CN113374634B - Wind turbine yaw wind alignment method under anemoscope fault mode - Google Patents
Wind turbine yaw wind alignment method under anemoscope fault mode Download PDFInfo
- Publication number
- CN113374634B CN113374634B CN202110747742.0A CN202110747742A CN113374634B CN 113374634 B CN113374634 B CN 113374634B CN 202110747742 A CN202110747742 A CN 202110747742A CN 113374634 B CN113374634 B CN 113374634B
- Authority
- CN
- China
- Prior art keywords
- wind
- wind direction
- wind turbine
- fault
- turbine generator
- 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.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 13
- 238000005315 distribution function Methods 0.000 claims abstract description 28
- 239000011159 matrix material Substances 0.000 claims abstract description 27
- 230000001133 acceleration Effects 0.000 claims description 19
- 238000005259 measurement Methods 0.000 claims description 10
- 238000010586 diagram Methods 0.000 claims description 9
- 239000013598 vector Substances 0.000 claims description 8
- 238000010248 power generation Methods 0.000 claims description 4
- 238000012417 linear regression Methods 0.000 claims description 3
- 238000000611 regression analysis Methods 0.000 claims description 3
- 238000012795 verification Methods 0.000 claims description 3
- 150000001875 compounds Chemical class 0.000 claims description 2
- 230000009286 beneficial effect Effects 0.000 abstract description 2
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 241001391944 Commicarpus scandens Species 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 125000004432 carbon atom Chemical group C* 0.000 description 1
- 238000005314 correlation function Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 239000012530 fluid Substances 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
Images
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
- F03D7/0204—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
-
- 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
-
- 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
-
- 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
- F03D7/045—Automatic control; Regulation by means of an electrical or electronic controller characterised by the type of control logic with model-based controls
-
- 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
- F05B2270/329—Azimuth or yaw angle
-
- 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
Landscapes
- 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)
- Indicating Or Recording The Presence, Absence, Or Direction Of Movement (AREA)
- Wind Motors (AREA)
Abstract
The invention relates to a wind turbine yawing and wind aligning method under a failure mode of a anemoscope, which comprises the following steps: before a fault occurs, establishing a wind direction related matrix group among the units; after a fault occurs, establishing a distribution function regression model of the wind direction of the fault unit to obtain a wind direction value of a machine position where the fault unit is located; checking the wind direction of the fault unit through a cabin vibration sensor; and comparing the wind direction included angle and the wind direction value of the unit in real time, and carrying out fault-tolerant control on the fault unit. The beneficial effects of the invention are: and pre-constructing a wind direction correlation matrix among the wind turbine points, and establishing a wind direction distribution function relation among the wind turbine units. The yaw control of the wind turbines is mainly performed on a small time scale, and when anemoscope equipment equipped for some wind turbines in the wind power plant is in fault, wind direction signals of other wind turbines normally working by the anemoscope can be used as yaw wind control input of a fault fan according to a wind direction distribution function relation.
Description
Technical Field
The invention belongs to the field of wind turbine yaw alignment, and particularly relates to a wind turbine yaw alignment method in a anemoscope fault mode.
Background
Yaw is the core control function of the wind turbine generator, and the front windward state is kept through the control unit, so that wind energy is captured to the maximum extent, the asymmetric load of the wind turbine generator can be effectively reduced, and the safe operation of the wind turbine generator is protected. The anemoscope is arranged at the top of the cabin of the wind turbine generator, and the wind direction is monitored in real time, so that the wind turbine generator can track the change of the wind direction in time and keep accurate wind alignment through a yaw wind alignment control function. However, the anemoscope is very easy to break down or damage due to long-term exposure to severe environments, so that the wind turbine cannot acquire effective wind direction input, the wind turbine cannot accurately face wind, even the wind turbine needs to be stopped for maintenance, the reliability of the wind turbine is reduced, and the generated energy loss is caused.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provides a wind turbine yawing and aligning method under a failure mode of a anemoscope.
