CN111817626A - Distributed state fusion estimation method of wind driven generator - Google Patents

Distributed state fusion estimation method of wind driven generator Download PDF

Info

Publication number
CN111817626A
CN111817626A CN202010610600.5A CN202010610600A CN111817626A CN 111817626 A CN111817626 A CN 111817626A CN 202010610600 A CN202010610600 A CN 202010610600A CN 111817626 A CN111817626 A CN 111817626A
Authority
CN
China
Prior art keywords
stator
rotor
generator
fusion
state
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202010610600.5A
Other languages
Chinese (zh)
Inventor
陈博
杨盛伟
王如生
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Zhejiang University of Technology ZJUT
Original Assignee
Zhejiang University of Technology ZJUT
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Zhejiang University of Technology ZJUT filed Critical Zhejiang University of Technology ZJUT
Priority to CN202010610600.5A priority Critical patent/CN111817626A/en
Publication of CN111817626A publication Critical patent/CN111817626A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • 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
    • H02P9/007—Control circuits for doubly fed generators
    • 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
    • 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
    • H02P2103/00—Controlling arrangements characterised by the type of generator
    • H02P2103/10—Controlling arrangements characterised by the type of generator of the asynchronous type

Landscapes

  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Control Of Eletrric Generators (AREA)

Abstract

A distributed state fusion estimation method for a wind driven generator under the condition of unknown noise statistical characteristics provides a mathematical model of a doubly-fed induction generator; establishing a state space model of the generator; designing a distributed fusion estimation method with unknown noise statistical characteristics; setting motor parameters, and solving two optimization problems to obtain local optimal gain and a distributed weighted fusion matrix; and carrying out iterative updating to obtain a fusion estimation of the generator state. The invention provides a novel distributed fusion estimation method for solving the problem of state estimation of a wind driven generator when the noise statistical characteristics cannot be accurately obtained, and the method has high estimation precision.

