CN116753116B - Yaw wake flow control method and device for fans in wind farm - Google Patents
Yaw wake flow control method and device for fans in wind farm Download PDFInfo
- Publication number
- CN116753116B CN116753116B CN202310853932.XA CN202310853932A CN116753116B CN 116753116 B CN116753116 B CN 116753116B CN 202310853932 A CN202310853932 A CN 202310853932A CN 116753116 B CN116753116 B CN 116753116B
- Authority
- CN
- China
- Prior art keywords
- wind
- optimal
- wake
- wind power
- speed
- 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 39
- 230000006698 induction Effects 0.000 claims abstract description 60
- 238000005457 optimization Methods 0.000 claims abstract description 46
- 230000000694 effects Effects 0.000 claims abstract description 15
- 230000006870 function Effects 0.000 claims description 31
- 239000002245 particle Substances 0.000 claims description 24
- 238000010248 power generation Methods 0.000 claims description 10
- 230000008878 coupling Effects 0.000 claims description 7
- 238000010168 coupling process Methods 0.000 claims description 7
- 238000005859 coupling reaction Methods 0.000 claims description 7
- 238000004590 computer program Methods 0.000 claims description 6
- 230000000153 supplemental effect Effects 0.000 claims description 5
- 230000006735 deficit Effects 0.000 claims description 2
- 238000004364 calculation method Methods 0.000 description 7
- 238000004422 calculation algorithm Methods 0.000 description 4
- 230000008901 benefit Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 230000008569 process Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000033228 biological regulation Effects 0.000 description 1
- 230000015556 catabolic process Effects 0.000 description 1
- PMGQWSIVQFOFOQ-YKVZVUFRSA-N clemastine fumarate Chemical compound OC(=O)\C=C\C(O)=O.CN1CCC[C@@H]1CCO[C@@](C)(C=1C=CC(Cl)=CC=1)C1=CC=CC=C1 PMGQWSIVQFOFOQ-YKVZVUFRSA-N 0.000 description 1
- 230000001276 controlling effect Effects 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 238000006731 degradation reaction Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000005684 electric field Effects 0.000 description 1
- 238000009429 electrical wiring Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000000047 product Substances 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
- 238000011144 upstream manufacturing Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
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
- F03D7/04—Automatic control; Regulation
- F03D7/042—Automatic control; Regulation by means of an electrical or electronic controller
- F03D7/048—Automatic control; Regulation by means of an electrical or electronic controller controlling wind farms
-
- 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
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)
- Wind Motors (AREA)
Abstract
The invention discloses a yaw wake flow control method and equipment of a fan in a wind power plant, wherein the method comprises the following steps: s1: inputting basic data of a wind farm; s2: building a wind power plant output optimization model based on axial induction factors and constraint conditions through the wind power plant basic data; s3: solving the wind power plant output optimization model to obtain an optimization result, wherein the optimization result comprises an optimal yaw angle and an optimal axial induction factor; s4: converting the optimal axial induction factor into an optimal adjustment parameter; s5: and setting the operation of each fan according to the optimal yaw angle and the optimal adjustment parameters so as to reduce the wake effect of the wind power plant and maximize the output of the wind power plant. The wind power plant wake effect can be reduced, so that the wind power plant output is maximized.
Description
Technical Field
The invention relates to the technical field of wind power plant operation and control, in particular to a yaw wake flow control method and device of a fan in a wind power plant.
Background
The wake effect is a phenomenon that after an upstream wind driven generator absorbs kinetic energy of natural wind and converts the kinetic energy into electric energy, the wind speed at the downstream of a rotor can be reduced, and the power generation capacity of a downstream unit is further affected. With the rapid development of wind power generation, particularly offshore wind power, more and more researches indicate that the loss caused by wake effect is great, for example, the loss of power generation caused by wake of a downstream generator in a Horns Rev wind farm of denmark is as high as 40%.
To overcome the serious problem of wind farm output degradation caused by wake effects, existing studies have generally optimized the microscopic layout of the wind farm to be built or optimized the operational characteristics of the built wind farm. For an established wind power plant, two operation optimization strategies are generally adopted, namely yaw angle optimization and axial induction factor optimization, and meanwhile, models with lower accuracy such as a Jensen model and the like are used in the operation process of optimizing the wind power plant in the existing research, so that the wake model has poor compliance with wake data of an actual wind power plant.
Disclosure of Invention
Technical problem
The invention aims to solve the technical problem of larger loss caused by wake effect between fans in a wind power plant, and provides a yaw wake control method and device for fans in the wind power plant.
