CN109162878B - Intelligent blade measurement and control method and system for wind driven generator - Google Patents
Intelligent blade measurement and control method and system for wind driven generator Download PDFInfo
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- 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
- F03D17/00—Monitoring or testing of wind motors, e.g. diagnostics
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- 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
- F03D7/02—Controlling wind motors the wind motors having rotation axis substantially parallel to the air flow entering the rotor
- F03D7/0204—Controlling wind motors the wind motors having rotation axis substantially parallel to the air flow entering the rotor for orientation in relation to wind direction
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- 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
- F03D7/02—Controlling wind motors the wind motors having rotation axis substantially parallel to the air flow entering the rotor
- F03D7/022—Adjusting aerodynamic properties of the blades
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- 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
- F03D7/02—Controlling wind motors the wind motors having rotation axis substantially parallel to the air flow entering the rotor
- F03D7/04—Automatic control; Regulation
- F03D7/042—Automatic control; Regulation by means of an electrical or electronic controller
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- 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
- F03D80/00—Details, components or accessories not provided for in groups F03D1/00 - F03D17/00
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- 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
- F03D9/00—Adaptations of wind motors for special use; Combinations of wind motors with apparatus driven thereby; Wind motors specially adapted for installation in particular locations
- F03D9/20—Wind motors characterised by the driven apparatus
- F03D9/25—Wind motors characterised by the driven apparatus the apparatus being an electrical generator
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- 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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Abstract
The embodiment of the invention discloses an intelligent blade measurement and control method for a wind driven generator, which comprises the steps of comparing a read real-time lifting force value y (k) of a blade with a target lifting force value r (k) to obtain a deviation e (k) r (k) -y (k), designing a single neuron incremental PID controller, and calculating an increment delta theta of a flap angle control quantityr(k) Obtaining an angle target value theta of a trailing edge flap on the blade through z transformationr(k) (ii) a The difference e between the target deflection angle of the flap and the real-time deflection angle of the flapθ(k) And calculating a control quantity delta u (k) through a PI controller, and obtaining a motor shaft target angle value u (k) of the servo motor for controlling the trailing edge flap through z conversion. The embodiment of the invention also discloses a system for measuring and controlling the intelligent blade of the wind driven generator. By adopting the invention, the lift force of the intelligent blade can be automatically controlled by adjusting the deflection angle of the trailing edge flap according to the performance index of the control system, and meanwhile, the lift force of the intelligent blade and the deflection angle of the trailing edge flap can be monitored in real time. The invention has the advantages of high precision, strong anti-interference capability, low cost, better system dynamic characteristic and the like.
Description
Technical Field
The invention relates to the field of blade control of wind driven generators, in particular to an intelligent blade measurement and control method and an intelligent blade measurement and control system for a wind driven generator.
Background
On the blade structure of a wind driven generator, as the size of a wind turbine blade is increased, the flexibility and inertia of the blade are increased, and the conventional variable pitch control technology is full-blade variable pitch and is difficult to apply quick and effective control on the pneumatic performance of the blade. The intelligent blade is characterized in that a trailing edge flap is added at the trailing edge of the blade, the inertia of the trailing edge flap is smaller than that of the blade, and the control scheme of the intelligent blade can make up the defects of large inertia, strong lag and difficulty in realizing local adjustment of variable pitch control to a certain extent. The existing intelligent blade technology only carries out static measurement on aerodynamic parameters and lift force of the intelligent blade, researches the change of the aerodynamic parameters of flap deflection at different angles, and does not realize dynamic closed-loop control with a certain target, namely, the aerodynamic performance and the lift force change condition of the blade at different angles of the trailing edge flap are not embedded into the intelligent blade by an automatic control system.
In addition, the existing variable pitch control technology is also a scheme for realizing the lift control of the blades, but the variable pitch control has stronger hysteresis, and when the wind speed changes greatly, a variable pitch control system is difficult to respond in time. The reason is that the pitch control adjusts the whole blade, when the blade is longer, the inertia and flexibility of the blade are larger, and the lag of the pitch control system in adjusting the pitch angle is more serious. Meanwhile, the actuator is required to provide larger torque for adjusting the pitch angle, so that an actuating motor with larger volume is also required.
