Method and system for improving life cycle power of offshore wind plant

Abstract

The invention discloses an offshore wind power plant whole life cycle power increasing method and system, and the method comprises the steps: carrying out the analysis of a two-dimensional wake flow model of a wind turbine generator according to the wind speed information collected at an offshore wind power plant; aiming at an analysis result, constructing a fan power model and a fatigue load model; and performing optimization control on output power according to the fan power model and the fatigue load model. And the output power of the wind power plant is improved to a certain extent, so that the fatigue load distribution among the units is more uniform. The improvement of the output power of the wind power plant is emphasized at low wind speed, and the uniformization of the fatigue load between units is emphasized at high wind speed. And the unit maintenance frequency and the maintenance cost are reduced, so that the service life of the wind power plant unit is prolonged, and the output power in the whole life cycle of the offshore wind power plant is improved considerably.

Description

Technical Field

[0001] The present invention relates to the technical field of power optimization and improvement of offshore wind farms, and in particular to a method and system for improving power of offshore wind farms throughout their entire life cycle. Background Art

[0002] The penetration rate of renewable energy sources such as wind power and photovoltaics in the power grid continues to increase. Offshore wind power has become an important part of the energy transition due to its high energy efficiency and suitability for centralized development. However, the centralized layout and installation of offshore wind farms has led to an intensification of the wake effect between units, and a significant decrease in the wind speed of downstream wind turbines, which in turn leads to output power loss in wind farms and uneven fatigue loads between wind turbines. Effectively reducing the impact of wind farm wake effects and uneven fatigue loads, improving overall output power and extending the life of wind farms have become key issues in current wind power technology research.

[0003] Usually, wind farms adopt the maximum power tracking strategy to capture as much wind energy as possible and increase the output power of wind turbines. However, this strategy only considers the maximization of the output power of a single wind turbine, but does not consider the impact of the wake effect on the downstream wind turbines, resulting in the output power of the entire wind farm not being optimal. To solve this problem, some studies have optimized the axial induction factor of the wind turbine to limit the wind energy capture capability of the upstream wind turbine, thereby increasing the wind speed and power of the downstream wind turbines, and thus increasing the overall output power of the wind farm. Other studies have proposed a method for calculating the wake distribution under different wind directions and wind speeds to improve the calculation accuracy and the output power of the wind farm.

[0004] In addition, some studies have parameterized and topologically characterized the coupling relationship of wind farm units based on graph theory and wake effects, and proposed a two-step optimization method to achieve distributed power optimization. Other studies have coordinated wind turbines affected by wake in wind farms based on wind speed conditions, and responded to external dispatch commands through the rotational kinetic energy and pitch angle of the wind turbines. However, these studies mainly focus on the problem of optimizing wake between wind turbines to increase power, and pay less attention to the distribution of fatigue loads.

[0005] Long-term uneven distribution of fatigue loads can easily lead to premature damage of some wind turbines in offshore wind farms, frequent maintenance, serious impact on the life cycle of wind farms and economic losses. Due to the complex offshore environment and high maintenance costs, it is also urgent to consider the impact of fatigue load distribution between wind turbines on offshore wind farms. Some studies optimize fatigue distribution through variable pitch and yaw control, but increase the overall fatigue damage. Other studies have proposed an output power control method that takes fatigue loss into account, thereby improving the ability to capture wind energy. In addition, the influence of wake effect on power generation and fatigue load is optimized through the single-machine power control strategy of rotor speed change and variable pitch, but the yaw adjustment response is slow. Other studies have used parametric analysis of different layout forms of lattice towers to find the optimal floating foundation layout to improve the structural lateral stiffness and load capacity, but the blade load conditions are not considered.

[0006] In summary, existing research focuses on the optimization of output power when reducing wind turbine fatigue load or increasing load capacity. However, under the influence of wake, the balanced coupling degree between wind farm output power and fatigue load between wind turbines under different wind conditions is low. Therefore, power optimization considering the uniformity of fatigue load between wind turbines is particularly important. Although it weakens the increase of wind farm power to a certain extent, it can reduce maintenance frequency and cost, extend the operating life of wind turbines, and improve the power output of offshore wind farms throughout their life cycle. Summary of the invention

[0007] In view of the above-mentioned problems, the present invention is proposed.

[0008] Therefore, the technical problem solved by the present invention is: the optimization problem of the short life cycle of existing offshore wind farms.

[0009] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for increasing the power of an offshore wind farm throughout its life cycle, comprising:

[0010] Based on the wind speed information collected at the offshore wind farm, the two-dimensional wake model of the wind turbine is analyzed;

[0011] Based on the analysis results, the wind turbine power model and fatigue load model are constructed;

[0012] The output power is optimized and controlled according to the wind turbine power model and the fatigue load model.