Solution scheme
In order to achieve the above purpose, the present invention adopts the following technical scheme:
A yaw wake flow control method of a fan in a wind farm comprises the following steps: s1: inputting basic data of a wind farm; s2: building a wind power plant output optimization model based on axial induction factors and constraint conditions through the wind power plant basic data; s3: solving the wind power plant output optimization model to obtain an optimization result, wherein the optimization result comprises an optimal yaw angle and an optimal axial induction factor; s4: converting the optimal axial induction factor into an optimal adjustment parameter; s5: and setting the operation of each fan according to the optimal yaw angle and the optimal adjustment parameters so as to reduce the wake effect of the wind power plant and maximize the output of the wind power plant.
In some embodiments of the present invention, in step S2, the wind farm output optimization model is constructed, including the following steps: s21: establishing a single-fan Gaussian wake model, and obtaining wake wind speed loss; s22: establishing a multi-fan wake superposition coupling model according to the wake wind speed loss, and solving to obtain the incoming wind speed of each fan; s23: establishing an objective function of a wind power plant output model and the constraint condition according to the axial induction factor and the incoming wind speed; s24: and solving an objective function of the wind power plant output model based on the constraint condition to obtain the wind power plant output optimization model.
In some embodiments of the invention, in step S24, solving an objective function of the wind farm output model comprises the steps of: s241: arbitrarily selecting an initial solution as the latest particle population in a solution space of an objective function of the wind power plant output model; s242: calculating the wind power plant output of each particle in the latest particle population; s243: iteratively updating the historical optimal wind farm output and the historical optimal control variable of each particle; s244: judging whether the iteration times reach a set value, if not, updating the particle population and executing step S242; if the set value is reached, step S3 is executed.
In some embodiments of the invention, the objective function expression of the wind farm output model is as follows:
Wherein P wf represents the total output of the whole wind power plant, n is the total number of wind power generators, P wt(i) represents the output of the ith wind power generator, ρ represents the air density, d 0 represents the wind wheel diameter, alpha wt(i) represents the axial induction factor of the ith wind power generator, η represents the power generation efficiency of the wind power generator, V wt(i) represents the incoming wind speed of the ith wind power generator, and gamma wt(i) is the yaw angle of the ith wind power generator;
the expression of the single-fan Gaussian wake model is as follows:
Wherein Δv wt(k),wt(j) represents the wake wind speed deficit of the kth wind turbine at the jth wind turbine, V wt(k) represents the wake wind speed of the kth wind turbine, C T,wt(k) represents the thrust coefficient of the kth wind turbine, γ wt(k) represents the yaw angle of the kth wind turbine, σ y,wt(k),wt(j) represents the standard deviation of the wake wind speed of the kth wind turbine at the jth wind turbine in the y direction, σ z,wt(k),wt(j) represents the standard deviation of the wake wind speed of the kth wind turbine at the jth wind turbine in the z direction, y d,wt(k),wt(j) represents the wake core offset of the wake of the kth wind turbine at the jth wind turbine, x wt(j)、ywt(j) represents the coordinates of the kth wind turbine on the x-axis and the y-axis, respectively, x wt(k)、ywt(k) represents the coordinates of the kth wind turbine on the x-axis and the y-axis;
the expression of the multi-fan tail flow superposition coupling model is as follows:
Where V wt(j) represents the incoming wind speed of the j-th wind generator and V 0 represents the initial wind speed into the wind farm.
In some embodiments of the invention, the constraint comprises an axial induction factor supplemental constraint, the axial induction factor supplemental constraint expressed as follows:
Wherein η represents the power generation efficiency of the wind power generator, α wt(i) represents the axial induction factor of the ith wind power generator, and C' P,wt(i),max represents the maximum value of the actual power coefficient of the ith wind power generator.
In some embodiments of the invention, the constraints further include yaw angle upper and lower limit constraints, axial induction factor upper and lower limit constraints, and rated power constraints.
In some embodiments of the invention, the optimal adjustment parameters include an optimal pitch and an optimal rotational speed;
Converting the optimal axial induction factor to an optimal rotational speed comprises:
for each wind driven generator, calculating an actual power coefficient according to an optimal axial induction factor, assuming that the optimal pitch is zero, calculating a tip speed ratio by using a pitch and tip speed ratio function, calculating a rotating speed by using a rotating speed solving formula, and if the rotating speed is higher than a rated rotating speed, setting the optimal rotating speed as the rated rotating speed; otherwise, the optimal rotating speed is the calculated rotating speed; the expression of the rotation speed solving formula is as follows:
Wherein ω wt(i) represents the rotational speed of the ith wind turbine, V wt(i) represents the incoming wind speed of the ith wind turbine, γ wt(i) represents the yaw angle of the ith wind turbine, λ wt(i) represents the tip speed ratio of the ith wind turbine, and d 0 represents the rotor diameter;
the expression of the pitch and tip speed ratio function is as follows:
Wherein C p' (beta, lambda) represents an actual power coefficient, beta represents pitch, lambda represents tip speed ratio, lambda 0 has no actual meaning, and lambda 0 has the expression of
In some embodiments of the invention, converting the optimal axial induction factor to an optimal pitch comprises: for each wind driven generator, calculating a blade tip speed ratio according to the rotating speed through the rotating speed solving formula, and then calculating the optimal pitch by combining the pitch with a blade tip speed ratio function and an actual power coefficient solving formula;
The expression of the actual power coefficient solving formula is as follows:
Cp,wt(i)'＝4ηαwt(i)(1-αwt(i))2
Wherein C p,wt(i)' represents the actual power coefficient of the ith wind power generator, eta represents the power generation efficiency of the wind power generator, and alpha wt(i) represents the axial induction factor of the ith wind power generator.
