CN105888971B - A kind of large scale wind power machine blade active load shedding control system and method - Google Patents
A kind of large scale wind power machine blade active load shedding control system and method Download PDFInfo
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- CN105888971B CN105888971B CN201610274672.0A CN201610274672A CN105888971B CN 105888971 B CN105888971 B CN 105888971B CN 201610274672 A CN201610274672 A CN 201610274672A CN 105888971 B CN105888971 B CN 105888971B
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Classifications
-
- 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
- F03D7/0232—Adjusting aerodynamic properties of the blades with flaps or slats
-
- 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
- F03D7/043—Automatic control; Regulation by means of an electrical or electronic controller characterised by the type of control logic
- F03D7/044—Automatic control; Regulation by means of an electrical or electronic controller characterised by the type of control logic with PID control
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
- F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
- F05B2270/00—Control
- F05B2270/30—Control parameters, e.g. input parameters
- F05B2270/32—Wind speeds
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
- F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
- F05B2270/00—Control
- F05B2270/70—Type of control algorithm
- F05B2270/706—Type of control algorithm proportional-integral-differential
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
- F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
- F05B2270/00—Control
- F05B2270/80—Devices generating input signals, e.g. transducers, sensors, cameras or strain gauges
- F05B2270/808—Strain gauges; Load cells
-
- 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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- Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Sustainable Development (AREA)
- Sustainable Energy (AREA)
- Chemical & Material Sciences (AREA)
- Combustion & Propulsion (AREA)
- Mechanical Engineering (AREA)
- General Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Fluid Mechanics (AREA)
- Wind Motors (AREA)
Abstract
The present invention relates to a kind of large scale wind power machine blade active load shedding control system and methods.The stress value of fibre optic strain sensor measurement root of blade, it is transmitted to control unit, control unit includes the fuzzy controller for being carried out the PID controller of parameter optimization using learning aid algorithm and being handled the strain force signal of root of blade, control is switched over to wing flap pivot angle by fuzzy controller and PID controller, combines fuzzy control and the advantage of PID control respectively；Meanwhile wind speed and it being transmitted to control unit before wind field sensing device measuring wind energy conversion system before machine, wind speed changes before the feedforward controller in control unit passes through real-time monitoring machine, calculates the control amount because of needed for uneven load caused by reducing RANDOM WIND or turbulent wind；Control unit couples two parts control amount, completes the control to wing flap pivot angle.The present invention effectively reduces the adaptability to changes of root of blade, extends the service life of blade, reduces the use cost of Wind turbine.
Description
Technical field
The invention belongs to technical field of wind power generation, in particular to a kind of large scale wind power machine blade active load shedding control system
And method.
Background technique
With the high speed development of wind-power electricity generation, for wind energy conversion system also gradually towards offshore, the trend development of enlargement, this is just right
More stringent requirements are proposed for core component-fan blade of wind energy conversion system.Blower enlargement mean to increase blower load and
The quality of system, sharply increases fatigue load and ultimate load, seriously reduces the service life of unit, also increases simultaneously
Hardware and engineering cost and late maintenance cost.
In order to reduce the fatigue load and ultimate load of blower, fan life, the stability of generated energy and power output are improved, closely
A kind of large scale wind power machine blade containing active trailing edge flaps, hardware configuration and control method and traditional leaf are produced over year
Piece is not quite similar.Due to the complexity of the blade construction, establishing its accurate mathematical model is very difficult, and RANDOM WIND
It is very important with loading effect of the turbulent wind to fan blade, therefore conventional single-stage PID control is difficult to meet the load shedding of blade
Demand.
Summary of the invention
In view of the shortcomings of the prior art, the present invention provides a kind of large scale wind power machine blade active load shedding control system and sides
Method.