The wind turbine yaw wind aligning method under the anemoscope fault mode comprises the following steps:
step 1.1, assuming that the field totally has N sets, and when the wind direction measured by the wind measuring tower is located in the ith wind direction sector in the equally divided wind direction sectors, the wind direction correlation coefficient of the set k and the rest N-1 sets in the field is as follows:
in the above formula, i is 1,2, …, and 16 represents 16 equal wind tower wind direction sectors; dir k (i) The wind direction time sequence of the unit k at the corresponding moment; dir m (i) A wind direction time sequence of a corresponding time unit m (m is not equal to k); r [ Dir ] k (i),Dir m (i)]The correlation coefficient of the set k and the wind direction of any one set of the rest N-1 sets in the field area is shown; cov [ Dir ] k (i),Dir m (i)]Is Dir k (i) And Dir m (i) Covariance of (2), var [ Dir k (i)]、var[Dir m (i)]Are respectively Dir k (i)、Dir m (i) The variance of (a); n is the total number of the units in the field;
step 1.2, the obtained wind direction correlation coefficients are sequentially arranged, and wind direction correlation column vectors of N-1 rows of the unit k are established; according to 16 wind directions of a wind field anemometry tower and a station wind direction rose diagram, 16 wind directions with the size of (N-1) multiplied by 16 are established for any unitWind direction related matrix group A (N-1)×16×16 ：
A (N-1)×16×16 ＝(a rst ) (N-1)×16×16 , a rst ∈R 3 (2)
Wherein, the matrix group element a rst The correlation coefficient of the wind direction of the unit k and the wind direction of other units under different wind directions, R 3 Is a three-dimensional real number vector space; the unit k searches a unit number with the maximum wind direction correlation coefficient under one wind direction from the wind direction correlation matrix group;
step 2, after a fault occurs, establishing a distribution function regression model of the wind direction of the fault unit, so as to obtain a wind direction value of the position of the fault unit;
step 2.1, supposing that a certain unit anemoscope in the wind field has a fault, the unit cannot effectively face wind, and the unit number is marked as k; the unit k indirectly calculates an included angle theta between an absolute wind direction and a central axis of the engine room through an engine room vibration sensor, a unit m with a wind direction value closest to that of the unit m is searched in a wind direction related matrix group of the unit, and a wind direction instrument of the unit m normally works;
step 2.2, performing regression analysis on historical wind measurement data of the fault unit k and wind measurement data of the normal unit m, and establishing a distribution function regression model of the wind direction of the fault unit; calculating the wind direction value theta of the position of the fault unit according to the wind direction value of the existing normal unit through a distribution function regression model m ；
Step 3, checking the wind direction of the fault unit through a cabin vibration sensor;
step 4, comparing the wind direction included angle theta and the wind direction value theta of the unit k in real time m And carrying out fault-tolerant control on the fault unit.
Preferably, the regression model of the distribution function of the wind direction of the fault unit in the step 2.2 is a one-dimensional linear regression model, and the mathematical expression of the regression model is as follows:
in the above formula, the first and second carbon atoms are,
the predicted value of the wind direction of the wind vane fault unit k is shown, X is the measured value of the wind direction of the unit m, and a and b are the parameters of a regression model of the distribution function of the wind direction of the unit.