[0013] As a preferred solution of the method for improving the power of an offshore wind farm throughout its life cycle described in the present invention, the two-dimensional wake model of the wind turbine set includes: a Jensen wake model and a 2D-Jensen wake model, the wake radii of the two wake models are the same; the areas of the two wake models under the velocity type are equal; the wind speed of the wake cross section of the 2DJensen wake model is distributed in a cosine function type;

[0014] In the Jensen wake model, the wind speed v at the position x behind the wind rotor is expressed as:

[0015]

[0016] Where a is the axial induction factor; r1 is the initial radius of the wake, which is equal to the impeller radius r; v0 is the incoming wind speed; k is a dimensionless constant, which is generally taken as 0.05 in offshore wind farms and represents the expansion speed of the wake area;

[0017] The 2D-Jensen wake model is improved from the Jensen model. The wind speed of the Jensen model wake cross section is consistent, and the wind speed distribution of the 2D-Jensen model cross section is expressed as:

[0018] v xr =(v0-v)cos[(π / r x )·r' x +π]+v(2)

[0019] Among them, r' x is the distance from the wake center to the wake section at the x position behind the impeller;

[0020] Mark the real-time position of each wind turbine in the XOY plane of the coordinate axis;

[0021] When the incoming wind direction changes, coordinate transformation is performed on all units;

[0022] [X1,Y1]=S[X,Y] (3)

[0023]

[0024] Among them, [X1, Y1] is the new coordinate of the wind turbine after transformation corresponding to the change of incoming wind direction; [X, Y] is the coordinate of the wind turbine corresponding to the wind direction of 0°; S is the transformation matrix; α is the angle of the wind direction rotating counterclockwise relative to the positive direction of the X-axis;

[0025] When the incoming wind direction changes, the windward order of the wind turbines is determined according to the actual incoming wind direction and the new coordinates of the turbines are obtained after the coordinate transformation.

[0026] As a preferred solution of the method for improving the power of an offshore wind farm throughout its life cycle described in the present invention, the wind turbine power model includes the wind turbine set absorbing wind energy through blades and converting it into electrical energy, which is expressed as:

[0027]

[0028] λ=ωr / v (6)

[0029] Among them, ρ is the ocean air density, v is the effective wind speed absorbed by the wind wheel, ω is the impeller speed, β is the blade pitch angle, and λ is the tip speed ratio; when wind energy is converted into electrical power, there is a wind energy utilization coefficient C P , which is a complex function of the tip speed ratio and the pitch angle. According to the Betz limit C Pmax =0.593; when the wind speed is determined, C P According to the matching relationship of historical analysis, it is converted into multiple (β, ω) combinations.

[0030] As a preferred solution of the method for increasing the power of an offshore wind farm throughout its life cycle described in the present invention, the fatigue load model includes taking the cyclic stress caused by normal operation and the cyclic stress caused by turbulent wind as the fatigue load borne by the unit. According to the fatigue linear accumulation theory, the interaction between the two can be ignored, so the comprehensive fatigue load level is expressed as:

[0031] S=S w +S tur (7)

[0032] Where S represents the total fatigue load level of the unit; S w Indicates the fatigue load level generated by working operation; S tur Indicates the turbulent wind fatigue load level;

[0033]

[0034]

[0035] Among them, S w (t), S tur (t) are the unit working load level and turbulent wind load level at time t, respectively; P(δ) is the power of the wind turbine at time δ; are the design values ​​of wind turbine operating load level and turbulent wind load level respectively; P rated is the rated power of the fan; T life is the design life; w is the maintenance factor within the life range; γ is the turbulence disturbance coefficient; S eff (δ) is the effective turbulence intensity of the wind turbine at time δ, which is different from the ambient turbulence S of the offshore wind farm itself. a and the turbulence intensity S increased by the wake effect w Related; C T,i is the thrust coefficient of the unit; A is the area swept by the wind rotor.

[0036] As a preferred solution of the method for improving the power of an offshore wind farm throughout its life cycle described in the present invention, the optimization control includes maximum power tracking and power optimization control, which not only pays attention to the optimal output power of the entire wind farm but also pays attention to the fatigue load distribution state between the units to make the load between the units uniform;

[0037] The optimization objectives of offshore wind farms are to maximize total output power and minimize the standard deviation of fatigue load distribution between wind turbines. The decision variables include the effective wind speed, speed, and pitch angle captured by each wind turbine. The objective function of output power is:

[0038]

[0039] Among them, F1 is the overall output power of the offshore wind farm, P ∑ is the sum of the power of N wind turbines in the offshore wind farm, N is the number of wind turbines in the wind farm, P i is the real-time output power of fan i, V i is the incoming wind speed of unit i, ω i is the speed of fan i, λ i is the tip speed ratio of fan i, β i is the pitch angle of wind turbine i;

[0040] According to the fatigue load level model, the comprehensive fatigue load level of fan i at time t is obtained as formula (14);

[0041]

[0042] because The proportional relationship between the two, The fatigue load coefficient of the fan fatigue load level deformation is formula (15);

[0043]

[0044] The objective function of the standard deviation of fatigue loads between units in an offshore wind farm is:

[0045]

[0046]

[0047] Among them, F2 is the minimum standard deviation of fatigue load distribution of offshore wind farm, and its value range is [0,1]; is the average fatigue load of all units; f c,i is the overall fatigue load value of unit i; C T,i (ω i r / V I ,β i )=4a i (1-a i ) is a function of the wind turbine pitch angle and tip speed ratio.

[0048] The fatigue loads borne by offshore wind turbines are relatively large. In order to more intuitively compare the standard deviation of fatigue loads in subsequent simulations, the reference value f under the corresponding wind condition is selected.B The fatigue load values ​​are homogenized.

[0049]

[0050] As a preferred solution of the method for increasing the power of an offshore wind farm throughout its life cycle described in the present invention, the optimization control further includes that the overall constraint conditions of the offshore wind farm are:

[0051]

[0052] Among them, P rated is the rated power of the wind turbine, ω min ,ω rated are the minimum speed and rated speed of the wind turbine respectively, β max is the maximum pitch angle of the wind turbine.