Disclosure of Invention
The technical problem to be solved by the embodiment of the invention is to provide a method and a system for measuring and controlling an intelligent blade of a wind driven generator. The lift force of the intelligent blade can be automatically controlled by adjusting the deflection angle of the trailing edge flap according to the performance index of the control system, and the intelligent blade has good dynamic characteristic and steady-state characteristic. Meanwhile, the lift force of the intelligent blade and the deflection angle of the trailing edge flap can be monitored in real time. .
In order to solve the technical problem, an embodiment of the invention provides a measurement and control method for an intelligent blade of a wind driven generator, which comprises the following steps:
s1: comparing the read real-time lifting value y (k) of the blade with a target lifting value r (k) to obtain a deviation e (k) ═ r (k) -y (k)
S2: designing a single neuron incremental PID controller, and calculating the increment delta theta of the flap angle control quantityr(k) Obtaining an angle target value theta of a trailing edge flap on the blade through z transformationr(k)；
S3: the difference e between the target deflection angle of the flap and the real-time deflection angle of the flapθ(k) Calculating a control quantity delta u (k) through a PI controller, and obtaining a motor shaft target angle value of a servo motor for controlling the trailing edge flap through z conversionu(k)。
Further, the single neuron incremental PID controller is realized by using an S function, and the weight value is adjusted according to a supervised Hebb learning rule.
Further, the input vector of the S function is a vector consisting of integral, proportional and differential quantities:
x1＝e(k)
x2＝e(k)-e(k-1)
x3＝e(k)-2e(k-1)+e(k-2)
wherein e (k-1) and e (k-2) are respectively an offset value of one step length and two step lengths.
Further, the adjusting of the weight value by the supervised Hebb learning rule comprises the connection weight value omega between the neuronsiFor the state vector in the S function structure, the weight iteration equation is as follows:
ω1(k)＝ω1(k-1)+ηIe(k)y(k)x1(k)
ω2(k)＝ω2(k-1)+ηPe(k)y(k)x2(k)
ω3(k)＝x3(k-1)+ηDe(k)y(k)x3(k)
wherein, ηI、ηPAnd ηDIntegral, proportional and differential learning efficiencies, respectively, and y (k) is a real-time lift value.
Further, the absolute value processing is performed on the output of the single-neuron PID, so that the control quantity output of the single-neuron PID is:
θr(k)＝θr(k-1)+Δθr(k)
wherein, KuIs the single neuron proportionality coefficient.
Further, the method also comprises a comparative example coefficient KuAnd (3) carrying out nonlinear transformation online correction:
Ku＝K0+ξ|e(k)n|/r(k)
wherein, K0The initial steady state value of the proportionality coefficient is ξ, the adjustment coefficient is ξ, n is an integer, and r (k) is a set value of the lift force.
Further, the control method of the PI controller includes the following calculation formula:
eθ(k)＝θr(k)-θy(k)
Δu(k)＝KIeθ(k)+KP[eθ(k)-eθ(k-1)]
u(k)＝u(k-1)+Δu(k)。
correspondingly, the embodiment of the invention also provides a system for measuring and controlling the intelligent blade of the wind driven generator, which comprises a sensor, an upper computer, a lower computer and a control object,
the upper computer is in communication connection with the lower computer;
the sensor comprises an encoder, a limit switch, a reference point switch and a lift force sensor;
the sensor is electrically and mechanically connected with the lower part;
the control object comprises a tail edge flap arranged at the tail end of the blade, the tail edge flap is arranged on a flap shaft, and the flap shaft is connected with a driving device and the encoder;
the trailing edge flap is provided with a positioning sheet;
the limit switch is used for detecting the position of the trailing edge flap, so that the driving device can drive the trailing edge flap to be positioned at the reference point switch.
Furthermore, the limit switch and the reference point switch are infrared photogates.
Still further, the lift sensor is an ATI six component balance.
The embodiment of the invention has the following beneficial effects: the invention can automatically control the lift force of the intelligent blade by adjusting the deflection angle of the trailing edge flap according to the performance index of the control system, and has good dynamic characteristic and steady-state characteristic. Meanwhile, the lift force of the intelligent blade and the deflection angle of the trailing edge flap can be monitored in real time. The invention has the advantages of high precision, strong anti-interference capability, low cost, better system dynamic characteristic and the like.
Drawings
FIG. 1 is a schematic diagram of a measurement and control system according to the present invention;
FIG. 2 is a schematic structural diagram of the smart blade of the present invention;
fig. 3 is a schematic diagram of the control principle of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings.