Preferably, step 3 specifically comprises the following steps:
step 3.1, when the anemoscope of the cabin of the wind turbine generator normally works, finishing the verification; when a wind direction indicator of a cabin of the wind turbine generator system fails, an included angle theta exists between the absolute wind direction measured by the wind direction indicator and the central axis of the cabin, the wind direction is decomposed into a central axis component of the cabin and a plane component of a wind wheel, and then the loss power P of the wind turbine generator system is obtained s Comprises the following steps:
P s ＝P(1-cos 3 θ) (4)
in the above formula, P is the power of the wind turbine generator set, in which the included angle between the central axis of the wind wheel and the wind direction is 0 degree, and θ is the included angle between the absolute wind direction and the central axis of the engine room;
3.2, installing a vibration acceleration sensor in the engine room of the wind turbine generator, and accessing a vibration signal of the vibration acceleration sensor into a main control system of the wind turbine generator; the main control system of the unit monitors the vibration value of the engine room in real time and compares the vibration value with a set threshold value; when the vibration value of the nacelle exceeds a set threshold value, triggering the wind turbine generator to stop; measuring the vibration numerical values of the axial force and the tangential force of the engine room through a vibration acceleration sensor, and acquiring the yaw error of the wind turbine generator:
in the above formula, the radial vibration acceleration of the nacelle is represented as a 1 Axial vibration acceleration of the nacelle is denoted as a 2 (ii) a And (3) checking the wind direction value of the position where the fault unit is located, which is obtained in the step (2), through calculating the included angle theta between the absolute wind direction and the central axis of the cabin.
Preferably, step 4 specifically comprises the following steps:
step 4.1, when the absolute wind direction difference value is theta-theta m Is less than a set threshold M and has a duration t M Then, the unit k adopts the wind of the unit mRegression model to the distribution function, see equation (3); inputting wind direction data of a regression model of a wind direction distribution function of the unit m into a unit main control system of the unit k, so that the unit k can carry out fault-tolerant controlled power generation under the fault state of a wind direction indicator;
step 4.2, when the absolute wind direction difference value is theta-theta m L exceeds or equals a set threshold value M, and a duration t M Then, the unit k inquires the wind direction related matrix group again, and the unit number m with the maximum wind direction related coefficient with the unit k and the wind direction value theta are searched in the wind direction related matrix group m And returns to perform step 4.1 until theta-theta m And | is less than the set threshold value M.
The invention has the beneficial effects that: according to the invention, wind direction correlation matrixes are pre-constructed among the wind turbine points, and a wind direction distribution function relation among the wind turbine units is established. The yaw control of the wind turbines is mainly performed on a small time scale, and when anemoscope equipment equipped for some wind turbines in the wind power plant is in fault, wind direction signals of other wind turbines normally working by the anemoscope can be used as yaw wind control input of a fault fan according to a wind direction distribution function relation.
Drawings
FIG. 1 is a wind direction rose diagram of a wind tower and a machine position at a site of a wind farm in an embodiment;
FIG. 2 is a schematic diagram of a wind direction related matrix group of a fault unit of a wind direction instrument of a wind power plant in the embodiment;
FIG. 3 is a flow chart of obtaining wind direction through unit vibration and a wind direction correlation matrix in the embodiment;
FIG. 4 is a schematic diagram of a wind farm location distribution according to an embodiment;
fig. 5 is a schematic diagram of a wind direction correlation function of the 36# unit (k-36) and the 5# unit (m-5) in the second embodiment;
fig. 6 is a schematic diagram of the vibration acceleration of the 36# machine set (k-36) in the second embodiment.
Detailed Description
The present invention will be further described with reference to the following examples. The following examples are set forth merely to aid in the understanding of the invention. It should be noted that, for a person skilled in the art, several modifications can be made to the invention without departing from the principle of the invention, and these modifications and modifications also fall within the protection scope of the claims of the present invention.
The wind direction distribution characteristics in the wind power plant mainly depend on local atmospheric circulation, local geographical environment and arrangement influence of wind power generation sets. On a large time scale (time and day), the whole wind direction change of the wind field follows the atmospheric circulation change, but on a small time scale (second and minute), the local geographical environment of the wind field and the arrangement of the wind generating sets are decisive factors of the wind direction distribution of each fan point position in the wind field, a wind direction correlation matrix can be pre-constructed among each fan point position, and a wind direction distribution function relation among the wind generating sets is established. The yaw control of the wind turbines is mainly performed on a small time scale, so that when anemoscope equipment equipped for some wind turbines in a wind power plant fails, wind direction signals of other wind turbines normally working by using the anemoscope can be used as the yaw wind control input of a failed fan according to the wind direction distribution function relation.