Description

Distributed state fusion estimation method of wind driven generator
Technical Field
The method mainly aims at the state monitoring of the wind driven generator, and estimates the state of the wind driven generator through a multi-sensor fusion technology.
Background
With the increasing severity of the environmental problems in the world, the international society has paid more and more attention to the problems of energy safety, ecological pollution, climate abnormality and the like, so that the use of fossil energy is reduced, and the development and utilization of renewable energy are accelerated, which has become a common consensus in all countries in the world. Among them, wind power generation plays an important role in the development of renewable energy, and has been applied on a large scale on a global scale. The wind power generation is a process of converting wind kinetic energy into mechanical kinetic energy and then converting the mechanical kinetic energy into electric energy, and the wind power generation has the working principle that the wind power is used for pushing a windmill blade to rotate, and then the speed of the windmill blade is increased through a speed increasing machine to drive a generator to generate electricity. Typically, various sensors are embedded within the wind turbine for measuring the condition of the electrical machine for monitoring and maintenance purposes.
Currently, there have been a number of research efforts to propose various estimation algorithms and models to estimate the state of the generator. The dynamic state estimation method based on Kalman filtering solves the problem of state estimation of the wind turbine model under the condition of unknown wind speed. And the extended Kalman filter adjusts a feedforward feedback optimal controller, and is used for tracking the power point of the permanent magnet synchronous generator under the interference of multiple wind speeds. It should be noted that kalman filter, extended kalman filter, unscented kalman filter, etc. are commonly used to process white noise with known covariance. However, in practical wind power systems, the generator may be subject to a variety of unknown disturbances, resulting in noise statistics that are not readily available with precision. Meanwhile, the estimation method only considers the situation of a single sensor, but with the development of information technology, the estimation precision of the single sensor cannot meet the requirement of the estimation precision of the system. Therefore, the technology provides a multi-sensor distributed fusion estimation method with unknown noise statistical characteristics, and the method is used for estimating the state of the wind driven generator.
Disclosure of Invention
In order to solve the problem that the noise statistical characteristics are known and improve the state estimation precision, the invention provides a distributed fusion estimation method with unknown noise statistical characteristics.
In order to achieve the purpose, the invention provides the following technical scheme:
a distributed state fusion estimation method of a wind driven generator comprises the following steps:
the method comprises the following steps: a mathematical model of the doubly-fed induction generator is established, and the process is as follows:
the mathematical model of the doubly-fed induction generator is a high-order, nonlinear and strongly coupled multivariable system, and in order to establish the mathematical model, the following assumptions are made: 1.1. neglecting space harmonic wave, the magnetic potential is distributed along the circumference of the air gap according to sine; 1.2. neglecting magnetic circuit saturation, the self inductance and mutual inductance of each winding are linear; 1.3. the influence of frequency and temperature change on the resistance of the winding is not considered; 1.4. when the stator current flows to the motor, the current value is a negative value; 1.5. using a synchronous rotation two-phase dq coordinate system derivation equation, wherein the d axis and the q axis are different by 90 degrees, and under the assumption, a double-fed induction motor mathematical model is derived, and the expression is as follows:
Figure BDA0002561972240000021
ψds＝-Lsids+Lmidr
Figure BDA0002561972240000022
ψqs＝-Lsiqs+Lmiqr
Figure BDA0002561972240000023
ψdr＝-Lsidr-Lmids
Figure BDA0002561972240000024
ψqr＝-Lsiqr-Lmiqs
wherein idsAnd idrIs the component of the stator current and rotor current on the d-axis; i.e. iqsAnd iqrIs the component of the stator current and rotor current on the q-axis; v. ofdsAnd vdrIs the component of the stator voltage and the rotor voltage on the d-axis; v. ofqsAnd vqrIs the component of the stator voltage and the rotor voltage on the q-axis; omegab，ωs，ωrThe angular velocities of the base stage, the stator and the rotor respectively; l ismFor the stator and rotor in a generatorEquivalent mutual inductance coefficients between the same phase winding axes; l iss,LrEquivalent inductances of the stator and the rotor in dq rotation coordinate system respectively; rs,RrRespectively the resistance of the stator and the rotor; psids,ψqs,ψdr,ψqrRespectively are flux linkages of the stator and the rotor under a dq coordinate system;