As shown in fig. 1 and 2, the system for measuring and controlling the intelligent blade of the wind driven generator according to the embodiment of the invention can normally operate in a wind tunnel environment, and mainly comprises four parts, namely a sensor, a lower computer siemens S7-200 PLC, an upper computer Matlab/Simulink and a control object. The sensors include 07 encoders, 08 limit switches, 09 reference point switches and 10 lift sensors. A 07 encoder signal wire is connected with a PLC high-speed counter terminal, a 07 encoder shaft is connected with an output shaft of a worm and gear reduction box through a coupler, the worm and gear reduction box is a bidirectional output shaft, one end of the worm and gear reduction box is connected with an encoder, and the other end of the worm and gear reduction box is connected with a flap shaft; the 08 limit switch and the 09 reference point switch are both selected from infrared photoelectric gates and are connected with a PLC digital quantity input terminal, a positioning sheet is added at the tail edge of a deflectable flap, when the positioning sheet 11 moves along with the flap to enable the limit switch to block light and conduct, the flap stops deflecting to limit the deflection angle of the flap, the damage to a model caused by excessive adjustment in the running process is prevented, and the reference point switch is used for starting or returning to a mechanical reference point after a fault occurs; the lift sensor 10 selects an ATI six-component balance which is connected with an end plate of the lower end face, the ATI six-component balance is connected with a matched ATIF/T controller, and the ATIF/T controller is communicated with the lower computer through an RS232 cable to write a real-time lift value into the lower computer; the lower computer is responsible for receiving the lift data and controlling the motion; the upper computer 01Matlab/Simulink carries out data analysis and processing, Matlab/GUI carries out state monitoring and parameter setting, and the PLC is connected with the Matlab/Simulink through a 02OPC server to realize real-time communication, wherein the OPC server uses PCAcess.
The tail edge flap is rigidly connected with a rigid flap shaft through a screw. The upper end surface and the lower end surface of each blade are connected with two end plates, and the end plates are used for reducing boundary airflow loss. The lower end plate is connected with the six-component balance and used for measuring the lifting force. The upper end plate is provided with a motor and a worm and gear reduction box transmission mechanism, and the output shaft of the reduction box is connected with the flap shaft through a coupling. The rest of the control system is located remotely from the smart blade.
The system is used for mechanically returning to zero when the system is started or has a fault through a button connected with a PLC digital value input end, and when the button is pressed, a mechanical return to zero (namely, a mechanical reference point) program is triggered. When wind acts on the smart blade, the blade generates lift force, and when the wind changes, the blade lift force will change with the wind, thereby generating deviation with the expected lift force. The moment and force in each direction are measured by a six-component balance, and the real-time lifting force value is calculated by a matched ATIF/T system controller. And the ATIF/T controller is communicated with the PLC through an RS232 cable to write the real-time lift force value into the PLC. Encoder detects real-time angle thetay(t) transmitting the analog quantity signal to the PLC, and obtaining a flap real-time angle value theta by the PLC through A/D conversiony(k) In that respect Matlab/Simulink reads a real-time lifting value y (k) and a flap real-time angle value theta from a register of the PLC through PCAcessy(k) And using the data to design the controller in Matlab/Simulink. After the controller calculates a target angle value u (k) of the servo motor, the data is transmitted to the PLC through PCAcess, the PLC executes an operation control instruction, the servo motor deflects, and the tail edge flap is driven to deflect through the worm gear reduction box, so that the aerodynamic characteristics of the intelligent blade are influenced, and dynamic closed-loop control of the lift force is realized.
As shown in fig. 3, the method for measuring and controlling the intelligent blade of the wind turbine according to the embodiment of the present invention mainly includes: an angle control ring of the flap adopts a PI control method; outer ring: the lift force control loop adopts a single neuron PID controller.
The real-time lift value y (k) is compared with the target lift value r (k) to obtain a deviation e (k) r (k) -y (k).
From e (k), y(k) And r (k) designing a single neuron incremental PID controller, and calculating the increment delta theta of the flap angle control quantity by the controllerr(k) Obtaining an angle target value theta through z transformationr(k)。
In Matlab/Simulink, a single neuron PID algorithm is realized by using an S function, and weight adjustment is realized according to a supervised Hebb learning rule, the core idea of the weight adjustment is that the increment of the connection weight between two neurons is in direct proportion to the product of excitation of the neurons, and teacher supervision information is added to the Hebb learning rule on the basis of the core idea. When realizing single neuron PID, the input vector in the S function structure is the vector composed of integral, proportional and differential quantities, which is:
x1＝e(k)
x2＝e(k)-e(k-1)
x3＝e(k)-2e(k-1)+e(k-2)
where e (k) is the difference between the target lift value r (k) and the real-time lift value y (k), i.e., e (k) r (k) -y (k). e (k-1) and e (k-2) are respectively a step length and an offset value before two step lengths.