A kind of large scale wind power machine blade active load shedding control system, the system comprises signal acquisition modules, control module
With electrical servo module；
The signal acquisition module includes wind field sensing equipment and fibre strain signal before fibre optic strain sensor, machine
Processing equipment；The control module includes No. 5 low-pass filters, No. 5 analog-digital converters, 4 tunnel control units, PLC, 4 number moulds
Converter and 4 road signal isolators；The electrical servo module includes 4 road wing flap actuator driving circuits and 4 road wing flap actuations
Device；
The fibre optic strain sensor is mounted on pneumatic equipment bladess root, and connect with fibre strain signal handling equipment；
The 4 road signals that 1 road signal of fibre strain signal handling equipment exports 4 road wing flap corresponding with wind field sensing equipment before machine are defeated
No. 1 low-pass filter is respectively corresponded out and No. 1 analog-digital converter is sequentially connected with；Corresponding with fibre strain signal handling equipment 1
Road analog-digital converter is respectively connected to 4 tunnel control units, No. 4 analog-digital converter one corresponding with wind field sensing equipment before machine
One is correspondingly connected to 4 tunnel control units, and every road control unit respectively corresponds 1 number mode converter, 1 tunnel signal isolation by PLC
Device, 1 road wing flap actuator driving circuit and 1 road wing flap actuator are sequentially connected with；
The fibre optic strain sensor is used to acquire the strain force signal of root of blade, and the fibre strain signal processing is set
It is ready for use on and the signal of fibre optic strain sensor acquisition is converted into voltage signal；Wind field sensing equipment is for surveying before the machine
Wind speed before amount wind energy conversion system；Wind field sensing equipment transmits signals to correspondence respectively before fibre strain signal handling equipment and machine
Low-pass filter, the low-pass filter is for filtering high-frequency interferencing signal；The analog-digital converter is used for analog signal
It is converted into digital signal；Described control unit includes using learning aid to the feedforward controller that wind field signal is handled before machine
The fuzzy controller that algorithm carries out the PID controller of parameter optimization and handled the strain force signal of root of blade, is used for
Reduce the control operation of root stress；The PLC is used to switch the output signal of PID controller and fuzzy controller, and
Couple the control signal from feedforward controller；The digital analog converter is for converting digital signals into analog signal；It is described
Signal isolator is for keeping apart control system output signal and electrical servo module；The wing flap actuator driving circuit produces
The electric signal of raw driving wing flap actuator；The wing flap actuator is according to wing flap actuator driving circuit output signal regulating flap
It is set to generate different pivot angles.
The feedforward controller is changed by wind speed before real-time monitoring machine, is calculated in wind power generating set operation
The control amount because of needed for uneven load caused by reducing RANDOM WIND or turbulent wind.
A kind of control method of above-mentioned large scale wind power machine blade active load shedding control system, specifically includes the following steps:
Step 1: to fuzzy controller FCi, feedforward controller FBiWith PID controller PIDiIt is initialized, i=1,2,3,
4；
Step 2: the stress value of current wind speed and root of blade is read, by obtained root stress value y (k) and blade
The specified stress value r (k) in root carries out difference operation, obtains stress-deviation e (k) and deviation variation rate ec (k), wherein blade is specified
Stress value r (k) is that experiment measures before being dispatched from the factory by blower；
Step 21: stress-deviation e (k), the deviation variation rate ec (k) that step 2 is obtained are used as fuzzy controller FCiIt is defeated
Enter variable；
Step 22: selection subordinating degree function is blurred, and obtains the control amount of wing flap, Anti-fuzzy according to fuzzy rule
Fuzzy controller FC is acquired after changeiOutput variable, this output be wing flap control expectation angle θ 1i；
Step 23: regarding the stress-deviation e (k) of step 21 as PID controller PIDiInput signal, sought using learning aid
Excellent algorithm is to PIDiParameter KPi, KIi, KDiCarry out online self-tuning, the PID controller PIDiOutput variable be wing flap control
It is expected that angle θ 2i；
Step 24: wind speed v (t) before the machine of wind power generating set is acquired, as independent variable, by wing flap angle of oscillation θiMake
For dependent variable, flap angle-wind speed is fitted, establishes flap angle-wind speed feedforward controller FBiModel: θi(v)=
a0+a1v+a2v2+L+anvn, each term coefficient is determined using least square method；Using wind speed as feedforward controller FBiInput letter
Number, then feedforward controller FBiOutput variable be wing flap control expectation angle θ 3i；
Step 3: the wing flap expectation angle control signal that step 22 is obtained with step 23 is sent to PLC respectively and is handled,
Handoff algorithms are set in PLC: when blade root stress error is greater than the set value using from fuzzy controller FCiSignal θ 1i, come
From PID controller PIDiControl signal θ 2iIt will not work；It is controlled when blade root stress error is less than setting value using from PID
Device PID processediControl signal θ 2i, come from fuzzy controller FCiSignal θ 1iIt will not work；It is come from finally, PLC is coupled again
Feedforward controller FBiControl signal θ 3i, and these signals are transferred to corresponding wing flap actuator driving circuit；
4:4 wing flap actuator of step receives the signal from 4 wing flap actuator driving circuits respectively, executes wing flap pendulum
Movement is to reduce root stress；
Above-mentioned steps 2-4 is run repeatedly, surveys root stress value y (k) equal or close to experiment before wind energy conversion system factory
The specified stress value r (k) of root of blade obtained, until completing control task.