step two: establishing a state space model of the generator, wherein the process is as follows:
selecting the current x ═ i of the generatordsiqsidriqr]TAs the state variable, the voltage u ═ vdsvqsvdrvqr]TAs input variables, a linear state space model is obtained, the expression of which is
Figure BDA0002561972240000025
Where γ is a coefficient matrix of process noise; w (t) is unknown bounded process noise, wT(t)w(t)<Is unknown, according to a mathematical model of a doubly-fed induction generator, A and B are
Figure BDA0002561972240000031
Figure BDA0002561972240000032
Wherein
Figure BDA0002561972240000033
s＝(ωs-ωr)/ωb；
Discretizing the linear state space model (1)
x(t+1)＝Adx(t)+Bdu(t)+γw(t) (2)
Wherein
Figure BDA0002561972240000034
T is a sampling period;
in addition, the sensor measuresThe information includes stator voltage, rotor voltage, etc. let yi(t) is the measurement output of the ith sensor, and the observation equation is defined as
yi(t)＝Cix(t)+γivi(t)(i＝1,2,...,L) (3)
Wherein C isiIs the measurement matrix of the i-th sensor, gammaiCoefficient matrix of measurement noise, v, for sensor ii(t) is the unknown bounded process noise of sensor i,
Figure BDA0002561972240000035
κ is unknown; each sensor can send the measured data to a control center in real time so that maintenance personnel can monitor the state of the generator remotely;
step three: the distributed fusion estimation method comprises the following processes:
aiming at (2) - (3), a distributed fusion estimation method with unknown noise statistical characteristics is designed and comprises the following steps:
3.1, prediction: is provided with
Figure BDA0002561972240000036
Predicting the status of the ith sensor
Figure BDA0002561972240000037
3.2, estimation: is provided with
Figure BDA0002561972240000038
Estimating the state of the ith sensor
Figure BDA0002561972240000039
3.3, fusion: is provided with
Figure BDA00025619722400000310
Estimating for fusion states
Figure BDA0002561972240000041
Wherein the optimum gain in (5)
Figure BDA0002561972240000042
Distributed weighted fusion matrix omega in sum (6)i(t) (i ═ 1, 2.., L) was obtained by solving the following two convex optimization problems, first defined
Figure BDA0002561972240000043
Optimum gain
Figure BDA0002561972240000044
Obtained by solving the following convex optimization problem:
Figure BDA0002561972240000045
weighted fusion matrix omegai(t) (i ═ 1, 2.., L) was obtained by solving the following convex optimization problem:
Figure BDA0002561972240000046
wherein
Figure BDA0002561972240000047
The invention has the following beneficial effects: a distributed fusion estimation method of unknown noise statistical information is provided for a state model of a wind driven generator. By solving the two convex optimization problems, the optimal gain and weighting fusion matrix can be obtained, and compared with the existing estimation method, the method can better estimate the state of the wind driven generator under the condition that the noise statistical information cannot be accurately obtained, so that a worker can accurately monitor and maintain the generator.
Drawings
FIG. 1 is a flow chart of an embodiment of the method of the present invention.
Fig. 2 is a block diagram of a distributed fusion architecture.
FIG. 3 is a diagram of a true state trajectory and a fused estimated state trajectory of a wind turbine.
Fig. 4 shows the distributed fusion estimation error.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
Referring to fig. 1 to 4, a method for estimating distributed state fusion of a wind turbine includes the following steps:
the method comprises the following steps: a mathematical model of the doubly-fed induction generator is established, and the process is as follows:
the mathematical model of the doubly-fed induction generator is a high-order, nonlinear and strongly coupled multivariable system, and in order to establish the mathematical model, the following assumptions are made: 1.1. neglecting space harmonic wave, the magnetic potential is distributed along the circumference of the air gap according to sine; 1.2. neglecting magnetic circuit saturation, the self inductance and mutual inductance of each winding are linear; 1.3. the influence of frequency and temperature change on the resistance of the winding is not considered; 1.4. when the stator current flows to the motor, the current value is a negative value; 1.5. the equation is derived using a synchronously rotating two-phase dq coordinate system, with the d-axis and q-axis being 90 ° apart. Under the assumption, a mathematical model of the doubly-fed induction motor is developed, and the expression is as follows:
Figure BDA0002561972240000051
ψds＝-Lsids+Lmidr
Figure BDA0002561972240000052
ψqs＝-Lsiqs+Lmiqr
Figure BDA0002561972240000053
ψdr＝-Lsidr-Lmids
Figure BDA0002561972240000054
ψqr＝-Lsiqr-Lmiqs