[0053] As a preferred solution of the method for improving the power of an offshore wind farm throughout its life cycle described in the present invention, the rotation speed and pitch angle of each unit are adjusted to balance the relationship between the total output power of the offshore wind farm and the fatigue load distribution between the units. In order to unify the dimensions, the overall collaborative optimization objective function is constructed as follows:

[0054] maxF=μ1F1 / F j -μ2F2(20)

[0055] Among them, μ1 and μ2 are the wind condition adaptive weight coefficients of the output power and load distribution between units in the offshore wind farm, μ1+μ2=1; F1 is the overall output power of the wind farm; F j is the selected power reference value; F2 is the standard deviation of the unit fatigue load.

[0056] A full life cycle power boosting system for an offshore wind farm using the method of the present invention is characterized in that:

[0057] The acquisition unit analyzes the two-dimensional wake model of the wind turbine according to the wind speed information collected at the offshore wind farm;

[0058] Model analysis unit, which constructs wind turbine power model and fatigue load model based on the analysis results;

[0059] According to the wind turbine power model and the fatigue load model, the output power is optimized and controlled.

[0060] A computer device comprises: a memory and a processor; the memory stores a computer program, wherein: the processor implements the steps of any one of the methods of the present invention when executing the computer program.

[0061] A computer-readable storage medium stores a computer program, wherein: when the computer program is executed by a processor, the steps of any one of the methods of the present invention are implemented.

[0062] Beneficial effects of the present invention: The method for improving the power of an offshore wind farm over its entire life cycle provided by the present invention improves the output power of the wind farm to a certain extent, and makes the fatigue load distribution between units more uniform. The use of an adaptive weighting method can achieve rapid optimization in different wind conditions, and well balance the relationship between the total output power and the load distribution between units, focusing on improving the output power of the wind farm at low wind speeds and on equalizing the fatigue load between units at high wind speeds. In addition to effectively improving the output power of offshore wind farms and making the load distribution between units uniform, this collaborative optimization strategy also reduces the maintenance frequency of units and reduces maintenance costs, thereby extending the life of wind farm units. The output power improvement over the entire life cycle of offshore wind farms is considerable. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0064] Figure 1 An overall flow chart of a method for increasing the power of an offshore wind farm throughout its life cycle provided by the first embodiment of the present invention;

[0065] Figure 2 A schematic diagram of a two-dimensional wake model of a single machine in a method for increasing the power of an offshore wind farm over its entire life cycle provided by the first embodiment of the present invention;

[0066] Figure 3 A coordinate transformation diagram when wind direction changes in a method for increasing power of an offshore wind farm throughout its life cycle provided by the first embodiment of the present invention;

[0067] Figure 4 The wind energy utilization coefficient C in a method for improving the power of an offshore wind farm throughout its life cycle provided by the first embodiment of the present invention is P The relationship diagram between β and λ;

[0068] Figure 5 A diagram showing the relationship between the weight coefficient μ1 and the wind speed in a method for increasing the power of an offshore wind farm over its entire life cycle provided by the first embodiment of the present invention;

[0069] Figure 6 A control process for different wind conditions in a wind farm in a method for increasing power over the entire life cycle of an offshore wind farm provided by the first embodiment of the present invention;

[0070] Figure 7 A wind turbine layout of an offshore wind farm base in a method for increasing the power of an offshore wind farm throughout its life cycle provided by a second embodiment of the present invention;

[0071] Figure 8 The effective wind speed of each unit in row 4 under three scenarios of wind condition a in a method for improving the power of an offshore wind farm throughout its life cycle provided by the second embodiment of the present invention;

[0072] Fig. 9 The output power of the fifth row of wind turbines under three scenarios of wind condition a in a method for improving the power of an offshore wind farm throughout its life cycle provided by the second embodiment of the present invention;

[0073] Fig.10 The homogenized load value of the wind turbine in row 5 of wind condition a in a method for improving the power of an offshore wind farm throughout its life cycle provided by the second embodiment of the present invention;

[0074] Fig.11 The wind speed distribution of each unit in the wind farm in wind condition B in a method for improving the power of an offshore wind farm throughout its life cycle provided by the second embodiment of the present invention;

[0075] Fig.12 The output power of each unit in wind condition b scenario 2 and 3 in a method for improving the power of an offshore wind farm throughout its life cycle provided by the second embodiment of the present invention;

[0076] Fig.13 A second embodiment of the present invention provides a method for improving the power of an offshore wind farm over its entire life cycle, in which (a) is the fatigue load distribution of the offshore wind farm wind turbines in scenario 2, and (b) is the fatigue load distribution of the offshore wind farm wind turbines in scenario 3. DETAILED DESCRIPTION

[0077] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0078] Example 1, reference Figure 1-6 , which is an embodiment of the present invention, provides a method for increasing the power of an offshore wind farm throughout its life cycle, comprising:

[0079] S1: Based on the wind speed information collected at the offshore wind farm, the two-dimensional wake model of the wind turbine is analyzed.

[0080] Specifically, the wake effect refers to the fact that the airflow generated by the wind turbine when it is working will diffuse backwards and affect other wind turbines behind it. In the Jensen wake model, the wind speed in the wake zone is only related to the distance x from the wind turbine, and the wind speed of the wake cross section is the same, so it is called a one-dimensional wake model. However, according to a large number of experimental observations, the wind speed of the real wind turbine wake cross section is Gaussian or parabolic distribution. Therefore, some studies have proposed a two-dimensional wake model based on the Jensen wake model that is closer to the actual wake distribution, namely the 2D-Jensen wake model. The 2D-Jensen wake model makes the following assumptions: the wake radii of the two wake models are the same; the areas of the two wake models under the velocity type are equal; the wind speed of the wake cross section of the 2DJensen wake model is distributed in a cosine function type, and the principle of its single-machine wake model is as follows: Figure 2 shown.