In some embodiments of the invention, the wind farm basic data includes wind wheel diameter, wind turbine hub height, wind direction and speed, wind turbine rated power, wind turbine alignment position, incoming wind turbulence intensity, cut-in wind speed, cut-out wind speed, rated rotational speed, air density, power coefficient.
The invention also provides yaw wake control equipment of a fan in a wind power plant, which comprises a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, wherein the yaw wake control method of the fan in the wind power plant is realized when the processor executes the computer program.
Advantageous effects of the invention
The invention has the following beneficial effects: according to the yaw wake flow control method of the fan in the wind power plant, after basic data of the wind power plant are input, a wind power plant output optimization model is built based on the axial induction factors and constraint conditions, and the wind power plant output optimization model is solved to obtain an optimization result, wherein the optimization result comprises an optimal yaw angle and an optimal axial induction factor; converting the optimal axial induction factor into an optimal regulation parameter which can be regulated and controlled in practice; and setting the operation of each fan according to the optimal yaw angle and the optimal adjustment parameters, so that the wake effect of the wind power plant can be reduced, and the output of the wind power plant is maximized. Compared with the traditional method (namely, each fan is controlled by a single fan maximum power point and works in an optimal tip speed ratio state under the condition of zero yaw angle), the wind power plant output of the method is greatly improved, and the method has wide application scenes and can be used for different wind power plants and under the atmospheric condition, and the wind power plant basic data can be used only by modifying the wind power plant basic data.
Furthermore, in some embodiments, the following benefits are also provided: by improving wake wind speed loss, an improved single-fan Gaussian wake model is established, and compared with the existing method, the method has the advantages that the distribution fit with the actual wake speed is more accurate, and the finally obtained result is more accurate.
The axial induction factors are more comprehensively restrained, so that the actual physical conditions are met, and particularly the output condition of the wind driven generator at high wind speed is met; the uncontrollable virtual parameter axial induction factors are further converted into directly controllable wind driven generator parameters, namely the rotating speed and the pitch, through a certain strategy, so that the method has direct practical application significance.
In addition to taking axial induction factors into account, the control of the yaw angle is taken into account, so that the control of the wind driven generator is more comprehensive.
Other advantages of embodiments of the present invention are further described below.
Drawings
FIG. 1 is a flow chart of the steps of a method for yaw wake control of a fan in a wind farm in accordance with an embodiment of the present invention;
FIG. 2 is a graph of predicted wind speed versus actual wind speed using a single-fan Gaussian wake model in example 1;
FIG. 3 is a wind farm layout in example 1;
FIG. 4 is a bar graph of the wind generator output of example 1 compared to the wind generator output of the conventional method;
FIG. 5 is a graph of total electric field output optimization versus example 1;
FIG. 6 is a flow chart of the steps of a method for yaw wake control of a fan within a wind farm in accordance with example 1.
Detailed Description
The application will be further described with reference to the following drawings in conjunction with the preferred embodiments. It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other.
It should be noted that, in this embodiment, the terms of left, right, upper, lower, top, bottom, etc. are merely relative terms, or refer to the normal use state of the product, and should not be considered as limiting.
For an established wind farm, there are two operation optimization strategies, namely, yaw angle optimization and axial induction factor optimization, but few researches combine the two optimization strategies, and the optimization of the axial induction factor is not converted into actual wind farm control parameters, so that the practical significance is lacking.
Therefore, the following embodiment of the present invention provides a yaw wake control method of a wind turbine in a wind farm, referring to fig. 1, including the following steps: s1: inputting basic data of a wind farm; s2: building a wind power plant output optimization model based on axial induction factors and constraint conditions through the wind power plant basic data; s3: solving the wind power plant output optimization model to obtain an optimization result, wherein the optimization result comprises an optimal yaw angle and an optimal axial induction factor; s4: converting the optimal axial induction factor into an optimal adjustment parameter; s5: and setting the operation of each fan according to the optimal yaw angle and the optimal adjustment parameters so as to reduce the wake effect of the wind power plant and maximize the output of the wind power plant.