wherein idsAnd idrIs the component of the stator current and rotor current on the d-axis; i.e. iqsAnd iqrIs the component of the stator current and rotor current on the q-axis; v. ofdsAnd vdrIs the component of the stator voltage and the rotor voltage on the d-axis; v. ofqsAnd vqrIs the component of the stator voltage and the rotor voltage on the q-axis; omegab，ωs，ωrThe angular velocities of the base stage, the stator and the rotor respectively; l ismEquivalent mutual inductance coefficients of the stator and the rotor between the same phase winding axis of the generator are obtained; l iss,LrEquivalent inductances of the stator and the rotor in dq rotation coordinate system respectively; rs,RrRespectively the resistance of the stator and the rotor; psids,ψqs,ψdr,ψqrRespectively are flux linkages of the stator and the rotor under a dq coordinate system;
step two: establishing a state space model of the generator, wherein the process is as follows:
selecting the current x ═ i of the generatordsiqsidriqr]TAs the state variable, the voltage u ═ vdsvqsvdrvqr]TAs input variables, a linear state space model is obtained, the expression of which is
Figure BDA0002561972240000061
Where γ is a coefficient matrix of process noise; w (t) is unknown bounded process noise, wT(t)w(t)<Is unknown, according to a mathematical model of a doubly-fed induction generator, A and B are
Figure BDA0002561972240000062
Figure BDA0002561972240000063
Wherein
Figure BDA0002561972240000064
s＝(ωs-ωr)/ωb；
Discretizing the linear state space model (1)
x(t+1)＝Adx(t)+Bdu(t)+γw(t) (2)
Wherein A isd＝eAT，
Figure BDA0002561972240000065
T is a sampling period;
in addition, the sensor measurement information includes stator voltage and rotor voltage, etc. for yi(t) is the measurement output of the ith sensor, and the observation equation is defined as
yi(t)＝Cix(t)+γivi(t)(i＝1,2,...,L) (3)
Wherein C isiIs the measurement matrix of the i-th sensor, gammaiCoefficient matrix of measurement noise, v, for sensor ii(t) is the unknown bounded process noise of sensor i,
Figure BDA0002561972240000066
κ is unknown; each sensor can send the measured data to a control center in real time so that maintenance personnel can monitor the state of the generator remotely;
step three: the distributed fusion estimation method comprises the following processes:
aiming at (2) - (3), a distributed fusion estimation method with unknown noise statistical characteristics is designed and comprises the following steps:
3.1, prediction: is provided with
Figure BDA0002561972240000071
Predicting the status of the ith sensor
Figure BDA0002561972240000072
3.2, estimation: is provided with
Figure BDA0002561972240000073
Estimating the state of the ith sensor
Figure BDA0002561972240000074
3.3, fusion: is provided with
Figure BDA0002561972240000075
Estimating for fusion states
Figure BDA0002561972240000076
Wherein the optimum gain in (5)
Figure BDA0002561972240000077
Distributed weighted fusion matrix omega in sum (6)i(t) (i ═ 1, 2.., L) was obtained by solving the following two convex optimization problems, first defined
Figure BDA0002561972240000078
Optimum gain
Figure BDA0002561972240000079
Obtained by solving the following convex optimization problem:
Figure BDA00025619722400000710
weighted fusion matrix omegai(t) (i ═ 1, 2.., L) was obtained by solving the following convex optimization problem:
Figure BDA00025619722400000711
Figure BDA0002561972240000081
wherein
Figure BDA0002561972240000082
To verify the effectiveness of the method designed by the present invention, the following example was used for verification.
Setting basic parameters of the motor: f. ofb＝50Hz,fs＝50Hz,fr＝40Hz,Rs＝0.004Ω,Rr＝0.005Ω,Ls＝0.09Ω,Lr＝0.08Ω,LbSubstituting 3.95 omega into the state space model of the wind driven generator. The input parameters are set as follows: stator d-axis voltage vds0.02V, q-axis voltage Vqs0.99V; rotor d-axis voltage vdr0.02V, q-axis voltage Vdr0.206V. Four sensors are adopted to measure four current components of the generator, and the measurement matrixes are respectively C1＝[1 0 0 0]、C2＝[0 1 0 0]、C3＝[0 0 1 0]、C4＝[0 0 0 1]. The coefficient matrixes of the process noise and the observation noise are unit matrixes, and the assumed bounded noise is
Figure BDA0002561972240000083
Wherein p is1(t)∈[0,1],p2(t)∈[0,1],p3(t)∈[0,1],p4(t)∈[0,1],pv1(t)∈[0,1],pv2(t)∈[0,1],pv3(t)∈[0,1],pv4(t)∈[0,1]
Solving (8) by LMI toolkit to obtain optimal gain
Figure BDA0002561972240000084
Solving (9) to obtain a weighted fusion matrix omegai(t) (i ═ 1, 2.., L), according to solution
Figure BDA0002561972240000085
And Ωi(t) (i ═ 1, 2.., L), local state estimates can be calculated by combining equations (5) and (6)
Figure BDA0002561972240000086
And fusion of the estimates
Figure BDA0002561972240000087
The real track and the distributed fusion estimation track of the drawing system are shown in fig. 3, and it can be seen from the figure that the fusion estimation method provided by the invention can better estimate the state of the starting motor. In addition, the error of the actual state and the fusion estimation state is calculated, as shown in fig. 4, the estimation error is small, which shows that the fusion estimation algorithm of the present invention has high estimation accuracy. The experimental result shows that when the statistical characteristics of the noise cannot be accurately obtained, the state of the wind driven generator can be accurately estimated by the method. Therefore, the distributed fusion estimation method designed by the invention is more suitable for the actual wind power generation system.