[0081] In the Jensen wake model, the wind speed v at position x behind the wind rotor is as follows:

[0082]

[0083] Among them, a is the axial induction factor; r1 is the initial radius of the wake, which is equal to the impeller radius r; v0 is the incoming wind speed; k is a dimensionless constant, which is generally taken as 0.05 in offshore wind farms, indicating the expansion speed of the wake area.

[0084] The 2D-Jensen wake model is an improvement of the Jensen model, but the wind speed in the wake cross section is quite different. The wind speed in the wake cross section of the Jensen model is consistent, while the wind speed distribution in the cross section of the 2D-Jensen model is as follows:

[0085] v xr =(v0-v)cos[(π / r x )·r' x +π]+v (2)

[0086] Where: r' x It is the distance from the wake section at the x position behind the impeller to the wake center.

[0087] Figure 3 For a 4×4 wind turbine matrix layout, the coordinate transformation of the wind turbines in the wind farm when the wind direction changes is as follows.

[0088] The change in wind direction causes the contact area between the wake and the downstream wind turbine rotor to change accordingly. The coordinate transformation steps are as follows:

[0089] (1) Mark the real-time position of each wind turbine in the XOY plane of the coordinate axis, and obtain the plane coordinates of the turbine when the wind direction is 0°: Figure 3 (a);

[0090] (2) When the incoming wind direction changes, the coordinate transformation of formula (3) is performed on all units;

[0091] [X1,Y1]=S[X,Y](3)

[0092]

[0093] Among them, [X1, Y1] is the new coordinate of the wind turbine after transformation corresponding to the change of incoming wind direction; [X, Y] is the coordinate of the wind turbine corresponding to the wind direction of 0°; S is the transformation matrix; α is the angle of the wind direction rotating counterclockwise relative to the positive direction of the X-axis, Figure 3 In (b), α=45°.

[0094] When the incoming wind direction changes, the windward order of the wind turbines is determined according to the actual incoming wind direction and the new coordinates of the turbines are obtained after the coordinate transformation.

[0095] S2: Based on the analysis results, construct the wind turbine power model and fatigue load model.

[0096] Wind turbines absorb natural wind energy through their blades and convert it into rotational mechanical energy to generate electricity to meet daily needs. The wind power calculation formula shows that the wind power absorbed by wind turbines in this process is closely related to environmental factors.

[0097]

[0098] λ=ωr / v (6)

[0099] Among them, ρ is the ocean air density, v is the effective wind speed absorbed by the wind wheel, ω is the impeller speed, β is the blade pitch angle, and λ is the tip speed ratio; when wind energy is converted into electrical power, there is a wind energy utilization coefficient C P , which is a complex function of the tip speed ratio and the pitch angle. According to the Betz limit C Pmax =0.593. Usually in the control system of wind turbines, the corresponding relationship between wind energy utilization coefficient and pitch angle and blade tip speed ratio is as follows: Figure 4 .

[0100] It can be concluded that the corresponding C P There can be multiple (β,λ) combinations of values. Different combinations correspond to different control methods of the fan. When the wind speed is determined, C P It is transformed into a variety of (β, ω) combinations.

[0101] The fatigue load of a wind turbine refers to the repeated loading and stress changes caused to the components of the unit during long-term operation. Offshore wind farms have a harsh environment and are difficult to operate and maintain. They are easily affected by factors such as typhoons and turbulence. At the same time, the increase in load caused by wake vortexes is mostly caused by wind turbulence mixing with ambient wind. Generally, the fatigue load borne by a wind turbine is proportional to its power output, but units with low output power may also be subjected to larger loads under the influence of wake vortexes. Therefore, the fatigue load borne by the unit includes the cyclic stress caused by normal operation and the cyclic stress caused by turbulent wind. According to the fatigue linear accumulation theory, the interaction between the two can be ignored, so the comprehensive fatigue load level is as follows:

[0102] S=S w +S tur (7)

[0103] Where: S represents the total fatigue load level of the unit; S w Indicates the fatigue load level generated by working operation; S tur Represents the turbulent wind fatigue load level.

[0104] The load level generated by the normal operation of wind turbines in offshore wind farms is proportional to their output power and has a cumulative effect. Before the wind turbines are put into operation, the design life value and fatigue load level design value will be obtained through simulation and other means; turbulent wind fatigue is caused by the alternating loads generated by the wind. In engineering, the fatigue load level is obtained by the approximate correlation between turbulent wind intensity and fatigue load level.

[0105]

[0106] Where: S w (t), S tur (t) are the unit working load level and turbulent wind load level at time t, respectively; P(δ) is the power of the wind turbine at time δ; They are the design values ​​of the fan operating load level and turbulent wind load level, and there is a certain proportional relationship between the two during design. P rated is the rated power of the fan; T life is the design life, which is generally 20 years; w is the maintenance coefficient within the life range; γ is the turbulence disturbance coefficient, which is related to the wind farm layout, unit materials and environment; S eff (δ) is the effective turbulence intensity of the wind turbine at time δ, which is different from the ambient turbulence S of the offshore wind farm itself. a and the turbulence intensity S increased by the wake effect w Related; C T,i is the thrust coefficient of the unit; A is the area swept by the wind rotor.

[0107] Since traditional fatigue load analysis is rather cumbersome and not applicable to wind farms, and since optimization of fatigue distribution within the farm is sufficient for comprehensive fatigue of wind turbines, this model is used in subsequent optimization control.