For pid parameter learning aid optimizing algorithm the following steps are included:
Step 1): setting initial parameter, region of search range are defined as X=(x1,x2,…,xd) ∈ [L, U], L=(L1,
L2,…,Ld) it is space lower bound, U=(U1,U2,…,Ud) it is the space upper bound, d is the dimension of optimization problem, i-th of d dimension space
Member is defined asStudent's scale is N, maximum number of iterations maxgen；
Step 2): the teaching phase of teacher:
Step 21): calculating the adaptive value of each student, selects best individual as teacher Xteacher, it is average to calculate individual
ValueThen learnt according to student and the difference of individual average level, such as following formula:
TFi=2-gen/maxgen (2)
In formula:WithRespectively indicate the preceding value with after study of i-th of student's study；W1=1-gen/maxgen
For adaptive weight coefficient；Random number of the ri between 0-1；TFiCertain number between 1-2, value with the number of iterations variation
And change；riAnd TFiFor regularized learning algorithm rate；Gen and maxgen is respectively current iteration number and maximum number of iterations；
Step 22): student updates:
IfAdaptive value ratioAdaptive value it is good, then usingInstead ofOtherwise, it continues to use
Step 3): mutually learn the stage between student:
Step 31): each student XiA learning object X is randomly selected in classj(j ≠ i), XiBy analyzing oneself
With student XjBetween difference carry out study adjustment, such as following formula:
If XiBetter than Xj,
If XjBetter than Xi,
In formula: w2=1-gen/maxgen is adaptive weight coefficient；riRandom number between 0-1；
Step 32): student updates:
IfAdaptive value ratioAdaptive value it is good, then usingInstead ofOtherwise, it continues to use
Step 4): calculating the fitness value of each student according to fitness function, and formula is as follows:
In formula, e (t) is systematic error, the global optimum of student is updated according to fitness function, obtained by calculating most
The figure of merit reaches setting value or when algorithm reaches maximum number of iterations, exits learning aid optimizing algorithm, otherwise return step 2).
The invention has the benefit that
1. relative to traditional single-stage PID control system, in this system by introduce feedforward controller can effectively reduce by
The influence generated to root stress is fluctuated in the randomness of wind.2. using fuzzy control and PID control at setting value into
Row switching control, be both utilized fuzzy controller do not need establish accurately mathematical model, adapt to controlled device it is non-linear and
The advantages of time variation, and utilize the feature that PID controller algorithm is simple, stability is good.3. using learning aid algorithm speed of searching optimization
Fastly, the high feature of solving precision, three parameters of on-line optimization PID controller, to effectively reduce the strain of root of blade
Power extends the service life of blade, reduces the use cost of Wind turbine.4. present invention design is simple, using convenient, control
System is more accurate and reliable, is very suitable to the modeling and control of large scale wind power machine blade.
Detailed description of the invention
Fig. 1 is a kind of structural block diagram of large scale wind power machine blade active load shedding control system.
Fig. 2 is a kind of control principle block diagram of large scale wind power machine blade active load shedding control system.
Fig. 3 is the learning aid optimizing algorithm flow chart that online self-tuning is carried out to PID controller parameter.
Fig. 4 is a kind of comparing result of large scale wind power machine blade active load shedding control method and existing method of the present invention.
