CN107784371A - Nonlinear fitting-based intelligent self-adaptive control method for wind power yaw state - Google Patents
Nonlinear fitting-based intelligent self-adaptive control method for wind power yaw state Download PDFInfo
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Abstract
The invention relates to a nonlinear fitting-based intelligent self-adaptive control method for a wind power yaw state, which adopts the technical scheme that: the method comprises the steps of collecting real-time working state information of a fan by using a wind vane, an anemoscope, a yaw encoder and a converter, preprocessing long-term accumulated information of the working state of the fan to eliminate invalid working state points, performing regional statistical analysis on the wind yaw state by using a machine learning technology according to the physical characteristics of the wind yaw state, selecting optimal fitting parameters by using a nonlinear fitting method to obtain a wind yaw intelligent self-adaptive model, further performing real-time processing on newly collected sensor information to obtain a theoretical optimal wind yaw running angle under the current wind condition, finally performing output correction processing on the wind yaw intelligent self-adaptive model by using the wind speed constraint condition under the normal work of the fan, and judging whether abnormal states such as stall exist or not, thereby ensuring the running safety of equipment.
Description
Technical field
It is adaptive more particularly to a kind of wind-force driftage condition intelligent based on nonlinear fitting the invention belongs to technical field
Answer control method.
Background technology
Wind-force monitoring profit control has huge meaning for many commercial Application environment, such as the renewable sources of energy and Aero-Space
Deng.For in terms of with the renewable sources of energy, the wind wheel of wind-driven generator absorbs wind energy rotation, and then drives the generator rotation being connected
Generate electricity.The yaw system of wind power generating set can track the change of wind direction, and driving fan engine room rotates around tower top, makes wind wheel
Swept surface and wind direction keep vertical angle, so as to reach the absorption of wind energy ceiling capacity in theory.
Existing wind-force yaw control system be all by wind vane, anemobiagraph, driftage encoder and arithmetic unit simply
A yaw angle is calculated to be controlled, yet with the complicated landform of wind power plant, the arrangement mode of multiple Wind turbines
And the cause influence such as wind vane spot measurement error caused by wind tail flow-disturbing, in different wind speed sections, the blower fan calculated
Driftage may not be optimal, to wind actual deviation and be not zero, and not up to wind energy ceiling capacity absorbs, and in turn results in wind
The loss of machine generated output, so as to influence the runnability of whole unit, it would be highly desirable to improve.
The content of the invention
To there is provided a kind of wind-force based on nonlinear fitting inclined for technical problem present in solution known technology by the present invention
Navigate condition intelligent self-adaptation control method, and this method compared to existing technologies, can adaptively adjust wind-force yaw angle,
To realize in different wind speed section self-adoptive traces to windage losses optimum point, so as to absorb wind energy in maximum efficiency.
The present invention is adopted the technical scheme that to solve technical problem present in known technology：
A kind of wind-force driftage condition intelligent self-adaptation control method based on nonlinear fitting, is comprised the following steps that：
(1) system initialization：
Blower fan work status data is gathered in real time using anemobiagraph, wind vane, driftage encoder and current transformer, will be collected
Fourth as status data be sent to wind-force driftage intelligent adaptive equipment, be cached in wind-force driftage intelligent adaptive equipment data
In storehouse；
(2) data prediction：
For particular job point and wind speed work constraints, the operating state data collected in step (1) is carried out
Pretreatment, reject the hash point for influenceing nonlinear fitting precision；
(3) fitted area is extracted：
Working condition of being gone off course using machine learning techniques to wind-force in blower fan work status data treats that fitted area is carried out
Data point is extracted；
(4) nonlinear fitting：
Rectangular co-ordinate is established in the real-time power output point combination of real-time wind speed, wind-force yaw angle and blower fan to extraction
System, record coordinate (xi, yi, zi), (i=1,2 ... nonlinear fitting n) is carried out to the point of selection, finds functionMake
Function is obtained in point (xi, yi), (i=1,2 ... the n) functional value at placeWith gathered data ziDeviation reaches the optimal bar of fitting algorithm
Part value；
Wherein, xiRepresent the real-time wind speed corresponding to i-th of selected point；yiRepresent that the wind-force corresponding to i-th of selected point is inclined
Boat angle；ziRepresent the real-time power output of blower fan corresponding to i-th of selected point；
(5) intelligent adaptive model is chosen：
Functional value in step (4)With gathered data ziWhen deviation reaches the optimal conditions value of fitting algorithm, assert and intend
Close effect and reach requirement, Selection of FunctionFor best fit function, and as wind-force driftage intelligent adaptive model；
(6) intelligent adaptive model seeks optimal solution and updates the data storehouse：
After new real-time wind speed and direction yaw angle is collected, the intelligent adaptive model that is obtained by step (5)
Search theory maximum service rating point, and real-time wind-force yaw angle is repaiied with theoretical wind-force driftage state corresponding to the point
Just, optimal wind-force yaw angle is obtained；Simultaneously using the duty point of correlation as new data input, step (2) is updated
Database, perform and prepare for following cycle；
(7) optimal solution obtained to (6) carries out service condition constraint processing：
The optimal wind-force yaw angle after optimization is calculated, with reference to real-time wind speed, passes through blower fan normal operating condition
Constraint requirements, blower fan work state is controlled in real time, to reach the output of the maximum realtime power of blower fan.