[0108] S3: Optimizing and controlling the output power according to the wind turbine power model and the fatigue load model.

[0109] It should be noted that the centralized layout and installation of offshore wind farms exacerbate the wake effect between wind turbines, and cause uneven distribution of fatigue loads between wind turbines in the field, which seriously affects the output power of downstream wind turbines and causes obvious differences in fatigue damage of upstream and downstream wind turbines. It is necessary to design a reasonable power coordination optimization strategy to maximize the output power of offshore wind farms during the entire life cycle while taking into account the uniformity of fatigue loads between wind turbines.

[0110] Wind turbines adopt different operating modes in different offshore environments such as wind speed, wind direction, and turbulence, mainly including maximum power tracking and power optimization control, to better output active power and improve the output of the overall wind farm. In the coordinated optimization control of offshore wind farms, we must pay attention to both the optimal output power of the overall wind farm and the fatigue load distribution state between units to make the load between units uniform and improve the life cycle of offshore wind farms. Therefore, how to balance the relationship between the two in the optimization process is particularly important.

[0111] The optimization objectives of offshore wind farms are to maximize the total output power and minimize the standard deviation of fatigue load distribution between wind turbines. The decision variables include the effective wind speed, speed, pitch angle, etc. captured by each wind turbine. The objective function of its output power is:

[0112]

[0113] Where: F1 is the overall output power of the offshore wind farm, P ∑ is the sum of the power of N wind turbines in the offshore wind farm, N is the number of wind turbines in the wind farm, P i is the real-time output power of fan i, V i is the incoming wind speed of unit i, ω i is the speed of fan i, λ i is the tip speed ratio of fan i, β i is the pitch angle of wind turbine i.

[0114] According to the fatigue load level model, the comprehensive fatigue load level of fan i at time t is obtained as formula (14).

[0115]

[0116] because The fatigue load level of the fan can be transformed into the fatigue load coefficient as follows: formula (15).

[0117]

[0118] The objective function of the standard deviation of fatigue load between units in an offshore wind farm is:

[0119]

[0120] Where: F2 is the minimum standard deviation of fatigue load distribution of offshore wind farm, and its value range is [0,1]; is the average fatigue load of all units; f c,i is the overall fatigue load value of unit i; C T,i (ω i r / V I ,β i )=4a i (1-a i ) is a function of the wind turbine pitch angle and blade tip speed ratio, and is related to C P similar.

[0121] The fatigue loads borne by offshore wind turbines are relatively large. In order to more intuitively compare the standard deviation of fatigue loads in subsequent simulations, the reference value f under the corresponding wind condition is selected. B The fatigue load value is homogenized, and the formula is:

[0122]

[0123] The overall constraints of offshore wind farms are:

[0124]

[0125] By adjusting the speed and pitch angle of each unit to balance the relationship between the total output power of the offshore wind farm and the fatigue load distribution between units, in order to unify the dimensions, F1, that is, the overall output power of the wind farm, is divided by the selected power reference value F j ; F2 is the standard deviation of the fatigue load of the unit, which is dimensionless. Therefore, the overall collaborative optimization objective function is constructed as follows:

[0126] maxF=μ1F1 / F j -μ2F2(20)

[0127] Where: μ1 and μ2 are the wind condition adaptive weight coefficients of the output power and load distribution between units in the offshore wind farm, where μ1+μ2=1. μ1 is introduced by fitting to satisfy the S-type function. The relationship between the weight coefficient μ1 and the wind speed is shown in Figure 5 , and then optimize the target through the following wind conditions, see the flow chart Figure 6 .

[0128] Two extreme cases are analyzed: ① If the uniformity of fatigue load between wind turbines is not considered and only the maximum output power of the wind farm is pursued, then μ1=1, μ2=0, which will inevitably lead to an increase in the difference in fatigue damage of upstream and downstream wind turbines in the farm; ② If the uniformity of fatigue load between units is excessively pursued, the output of the units will be weak, resulting in a large amount of wind abandonment, then μ1=0, μ2=1.

[0129] When V in ≤V i ≤V rated When the wind speed is low, the load on the unit is not too serious. The wind turbine unit absorbs as much wind energy as possible to output more power. At this time, it is hoped that μ1>0.5. After many tests, this paper takes μ1=0.7,μ2=0.3 in wind condition a. The overall objective function is:

[0130] maxF=0.7F1 / F j -0.3F2(21)

[0131] When V rated <V i ≤V out When the wind turbines are running at , they hope to reduce the standard deviation of fatigue load between units as much as possible by adjusting the pitch angle. As the wind speed continues to increase, more and more units reach the rated power or close to the rated power. Similarly, the weight coefficients corresponding to wind condition b are selected for multiple tests: μ1=0.4, μ2=0.6. The overall objective function is:

[0132] maxF=0.4F1 / F j -0.6F2(22)

[0133] When V i >V out When an overload occurs, usually in extreme typhoon conditions, the wind turbine starts to shut down to protect itself from damage.

[0134] On the other hand, this embodiment also provides a full life cycle power enhancement system for an offshore wind farm, which includes:

[0135] The acquisition unit analyzes the two-dimensional wake model of the wind turbine according to the wind speed information collected at the offshore wind farm.

[0136] The model analysis unit constructs the wind turbine power model and fatigue load model based on the analysis results.

[0137] The output power is optimized and controlled according to the wind turbine power model and the fatigue load model.