Specific embodiment
The present invention will be further described with reference to the accompanying drawings and detailed description.It is emphasized that following the description
It is only exemplary, the range and its application being not intended to be limiting of the invention.
A kind of large scale wind power machine blade active load shedding control system as shown in Figure 1, the system comprises signal acquisition module,
Control module and electrical servo module；
The signal acquisition module includes wind field sensing equipment and fibre strain signal before fibre optic strain sensor, machine
Processing equipment；The control module includes No. 5 low-pass filters, No. 5 analog-digital converters, 4 tunnel control units, PLC, 4 number moulds
Converter and 4 road signal isolators；The electrical servo module includes 4 road wing flap actuator driving circuits and 4 road wing flap actuations
Device；
The fibre optic strain sensor is mounted on pneumatic equipment bladess root, and connect with fibre strain signal handling equipment；
The 4 road signals that 1 road signal of fibre strain signal handling equipment exports 4 road wing flap corresponding with wind field sensing equipment before machine are defeated
No. 1 low-pass filter is respectively corresponded out and No. 1 analog-digital converter is sequentially connected with；Corresponding with fibre strain signal handling equipment 1
Road analog-digital converter is respectively connected to 4 tunnel control units, No. 4 analog-digital converter one corresponding with wind field sensing equipment before machine
One is correspondingly connected to 4 tunnel control units, and every road control unit respectively corresponds 1 number mode converter, 1 tunnel signal isolation by PLC
Device, 1 road wing flap actuator driving circuit and 1 road wing flap actuator are sequentially connected with；
The fibre optic strain sensor is used to acquire the strain force signal of root of blade, and the fibre strain signal processing is set
It is ready for use on and the signal of fibre optic strain sensor acquisition is converted into voltage signal；Wind field sensing equipment is for surveying before the machine
Wind speed before amount wind energy conversion system；Wind field sensing equipment transmits signals to correspondence respectively before fibre strain signal handling equipment and machine
Low-pass filter, the low-pass filter is for filtering high-frequency interferencing signal；The analog-digital converter is used for analog signal
It is converted into digital signal；Described control unit is used to reduce the control operation of root stress comprising believes wind field before machine
Number feedforward controller handled, the feedforward controller are in wind power generating set operation, before real-time monitoring machine
Wind speed variation calculates the control amount because of needed for uneven load caused by reducing RANDOM WIND or turbulent wind；It is calculated using learning aid
The PID controller of method progress parameter optimization；And to the fuzzy controller that the strain force signal of root of blade is handled；It is described
PLC is used to switch the output signal of PID controller and fuzzy controller, and couples the control signal from feedforward controller；Institute
Digital analog converter is stated for converting digital signals into analog signal；The signal isolator is used for control system output signal
Keep apart with electrical servo module；The wing flap actuator driving circuit generates the electric signal of driving wing flap actuator；The flap
Wing actuator makes it generate different pivot angles according to wing flap actuator driving circuit output signal regulating flap.
A kind of large scale wind power machine blade active load shedding control method as shown in Figure 2, specifically includes the following steps:
Step 1: to fuzzy controller FCi, feedforward controller FBiWith PID controller PIDiIt is initialized, i=1,2,3,
4。
Step 2: the stress value of current wind speed and root of blade is read, by obtained root stress value y (k) and blade
The specified stress value r (k) in root carries out difference operation, obtains stress-deviation e (k) and deviation variation rate ec (k), wherein blade is specified
Stress value r (k) is that experiment measures before being dispatched from the factory by blower；
Step 21: stress-deviation e (k), the deviation variation rate ec (k) that step 2 is obtained are used as fuzzy controller FCiIt is defeated
Enter variable；
Step 22: selection subordinating degree function is blurred, and obtains the control amount of wing flap, Anti-fuzzy according to fuzzy rule
Fuzzy controller FC is acquired after changeiOutput variable, this output be wing flap control expectation angle θ 1i；
Step 23: regarding the stress-deviation e (k) of step 21 as PID controller PIDiInput signal, sought using learning aid