In a preferred embodiment, in step S2, the wind farm output optimization model is constructed, including the following steps: s21: establishing a single-fan Gaussian wake model, and obtaining wake wind speed loss; s22: establishing a multi-fan wake superposition coupling model according to the wake wind speed loss, and solving to obtain the incoming wind speed of each fan; s23: establishing an objective function of a wind power plant output model and the constraint condition according to the axial induction factor and the incoming wind speed; s24: and solving an objective function of the wind power plant output model based on the constraint condition to obtain the wind power plant output optimization model.
More preferably, in step S24, solving an objective function of the wind farm output model includes the steps of: s241: randomly selecting an initial solution latest particle population in a solution space of an objective function of the wind power plant output model; s242: calculating the wind power plant output of each particle in the latest particle population; s243: iteratively updating the historical optimal wind power plant output and the historical optimal control variable of each particle and the latest particle population; s244: judging whether the iteration times reach a set value, if not, updating the particle population and executing step S242; if the set value is reached, step S3 is executed.
The embodiment of the invention also provides yaw wake control equipment of the fan in the wind power plant, which comprises a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, wherein the yaw wake control method of the fan in the wind power plant is realized when the processor executes the computer program.
Example 1
According to the embodiment, an improved single-fan Gaussian wake model and a multi-fan wake superposition coupling model are firstly established, then a wind power plant output optimization model is provided based on the model, the axial induction factors provided in the wind power plant output optimization model supplement constraint, the constraint on the running condition under high wind speed is more practical, and then a particle swarm algorithm is utilized for optimization solving, so that the optimization result of the wind power plant output optimization model, namely the axial induction factors and yaw angles of each wind driven generator, is obtained, and the axial induction factors are further converted into the practically controllable pitch and rotation speed to be controlled, so that the wind power plant can reduce the wake effect, and the productivity is improved.
The method in the embodiment is applicable to wind speeds of 3m/s to 25m/s, and can be more suitable for realistic physical conditions than the existing case of optimizing the axial induction factors independently when the wind speed is higher than a certain value (such as 11m/s and related to specific situations of a wind power plant).
Referring to fig. 6, the method specifically includes the following steps:
S1: inputting wind farm basic data.
The wind farm basic data includes: wind wheel diameter, fan hub height, wind direction and speed, fan rated power, fan arrangement position, incoming wind turbulence intensity, cut-in wind speed, cut-out wind speed, rated rotation speed, air density and power coefficient. The arrangement positions of the fans comprise row spacing and column spacing of the fans, the wind direction is 0 degrees by positive south wind, and the positive direction of the wind direction is clockwise.
S2: and constructing a wind power plant output optimization model based on the axial induction factors and constraint conditions through the wind power plant basic data.
S21: an improved single-fan Gaussian wake model is established, relative to the incoming wind direction, the height of a hub of a certain fan at the forefront is taken as an original point, the incoming wind direction is taken as an x-axis, the direction of a horizontal direction and a vertical direction which are perpendicular to the incoming wind direction is taken as a y-axis, the vertical direction is taken as a z-axis, the coordinates (x wt(i),ywt(i),zwt(i)) of each fan i can be obtained according to the interval of each fan i, and when the influence of other fans is not considered, the wake wind speed loss delta V wt(k),wt(j) of a kth fan generator at a jth fan generator is:
1 (1)
Wherein x wt(j)、ywt(j) is the coordinate of the jth wind power generator on the x axis and the y axis, x wt(k)、ywt(k) is the coordinate of the kth wind power generator on the x axis and the y axis, gamma wt(k) is the yaw angle of the kth wind power generator, V wt(k) is the incoming wind speed of the kth wind power generator, d 0 is the diameter of the wind wheel, C T,wt(k) is the thrust coefficient of the kth wind power generator, sigma y,wt(k),wt(j) is the standard deviation of the wake wind speed of the kth wind power generator along the y direction at the jth wind power generator, sigma z,wt(k),wt(j) is the standard deviation of the wake wind speed of the kth wind power generator along the z direction at the jth wind power generator, and y d,wt(k),wt(j) is the wake core offset of the wake of the kth wind power generator at the jth wind power generator. The parameters described above can be calculated from equations 2-5.