[0138] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0139] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0140] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0141] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0142] Example 2, reference Figure 7-13 , which is an embodiment of the present invention, provides a method for improving the power of an offshore wind farm throughout its life cycle. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0143] The present invention constructs a test case based on the above model and an offshore wind farm in a certain area. Figure 7 As shown in Table 1, there are 62 wind turbines in the offshore wind farm, of which 34 wind turbines with a rated capacity of 3.0MW are included in the first phase of the project, and 27 wind turbines with a rated capacity of 3.6MW and 1 wind turbine with a rated capacity of 5MW are included in the second phase of the project. The specific data are shown in Table 1. The effectiveness of the coordinated optimization control strategy of output power and fatigue load distribution proposed in this paper is verified by conducting simulation analysis on this offshore wind farm under two wind conditions.

[0144] Table 1 Data of an offshore wind farm

[0145]

[0146] Two wind conditions are selected in the simulation: a∈A wind speed 9m / s, wind direction 0°; b∈B wind speed 14m / s, wind direction 45°, and the following three scenarios are set for comparison: Scenario 1: power output without considering wake effect and fatigue load; Scenario 2: power output considering wake effect but not considering fatigue load; Scenario 3: power output considering both wake effect and fatigue load.

[0147] Two wind conditions were selected: wind speed 9m / s, wind direction 0° and wind speed 14m / s, wind direction 45°, to simulate and verify the offshore wind farm.

[0148] When the wind direction is 0°, the incoming wind is basically parallel to the rows (X axis) of the offshore wind farm. Therefore, the wake generated by the wind turbine will only affect the rows (Y axis) and the rows. There will be no wake superposition between rows. The upstream wind turbine only affects the downstream wind turbines in the same row but different rows. When the wind speed is 9m / s and the wind direction is 0°, the first column is defined as the upstream wind turbine. The wind speed captured by the wind turbine is equal to the incoming wind speed at this time. The 2nd, 3rd, 14th, and 15th columns are all downstream wind turbines. Under this wind condition, since the wind turbines in each row have the same properties when capturing wind energy, this paper selects the 5th row as the research object. In order to absorb wind energy to the maximum extent, the pitch angle of the wind turbines in the offshore wind farm is set to 0°. At this time, the speed adjustment between the units can weaken the fatigue load effect while ensuring power increase.

[0149] Figure 8 The effective wind speed of 14 wind turbines in the fifth row of the offshore wind farm under the three scenarios is 9 m / s when the wind speed is 9 m / s and the wind direction is 0°. Since the first row of wind turbines is not affected by the wake, its effective wind speed is the incoming wind speed of the offshore wind farm under the three scenarios, which is 9 m / s. However, scenario 1 does not optimize the wake effect generated by the upstream wind turbines, and the effective wind speed of the downstream units is most seriously attenuated. By the last row of wind turbines, its captured wind speed has dropped to 3.41 m / s. Scenarios 2 and 3 coordinate the speed control between the upstream and downstream wind turbines. The speed control greatly weakens the wake effect between the rows of wind turbines and increases the effective wind speed of each downstream wind turbine, thereby improving the overall output power of the offshore wind farm.

[0150] Fig. 9 , Fig.10 The output power and uniform fatigue load of the 14 wind turbines in the fifth row under three scenarios under wind condition a are compared. The fatigue load values ​​of the wind turbines in the three scenarios under wind condition a are calculated by formula (17), and the benchmark values ​​under the corresponding scenarios are selected by formula (18) for uniform processing. The 32# wind turbine is taken as an example, as shown in Table 2. Due to the influence of the wake, the output power of the wind turbines basically shows a decreasing trend from column to column. Scenario 1 does not consider the wake and load, so the output power of the 33# wind turbine in the second column is 15.61% lower than that of the 32# wind turbine in the first column, which is the most obvious. The sum of the output power of the wind turbines in the fifth row is 17.51MW. Scenario 2 starts from optimizing the wake effect and uses PSO to adjust the speed of each wind turbine. While slightly sacrificing the wind energy utilization rate of the upstream wind turbine, the overall output power of the downstream wind turbine is increased. The results show that the output power is increased by 7.94% compared with scenario 1. On the one hand, Scenario 3 improves the total power output by optimizing the wake, and on the other hand, considering the fatigue load distribution between wind turbines, although the output power is lower than that of Scenario 2, the fatigue load distribution between wind turbines is the most uniform, which can reduce the investment in unit maintenance and increase the life of the wind farm. In terms of the entire life cycle, the output power of Scenario 3 will reach the maximum.

[0151] Table 2 Homogenization of loads on 32# wind turbine in three scenarios of wind condition a

[0152] scene Fatigue load value / Nm Load reference value / Nm Homogenized load value 1 2.543×107 2.75×107 0.924 2 2.282×107 2.50×107 0.912 3 1.972×107 2.35×107 0.839

[0153] From the data in Table 3, we can see that under the optimized control of scenarios 2 and 3, the total output power of the offshore wind farm has been improved to a certain extent compared with scenario 1. The smaller the standard deviation of fatigue load, the closer the load level of each unit in the farm is and the more uniform the distribution is. Therefore, maintenance can be arranged in the same time period. When considering both power and load, scenario 3 is undoubtedly the best strategy.

[0154] Table 3 Offshore wind farm power and fatigue load distribution in three scenarios of wind condition a

[0155] scene Power output / MW Fatigue load standard deviation 1 84.27 (base value) 0.143 2 89.60(6.32％) 0.139 3 87.01(3.25％) 0.087

[0156] When the wind direction is 45°, the wake coupling relationship of the offshore wind farm becomes oblique coupling, and the distance between the upstream and downstream wind turbines becomes the original times, the number of upstream wind turbines has increased significantly, and their effective wind speed has exceeded the rated wind speed, the speed has also reached the rated value, and the output power is the rated power of the wind turbine. Taking the 62 wind turbines in the entire wind farm as the research object, the power output is improved by optimizing the pitch angle and speed of the unit and the fatigue load distribution between the units is made as uniform as possible.