Claims (1)

1. A method for distributed state fusion estimation of a wind turbine, the method comprising the steps of:
the method comprises the following steps: a mathematical model of the doubly-fed induction generator is established, and the process is as follows:
the mathematical model of the doubly-fed induction generator is a high-order, nonlinear and strongly coupled multivariable system, and in order to establish the mathematical model, the following assumptions are made: 1.1. neglecting space harmonic wave, the magnetic potential is distributed along the circumference of the air gap according to sine; 1.2. neglecting magnetic circuit saturation, the self inductance and mutual inductance of each winding are linear; 1.3. the influence of frequency and temperature change on the resistance of the winding is not considered; 1.4. when the stator current flows to the motor, the current value is a negative value; 1.5. using a synchronous rotation two-phase dq coordinate system derivation equation, wherein the d axis and the q axis are different by 90 degrees, and under the assumption, a double-fed induction motor mathematical model is derived, and the expression is as follows:
Figure FDA0002561972230000011
ψds＝-Lsids+Lmidr
Figure FDA0002561972230000012
ψqs＝-Lsiqs+Lmiqr
Figure FDA0002561972230000013
ψdr＝-Lsidr-Lmids
Figure FDA0002561972230000014
ψqr＝-Lsiqr-Lmiqs
wherein idsAnd idrIs the component of the stator current and rotor current on the d-axis; i.e. iqsAnd iqrIs the component of the stator current and rotor current on the q-axis; v. ofdsAnd vdrIs the component of the stator voltage and the rotor voltage on the d-axis; v. ofqsAnd vqrIs the component of the stator voltage and the rotor voltage on the q-axis; omegab，ωs，ωrThe angular velocities of the base stage, the stator and the rotor respectively; l ismEquivalent mutual inductance coefficients of the stator and the rotor between the same phase winding axis of the generator are obtained; l iss,LrEquivalent inductances of the stator and the rotor in dq rotation coordinate system respectively; rs,RrRespectively the resistance of the stator and the rotor; psids,ψqs,ψdr,ψqrRespectively are flux linkages of the stator and the rotor under a dq coordinate system;
step two: establishing a state space model of the generator, wherein the process is as follows:
selecting the current x ═ i of the generatordsiqsidriqr]TAs the state variable, the voltage u ═ vdsvqsvdrvqr]TAs input variables, a linear state space model is obtained, the expression of which is
Figure FDA0002561972230000021
Where γ is a coefficient matrix of process noise; w (t) is unknown bounded process noise, wT(t)w(t)<Is unknown, according to a mathematical model of a doubly-fed induction generator, A and B are
Figure FDA0002561972230000022
Figure FDA0002561972230000023
Wherein
Figure FDA0002561972230000024
s＝(ωs-ωr)/ωb；
Discretizing the linear state space model (1)
Figure FDA0002561972230000025
Wherein A isd＝eAT，
Figure FDA0002561972230000026
T is a sampling period;
in addition, the sensor measurement information includes stator voltage and rotor voltage, etc. for yi(t) is the measurement output of the ith sensor, and the observation equation is defined as
yi(t)＝Cix(t)+γivi(t)(i＝1,2,...,L) (3)
Wherein C isiIs the measurement matrix of the i-th sensor, gammaiCoefficient matrix of measurement noise, v, for sensor ii(t) is the unknown bounded process noise of sensor i,
Figure FDA0002561972230000029
κ is unknown; each passing throughThe sensor sends the measured data to the control center in real time so that maintenance personnel can monitor the state of the generator remotely;
step three: the distributed fusion estimation method comprises the following processes:
aiming at (2) - (3), a distributed fusion estimation method with unknown noise statistical characteristics is designed and comprises the following steps:
3.1, prediction: is provided with
Figure FDA0002561972230000027
Predicting the status of the ith sensor
Figure FDA0002561972230000028
3.2, estimation: is provided with
Figure FDA0002561972230000031
Estimating the state of the ith sensor
Figure FDA0002561972230000032
3.3, fusion: is provided with
Figure FDA0002561972230000033
Estimating for fusion states
Figure FDA0002561972230000034
Wherein the optimum gain in (5)
Figure FDA0002561972230000035
Distributed weighted fusion matrix omega in sum (6)i(t) (i ═ 1, 2.., L) was obtained by solving the following two convex optimization problems, first defined
Figure FDA0002561972230000036
Optimum gain
Figure FDA0002561972230000037
Obtained by solving the following convex optimization problem:
Figure FDA0002561972230000038
Figure FDA0002561972230000039
weighted fusion matrix omegai(t) (i ═ 1, 2.., L) was obtained by solving the following convex optimization problem:
Figure FDA00025619722300000310
Figure FDA00025619722300000311
wherein
Figure FDA00025619722300000312
CN202010610600.5A 2020-06-30 2020-06-30 Distributed state fusion estimation method of wind driven generator Pending CN111817626A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010610600.5A CN111817626A (en) 2020-06-30 2020-06-30 Distributed state fusion estimation method of wind driven generator

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010610600.5A CN111817626A (en) 2020-06-30 2020-06-30 Distributed state fusion estimation method of wind driven generator

Publications (1)