Excellent algorithm is to PIDiParameter KPi, KIi, KDiCarry out online self-tuning, the PID controller PIDiOutput variable be wing flap control
It is expected that angle θ 2i；
As shown in figure 3, for pid parameter learning aid optimizing algorithm the following steps are included:
Step 1): setting initial parameter, region of search range are defined as X=(x1,x2,…,xd) ∈ [L, U], L=(L1,
L2,…,Ld) it is space lower bound, U=(U1,U2,…,Ud) it is the space upper bound, d is the dimension of optimization problem, i-th of d dimension space
Member is defined asStudent's scale is N, maximum number of iterations maxgen；
Step 2): the teaching phase of teacher:
Step 21): calculating the adaptive value of each student, selects best individual as teacher Xteacher, it is average to calculate individual
ValueThen learnt according to student and the difference of individual average level, such as following formula:
TFi=2-gen/maxgen (2)
In formula:WithRespectively indicate the preceding value with after study of i-th of student's study；W1=1-gen/maxgen
For adaptive weight coefficient；riRandom number between 0-1；TFiCertain number between 1-2, value with the variation of the number of iterations and
Variation；riAnd TFiFor regularized learning algorithm rate；Gen and maxgen is respectively current iteration number and maximum number of iterations；
Step 22): student updates:
IfAdaptive value ratioAdaptive value it is good, then usingInstead ofOtherwise, it continues to use
Step 3): mutually learn the stage between student:
Step 31): each student XiA learning object X is randomly selected in classj(j ≠ i), XiBy analyzing oneself
With student XjBetween difference carry out study adjustment, such as following formula:
If XiBetter than Xj,
If XjBetter than Xi,
In formula: w2=1-gen/maxgen is adaptive weight coefficient；riRandom number between 0-1；
Step 32): student updates:
IfAdaptive value ratioAdaptive value it is good, then usingInstead ofOtherwise, it continues to use
Step 4): calculating the fitness value of each student according to fitness function, and formula is as follows:
In formula, e (t) is systematic error, the global optimum of student is updated according to fitness function, obtained by calculating most
The figure of merit reaches setting value or when algorithm reaches maximum number of iterations, exits learning aid optimizing algorithm, otherwise return step 2)；
Step 24: wind speed v (t) before the machine of wind power generating set is acquired, as independent variable, by wing flap angle of oscillation θiMake
For dependent variable, flap angle-wind speed is fitted, establishes flap angle-wind speed feedforward controller FBiModel: θi(v)=
a0+a1v+a2v2+L+anvn, each term coefficient is determined using least square method；Using wind speed as feedforward controller FBiInput letter
Number, then feedforward controller FBiOutput variable be wing flap control expectation angle θ 3i。
Step 3: the wing flap expectation angle control signal that step 22 is obtained with step 23 is sent to PLC respectively and is handled,
Handoff algorithms are set in PLC: when blade root stress error is greater than the set value using from fuzzy controller FCiSignal θ 1i, come
From PID controller PIDiControl signal θ 2iIt will not work；It is controlled when blade root stress error is less than setting value using from PID
Device PID processediControl signal θ 2i, come from fuzzy controller FCiSignal θ 1iIt will not work；It is come from finally, PLC is coupled again
Feedforward controller FBiControl signal θ 3i, and these signals are transferred to corresponding wing flap actuator driving circuit.
4:4 wing flap actuator of step receives the signal from 4 wing flap actuator driving circuits respectively, executes wing flap pendulum
Movement is to reduce root stress.
Above-mentioned steps 2-4 is run repeatedly, surveys root stress value y (k) equal or close to experiment before wind energy conversion system factory
The specified stress value r (k) of root of blade obtained, until completing control task.
5WM refers to wind energy conversion system in the case where wind regime is the turbulent wind of 11.4m/s, using the blade root moment of flexure of blade as wind energy conversion system load shedding
Target, using a kind of comparing result such as Fig. 4 institute of large scale wind power machine blade active load shedding control method and existing method of the present invention
Show, it is seen that the adaptability to changes of root of blade is effectively reduced using method of the invention.
The above is only presently preferred embodiments of the present invention, is not intended to limit the present invention in any form, any ripe
Know those skilled in the art in the technical scope disclosed by the present invention, can readily occur in it is simple modification, equivalent variations,
It should be covered by the protection scope of the present invention.Therefore, protection scope of the present invention should be with scope of protection of the claims
Subject to.