[0157] Fig.11 The effective wind speed distribution captured by the wind turbines under the three scenarios of wind condition b. Under this wind condition, most of the wind turbines in the offshore wind farm are already above the rated wind speed. The wake optimization control of scenario 2 significantly increases the effective wind speed captured by the downstream wind turbines by reducing the wind energy captured by the upstream wind turbines. Under this wind condition, it is urgent to consider the sharp fatigue load impact of natural wind on wind turbines. Scenario 3 optimizes the fatigue load distribution between units in the wind farm, thereby reducing the maintenance frequency of the wind farm and repairing the wind turbines at the same time as much as possible, thereby reducing the investment cost and extending the overall life of the offshore wind farm.

[0158] In wind condition b, the distribution of fatigue loads between wind turbines should be focused on, so only the comparison between scenarios 2 and 3 is performed. Fig.11 It can be seen that scenario 3 reduces the wind energy utilization coefficient by optimizing the pitch angle of some units. Its output power decreases slightly compared with scenario 2, but the upstream wind turbine is basically maintained near the rated power. The other two scenarios both optimize wake control to reduce the wake effect between the upstream and downstream wind turbines, slightly reducing the output power of the upstream wind turbine and increasing the power of the downstream wind turbine.

[0159] After optimization, the total output power of the entire offshore wind farm is 179.62MW in scenario 3 and 185.25MW in scenario 2, which is about 3.01% lower than scenario 2. This is because scenario 3 takes more consideration of the optimization of fatigue loads. Although a small amount of power output is sacrificed, the fatigue load distribution of the entire offshore wind farm is more uniform, which can extend the operating life of the wind turbines in the offshore wind farm to a great extent, and the output power over its entire life cycle will be significantly improved.

[0160] Fig.12 Figure 2 shows the homogenized fatigue load contour distribution of all wind turbines in the offshore wind farm in scenarios 2 and 3. The homogenization process refers to wind condition a. Fig.13 (a) shows that the fatigue load uniformity between fans is not considered, resulting in large differences in fatigue loads between upstream and downstream fans and uneven distribution. The red area indicates that the fan is subjected to the most severe fatigue load, and the lighter the color, the smaller the fatigue load value. The consequence of this scenario is that after a period of operation, the damage between fans is different, which in turn affects the life of each unit, causing differences in the maintenance time of the units, and correspondingly increasing the maintenance cost, which is disadvantageous from an overall perspective. Fig.13 (b) shows that by optimizing the fatigue load between wind turbines, the overall fatigue load distribution of the wind farm is relatively uniform. Therefore, the offshore wind turbines can be scheduled for maintenance in the same time period, reducing maintenance costs while extending the life of some units. The standard deviation of the homogenized load in scenario 2 is 0.1003, and the standard deviation of the homogenized load in scenario 3 is 0.0417. The smaller the standard deviation, the more uniform the fatigue load distribution between wind turbines. Therefore, it is necessary to consider the uniformity of fatigue load between wind turbines in the power optimization of offshore wind farms. The comparison of the output power and fatigue load standard deviation of offshore wind farms in scenarios 2 and 3 under wind condition b is shown in Table 4.

[0161] Table 4 Wind farm power and load distribution in two scenarios of wind condition b

[0162] scene Power output / MW Fatigue load standard deviation 2 185.25 (base value) 0.0711 3 179.62(-3.01％) 0.0416

[0163] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for increasing the power of an offshore wind farm over its entire life cycle, characterized in that: include: Based on the wind speed information collected at the offshore wind farm, the two-dimensional wake model of the wind turbine is analyzed; Based on the analysis results, the wind turbine power model and fatigue load model are constructed; The output power is optimized and controlled according to the wind turbine power model and the fatigue load model.

2. The method for increasing the power of an offshore wind farm throughout its life cycle as claimed in claim 1, characterized in that: The two-dimensional wake model of the wind turbine generator set includes: a Jensen wake model and a 2D-Jensen wake model, the wake radii of the two wake models are the same; the areas of the two wake models under the velocity type are equal; the wind speed of the wake cross section of the 2D Jensen wake model is distributed in a cosine function type; In the Jensen wake model, the wind speed v at the position x behind the wind rotor is expressed as: Where a is the axial induction factor; r1 is the initial radius of the wake, which is equal to the impeller radius r; v0 is the incoming wind speed; k is a dimensionless constant, which is generally taken as 0.05 in offshore wind farms and represents the expansion speed of the wake area; The 2D-Jensen wake model is improved from the Jensen model. The wind speed of the Jensen model wake cross section is consistent, and the cross-sectional wind speed distribution of the 2D-Jensen model is expressed as: v xr =(v0-v)cos[(π / r x )·r x '+π]+v (2) Among them, r x ' is the distance from the wake center to the wake section at the x position behind the impeller; Mark the position of each wind turbine in the XOY plane; When the incoming wind direction changes, coordinate transformation is performed on all units; [X1,Y1]=S[X,Y] (3) Among them, [X1, Y1] is the new coordinate of the wind turbine after transformation corresponding to the change of incoming wind direction; [X, Y] is the coordinate of the wind turbine corresponding to the wind direction of 0°; S is the transformation matrix; α is the angle of the wind direction rotating counterclockwise relative to the positive direction of the X-axis; When the incoming wind direction changes, the windward order of the wind turbines is determined according to the actual incoming wind direction and the new coordinates of the turbines are obtained after the coordinate transformation.