By the connection weight omega between neuronsiFor the state vector in the S function structure, the weight iteration equation is as follows:
ω1(k)＝ω1(k-1)+ηIe(k)y(k)x1(k)
ω2(k)＝ω2(k-1)+ηPe(k)y(k)x2(k)
ω3(k)＝x3(k-1)+ηDe(k)y(k)x3(k)
wherein, ηI、ηPAnd ηDRespectively, integral, proportional and differential learning efficiencies. y (k) is a real-time lift value.
Negative positive feedback may occur during weight self-tuning, so that the system diverges. And (3) carrying out absolute value processing on the output of the single-neuron PID, wherein the control quantity output of the single-neuron PID is as follows:
θr(k)＝θr(k-1)+Δθr(k)
the influence of the learning efficiency and the initial weight of the controller is greatly reduced through a self-learning algorithm, and the parameter which has the greatest influence on the performance of the controller is a single neuron proportional coefficient Ku。
In order to facilitate field setting and enable the controller to obtain better self-adaptive characteristic, the proportional coefficient K is compareduAnd (3) carrying out nonlinear transformation online correction:
Ku＝K0+ξ|e(k)n|/r(k)
wherein, K0The initial steady state value of the proportionality coefficient is ξ, the adjustment coefficient is ξ, n is an integer, and r (k) is a set value of the lift force.
The angle control loop of the flap adopts a PI controller. The difference between the flap target deflection angle and the flap real-time deflection angle is eθ(k) And the PI controller calculates the control quantity delta u (k) and obtains a motor shaft target angle value u (k) of the servo motor through Z conversion. The algorithm is as follows:
eθ(k)＝θr(k)-θy(k)
Δu(k)＝KIeθ(k)+KP[eθ(k)-eθ(k-1)]
u(k)＝u(k-1)+Δu(k)
the invention has the following advantages:
①, the precision is high, the anti-interference ability is strong, the transmission scheme of 'PLC-servo motor-worm gear reduction box-flap shaft' is used, the PLC is used as a lower computer, the anti-interference ability is strong, the control precision of the servo motor is high, the worm gear reduction box is an NMRV standard component, and the transmission precision is high.
② the cost is low, the function of the touch screen is realized by a personal computer, namely Matlab/Simulink is used as an upper computer, and a control algorithm is designed in the upper computer without additionally purchasing a matched touch screen.
③ the system has better dynamic characteristics, the control system uses the increment type nonlinear PID controller with simple structure and self-adaptation, namely a single-neuron PID controller.
④ the invention uses single neuron PID controller to realize control, in order to reduce the accumulated error, uses increment type single neuron PID, the single neuron PID is characterized in that it can obtain the effect better than the classic PID controller under the condition of ensuring the simple structure, it has stronger engineering practicability, the classic PID of fixed parameter can not meet the control demand of the non-linear, strong interference lift force control system.
While the invention has been described in connection with what is presently considered to be the most practical and preferred embodiment, it is to be understood that the invention is not to be limited to the disclosed embodiment, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
Claims (5)
1. An intelligent blade measurement and control method for a wind driven generator is characterized by comprising the following steps:
s1: comparing the read real-time lift value y (k) of the blade with a set value r (k) of the lift force to obtain a deviation e (k) ═ r (k) -y (k)
S2: designing a single neuron incremental PID controller, and calculating the increment delta theta of the flap angle control quantityr(k) Obtaining an angle target value theta of a trailing edge flap on the blade through z transformationr(k)；
S3: the flap target deflection angle and the flap real-time deflection angle thetay(k) Difference of difference eθ(k) Calculating a control quantity delta u (k) through a PI controller, and obtaining a motor shaft target angle value u (k) of a servo motor for controlling the trailing edge flap through z transformation;
the single neuron incremental PID controller is realized by using an S function, and the weight value is adjusted according to a supervised Hebb learning rule;
the input vector of the S function is a vector consisting of integral, proportional and differential quantities:
x1＝e(k)
x2＝e(k)-e(k-1)
x3＝e(k)-2e(k-1)+e(k-2)
wherein e (k-1) and e (k-2) are respectively a step length and an offset value before two step lengths;
the supervised Hebb learning rule realizes the adjustment of the weight value through the following formula:
ω1(k)＝ω1(k-1)+ηIe(k)y(k)x1(k)
ω2(k)＝ω2(k-1)+ηPe(k)y(k)x2(k)
ω3(k)＝x3(k-1)+ηDe(k)y(k)x3(k)
wherein, ω is1、ω2、ω3Connection weights of the neurons, η, respectivelyI、ηPAnd ηDIntegral, proportional and differential learning efficiencies, respectively, and y (k) is a real-time lift value.