2C T,wt(k)＝4αwt(k)(1-αwt(k))
3 Sigma y,wt(k),wt(j)＝kv,wt(k)(xwt(j)-xwt(k)-x0,wt(k))+σy0,wt(k)
4 Sigma z,wt(k),wt(j)＝kv,wt(k)(xwt(j)-xwt(k)-x0,wt(k))+σz0,wt(k)
Wherein, α wt(k) is an axial induction factor of the kth wind power generator, k v,wt(k) is a wake standard deviation diffusivity of the kth wind power generator (the calculation method is shown in formula 6), x 0,wt(k) is a near wake area length of the kth wind power generator (the calculation method is shown in formula 7), σ y0,wt(k) is a standard deviation limit parameter of wake horizontal wind speed of the kth wind power generator along y direction (the calculation method is shown in formula 8), σ z0,wt(k) is a standard deviation limit parameter of wake horizontal wind speed of the kth wind power generator along z direction (the calculation method is shown in formula 9), sgn is a sign function (the expression is shown in formula 10), θ wt(k) is a wake deviation angle of the kth wind power generator (the calculation method is shown in formula 11), and y d1,wt(k),wt(j) and y d2,wt(k),wt(j) are wake deviation parameters (the calculation methods are shown in formula 12 and formula 13).
6 K v,wt(k)＝0.11CT,wt(k) 1.07Ia 0.20
Wherein I a is the turbulence intensity of incoming wind.
The accuracy of the single-fan gaussian wake model is higher than that of the conventional Jensen wake model and the Frandsen wake model, the improved single-fan gaussian wake model provided by the embodiment has better effect than that of the conventional single-fan gaussian wake model, the function of wake wind speed loss is more accurate in accordance with the distribution of actual wake speed relative to the conventional function, the comparison effect at 8d 0 of the downstream of the fan at the yaw angle of 20 DEG is shown in fig. 2, the abscissa in fig. 2 is the ratio of the distance y (positive and negative only represent different directions on two sides) of the spanwise direction from the wind wheel core to the wind wheel diameter at the position, and the ordinate is the ratio of the wake wind speed v to the initial wind speed v 0.
S22: and (3) establishing a multi-fan tail flow superposition coupling model, wherein the incoming flow wind speed of each fan can be calculated by a formula 14.
Where n is the total number of wind generators, V wt(j) is the incoming wind speed of the jth wind generator, and V 0 is the initial wind speed flowing into the wind farm.
S23: and establishing an objective function (see formula 15) and constraint conditions (see formula 16-formula 19) of the wind power plant output model based on the axial induction factors.
Wherein, P wf is the total output of the whole wind power plant, P wt(i) is the output of the ith wind power generator, ρ is the air density (generally taking 1.225kg/m 3),αwt(i) as the axial induction factor of the ith wind power generator, η is the power generation efficiency of the wind power generator (generally taking 0.768), and γ wt(i) is the yaw angle of the ith wind power generator.
The constraint conditions include: yaw angle upper and lower limit constraints (see formula 16), axial induction factor upper and lower limit constraints (see formula 17), rated power constraints (see formula 18), axial induction factor supplemental constraints (see formula 19).
Wherein, gamma min is generally 25 degrees, gamma max is generally 25 degrees, alpha min is 0, alpha max is 1/3, P rate is the rated power of the fan, and C' P,wt(i),max is the maximum value of the actual power coefficient (the calculation method is shown in formula 20-formula 23).
Wherein, lambda wt(i),max is the maximum value of the blade tip speed ratio of the i-th wind driven generator, lambda 0,opt is the optimal blade tip speed ratio (such as 7.21) when the blade tip speed ratio is zero, lambda 0 has no practical meaning, is represented by characters set for simplicity of a formula 22, omega n is the rated rotation speed of the wind driven generator, wherein the formula 22 is a function of the blade tip speed ratio and the blade pitch, wherein C p' (beta, lambda) is an actual power coefficient which is a function of the actual power coefficient, the adjusting parameter blade pitch beta and the blade tip speed ratio lambda.
S24: and solving an objective function of the wind power plant output model based on the constraint condition to obtain the wind power plant output optimization model.
Because the objective function and constraint conditions of the wind farm output model based on the axial induction factors have highly nonlinear characteristics, the embodiment adopts the existing particle swarm algorithm to solve the objective function and constraint conditions. The solving steps are as follows:
S241: selecting any hundred initial solutions (namely axial induction factors and yaw angles of each wind driven generator) as initial particle populations in the solution space, wherein the initial particle populations are the current latest particle populations;
s242: calculating the fitness (namely wind farm output) of each latest particle;
S243: updating the historical optimal fitness (namely the optimal wind farm output) and the historical optimal position (namely the optimal control variable) of the individuals and the population, wherein the control variables comprise yaw angle and axial induction factors;
S244: judging whether the iteration times reach a set value, if not, updating the population based on the existing particle swarm algorithm theory, and returning to the step S242; if the set value is reached, the process goes to step S3.
S3: and solving the wind power plant output optimization model by a particle swarm algorithm to obtain an optimization result, wherein the optimization result comprises an optimal yaw angle and an optimal axial induction factor. And obtaining a yaw angle based on the wind power plant output optimization model, namely obtaining the optimal yaw angle of the adjusting parameter for controlling the wind power plant finally.