3. The method for increasing the power of an offshore wind farm throughout its life cycle as claimed in claim 2, characterized in that: The wind turbine power model includes that the wind turbine absorbs wind energy through the blades and converts it into electrical energy, which is expressed as: λ=ωr / v(6) Among them, ρ is the ocean air density, v is the effective wind speed absorbed by the wind wheel, ω is the impeller speed, β is the blade pitch angle, and λ is the tip speed ratio; when wind energy is converted into electrical power, there is a wind energy utilization coefficient C P , is a complex function of the tip speed ratio and pitch angle. According to the Betz limit C Pmax =0.593, when the wind speed is determined, C P According to the matching relationship of historical analysis, it is transformed into multiple (β, ω) combinations.

4. The method for increasing the power of an offshore wind farm throughout its life cycle as claimed in claim 3, characterized in that: The fatigue load model includes taking the cyclic stress caused by normal operation and the cyclic stress caused by turbulent wind as the fatigue load borne by the unit. According to the fatigue linear accumulation theory, the interaction between the two can be ignored, so the comprehensive fatigue load level is expressed as: S=S w +S tur (7) Where S represents the total fatigue load level of the unit; S w Indicates the fatigue load level generated by working operation; S tur Represents the turbulent wind fatigue load level. Among them, S w (t), S tur (t) are the unit working load level and turbulent wind load level at time t, respectively; P(δ) is the power of the wind turbine at time δ; are the design values ​​of wind turbine operating load level and turbulent wind load level respectively; P rated is the rated power of the fan; T life is the design life; w is the maintenance factor within the life range; γ is the turbulence disturbance coefficient; S eff (δ) is the effective turbulence intensity of the wind turbine at time δ, which is different from the ambient turbulence S of the offshore wind farm itself. a and the turbulence intensity S increased by the wake effect w Related; C T,i is the thrust coefficient of the unit; A is the area swept by the wind rotor.

5. The method for increasing the power of an offshore wind farm throughout its life cycle as claimed in claim 4, characterized in that: The optimization control includes maximum power tracking and power optimization control, which not only focuses on the optimal output power of the entire wind farm, but also pays attention to the fatigue load distribution state between the units to make the load between the units uniform; The optimization objectives of offshore wind farms are to maximize total output power and minimize the standard deviation of fatigue load distribution between wind turbines. The decision variables include the effective wind speed, speed, and pitch angle captured by each wind turbine. The objective function of output power is: Among them, F1 is the overall output power of the offshore wind farm, P ∑ is the sum of the power of N wind turbines in the offshore wind farm, N is the number of wind turbines in the wind farm, P i is the real-time output power of fan i, V i is the incoming wind speed of unit i, ω i is the speed of fan i, λ i is the tip speed ratio of fan i, β i is the pitch angle of wind turbine i; According to the fatigue load level model, the comprehensive fatigue load level of fan i at time t is obtained as formula (14); because The proportional relationship between the two, The fatigue load coefficient of the fan fatigue load level deformation is formula (15); The objective function of the standard deviation of fatigue loads between units in an offshore wind farm is: Among them, F2 is the minimum standard deviation of fatigue load distribution of offshore wind farm, and its value range is [0,1]; is the average fatigue load of all units; f c,i is the overall fatigue load value of unit i; C T,i (ω i r / V I ,β i )=4a i (1-a i ) is a function between the wind turbine pitch angle and the tip speed ratio; The fatigue loads borne by offshore wind turbines are relatively large. In order to more intuitively compare the standard deviation of fatigue loads in subsequent simulations, the reference value f under the corresponding wind condition is selected. B Homogenize the fatigue load values:

6. The method for increasing the power of an offshore wind farm throughout its life cycle as claimed in claim 5, characterized in that: The optimization control also includes that the overall constraints of the offshore wind farm are: Among them, P rated is the rated power of the wind turbine, ω min ,ω rated are the minimum speed and rated speed of the wind turbine respectively, β max is the maximum pitch angle of the wind turbine.

7. The method for increasing the power of an offshore wind farm throughout its life cycle as claimed in claim 6, characterized in that: By adjusting the speed and pitch angle of each unit to balance the relationship between the total output power of the offshore wind farm and the fatigue load distribution between units, in order to unify the dimensions, the overall collaborative optimization objective function is constructed as follows: max F=μ1F1 / F j -μ2F2 (19) Among them, μ1 and μ2 are the wind condition adaptive weight coefficients of the output power and load distribution between units in the offshore wind farm, μ1+μ2=1; F1 is the overall output power of the wind farm; F j is the selected power reference value; F2 is the standard deviation of the unit fatigue load.

8. A full life cycle power boosting system for an offshore wind farm using the method according to any one of claims 1 to 7, characterized in that: The acquisition unit analyzes the two-dimensional wake model of the wind turbine according to the wind speed information collected at the offshore wind farm; Model analysis unit, which constructs wind turbine power model and fatigue load model based on the analysis results; The output power is optimized and controlled according to the wind turbine power model and the fatigue load model.

9. A computer device comprising: Memory and processor; The memory stores a computer program, characterized in that: when the processor executes the computer program, steps of a method for improving the power of an offshore wind farm throughout its life cycle are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, steps of a method for improving the power of an offshore wind farm over its entire life cycle are implemented.

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