Example one
The embodiment of the application provides a wind turbine yaw wind alignment method under a anemoscope fault mode, which comprises the following steps:
step 1.1, assuming that the field totally has N sets, and when the wind direction measured by the wind measuring tower is located in the ith wind direction sector in the equally divided wind direction sectors, the wind direction correlation coefficient of the set k and the rest N-1 sets in the field is as follows:
in the above formula, i is 1,2, …, and 16 represents 16 equal wind tower wind direction sectors; dir k (i) The time sequence of the wind direction of the unit k at the corresponding moment is shown; dir m (i) A wind direction time sequence of a corresponding time unit m (m is not equal to k); r [ Dir ] k (i),Dir m (i)]The correlation coefficient of the set k and the wind direction of any one set of the rest N-1 sets in the field area is shown; cov[Dir k (i),Dir m (i)]Is Dir k (i) And Dir m (i) Covariance of (2), var [ Dir k (i)]、var[Dir m (i)]Are respectively Dir k (i)、Dir m (i) The variance of (a); n is the total number of the units in the field;
step 1.2, the obtained wind direction correlation coefficients are sequentially arranged, and wind direction correlation column vectors of N-1 rows of the unit k are established; as shown in fig. 1, wind measurement data of a wind measurement tower and representative year wind resource data of all machine positions are sorted out according to wind measurement data of a wind measurement representative year of a wind farm site and combined with a CFD (computational fluid dynamics) simulation result. Dividing a wind power plant site and a representative annual wind direction rose diagram of all machine positions into 16 sectors, and clockwise dividing wind directions N, NNE, NE, ENE, E, ESE, SE, SSE, S, SSW, SW, WSW, W, WNW, NW and NNW; according to 16 wind directions of a wind field anemometry tower and a station wind direction rose diagram, for any unit, as shown in figure 2, 16 wind direction related matrix groups A with the size of (N-1) multiplied by 16 are established (N-1)×16×16 ：
A (N-1)×16×16 ＝(a rst ) (N-1)×16×16 ,a rst ∈R 3 (2)
Wherein, the matrix group element a rst The wind direction correlation coefficient R of the unit k and other units under different wind directions 3 Is a three-dimensional real vector space.
Finding one unit number with the largest downwind related coefficient from the wind direction related matrix group, and predicting the wind direction of the unit by using wind direction data corresponding to the unit number with the largest downwind related coefficient; the anemometer tower wind direction can be divided into 16 sectors, and the anemoscope fault unit also has 16 wind direction sectors. The anemometer fault unit wind direction and the anemometer wind direction are not completely consistent and the wind direction sectors of the anemometer fault unit and the anemometer wind direction are possibly inconsistent; the wind direction of the anemometer tower is divided into 16 sectors, so that 16 matrixes exist; in each matrix in fig. 2, a column vector is taken as a unit to represent a correlation coefficient between a certain fault unit and the rest N-1 units in the machine position sector, and the fault unit has 16 wind direction sectors, so that 16 column vectors exist;
step 2, after a fault occurs, establishing a distribution function regression model of the wind direction of the fault unit to obtain a wind direction value of the machine position where the fault unit k is located;
step 2.1, supposing that a certain unit anemoscope in the wind field has a fault, the unit cannot effectively face wind, and the unit number is marked as k; the unit k indirectly calculates an included angle theta between an absolute wind direction and a central axis of the engine room through an engine room vibration sensor, a unit m with a wind direction value closest to that of the unit m is searched in a wind direction related matrix group of the unit, and a wind direction instrument of the unit m normally works;
step 2.2, performing regression analysis on historical wind measurement data of the fault unit k and wind measurement data of a normal unit, and establishing a distribution function regression model of the wind direction of the fault unit; calculating the wind direction value of the machine position where the fault machine set k is located according to the wind direction value of the existing normal machine set through a distribution function regression model; the regression model of the distribution function of the wind direction of the fault unit is a mature one-dimensional linear regression model, and the mathematical expression of the regression model is as follows:
in the above-mentioned formula, the compound has the following structure,
the wind direction prediction value of a wind vane fault unit k is shown, X is a wind direction measured value of a unit m, and a and b are distribution function regression model parameters of the unit wind direction;