Claims (4)
1. a kind of large scale wind power machine blade active load shedding control system, which is characterized in that the system comprises signal acquisition module,
Control module and electrical servo module；
The signal acquisition module includes wind field sensing equipment and fibre strain signal processing before fibre optic strain sensor, machine
Equipment；The control module includes No. 5 low-pass filters, No. 5 analog-digital converters, 4 tunnel control units, PLC, 4 tunnel digital-to-analogue conversions
Device and 4 road signal isolators；The electrical servo module includes 4 road wing flap actuator driving circuits and 4 road wing flap actuator；
The fibre optic strain sensor is mounted on pneumatic equipment bladess root, and connect with fibre strain signal handling equipment；Optical fiber
1 road signal of strain signal processing equipment exports the 4 road signals output point of 4 road wing flap corresponding with wind field sensing equipment before machine
Not Dui Ying No. 1 low-pass filter and No. 1 analog-digital converter be sequentially connected with；1 road mould corresponding with fibre strain signal handling equipment
Number converter is respectively connected to 4 tunnel control units, and No. 4 analog-digital converter one corresponding with wind field sensing equipment before machine is a pair of
4 tunnel control units should be connected to, every road control unit respectively corresponds 1 number mode converter, 1 road signal isolator, 1 by PLC
Road wing flap actuator driving circuit and 1 road wing flap actuator are sequentially connected with；
The fibre optic strain sensor is used to acquire the strain force signal of root of blade, and the fibre strain signal handling equipment is used
Voltage signal is converted in the signal for acquiring fibre optic strain sensor；Wind field sensing equipment is for measuring wind before the machine
Wind speed before power machine；Wind field sensing equipment transmits signals to corresponding low respectively before fibre strain signal handling equipment and machine
Bandpass filter, the low-pass filter is for filtering high-frequency interferencing signal；The analog-digital converter is for converting analog signal
At digital signal；Described control unit includes using learning aid algorithm to the feedforward controller that wind field signal is handled before machine
The fuzzy controller for carrying out the PID controller of parameter optimization and the strain force signal of root of blade being handled, for reducing
The control operation of root stress；The PLC is used to switch the output signal of PID controller and fuzzy controller, and couples
Control signal from feedforward controller；The digital analog converter is for converting digital signals into analog signal；The signal
Isolator is for keeping apart control system output signal and electrical servo module；The wing flap actuator driving circuit generates drive
The electric signal of dynamic wing flap actuator；The wing flap actuator makes it according to wing flap actuator driving circuit output signal regulating flap
Generate different pivot angles.
2. a kind of large scale wind power machine blade active load shedding control system according to claim 1, which is characterized in that the feedforward
Controller is changed by wind speed before real-time monitoring machine, is calculated because reducing RANDOM WIND or rapids in wind power generating set operation
Control amount needed for flowing wind-induced uneven load.
3. a kind of controlling party of large scale wind power machine blade active load shedding control system described in claim 1-2 any claim
Method, which is characterized in that specifically includes the following steps:
Step 1: to fuzzy controller FCi, feedforward controller FBiWith PID controller PIDiIt is initialized, i=1,2,3,4；
Step 2: the stress value of current wind speed and root of blade is read, by obtained root stress value y (k) and root of blade
Specified stress value r (k) carries out difference operation, obtains stress-deviation e (k) and deviation variation rate ec (k), wherein the specified stress of blade
Value r (k) is that experiment measures before being dispatched from the factory by blower；
Step 21: stress-deviation e (k), the deviation variation rate ec (k) that step 2 is obtained are used as fuzzy controller FCiInput become
Amount；
Step 22: selection subordinating degree function is blurred, and obtains the control amount of wing flap according to fuzzy rule, after anti fuzzy method
Acquire fuzzy controller FCiOutput variable, this output be wing flap control expectation angle θ 1i；
Step 23: regarding the stress-deviation e (k) of step 21 as PID controller PIDiInput signal, utilize learning aid optimizing calculate