S4: the optimal axial induction factor is converted into optimal adjustment parameters, wherein the optimal adjustment parameters comprise optimal pitch and optimal rotating speed.
However, the optimal axial induction factor needs to be further converted into the actual optimal adjustment parameters, namely the optimal pitch and the optimal rotation speed. For each wind driven generator, the actual power coefficient is calculated according to the optimal axial induction factor by using an actual power coefficient solving formula 24, then the optimal pitch is assumed to be 0, the tip speed ratio is reversely solved by using a pitch and tip speed ratio function formula 22, and the rotating speed is calculated by using a rotating speed solving formula 25.
24C p,wt(i)'＝4ηαwt(i)(1-αwt(i))2
Wherein, formula 24 is the relation between the actual power coefficient and the axial induction factor, C p,wt(i)' is the actual power coefficient of the ith wind power generator, lambda wt(i) is the tip speed ratio of the ith wind power generator, omega wt(i) is the rotating speed of the ith wind power generator, and V wt(i) is the incoming wind speed of the ith wind power generator.
If the calculated rotating speed of the typhoon generator is higher than the rated rotating speed, the optimal rotating speed is the rated rotating speed, the tip speed ratio is calculated by utilizing a rotating speed solving formula 25 according to the rotating speed, and then the optimal pitch is reversely calculated by combining the pitch and tip speed ratio function formula 22 and the actual power coefficient solving formula 24; otherwise, the optimal rotating speed is the calculated rotating speed, and the optimal pitch is zero.
And outputting the optimal pitch, the optimal rotating speed and the optimal yaw angle of each fan in the wind power plant.
S5: and setting the pitch, the rotating speed and the yaw angle of each fan to the calculated optimal operating points, namely the optimal pitch, the optimal rotating speed and the optimal yaw angle according to the optimal yaw angle and the optimal adjusting parameters so as to reduce the wake effect of the wind power plant and further maximize the output of the wind power plant.
Experiment verification
Analysis was performed with a wind farm containing 12 NREL 5MW wind turbines, the electrical wiring diagram of which is shown in fig. 3, specific data of which are shown in table 1, and the effectiveness of the proposed method was verified by simulation.
TABLE 1
By using the method provided by the embodiment of the invention, the output of different wind driven generators is shown in fig. 4, the abscissa in the diagram is the number of fans, the ordinate is the power/megawatt (P/MW), the output iteration condition of the whole wind power plant is shown in fig. 5, the abscissa in the diagram is the iteration times, the ordinate is the power/megawatt (P/MW), the maximum total output of the wind power plant is 12.1057MW, and as can be seen from fig. 4 and 5, the total output of the traditional method (namely, each fan is controlled by a single fan maximum power point and works in an optimal tip speed ratio state and a zero yaw angle) is 11.1549MW, and the output of the whole wind power plant is improved by 8.52% compared with the output of the wind power plant by using the traditional method by using the method in the embodiment.
The above results show that, compared with the traditional control method, the method provided by the embodiment of the invention can improve the total output of the wind power plant by properly reducing the output of the front exhaust wind power generator, thereby illustrating the effectiveness of the method in the embodiment of the invention.
The foregoing is a further detailed description of the invention in connection with the preferred embodiments, and it is not intended that the invention be limited to the specific embodiments described. It will be apparent to those skilled in the art that several equivalent substitutions and obvious modifications can be made without departing from the spirit of the invention, and the same should be considered to be within the scope of the invention.