step 3, as shown in FIG. 3, checking the wind direction of the fault unit through a cabin vibration sensor;
3.1, under an ideal wind alignment condition, the included angle between the central axis of the wind wheel of the wind turbine generator and the wind direction is 0 degree, the power of the wind turbine generator is maximum, and the load of the wind turbine generator is minimum; when the anemoscope of the cabin of the wind turbine generator normally works, finishing the verification; when a anemoscope of a cabin of the wind turbine generator system fails, the wind turbine generator system loses the input condition of the wind direction, and under the working conditions, the wind direction cannot be effectively tracked by the wind turbine generator system after being changed; the absolute wind direction measured by the wind direction indicator forms an included angle theta with the central axis of the engine room, and the wind direction is decomposed intoThe central axis component and the plane component of the wind wheel of the engine room, the loss power P of the wind turbine s Comprises the following steps:
P s ＝P(1-cos 3 θ) (4)
in the above formula, P is the power of the wind turbine generator set with the included angle of the central axis of the wind wheel and the wind direction of 0 degree (right against the wind), and θ is the included angle of the absolute wind direction and the central axis of the engine room;
3.2, installing a vibration acceleration sensor in the engine room of the wind turbine generator, and accessing a vibration signal of the vibration acceleration sensor into a main control system of the wind turbine generator; the unit main control system monitors the vibration value of the engine room in real time and compares the vibration value with a set threshold value; when the vibration value of the nacelle exceeds a set threshold value, triggering the wind turbine generator to stop, and taking the stop as soft protection of the over-vibration of the structure of the wind turbine generator; due to the existence of the wind direction included angle theta, the wind acts on the engine room to generate an axial force and a tangential force; measuring the vibration numerical values of the axial force and the tangential force of the engine room through a vibration acceleration sensor, and acquiring the yaw error of the wind turbine generator:
in the above formula, the radial vibration acceleration of the nacelle is represented as a 1 Axial vibration acceleration of the nacelle is denoted as a 2 (ii) a Checking the wind direction value of the machine position where the fault unit is located obtained in the step 2 through calculating the included angle theta between the absolute wind direction and the central axis of the engine room;
step 4, comparing the wind direction included angle theta and the wind direction value theta of the unit k in real time m And carrying out fault-tolerant control on the fault unit. Step 4.1, when the absolute wind direction difference value is theta-theta m Is less than a set threshold M and has a duration t M Then, the unit k adopts a regression model of a wind direction distribution function of the unit m, which is shown in a formula (3); inputting wind direction data of a regression model of a wind direction distribution function of the unit m into a unit main control system of the unit k, so that the unit k can carry out fault-tolerant controlled power generation under the fault state of a wind direction indicator;
step 4.2, when the absolute wind direction difference value is equal to | theta-theta m I exceeds a set threshold M and lasts for a time t M After, machineThe group k inquires the wind direction related matrix group again, and the unit number and the wind direction value theta with the maximum wind direction related coefficient of the unit k are searched in the wind direction related matrix group m And returns to perform step 4.1 until theta-theta m And | is less than the set threshold value M.
Example two
On the basis of the first embodiment, the second embodiment of the present application provides an application example of the method in the first embodiment:
an existing wind power plant (as shown in fig. 4) has 74 units (N-74), wherein the 5 unit (m-5) and the 36 unit (k-36) have large wind direction correlations in wind direction sectors 0 ° to 135 ° and 225 ° to 330 ° (as shown in fig. 5). The anemoscope has faults and cannot provide effective wind direction input data for the unit, and the wind direction data of the 36# unit in the two sectors are indirectly calculated by using the wind measuring data of the 5# unit in the wind direction sectors of 0-135 degrees and 225-330 degrees. The wind direction regression model of the 36# unit is obtained by fitting in the figure 5
In the formula, the wind direction of the 36# unit is
The wind direction of the 5# unit is X. Now, suppose that the measured wind direction of the 5# unit at a certain time is 45 °, the wind direction of the 36# unit is 54.78 °, other wind direction calculation values falling into relevant wind direction sectors, and so on.