2. The intelligent blade measurement and control method for the wind driven generator according to claim 1, wherein the absolute value of the output of the single-neuron PID is processed, so that the output of the control quantity of the single-neuron PID is:
θr(k)＝θr(k-1)+Δθr(k)
wherein, KuIs the single neuron proportionality coefficient.
3. The intelligent blade measurement and control method for the wind driven generator according to claim 2, characterized by further comprising a comparative example coefficient KuAnd (3) carrying out nonlinear transformation online correction:
Ku＝K0+ξ|e(k)n|/r(k)
wherein, K0Is the initial steady state of the proportionality coefficientThe value ξ is the adjustment factor, n is an integer, and r (k) is the set point for lift.
4. The intelligent blade measurement and control method of the wind driven generator according to any one of claims 1-3, wherein the control method of the PI controller comprises the following calculation formula:
eθ(k)＝θr(k)-θy(k)
Δu(k)＝KIeθ(k)+KP[eθ(k)-eθ(k-1)]
u(k)＝u(k-1)+Δu(k)
wherein, KI、KPRespectively, the integral gain and the proportional gain of the PI controller.
5. A system for the intelligent blade measurement and control method of the wind driven generator as claimed in any one of claims 1 to 4, which is characterized by comprising a sensor, an upper computer, a lower computer and a control object,
the upper computer is in communication connection with the lower computer;
the sensor comprises an encoder, a limit switch, a reference point switch and a lift force sensor;
the sensor is electrically and mechanically connected with the lower part;
the control object comprises a tail edge flap arranged at the tail end of the blade, the tail edge flap is arranged on a flap shaft, and the flap shaft is connected with a driving device and the encoder;
the trailing edge flap is provided with a positioning sheet;
the limit switch is used for detecting the position of the trailing edge flap, so that the driving device can drive the trailing edge flap to be positioned at the reference point switch;
the limit switch and the reference point switch are infrared photoelectric gates;
the lift sensor is an ATI six-component balance.
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| CN110500240B (en) * | 2019-09-27 | 2020-06-30 | 扬州大学 | Method for measuring aerodynamic characteristics of low-power wind turbine |
| CN111796510A (en) * | 2020-06-29 | 2020-10-20 | 邯郸钢铁集团有限责任公司 | Application method of PID controller in primary control system PLC |
| CN111707442B (en) * | 2020-06-30 | 2022-06-07 | 中国科学院工程热物理研究所 | Supersonic wind tunnel propeller numerical model measurement verification system and control method thereof |
| CN114000976B (en) * | 2021-10-29 | 2023-11-03 | 重庆科凯前卫风电设备有限责任公司 | Additional proportional term incremental PID control method for wind generating set pitch controller |
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| EP2333322A3 (en) * | 2009-11-30 | 2017-02-22 | Vestas Wind Systems A/S | Measuring loads on wind turbine blades |
| DK177250B1 (en) * | 2010-12-21 | 2012-08-13 | Envision Energy Denmark Aps | A Wind Turbine Blade |
| CN104199289A (en) * | 2014-08-04 | 2014-12-10 | 江苏科技大学 | Identification-free single neuron self-adaption PID (Proportion Integration Differentiation) control method for magnet power source rectification system |
| CN105888971B (en) * | 2016-04-28 | 2018-12-18 | 华北电力大学 | A kind of large scale wind power machine blade active load shedding control system and method |
| CN107559143B (en) * | 2017-07-27 | 2019-08-27 | 华北电力大学 | A kind of large scale wind power machine trailing edge flaps structural parameters optimizing and multiple target wing flap optimal control method |
| CN107762730B (en) * | 2017-08-23 | 2019-06-18 | 华北电力大学 | A kind of large-scale variable pitch turbine control system and control method with trailing edge flaps |
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