The present invention can also use following technical measures：Can Selection of Function in step (4)Method meet under
Formula：
To realize optimization.
The present invention can also use following technical measures：Also need to judge that accumulation data point is between step (3) and (4)
It is no to reach data volume needed for nonlinear fitting, if accumulation data point reaches required data volume, perform step (4)；If accumulate number
The not up to required data volume in strong point, then re-execute step (2).
The present invention has the advantages and positive effects of：
In order to overcome the above-mentioned problems of the prior art, the present invention proposes the wind-force driftage shape based on nonlinear fitting
State intelligent adaptive control method, the technical scheme that this method uses are：Using wind vane, anemobiagraph, driftage encoder and
Current transformer gathers blower fan instantaneous operating conditions information, by being located in advance to blower fan work state long term accumulation information
Reason rejects non-active operating status point, the physical characteristics for state of being gone off course according to wind-force, using machine learning techniques to wind-force driftage shape
State carries out range statistics analysis, and best fit parameters are chosen using non-linear fitting method, obtains wind-force driftage intelligent adaptive
Model, and then the sensor information come in new collection is handled in real time, is obtained theoretical optimal under current wind condition
Wind-force driftage operation angle, finally carries out output calibration processing to it using the wind speed constraints under blower fan normal work, sentences
Read with the presence or absence of abnormalities such as stall, so as to ensure that equipment is safe for operation.
Technical scheme provided by the invention has weight to the economic benefit for improving wind turbine power generation power and whole wind power plant
Big meaning.The former existing and equipment of same control also can be widely used to space flight and aviation etc. and wind-force be detected and controlled the work required
Industry application environment.
Brief description of the drawings
Fig. 1 is a kind of flow of the wind-force driftage condition intelligent self-adaptation control method based on nonlinear fitting of the present invention
Figure.
Embodiment
The embodiment of the present invention is described further below in conjunction with the accompanying drawings.Herein it should be noted that for
The explanation of these embodiments is used to help understand the present invention, but does not form limitation of the invention.It is in addition, disclosed below
As long as each embodiment of the invention in the technical characteristic that is related to do not form conflict can each other and be mutually combined.
A kind of flow of wind-force driftage condition intelligent self-adaptation control method based on nonlinear fitting as shown in Figure 1
Figure, its entirety is that technical scheme is：Utilize wind vane, anemobiagraph, driftage encoder and current transformer collection blower fan real-time working
Status information, non-active operating status point is rejected by carrying out pretreatment to blower fan work state long term accumulation information, according to wind-force
The physical characteristics of driftage state, range statistics analysis is carried out to wind-force driftage state using machine learning techniques, use is non-linear
Approximating method chooses best fit parameters, obtains wind-force driftage intelligent adaptive model, and then gather the sensor of coming in new
Information is handled in real time, obtains theoretical optimal wind-force driftage operation angle under current wind condition.
Comprise the following steps that：
(1) system initialization：
Blower fan work status data is gathered in real time using anemobiagraph, wind vane, driftage encoder and current transformer, will be collected
Operating state data be sent to wind-force driftage intelligent adaptive equipment, be cached in wind-force driftage intelligent adaptive equipment data
In storehouse；
In specific implement, anemobiagraph, wind vane, driftage encoder and current transformer are the basis biographies that the present invention is relied on
Feel equipment, be respectively used to obtain the wind speed being in the course of work, wind direction, yaw angle, the real-time power output of blower fan.