Method is to PIDiParameter KPi, KIi, KDiCarry out online self-tuning, the PID controller PIDiOutput variable be wing flap control expectation
Angle θ 2i；
Step 24: wind speed v (t) before the machine of wind power generating set is acquired, as independent variable, by wing flap angle of oscillation θiAs because
Variable is fitted flap angle-wind speed, establishes flap angle-wind speed feedforward controller FBiModel: θi(v)=a0+
a1v+a2v2+L+anvn, each term coefficient is determined using least square method；Using wind speed as feedforward controller FBiInput signal, then
Feedforward controller FBiOutput variable be wing flap control expectation angle θ 3i；
Step 3: the wing flap expectation angle control signal that step 22 is obtained with step 23 being sent to PLC respectively and is handled, in PLC
Handoff algorithms are set: when blade root stress error is greater than the set value using from fuzzy controller FCiSignal θ 1i, come from PID
Controller PIDiControl signal θ 2iIt will not work；When blade root stress error is less than setting value using from PID controller
PIDiControl signal θ 2i, come from fuzzy controller FCiSignal θ 1iIt will not work；Finally, PLC is coupled again from feedforward
Controller FBiControl signal θ 3i, and these signals are transferred to corresponding wing flap actuator driving circuit；
4:4 wing flap actuator of step receives the signal from 4 wing flap actuator driving circuits respectively, and it is dynamic to execute wing flap swing
Make to reduce root stress；
Above-mentioned steps 2-4 is run repeatedly, measures root stress value y (k) equal or close to experiment before wind energy conversion system factory
The specified stress value r (k) of root of blade, until completing control task.
4. a kind of control method according to claim 3, which is characterized in that the learning aid optimizing algorithm packet for pid parameter
Include following steps:
Step 1): setting initial parameter, region of search range are defined as X=(x1,x2,…,xd) ∈ [L, U], L=(L1,L2,…,
Ld) it is space lower bound, U=(U1,U2,…,Ud) it is the space upper bound, d is the dimension of optimization problem, and i-th of student of d dimension space is fixed
Justice isStudent's scale is N, maximum number of iterations maxgen；
Step 2): the teaching phase of teacher:
Step 21): calculating the adaptive value of each student, selects best individual as teacher Xteacher, calculate individual average valueThen learnt according to student and the difference of individual average level, such as following formula:
TFi=2-gen/maxgen (2)
In formula:WithRespectively indicate the preceding value with after study of i-th of student's study；W1=1-gen/maxgen is certainly
Adapt to weight coefficient；riRandom number between 0-1；TFiCertain number between 1-2, value become with the variation of the number of iterations
Change；riAnd TFiFor regularized learning algorithm rate；Gen and maxgen generation number and maximum number of iterations；
Step 22): student updates:
IfAdaptive value ratioAdaptive value it is good, then usingInstead ofOtherwise, it continues to use
Step 3): mutually learn the stage between student:
Step 31): each student XiA learning object X is randomly selected in classj(j ≠ i), XiBy analyzing oneself and learning
Member XjBetween difference carry out study adjustment, such as following formula:
If XiBetter than Xj,
If XjBetter than Xi,
In formula: w2=1-gen/maxgen is adaptive weight coefficient；riRandom number between 0-1；
Step 32): student updates:
IfAdaptive value ratioAdaptive value it is good, then usingInstead ofOtherwise, it continues to use
Step 4): calculating the fitness value of each student according to fitness function, and formula is as follows:
In formula, e (t) is systematic error, the global optimum of student is updated according to fitness function, the optimal value obtained by calculating
Reach setting value or when algorithm reaches maximum number of iterations, exit learning aid optimizing algorithm, otherwise return step 2).
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| 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 |
| CN109958574B (en) * | 2017-12-22 | 2020-05-05 | 北京金风科创风电设备有限公司 | Load reduction control method and device for wind generating set |
| CN109162878B (en) * | 2018-08-01 | 2020-06-30 | 汕头大学 | Intelligent blade measurement and control method and system for wind driven generator |
| EP3667076A1 (en) * | 2018-12-13 | 2020-06-17 | Siemens Gamesa Renewable Energy A/S | Estimating wind speed |
| CN111749845B (en) * | 2019-03-29 | 2022-04-05 | 北京金风科创风电设备有限公司 | Load reduction control method and device for wind turbine generator |
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