,Kp2,Kp3And Kp4Is the proportionality coefficient of the corresponding PI controller; psAnd QsThe active power and the reactive power of the stator winding are respectively; vsdAnd VsqD-axis and q-axis components of the stator winding voltage, respectively; vrdVrqAnd d-axis and q-axis components of the rotor winding voltage, respectively; i issdAnd IsqD-axis and q-axis components of the stator winding current, respectively; i isrdAnd IrqAre the d-axis and q-axis components of the rotor winding current, respectively. XmIs a magnetizing inductance; xrrIs the self-inductance of the rotor winding; xssIs the self-inductance of the rotor winding;
Figure BDA0002431072850000063
swis the rotor slip. The superscript "ref" denotes a reference value for the corresponding physical quantity.
The rotor-side converter generally adopts vector control, and the PI controllers output by the integral links in FIG. 2 are x respectively1,x2,x3And x4The following controller-related dynamic equations are available:
Figure BDA0002431072850000061
wherein Ki1,Ki2,Ki3And Ki4Respectively, the integral coefficients of the respective PI-controllers.
(2) Grid Side Converter (GSC) controller
Fig. 3 shows a control strategy of the DFIG grid-side converter, and the algebraic equation of the controller is as follows:
Figure BDA0002431072850000062
in the formula, Kp5,Kp6And Kp7Is corresponding PI controlScaling factor of the system. VdcIs the dc capacitor voltage; vcdAnd VcqD-axis and q-axis components of the ac side voltage of the grid side converter, respectively; i isr3d and Ir3qAre the d-axis and q-axis components of the ac side output current of the grid side converter, respectively. Xr3Is the filter reactance. The superscript "ref" denotes a reference value for the corresponding physical quantity.
Vector control is applied to a grid-side converter, and the output of the integral link of the PI controller in the figure 3 is respectively used as x5,x6And x7By introduction, the following dynamic equations relating to the controller can be derived:
Figure BDA0002431072850000071
in the formula, Ki5,Ki6And Ki7Is the integral coefficient of the corresponding PI controller.
Step 2: determining the parameters to be identified, specifically including each integral coefficient and proportional coefficient K in controller PI linkp1～Kp7，Ki1～Ki7。
And step 3: the identifiability and the identification difficulty degree of the control parameters are analyzed based on the track sensitivity of the parameters, and the specific process is as follows:
the controller parameter KjCalculating the sensitivity of the dynamic response;
will KjIncrease in value of Δ KjObtaining an observed value Y by simulating Y (k);
will KjDecrease of value of Δ KjObtaining an observed value Y' by simulating Y (k);
according to
Figure BDA0002431072850000072
Calculating KjTrack sensitivity S ofjWherein Y is0Is KjTake the original value Kj0Then, observing a steady state value corresponding to Y; Δ KjTaking 10% Kj；
And summing all sampling values of the track sensitivity curve of the parameters, wherein the absolute value represents the sensitivity of the parameters, the parameter with higher sensitivity is less difficult to identify, and the parameter with lower sensitivity is more difficult to identify.
And 4, step 4: the iterative process based on Extended Kalman Filtering (EKF) utilizes phasors which can be directly measured in the actual operation process, and voltages and currents of a rotor side, a stator side and a power grid side to establish a parameter identification model of the control system, and the specific process is as follows:
(1) RSC controller parameter identification model
The rotor-side controller shown in fig. 2 achieves decoupling of the d-axis and q-axis. As can be seen from the expressions (1) to (2), the parameter to be identified is θ ═ Kp1,Ki1,Kp2,Ki2,Kp3,Ki3,Kp4,Ki4]. In order to reduce errors caused by simultaneous identification of excessive parameters, step-by-step identification is adopted for control parameters. The cascaded PI controller parameters are divided into two groups, [ K ]p1,Ki1,Kp2,Ki2]And [ K ]p3,Ki3,Kp4,Ki4]。
1) Active power control outer ring and q-axis current inner ring
Selecting an initial state variable X1＝[x1,x2,Isd,Isq,Irq]Extended State variable X'1＝[Kp1,Ki1,Kp2,Ki2]Deterministic input variable u1＝[Vsd,Vsq,Ird,sw]Observation variable Z1＝[Vrq,Isd,Isq,Irq]。
A discrete state space equation for the rotor side controller is established. Discretizing (1) and (2) with the sampling period T to obtain a discrete time equation:
Figure BDA0002431072850000081
the coefficient matrix A is thus9×9B and H4×9Comprises the following steps:
Figure BDA0002431072850000082
Figure BDA0002431072850000083
b is a matrix of zeros and a,
Figure BDA0002431072850000091
the algebraic equation of the RSC controller is collated as follows:
Figure BDA0002431072850000092
2) reactive power control outer ring and d-axis current inner ring
Selecting an initial state variable X2＝[x3,x4,Isd,Isq,Ird]Extended State variable X'2＝[Kp3,Ki3,Kp4,Ki4]Deterministic input variable u2＝[Vsd,Vsq,Irq,sw]Observation variable z2＝[Vrd,Isd,Isq,Ird]。
By discretization, the same discrete-time equation as in (5) can be obtained. Corresponding coefficient matrix A9×9B and H4×9Respectively as follows:
Figure BDA0002431072850000093
Figure BDA0002431072850000101
b is a matrix of zeros and a,
Figure BDA0002431072850000102
Figure BDA0002431072850000103
(2) GSC controller parameter identification model
The same method as the rotor side control parameter identification method, the cascade PI controller parameters are divided into two groups, [ K ]p5,Ki5,Kp6,Ki6]And [ K ]p7,Ki7]。
1) DC voltage control outer ring and q-axis current inner ring
Considering the dc voltage dynamics, the dynamic model of the intermediate capacitor is:
Figure BDA0002431072850000104
wherein C is an intermediate capacitor, PrActive power from the rotor-side converter to the intermediate capacitor; pr3The active power of the grid-side converter can be expressed as:
Figure BDA0002431072850000111
selecting an initial state variable X3＝[x5,x6,Vdc,Ir3q]Extended State variable X'3＝[Kp5,Ki5,Kp6,Ki6]Deterministic input variable u3＝[Vsd,Vsq,Isd,Isq,Ir3d,]Observation variable z3＝[Vcq,Vdc,Ir3q]。
By discretizing (3) to (4), the corresponding coefficient matrices a, B, and H of the discrete-time equation can be obtained:
Figure BDA0002431072850000112
A2i＝zerO5×3A22＝I5×5
Figure BDA0002431072850000113
b is a matrix of zeros and a,
Figure BDA0002431072850000114
the algebraic equation of the GSC controller is summarized as follows:
Figure BDA0002431072850000115
2) d-axis current inner ring
Selecting an initial state variable X4＝[x7,Ir3d]Extended State variable X'4＝[Kp7,Ki7]Deterministic input variable u4＝[Vsd,Ir3q]Observed variable z4＝[Vcd,Ir3d]. And has already:
Figure BDA0002431072850000121
b is a matrix of zeros and a,
Figure BDA0002431072850000122
5.4 detailed parameter identification Process
1) A small interference scenario is imposed in the digital simulation. Measuring s in time domain simulationw,Is,Ir,Ir3,Vs,Vr,Vc,VdcAnd introduced into the EFK algorithm as deterministic input vectors and observation vectors.
2) Selecting an initial matrix P under the principle of ensuring steady-state tracking and iteration non-divergence0Variance matrices Q and R.
3) And (3) realizing the online identification method of the DFIG controller parameters by utilizing the established EKF parameter identification model and combining the formulas (5) to (15).
And 5: through repeated identification and analysis of the experimental result, a final parameter identification result is obtained, and the specific process is as follows:
changing the iteration initial value of the identification model, and repeatedly identifying the parameters for 50 times;
taking the average value of the identification results of each time as the final identification result of the parameter;
and carrying out error analysis on the final identification result of the parameters.
The invention adopts the embodiment 1, and the effect of the method is verified as follows:
fig. 4 is a system structure diagram of embodiment 1 of the present invention, which is a four-zone two-machine system including a doubly-fed wind generator. Fig. 5 and fig. 6 show that the experiment is repeated 50 times by selecting different initial values, the gray curve represents each identification process, and the red curve represents the average value of each identification result, and the results show that the identification method has good convergence and stable controller parameter identification. As the recognition result of each parameter selection. The relative error between the respective identification values and the true values is shown in fig. 7 and 8. As shown, the parameter identification results obtained from each identification process are not identical. Due to the difference of the sensitivity of the parameters, the identification result of each parameter has different errors, and all the errors are within the acceptable range.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.