For the data under blower fan different working condition, wind-force driftage intelligent adaptive equipment can be cached, and be thought non-
Linear fit provides enough data volumes and supported.
(2) data prediction：
For particular job point and wind speed work constraints, the operating state data collected in step (1) is carried out
Pretreatment, reject the hash point for influenceing nonlinear fitting precision；
Particular job point mainly includes：Blower fan incision state, blower fan cut out state and artificial suspended state.Three of the above
State correspond to different wind speed work constraints respectively, and in the case where these three wind speed fourths make constraints, blower fan is in anon-normal
Normal working condition.
When outside wind speed in real time is less than blower fan incision wind speed, the inadequate fan blade own rotation of electric energy caused by generator
Required energy, now blower fan is idle；When outside real-time wind speed exceedes blower fan cut-out wind speed, fan blade rotated
It hurry up, be easy to cause the accident, now blower fan will also cut out working condition, i.e., do not work.As its name suggests, artificially stop when blower fan is in
During work state, blower fan is also idle.
So-called pre-process just is to discriminate between abnormal operating state point and normal operating conditions point, the Nonlinear Quasi in the present invention
Closing only for blower fan normal condition, that is, needs that wind speed work will be met about in the data of wind-force driftage intelligent adaptive equipment caching
The abnormal operating state point of beam condition is rejected.
(3) fitted area is extracted：
Working condition of being gone off course using machine learning techniques to wind-force in blower fan work status data treats that fitted area is carried out
Data point is extracted；
When blower fan real-time working, there is certain deviation in blower fan real-time working curve and blower fan theoretical work curve,
Fitted it cannot be guaranteed that all operating state data points can fall in step (4) around the curve come.
In order to ensure the robustness of fitting algorithm, it is necessary to which certain to fitted area progress define, so making in this step
With machine learning techniques (such as：Deep learning, AdaBoost etc.) fitted area is bound.
(4) nonlinear fitting：
Rectangular co-ordinate is established in the real-time power output point combination of real-time wind speed, wind-force yaw angle and blower fan to extraction
System, record coordinate (xi, yi, zi), (i=1,2 ... nonlinear fitting n) is carried out to the point of selection, finds functionMake
Function is obtained in point (xi, yi), (i=1,2 ... the n) functional value at placeWith gathered data ziDeviation reaches the optimal bar of fitting algorithm
Part value；
Wherein, xiRepresent the real-time wind speed corresponding to i-th of selected point；yiRepresent that the wind-force corresponding to i-th of selected point is inclined
Boat angle；ziRepresent the real-time power output of blower fan corresponding to i-th of selected point；
Nonlinear fitting can use such as least square method, Newton-Rapson algorithms, Levenberg-Marquardt to calculate
Method etc..
(5) intelligent adaptive model is chosen：
Functional value in step (4)With gathered data ziWhen deviation reaches the optimal conditions value of fitting algorithm, assert and intend
Close effect and reach requirement, Selection of FunctionFor best fit function, and as wind-force driftage intelligent adaptive model；
When evaluating best fit function, it is necessary to according to the constraints provided in step (4), i.e.,：When in step (4)
Functional valueWith gathered data ziWhen deviation reaches the optimal conditions value of fitting algorithm.
In a preferred embodiment, described in the step (4) can Selection of FunctionMethod meet following formula：
To realize optimization.
(6) intelligent adaptive model seeks optimal solution and updates the data storehouse：
After new real-time wind speed and direction yaw angle is collected, the intelligent adaptive model that is obtained by step (5)
Maximum service rating point is now discussed in search, and so-called theoretical maximum operating power point is exactly that the real-time wind speed and wind-force that will newly collect are inclined
The intelligent adaptive model that boat angle is updated in step (5) is obtained tangential maximum corresponding to current wind speed in real time on model curved surface
Performance number.
When obtaining wind-force yaw error corresponding to tangential maximum power value can lookup corresponding to blower fan, to driftage in real time
Angle is modified, i.e.,：Real-time wind-force yaw angle is modified with theoretical wind-force yaw error corresponding to the point, obtained most
Excellent wind-force yaw angle；Simultaneously by the duty point of correlation, i.e., real-time wind speed, wind-force yaw angle corresponding to the point and
The real-time power output of blower fan, as new data input, to update the database of step (2), perform and prepare for following cycle.