Claims (8)
1. A yaw wake flow control method of a fan in a wind power plant is characterized by comprising the following steps:
S1: inputting basic data of a wind farm;
S2: building a wind power plant output optimization model based on axial induction factors and constraint conditions through the wind power plant basic data;
S3: solving the wind power plant output optimization model to obtain an optimization result, wherein the optimization result comprises an optimal yaw angle and an optimal axial induction factor;
s4: converting the optimal axial induction factor into an optimal adjustment parameter;
S5: setting the operation of each fan according to the optimal yaw angle and the optimal adjustment parameters so as to reduce the wake effect of the wind power plant and maximize the output of the wind power plant;
In step S2, the wind farm output optimization model is constructed, including the following steps:
s21: establishing a single-fan Gaussian wake model, and obtaining wake wind speed loss;
S22: establishing a multi-fan wake superposition coupling model according to the wake wind speed loss, and solving to obtain the incoming wind speed of each fan;
s23: establishing an objective function of a wind power plant output model and the constraint condition according to the axial induction factor and the incoming wind speed;
S24: solving an objective function of the wind power plant output model based on the constraint condition to obtain the wind power plant output optimization model;
the objective function expression of the wind farm output model is as follows:
Wherein P wf represents the total output of the whole wind power plant, n is the total number of wind power generators, P wt(i) represents the output of the ith wind power generator, ρ represents the air density, d 0 represents the wind wheel diameter, alpha wt(i) represents the axial induction factor of the ith wind power generator, η represents the power generation efficiency of the wind power generator, V wt(i) represents the incoming wind speed of the ith wind power generator, and gamma wt(i) is the yaw angle of the ith wind power generator;
the expression of the single-fan Gaussian wake model is as follows:
Wherein Δv wt(k),wt(j) represents the wake wind speed deficit of the kth wind turbine at the jth wind turbine, V wt(k) represents the wake wind speed of the kth wind turbine, C T,wt(k) represents the thrust coefficient of the kth wind turbine, γ wt(k) represents the yaw angle of the kth wind turbine, σ y,wt(k),wt(j) represents the standard deviation of the wake wind speed of the kth wind turbine at the jth wind turbine in the y direction, σ z,wt(k),wt(j) represents the standard deviation of the wake wind speed of the kth wind turbine at the jth wind turbine in the z direction, y d,wt(k),wt(j) represents the wake core offset of the wake of the kth wind turbine at the jth wind turbine, x wt(j)、ywt(j) represents the coordinates of the kth wind turbine on the x-axis and the y-axis, respectively, x wt(k)、ywt(k) represents the coordinates of the kth wind turbine on the x-axis and the y-axis;
the expression of the multi-fan tail flow superposition coupling model is as follows:
Where V wt(j) represents the incoming wind speed of the j-th wind generator and V 0 represents the initial wind speed into the wind farm.
2. The yaw wake control method of a fan in a wind farm according to claim 1, wherein in step S24, solving an objective function of the wind farm output model comprises the steps of:
s241: arbitrarily selecting an initial solution as the latest particle population in a solution space of an objective function of the wind power plant output model;
S242: calculating the wind power plant output of each particle in the latest particle population;
s243: iteratively updating the historical optimal wind farm output and the historical optimal control variable of each latest particle population;
S244: judging whether the iteration times reach a set value, if not, updating the particle population and executing step S242; if the set value is reached, step S3 is executed.
3. The method of yaw wake control of a wind turbine in a wind farm according to claim 1, wherein the constraint condition comprises an axial induction factor supplemental constraint, the axial induction factor supplemental constraint expressed as follows:
Wherein η represents the power generation efficiency of the wind power generator, α wt(i) represents the axial induction factor of the ith wind power generator, and C' P,wt(i),max represents the maximum value of the actual power coefficient of the ith wind power generator.
4. A method of yaw wake control of a fan in a wind farm according to claim 3, wherein the constraints further comprise yaw angle upper and lower limit constraints, axial induction factor upper and lower limit constraints, rated power constraints.
5. The method of yaw wake control of a fan in a wind farm according to claim 1, wherein the optimal adjustment parameters include an optimal pitch and an optimal rotational speed;
Converting the optimal axial induction factor to an optimal rotational speed comprises:
for each wind driven generator, calculating an actual power coefficient according to an optimal axial induction factor, assuming that the optimal pitch is zero, calculating a tip speed ratio by using a pitch and tip speed ratio function, calculating a rotating speed by using a rotating speed solving formula, and if the rotating speed is higher than a rated rotating speed, setting the optimal rotating speed as the rated rotating speed; otherwise, the optimal rotating speed is the calculated rotating speed; the expression of the rotation speed solving formula is as follows:
Wherein ω wt(i) represents the rotational speed of the ith wind turbine, V wt(i) represents the incoming wind speed of the ith wind turbine, γ wt(i) represents the yaw angle of the ith wind turbine, λ wt(i) represents the tip speed ratio of the ith wind turbine, and d 0 represents the rotor diameter;
the expression of the pitch and tip speed ratio function is as follows:
Wherein C p' (beta, lambda) represents an actual power coefficient, beta represents pitch, lambda represents tip speed ratio, lambda 0 has no actual meaning, and lambda 0 has the expression of
6. The method for yaw wake control of a wind turbine in a wind farm according to claim 5, wherein,
Converting the optimal axial induction factor to an optimal pitch comprises: for each wind driven generator, calculating a blade tip speed ratio according to the rotating speed through the rotating speed solving formula, and then calculating the optimal pitch by combining the pitch with a blade tip speed ratio function and an actual power coefficient solving formula;
The expression of the actual power coefficient solving formula is as follows:
Cp,wt(i)'＝4ηαwt(i)(1-αwt(i))2
Wherein C p,wt(i)' represents the actual power coefficient of the ith wind power generator, eta represents the power generation efficiency of the wind power generator, and alpha wt(i) represents the axial induction factor of the ith wind power generator.