Fig. 6 shows the nacelle axial vibration acceleration and the radial vibration acceleration of the anemoscope fault unit # 36. The #36 unit calculates the ratio of the axial vibration acceleration and the radial vibration acceleration of the engine room to measure the wind direction of the machine position, compares the measured wind direction with the wind direction obtained by the unit regression model, and calculates the duration t when the difference between the measured wind direction and the wind direction is less than a predefined threshold M M Then, the #36 unit adopts a wind direction regression model
And predicting the wind direction of the airplane position.
Claims (4)
1. A wind turbine yaw wind aligning method under a failure mode of a anemoscope is characterized by comprising the following steps:
step 1, before a fault occurs, establishing a wind direction related matrix group between wind turbine generators;
step 1.1, assuming that the total number of N wind turbine generators in the field is N, and when the wind direction measured by the wind measuring tower is located in the ith wind direction sector in the equally divided wind direction sectors, the wind direction correlation coefficient of the wind turbine generator k and the wind direction correlation coefficients of the N-1 wind turbine generators in the field are as follows:
in the above formula, i is 1,2, …,16, and represents 16 wind measuring tower wind direction sectors which are equally divided; dir k (i) The wind direction time sequence of the wind turbine generator k at the corresponding moment is shown; dir m (i) The method comprises the steps of obtaining a wind direction time sequence of a wind turbine generator m at a corresponding moment, wherein m is not equal to k; r [ Dir ] k (i),Dir m (i)]The correlation coefficient of the wind direction of the wind turbine generator k and any one of the rest N-1 wind turbine generators in the field area is obtained; cov [ Dir ] k (i),Dir m (i)]Is Dir k (i) And Dir m (i) Covariance of (var [ Dir ] k (i)]、var[Dir m (i)]Are respectively Dir k (i)、Dir m (i) The variance of (a); n is the total number of the wind turbine generators in the field area;
step 1.2, arranging the obtained wind direction correlation coefficients in sequence, and establishing wind direction correlation column vectors of N-1 rows of the wind turbine generator k; according to 16 wind directions of a wind field anemometry tower and a station wind direction rose diagram, 16 wind direction related matrix groups A with the size of (N-1) multiplied by 16 are established for any wind turbine generator set (N-1)×16×16 ：
A (N-1)×16×16 ＝(a rst ) (N-1)×16×16 ,a rst ∈R 3 (2)
Wherein, the matrix group element a rst The correlation coefficient of the wind direction of the wind turbine generator k and the wind direction of the other wind turbine generators in different wind directions, R 3 Is a three-dimensional real number vector space; the wind turbine generator k searches a wind turbine generator set with the maximum wind direction correlation coefficient under one wind direction from the wind direction correlation matrix setNumber;
step 2, after a fault occurs, establishing a distribution function regression model of the wind direction of the fault wind turbine generator, so as to obtain a wind direction value of a machine position where the fault wind turbine generator is located;
step 2.1, supposing that a wind direction instrument of a certain wind turbine generator in a wind field has a fault, the wind turbine generator cannot effectively face the wind, and the serial number of the fault wind turbine generator is marked as k 1; the wind turbine generator k1 indirectly calculates an included angle theta between an absolute wind direction and a central axis of the engine room through an engine room vibration sensor, a wind turbine generator m1 closest to a wind direction value is searched in a wind direction related matrix group of the wind turbine generator, and a wind direction instrument m1 of the wind turbine generator works normally;
step 2.2, carrying out regression analysis on historical wind measurement data of the fault wind generation set k1 and wind measurement data of a normally working wind generation set m1, and establishing a distribution function regression model of the wind direction of the fault wind generation set k 1; calculating the wind direction value theta of the position where the fault wind turbine generator k1 is located according to the wind direction value of the existing normal wind turbine generator m1 through a distribution function regression model m ；
Step 3, checking the wind direction of the fault wind turbine generator through a cabin vibration sensor; comparing the included angle theta of the fault wind turbine generator k1 with the wind direction value theta in real time m And carrying out fault-tolerant control on the fault wind turbine generator.