(7) optimal solution obtained to (6) carries out service condition constraint processing：
The optimal wind-force yaw angle after optimization is calculated, with reference to real-time wind speed, passes through blower fan normal operating condition
Constraint requirements, blower fan work state is controlled in real time, to reach the output of the maximum realtime power of blower fan.
Also need to judge to accumulate whether data point reaches non-thread between step (3) and (4) in a preferred embodiment
Property data volume needed for fitting, if accumulation data point reaches required data volume, perform step (4)；If accumulation number grid point is not up to
Required data volume, then re-execute step (2).Because nonlinear fitting needs a number of data point just to draw finally
Solution, specific number are relevant with the nonlinear fitting model of foundation.Generally, model it is more complicated, it is necessary to number of data points
It is more.As long as the data volume of data point is more than number of parameters to be solved in model in principle.
Above instrument is the preferred embodiment of the present invention, it is noted that is come for those skilled in the art
Say, without departing from the technical principles of the invention, some improvements and modifications can also be made, these improvements and modifications also should
It is considered as protection scope of the present invention.
Claims (3)
- A kind of 1. wind-force driftage condition intelligent self-adaptation control method based on nonlinear fitting, it is characterised in that：Specific steps It is as follows：(1) system initialization：Blower fan work status data is gathered in real time using anemobiagraph, wind vane, driftage encoder and current transformer, the work that will be collected Wind-force driftage intelligent adaptive equipment is sent to as status data, is cached in the database of wind-force driftage intelligent adaptive equipment In；(2) data prediction：For particular job point and wind speed work constraints, the operating state data collected in step (1) is located in advance Reason, reject the hash point for influenceing nonlinear fitting precision；(3) fitted area is extracted：Working condition of being gone off course using machine learning techniques to wind-force in blower fan work status data treats that fitted area carries out data Point extraction；(4) nonlinear fitting：Rectangular coordinate system is established in the real-time power output point combination of real-time wind speed, wind-force yaw angle and blower fan to extraction, is remembered Record coordinate (xi, yi, zi), (i=1,2 ... nonlinear fitting n) is carried out to the point of selection, function f (x, y, z) is found and causes letter Number is in point (xi, yi), (i=1,2 ... the n) functional value at placeWith gathered data ziDeviation reaches the optimal conditions of fitting algorithm Value；Wherein, xiRepresent the real-time wind speed corresponding to i-th of selected point；yiRepresent the wind-force yaw angle corresponding to i-th of selected point Degree；ziRepresent the real-time power output of blower fan corresponding to i-th of selected point；(5) intelligent adaptive model is chosen：Functional value in step (4)With gathered data ziWhen deviation reaches the optimal conditions value of fitting algorithm, fitting effect is assert Fruit reaches requirement, Selection of Function f (x, y,) it is best fit function, and as wind-force driftage intelligent adaptive model；(6) intelligent adaptive model seeks optimal solution and updates the data storehouse：After new real-time wind speed and direction yaw angle is collected, the intelligent adaptive pattern search that is obtained by step (5) Theoretical maximum operating power point, and real-time wind-force yaw angle is modified with theoretical wind-force driftage state corresponding to the point, Obtain optimal wind-force yaw angle；Simultaneously using the duty point of correlation as new data input, the data of renewal step (2) Storehouse, perform and prepare for following cycle；(7) optimal solution obtained to (6) carries out service condition constraint processing：The optimal wind-force yaw angle after optimization is calculated, with reference to real-time wind speed, passes through the constraint of blower fan normal operating condition It is required that blower fan work state is controlled in real time, to reach the output of the maximum realtime power of blower fan.
- 2. the wind-force driftage condition intelligent self-adaptation control method according to claim 1 based on nonlinear fitting, it is special Sign is：Can Selection of Function in step (4)Method meet following formula：<mrow> <mi>min</mi> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <mo>|</mo> <mi>f</mi> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mi>i</mi> </msub> <mo>,</mo> <msub> <mi>y</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mo>-</mo> <msub> <mi>z</mi> <mi>i</mi> </msub> <msup> <mo>|</mo> <mn>2</mn> </msup> </mrow>To realize optimization.
- 3. the wind-force driftage condition intelligent self-adaptation control method according to claim 1 or 2 based on nonlinear fitting, its It is characterised by：Also need to judge to accumulate whether data point reaches data volume needed for nonlinear fitting between step (3) and (4), If accumulation data point reaches required data volume, step (4) is performed；If the not up to required data volume of data point is accumulated, again Perform step (2).
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