7. The method of claim 1, wherein the wind farm basic data comprises rotor diameter, wind wheel hub height, wind direction and speed, wind turbine rated power, wind turbine alignment position, incoming wind turbulence intensity, cut-in wind speed, cut-out wind speed, rated rotational speed, air density, power coefficient.
8. A yaw wake control device of a wind turbine in a wind farm, comprising a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, the processor implementing the yaw wake control method of a wind turbine in a wind farm according to any of claims 1 to 7 when the computer program is executed.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310853932.XA CN116753116B (en) | 2023-07-12 | 2023-07-12 | Yaw wake flow control method and device for fans in wind farm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310853932.XA CN116753116B (en) | 2023-07-12 | 2023-07-12 | Yaw wake flow control method and device for fans in wind farm |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116753116A CN116753116A (en) | 2023-09-15 |
| CN116753116B true CN116753116B (en) | 2024-08-20 |
Family
ID=87960930
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310853932.XA Active CN116753116B (en) | 2023-07-12 | 2023-07-12 | Yaw wake flow control method and device for fans in wind farm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116753116B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108708825A (en) * | 2018-05-15 | 2018-10-26 | 浙江运达风电股份有限公司 | A kind of wind power plant Yaw control method considering unit wake flow |
| CN112883652A (en) * | 2021-03-12 | 2021-06-01 | 重庆大学 | Design method of wind power plant active power off-line prediction controller considering dynamic wake flow |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9551322B2 (en) * | 2014-04-29 | 2017-01-24 | General Electric Company | Systems and methods for optimizing operation of a wind farm |
| CN114021382A (en) * | 2021-11-19 | 2022-02-08 | 华能新能源股份有限公司 | Wind power plant layout optimization method based on mathematical programming |
| CN114417692B (en) * | 2021-11-24 | 2022-12-13 | 中国电建集团华东勘测设计研究院有限公司 | Constrained wind power plant wake effect particle swarm optimization method |
-
2023
- 2023-07-12 CN CN202310853932.XA patent/CN116753116B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108708825A (en) * | 2018-05-15 | 2018-10-26 | 浙江运达风电股份有限公司 | A kind of wind power plant Yaw control method considering unit wake flow |
| CN112883652A (en) * | 2021-03-12 | 2021-06-01 | 重庆大学 | Design method of wind power plant active power off-line prediction controller considering dynamic wake flow |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116753116A (en) | 2023-09-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105354632B (en) | A kind of wind power optimization allocation strategy considering wake effect | |
| CN103244348A (en) | Power curve optimization method for variable-speed variable-pitch wind generating set | |
| CN109268205B (en) | Wind power plant optimization control method based on intelligent wind turbine | |
| Tian et al. | Active power dispatch method for a wind farm central controller considering wake effect | |
| CN112260325A (en) | Wind power plant active power control method considering fatigue load of wind driven generator | |
| CN107846030A (en) | A kind of double-fed fan motor field frequencies range control method for considering optimal rotor inertia kinetic energy | |
| Kim et al. | Power regulation of upstream wind turbines for power increase in a wind farm | |
| CN106777499A (en) | A kind of whole machine dynamic modelling method of dual-feed asynchronous wind power generator group | |
| Lu et al. | A dynamic wind turbine simulator of the wind turbine generator system | |
| Zhang et al. | Optimization of sizing parameters and multi-objective control of trailing edge flaps on a smart rotor | |
| CN105023099B (en) | A kind of wind-driven generator output appraisal procedure for considering turbulence intensity | |
| Xin et al. | Modified hill climbing method for active yaw control in wind turbine | |
| CN106026103A (en) | Probabilistic power flow calculation method with wind power integration | |
| CN116753116B (en) | Yaw wake flow control method and device for fans in wind farm | |
| Suryanarayanan et al. | On the dynamics of the pitch control loop in horizontal-axis large wind turbines | |
| Hao et al. | Aerodynamic and aeroacoustic optimization of wind turbine blade by a genetic algorithm | |
| CN114336665B (en) | Active power control method for improving inertia reserve level of wind power plant | |
| CN116658359A (en) | Real-time collaborative yaw control method for wind farm | |
| CN109611268A (en) | A kind of bilobed wheel horizontal-shaft wind turbine design optimization method | |
| Kim et al. | A Study on the Active Induction Control of Upstream Wind Turbines for total power increases | |
| Yusong et al. | The control strategy and simulation of the yaw system for MW rated wind turbine | |
| Zhang et al. | Fuzzy control used in variable speed wind turbine | |
| Chen et al. | Optimal placement of horizontal-and vertical-axis wind turbines in a wind farm for maximum power generation using a genetic algorithm | |
| Hosseinkhani et al. | Performance Prediction of a SANDIA 17-m Vertical Axis Wind Turbine Using Improved Double Multiple Streamtube | |
| Sakib | Aerodynamic design and control of vertical axis wind turbines |
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 |