Publication Number Publication Date
CN111817626A true CN111817626A (en) 2020-10-23

Family

ID=72855582

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010610600.5A Pending CN111817626A (en) 2020-06-30 2020-06-30 Distributed state fusion estimation method of wind driven generator

Country Status (1)

Country Link
CN (1) CN111817626A (en)

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102588211A (en) * 2012-02-29 2012-07-18 沈阳华人风电科技有限公司 Model prediction control method and model prediction control system for all working conditions of wind generating set
WO2017043143A1 (en) * 2015-09-10 2017-03-16 株式会社日立製作所 Device and method for estimating amount of electric power generated by distributed generators
CN108594652A (en) * 2018-03-19 2018-09-28 江苏大学 A kind of vehicle-state fusion method of estimation based on observer information iteration
CN208844491U (en) * 2018-09-03 2019-05-10 哈尔滨工程大学 A kind of comb type breakwater system for taking into account floating swinging type wave energy power generation

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102588211A (en) * 2012-02-29 2012-07-18 沈阳华人风电科技有限公司 Model prediction control method and model prediction control system for all working conditions of wind generating set
WO2017043143A1 (en) * 2015-09-10 2017-03-16 株式会社日立製作所 Device and method for estimating amount of electric power generated by distributed generators
CN108594652A (en) * 2018-03-19 2018-09-28 江苏大学 A kind of vehicle-state fusion method of estimation based on observer information iteration
CN208844491U (en) * 2018-09-03 2019-05-10 哈尔滨工程大学 A kind of comb type breakwater system for taking into account floating swinging type wave energy power generation

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
BO CHEN等: "A New Approach to Linear/Nonlinear Distributed", 《IEEE TRANSACTIONS ON AUTOMATIC CONTROL》 *
曾实等: "基于模型预测双馈电机并网策略", 《科学技术与工程》 *

Similar Documents

Publication Publication Date Title
Corradini et al. Robust control of variable-speed wind turbines based on an aerodynamic torque observer
Belmokhtar et al. Novel fuzzy logic based sensorless maximum power point tracking strategy for wind turbine systems driven DFIG (doubly-fed induction generator)
Li et al. On the rejection of internal and external disturbances in a wind energy conversion system with direct-driven PMSG
Corradini et al. Fully sensorless robust control of variable-speed wind turbines for efficiency maximization
Ciampichetti et al. Sliding mode control of permanent magnet synchronous generators for wind turbines
CN108365785B (en) Asynchronous motor repeated prediction control method
CN102611380B (en) Online identification method for parameters of double-fed motor
Taoussi et al. Low-speed sensorless control for wind turbine system
CN109194225A (en) A kind of double feedback electric engine parameter on-line identification method
Cimini et al. Control of variable speed wind energy conversion systems by a discrete-time sliding mode approach
Dang et al. Offset-free predictive control for variable speed wind turbines
Ciccarelli et al. Sliding mode control based robust observer of aerodynamic torque for variable-speed wind turbines
Corradini et al. Fault-tolerant sensorless control of wind turbines achieving efficiency maximization in the presence of electrical faults
Corradini et al. An aerodynamic torque observer for the robust control of variable-speed wind turbines
Nair et al. Parametrically robust identification based sensorless control approach for doubly fed induction generator
CN116738636A (en) Multi-machine equivalent method of doubly-fed wind power station considering impedance characteristics and synchronization mechanism
Beniss et al. Improvement of Power Quality Injected into the Grid by Using a FOSMC-DPC for Doubly Fed Induction Generator.
Sun et al. MPPT adaptive controller of DC-based DFIG in resistances uncertainty
CN108678902B (en) The straight disturbance sensing control method for driving PMSM wind generator system MPPT
Syskakis et al. MPPT for small wind turbines: Zero-oscillation sensorless strategy
Serhoud et al. Sensorless optimal power control of brushless doubly-fed machine in wind power generator based on extended kalman filter
He et al. Grey prediction pi control of direct drive permanent magnet synchronous wind turbine
CN104408223A (en) Tracing optimization control method for wind turbine
Wu et al. Backstepping terminal sliding mode control of DFIG for maximal wind energy captured
Zhou et al. A combined control strategy of wind energy conversion system with direct-driven PMSG

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
RJ01 Rejection of invention patent application after publication

Application publication date: 20201023

RJ01 Rejection of invention patent application after publication