Claims (6)

1. A doubly-fed wind generator (DFIG) converter control parameter identification method based on extended Kalman filtering is characterized by comprising the following steps:
constructing a complete double-fed fan converter control system model;
the parameters to be identified comprise all integral coefficients and proportional coefficients K in PI link of converter control systemp1～Kp7，Ki1～Ki7；
Carrying out identifiability and identification difficulty degree analysis on the converter control parameters based on the track sensitivity;
establishing a parameter identification model of the control system based on an iterative process of an Extended Kalman Filter (EKF) algorithm by utilizing phasors which can be directly measured in an actual operation process and voltages and currents at a rotor side, a stator side and a power grid side;
and obtaining a final identification result of the controller parameters by repeatedly identifying and analyzing the experimental result.
2. The method for identifying the control parameters of the doubly-fed wind generator converter based on the extended kalman filter is characterized in that the construction of the complete model of the doubly-fed wind generator converter control system specifically comprises the following steps:
establishing an algebraic equation and a dynamic equation of a Rotor Side Converter (RSC) control system;
and establishing an algebraic equation and a dynamic equation of a Grid Side Converter (GSC) control system.
3. The extended Kalman filtering-based doubly-fed wind generator converter control parameter identification method according to claim 1, characterized in that the parameters to be identified are determined, and specifically include each integral coefficient and proportionality coefficient K in controller PI linkp1～Kp7，Ki1～Ki7。
4. The method for identifying the control parameters of the doubly-fed wind generator converter based on the extended kalman filter according to claim 1, wherein the analysis of the identifiability and the identification difficulty of the control parameters based on the trajectory sensitivity of the parameters specifically comprises:
the controller parameter KjCalculating the sensitivity of the dynamic response;
will KjIncrease in value of Δ KjObtaining an observed value Y by simulating Y (k);
will KjDecrease of value of Δ KjObtaining an observed value Y' by simulating Y (k);
according to
Figure FDA0002431072840000021
Calculating KjTrack sensitivity S ofjWherein Y is0Is KjTake the original value Kj0Then, observing a steady state value corresponding to Y; Δ KjTaking 10% Kj；
And summing all sampling values of the track sensitivity curve of the parameters, wherein the absolute value represents the sensitivity of the parameters, the parameter with higher sensitivity is less difficult to identify, and the parameter with lower sensitivity is more difficult to identify.
5. The extended kalman filter-based doubly-fed wind generator converter control parameter identification method according to claim 1, wherein the Extended Kalman Filter (EKF) -based iterative process is performed by using phasors, voltages and currents on a rotor side, a stator side and a grid side, which can be directly measured in an actual operation process, to establish a parameter identification model of the control system, and specifically includes:
by adopting step identification, the RSC controller and the GSC controller parameters are respectively divided into two groups according to different control loops and cascade sequences, and the value is [ K ]p1,Ki1,Kp2,Ki2]，[Kp3,Ki3,Kp4,Ki4]And [ K ]p5,Ki5,Kp6,Ki6]，[Kp7,Ki7]；
Selecting different initial state variables X, input variables u and measurement variables z according to different groups;
establishing a discrete state space equation of the controller based on an iterative process of extended Kalman filtering;
setting an initial value of iteration under the principle of ensuring steady-state tracking and iteration not to diverge;
and identifying the parameters of the DFIG controller by using the established EKF parameter identification model.
6. The method for identifying the control parameters of the converter of the doubly-fed wind generator based on the extended kalman filter according to claim 1, wherein the final parameter identification result is obtained by repeatedly identifying and analyzing the experimental result, and specifically comprises the following steps:
changing the iteration initial value of the identification model, and repeatedly identifying the parameters for 50 times;
taking the average value of the identification results of each time as the final identification result of the parameter;
and carrying out error analysis on the final identification result of the parameters.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111769595A (en) * 2020-07-07 2020-10-13 南京工程学院 Parameter identification-based equivalent capacitance solving method for wind driven generator network side converter
CN115411775A (en) * 2022-09-27 2022-11-29 三峡大学 Doubly-fed wind turbine control parameter identification method based on LSTM neural network

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103701389A (en) * 2013-12-12 2014-04-02 河海大学 Method for identifying parameters of controllers of DFIG (doubly-fed induction generator)
CN106169747A (en) * 2016-07-20 2016-11-30 河海大学 A kind of double fed induction generators parameter identification method
CN109657380A (en) * 2018-12-26 2019-04-19 华北电力大学 A kind of double-fed fan motor field Dynamic Equivalence based on Extended Kalman filter
CN110011587A (en) * 2019-03-27 2019-07-12 哈尔滨理工大学 A kind of permanent magnet synchronous motor sensor-less vector control method based on Multiparameter
CN110492803A (en) * 2019-06-06 2019-11-22 新疆大学 Permanent magnet direct-driving aerogenerator parameter identification method based on MDPSO

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103701389A (en) * 2013-12-12 2014-04-02 河海大学 Method for identifying parameters of controllers of DFIG (doubly-fed induction generator)
CN106169747A (en) * 2016-07-20 2016-11-30 河海大学 A kind of double fed induction generators parameter identification method
CN109657380A (en) * 2018-12-26 2019-04-19 华北电力大学 A kind of double-fed fan motor field Dynamic Equivalence based on Extended Kalman filter
CN110011587A (en) * 2019-03-27 2019-07-12 哈尔滨理工大学 A kind of permanent magnet synchronous motor sensor-less vector control method based on Multiparameter
CN110492803A (en) * 2019-06-06 2019-11-22 新疆大学 Permanent magnet direct-driving aerogenerator parameter identification method based on MDPSO

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
张敏: "永磁直驱风电机组电气参数辨识研究", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》 *
潘学萍，等: "双馈风力发电机参数分步辨识及观测量的选择", 《中国电机工程学报》 *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111769595A (en) * 2020-07-07 2020-10-13 南京工程学院 Parameter identification-based equivalent capacitance solving method for wind driven generator network side converter
CN115411775A (en) * 2022-09-27 2022-11-29 三峡大学 Doubly-fed wind turbine control parameter identification method based on LSTM neural network
CN115411775B (en) * 2022-09-27 2024-04-26 三峡大学 Double-fed fan control parameter identification method based on LSTM neural network

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