2. The wind turbine yaw alignment method under the anemoscope fault mode as claimed in claim 1, wherein the distribution function regression model of the wind direction of the fault wind turbine in the step 2.2 is a one-dimensional linear regression model, and the mathematical expression of the regression model is as follows:
in the above-mentioned formula, the compound has the following structure,
is the wind direction predicted value of a wind turbine k1 with a anemoscope fault, X is the wind direction measured value of a wind turbine m1, and a and b are the distribution functions of the wind directions of the wind turbinesNumber regression model parameters.
3. The wind turbine yaw wind aligning method under the anemoscope fault mode according to claim 2, wherein the included angle θ in the step 3 is calculated in a manner that:
a 1 acceleration of radial vibration of the nacelle, a 2 Is the axial vibration acceleration of the engine room; the cabin vibration sensor is a vibration acceleration sensor; a vibration signal of the vibration acceleration sensor is connected to a master control system of the wind turbine generator; a main control system of the wind turbine generator monitors a vibration value of a cabin in real time; and when the anemoscope of the cabin of the wind turbine generator normally works, finishing the verification.
4. The wind turbine yaw wind aligning method under the anemoscope fault mode according to claim 3, wherein the step 3 specifically comprises the following steps:
step 3.1, when the absolute wind direction difference value is theta-theta m Is less than a set threshold M and has a duration t M Then, the fault wind turbine generator k1 adopts the distribution function regression model, and inputs wind direction data of the distribution function regression model into a master control system of the fault wind turbine generator k1, so that fault-tolerant controlled power generation is performed on the fault wind turbine generator k1 in a anemoscope fault state, and a wind direction value of the current fault wind turbine generator k1 is obtained through prediction;
step 3.2, when the absolute wind direction difference value is theta-theta m L exceeds or equals a set threshold M and lasts for a time t M Then, the wind direction related matrix group is inquired again by the fault wind turbine generator k1, the wind turbine generator number with the maximum wind direction related coefficient of the fault wind turbine generator k1 is searched in the wind direction related matrix group, and the wind direction value theta is obtained through recalculation m And returns to perform step 3.1 until theta-theta m And | is less than the set threshold value M.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110747742.0A CN113374634B (en) | 2021-07-01 | 2021-07-01 | Wind turbine yaw wind alignment method under anemoscope fault mode |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110747742.0A CN113374634B (en) | 2021-07-01 | 2021-07-01 | Wind turbine yaw wind alignment method under anemoscope fault mode |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113374634A CN113374634A (en) | 2021-09-10 |
| CN113374634B true CN113374634B (en) | 2022-09-06 |
Family
ID=77580628
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110747742.0A Active CN113374634B (en) | 2021-07-01 | 2021-07-01 | Wind turbine yaw wind alignment method under anemoscope fault mode |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113374634B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113653609B (en) * | 2021-09-17 | 2022-04-19 | 中节能风力发电股份有限公司 | Wind vane fault identification method, system, equipment and storage medium for wind turbine generator |
| CN115355142B (en) * | 2022-09-14 | 2023-03-28 | 中节能风力发电股份有限公司 | Wind vane fault detection method, system, equipment and medium for wind turbine generator |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105041570A (en) * | 2015-07-30 | 2015-11-11 | 北京天诚同创电气有限公司 | Yaw control method and device for wind turbine generator |
| CN109458305A (en) * | 2018-10-23 | 2019-03-12 | 北京金风科创风电设备有限公司 | Anemometer fault-tolerant control method and device and wind power plant controller |
| CN109458296A (en) * | 2018-12-31 | 2019-03-12 | 北京金风科创风电设备有限公司 | Wind vane fault-tolerant control method and device, controller and wind generating set |
| CN110210641A (en) * | 2018-02-28 | 2019-09-06 | 北京金风科创风电设备有限公司 | Wind direction prediction method and device for wind power plant |
| CN111680823A (en) * | 2020-05-12 | 2020-09-18 | 同济大学 | Wind direction information prediction method and system |
-
2021
- 2021-07-01 CN CN202110747742.0A patent/CN113374634B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105041570A (en) * | 2015-07-30 | 2015-11-11 | 北京天诚同创电气有限公司 | Yaw control method and device for wind turbine generator |
| CN110210641A (en) * | 2018-02-28 | 2019-09-06 | 北京金风科创风电设备有限公司 | Wind direction prediction method and device for wind power plant |
| CN109458305A (en) * | 2018-10-23 | 2019-03-12 | 北京金风科创风电设备有限公司 | Anemometer fault-tolerant control method and device and wind power plant controller |
| CN109458296A (en) * | 2018-12-31 | 2019-03-12 | 北京金风科创风电设备有限公司 | Wind vane fault-tolerant control method and device, controller and wind generating set |
| CN111680823A (en) * | 2020-05-12 | 2020-09-18 | 同济大学 | Wind direction information prediction method and system |
Non-Patent Citations (1)
| Title |
|---|
| 风电机组振动数据分析与偏航校正;高鑫;《热能动力工程》;20190630;第34卷(第06期);第172-177页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113374634A (en) | 2021-09-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113374634B (en) | Wind turbine yaw wind alignment method under anemoscope fault mode | |
| Oh et al. | A novel method and its field tests for monitoring and diagnosing blade health for wind turbines | |
| CN103279639B (en) | Receiving end Network Voltage Stability overall process Situation Assessment based on response and preventing control method | |
| Badihi et al. | Fault-tolerant individual pitch control for load mitigation in wind turbines with actuator faults | |
| CN105891546B (en) | The method of wind vane fault diagnosis in Wind turbines yaw system based on big data | |
| Li et al. | A novel wind speed-sensing methodology for wind turbines based on digital twin technology | |
| CN105909466B (en) | Wind generating set yaw error analysis method | |
| CN102165187B (en) | System for evaluating and controlling the efficiency of a wind turbine | |
| Pourmohammad et al. | Fault-tolerant control of wind turbine systems-a review | |
| US11313351B2 (en) | Methods and systems of advanced yaw control of a wind turbine | |
| CN112065668A (en) | Wind turbine generator state abnormity assessment method and system | |
| CN104074687A (en) | Load and performance testing method and device used for megawatt wind generation set | |
| Cosack | Fatigue load monitoring with standard wind turbine signals | |
| CN115549187A (en) | Stability evaluation method for wind power integration | |
| CN117212077A (en) | Wind wheel fault monitoring method, device and equipment of wind turbine and storage medium | |
| CN106246465B (en) | Wind turbine generator set wind speed and wind direction acquisition method and wind turbine generator set system | |
| CN111680823A (en) | Wind direction information prediction method and system | |
| Sun et al. | Wind turbine anomaly detection using normal behavior models based on SCADA data | |
| CN110765640B (en) | Method, system and equipment for calculating effective wind speed of rotor | |
| EP3526471A1 (en) | Determining loads on a wind turbine | |
| CN110273818A (en) | A kind of fan blade icing fault monitoring method based on the classification of principal axis transformation fineness degree | |
| Muto et al. | Model-based load estimation for wind turbine blade with Kalman filter | |
| CN105205492B (en) | A kind of fuzzy arest neighbors fusion diagnosis method of therrmodynamic system failure | |
| ES2905141T3 (en) | Method for monitoring wind farms | |
| CN114254834A (en) | Wind power plant wind speed prediction method based on machine learning |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |