Fan power scheduling optimization method and system

Abstract

The invention provides a fan power scheduling optimization method and system, and the method comprises the steps: building a stress characteristic model of a key part based on the operation characteristics of the key part of a fan and external environment factors; on the basis of the stress characteristic model of the key component, a four-point rain flow counting method and a residual wave series connection method are combined, stress circulation in fan operation is recognized and corrected, and accumulated fatigue damage of the key component of the fan is calculated on the basis of the fatigue damage theory; constructing a power scheduling model taking minimization of fan fatigue damage as an optimization target, defining a target function as a weighted minimum value of fatigue damage of a main shaft and a tower, and comprehensively considering constraints in all aspects; and solving the power scheduling model through a nonlinear programming method, and generating an optimal power scheduling strategy of each wind turbine generator. According to the method, the accumulation speed of the fatigue damage of the fan can be effectively reduced, the service life of key components of the fan is prolonged, and the comprehensive improvement of the operation economy and the equipment reliability of the wind power plant is realized by optimizing the power scheduling strategy.

Description

Technical Field

[0001] The present invention belongs to the field of wind power generation operation and dispatch optimization, and particularly relates to a method and system for optimizing the power dispatch of a wind turbine. More specifically, it relates to a method and system for optimizing the power dispatch of a wind turbine with the goal of minimizing the fatigue damage of the wind turbine. Background Art

[0002] The statements in this section merely provide background technical information related to the present invention and do not necessarily constitute prior art.

[0003] With the growth of global energy demand and the rapid development of renewable energy technologies, wind power generation, as a clean and sustainable energy form, has been widely applied and promoted. However, the operating environment of wind turbines (hereinafter referred to as wind turbines) is complex and variable, and factors such as wind speed fluctuations and wind direction changes cause the wind turbines to bear unstable loads, thereby leading to fatigue damage problems. This fatigue damage is mainly reflected in key components, and long-term accumulation may lead to component failure or wind turbine shutdown, seriously affecting the economic benefits and power generation reliability of wind farms.

[0004] Nowadays, with the rapid development of wind power, more large-scale wind turbines with high rated capacities are put into use. For large-scale wind power mechanical components, due to their stronger flexibility, the speed of fatigue damage accumulation is faster and they are more vulnerable to damage. Therefore, optimization means are needed to reduce the cumulative fatigue damage during the operation of wind turbines, thereby reducing the power generation loss caused by abnormal shutdowns due to wind turbine failures, reducing the cumulative fatigue damage during the operation of wind turbines, and reducing the maintenance frequency of mechanical components to save the costs of maintenance personnel and materials, so as to improve the economic and social benefits brought by wind farm operators.

[0005] During the operation of a wind farm, active power is distributed. The specific process is as follows: The power grid issues a dispatch instruction to the control terminal of the wind farm station, and then the Automatic Generation Control (AGC) of the station responds to the dispatch instruction issued by the power grid at a sampling rate of once per second. Subsequently, the AGC calculates and distributes the required power generation of each wind turbine in the field according to the instruction, and each wind turbine adjusts its mechanical parameters according to the allocated power generation power to track the power reference value.

[0006] Traditional wind turbine power dispatch methods usually aim to maximize power generation or economic benefits and pay less attention to the cumulative effect of wind turbine fatigue damage. However, during the entire life cycle of a wind turbine, the management and control of fatigue damage are of great significance for reducing maintenance costs and extending equipment life. In recent years, researchers have gradually recognized the close relationship between the power output strategy of wind turbines and fatigue damage and proposed some optimization methods based on load control. However, these methods still have the following problems in practical applications:

[0007] Load model complexity: The cumulative process of wind turbine fatigue damage is affected by various factors, including wind speed fluctuations, wind turbine operating conditions, control strategies, etc. Existing models are difficult to comprehensively and accurately describe this process.

[0008] Balance between power scheduling and fatigue damage: While pursuing maximum power generation, there is still a lack of systematic optimization methods for effectively reducing fatigue damage.

[0009] Real-time performance and operability: Existing optimization methods have deficiencies in terms of computational complexity and real-time performance, making it difficult to meet the actual operation requirements of large-scale wind farms. Summary of the Invention

[0010] The present invention proposes a method and system for optimizing the power scheduling of wind turbines. Aiming at the problem of cumulative fatigue damage caused by wind speed fluctuations during the operation of existing wind turbines, and the fact that traditional power scheduling methods do not fully consider the impact of wind turbine fatigue damage on equipment life and operation economy, the present invention optimizes the power scheduling with the goal of minimizing wind turbine fatigue damage, which can effectively reduce the cumulative speed of wind turbine fatigue damage, extend the service life of key components of the wind turbine, and comprehensively improve the operation economy and equipment reliability of the wind farm by optimizing the power scheduling strategy.

[0011] According to some embodiments, the present invention adopts the following technical solutions:

[0012] A method for optimizing the power scheduling of a wind turbine, comprising the following steps:

[0013] Based on the operating characteristics of key components of the wind turbine and external environmental factors, a stress characteristic model of the key components is constructed;

[0014] Based on the stress characteristic model of the key components, combined with the four-point rain flow counting method and the residual wave series method, the stress cycles during the operation of the wind turbine are identified and corrected, and based on the fatigue damage theory, the cumulative fatigue damage of the key components of the wind turbine is calculated;

[0015] A power scheduling model with the goal of minimizing wind turbine fatigue damage is constructed, and the objective function is defined as the weighted minimum value of the fatigue damage of the main shaft and the tower. The output power constraint of the wind farm, the grid scheduling requirement constraint, the wind turbine operating characteristic constraint, and the fatigue damage constraint are comprehensively considered;

[0016] The power scheduling model is solved by a nonlinear programming method to generate the optimal power scheduling strategy for each wind turbine unit.

[0017] As an alternative implementation, the process of constructing a force characteristic model for key components based on the operating characteristics of key components of a wind turbine and external environmental factors includes: obtaining the power dispatch instruction, wind speed, and status data of the wind turbine, estimating the main shaft torque of the wind turbine tower based on the obtained data, decomposing the force directly acting on the fan blade surface by the wind speed twice, once perpendicular to the fan blade surface and once parallel to the wind turbine rotation plane, to obtain the tangential component force along the main shaft of the fan blade rotation plane as the effective force, and realizing the estimation of the wind turbine tower thrust.

[0018] As an alternative implementation, based on the force characteristic model of the key components, the process of identifying and correcting the stress cycle during the operation of the wind turbine by combining the four-point rain flow counting method and the residual wave series method includes:

[0019] Preprocessing a series of tower thrust and main shaft torque data in the time series, only retaining the peak and valley points, and this reconstruction process needs to start from the highest peak or the lowest valley in the series;

[0020] Counting the peak and valley values in the peak-valley value series according to a predetermined rule, and forming the counted peak and valley values into a residual wave;

[0021] Connect the residual waves in chronological order, process the connection points using the PV principle, delete the connection points, and reapply the rain flow counting method to the series until all full cycles are determined and deleted in turn.

[0022] As an alternative implementation, in the process of identifying and correcting the stress cycle during the operation of the wind turbine by combining the four-point rain flow counting method and the residual wave series method based on the force characteristic model of the key components, the four-point rain flow counting method is executed once at each set moment in all time series, and the calculated cycle data and residual waves are recorded and stored; in the stress cycle calculation at any subsequent moment, the new time series stress value is concatenated with the residual wave at the previous set moment, and the four-point rain flow counting method is performed to obtain the new cycle data and residual waves, and the sum of the two segments of cycle data is the stress cycle data up to the target moment.

[0023] As an alternative implementation, the process of calculating the cumulative fatigue damage of key components of a wind turbine based on the fatigue damage theory includes: combining the actual stress amplitude and the mean stress of the material using the Goodman correction to correct the equivalent stress, calculating the equivalent fatigue load according to the corrected load amplitudes at all levels, substituting the load amplitudes at all levels corrected by the Goodman correction into the S-N curve to obtain the corresponding maximum number of cycle loads, and then calculating the loss caused by each cycle, and finally summing them up according to the Palmgren-Miner linear cumulative damage theory to obtain the cumulative fatigue damage value.

[0024] As an alternative implementation, the process of constructing a power scheduling model with the optimization goal of minimizing the fatigue damage of the wind turbine includes: solving the wind turbine power scheduling optimization problem, where the decision variable is the power scheduling instruction of the wind turbine, the objective function is to minimize the fatigue damage of the key components of the wind turbine, and it satisfies the wind farm output power constraint, grid scheduling demand constraint, wind turbine operation characteristic constraint, and fatigue damage constraint during system operation.

[0025] As an alternative implementation, the objective function of minimizing the fatigue damage of the key components of the wind turbine specifically means: defining the cumulative fatigue damage degree indicators corresponding to the main shaft torque and tower top thrust as two objectives of the fatigue damage of two components of the main shaft and the tower, and using the analytic hierarchy process to assign certain weights to the fatigue damage of the two components of the main shaft and the tower, and taking the minimum value of the sum of the products of the fatigue damage of the two and the weights as the objective function of the overall fatigue damage degree.

[0026] As a further defined implementation, the process of assigning certain weights to the fatigue damage of the two components of the main shaft and the tower includes that the fatigue damage weight of the main shaft is high, with a value range of [0.7, 0.8], the fatigue damage weight of the tower is low, which is [0.2, 0.3], and the sum of the weights of the main shaft and the tower under each criterion is 1.

[0027] A wind turbine power scheduling optimization system includes:

[0028] A wind turbine key component modeling module, configured to construct a force characteristic model of the key components based on the operation characteristics of the wind turbine key components and external environmental factors;

[0029] A fatigue damage calculation module, configured to identify and correct the stress cycle during the operation of the wind turbine based on the force characteristic model of the key components, combined with the four-point rain flow counting method and the residual wave series method, and calculate the cumulative fatigue damage of the wind turbine key components based on the fatigue damage theory;

[0030] A power scheduling model construction module, configured to construct a power scheduling model with the optimization goal of minimizing the fatigue damage of the wind turbine, define the objective function as the weighted minimum value of the fatigue damage of the main shaft and the tower, and comprehensively consider the wind farm output power constraint, grid scheduling demand constraint, wind turbine operation characteristic constraint, and fatigue damage constraint;

[0031] An optimization solution module, configured to solve the power scheduling model by a nonlinear programming method to generate the optimal power scheduling strategy for each wind turbine unit.

[0032] An electronic device includes a memory, a processor, and computer instructions stored on the memory and running on the processor. When the computer instructions are run by the processor, the steps in the above method are completed.

[0033] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0034] Based on the operating characteristics of the key components of the wind turbine and external environmental factors, the present invention constructs a force characteristic model of the key components, which can accurately describe the load effects of factors such as wind speed fluctuations on components such as the main shaft and tower.

[0035] The present invention combines the four-point rain flow counting method with the improved residual wave series method to identify and correct the stress cycles during the operation of the wind turbine, and based on the fatigue damage theory, quantitatively real-time accumulative fatigue damage of the key components of the wind turbine, which helps to ensure the accuracy of the solution results.

[0036] Construct a power scheduling model with the optimization goal of minimizing the fatigue damage of the wind turbine, define the objective function as the weighted minimum value of the fatigue damage of the main shaft and tower, and at the same time comprehensively consider the constraints of the wind farm output power, grid scheduling requirements, wind turbine operating characteristics and fatigue damage constraints. Solve the model through the nonlinear programming method to generate the optimal power scheduling strategy for each wind turbine unit. Thus, on the premise of meeting the grid power demand, effectively reduce the cumulative fatigue damage rate of the key components of the wind turbine, extend the service life of the key components of the wind turbine, improve the economic efficiency and reliability of the operation of the wind farm, provide important technical support for the operation optimization and equipment life management of wind power generation, and achieve the extension of the operation life of the wind turbine and the improvement of the economic benefits of the wind farm.

[0037] To make the above objects, features and advantages of the present invention more obvious and understandable, the following specifically gives preferred embodiments and, in conjunction with the accompanying drawings, makes a detailed description as follows. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The specification drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention.

[0039] Figure 1 is a flowchart of the optimization of the wind turbine power scheduling with the goal of minimizing fatigue damage in an embodiment;

[0040] Figure 2 is a flowchart of the estimation of the thrust of the wind turbine tower and the torque of the main shaft in an embodiment;

[0041] Figure 3 is a force analysis diagram of the wind turbine blade in an embodiment;

[0042] Figure 4 is a flowchart of the calculation of the fatigue damage quantification index in an embodiment;

[0043] Figure 5 is a waveform schematic diagram in an embodiment;

[0044] Figure 6 It is a schematic diagram of a waveform of another embodiment;

[0045] Figure 7 It is a schematic diagram of a residual wave waveform of an embodiment;

[0046] Figure 8 It is a schematic diagram of a residual wave waveform of another embodiment;

[0047] Figure 9 It is a schematic diagram of a series-connected residual wave waveform of an embodiment;

[0048] Figure 10 It is a schematic diagram for identifying stress cycles in a series-connected residual wave of an embodiment;

[0049] Figure 11 It is a schematic diagram of a remaining residual wave of an embodiment;

[0050] Figure 12 It is a flowchart of power scheduling optimization of an embodiment;

[0051] Figure 13 It is a flowchart of the analytic hierarchy process for fatigue damage of two components, namely the main shaft and the tower, of an embodiment;

[0052] Figure 14 It is the average value of fatigue damage of the tower and the main shaft at different times before and after optimization of an embodiment

[0053] Figure 15 It is a line graph showing the change over time of the variance value of the reference power between wind turbines before and after optimization of an embodiment. Detailed implementation manners

[0054] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0055] It should be noted that the following detailed descriptions are all illustrative and are intended to provide further explanations of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0056] It should be noted that the terms used herein are only for describing specific implementation manners and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0057] Without conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.

[0058] Embodiment 1

[0059] As described in the background art, wind power generation, as a representative of clean and renewable energy, plays an important role in the global energy transformation. However, with the continuous growth of wind power installed capacity, the operation and management of wind farms face more and more challenges. During the long-term operation of wind turbines, the cumulative fatigue damage of key components is caused by factors such as wind speed fluctuations and power output changes, which seriously affects the operation life and economy of the wind turbines. In particular, the fatigue damage problems of key components such as the main shaft and tower of the wind turbine have become important factors restricting the long-term stable operation of wind farms. How to effectively reduce the cumulative rate of fatigue damage of wind turbines through a scientific and reasonable power dispatch strategy while meeting the power demand of the power grid is the key technical problem faced by the current operation optimization of wind farms.

[0060] Currently, the research on wind turbine power dispatch mainly focuses on optimization methods aiming at maximizing the power output of wind farms or minimizing power fluctuations, and less attention is paid to the cumulative effect of fatigue damage of wind turbines. The existing methods have the following deficiencies: on the one hand, traditional power dispatch methods do not fully consider the fatigue damage characteristics of key components of wind turbines, which may lead to the premature failure of some wind turbines due to overoperation, increasing the operation and maintenance costs of wind farms; on the other hand, most of the existing optimization models are based on a single time scale, which is difficult to adapt to the dynamic changes of the wind farm operation environment and cannot effectively combine short-term real-time optimization with long-term life management. In addition, in the design of optimization objectives and constraint conditions, the existing methods lack comprehensive consideration of the operation characteristics of wind turbines, the power demand of the power grid and fatigue damage constraints, and it is difficult to balance economy, reliability and accuracy. Therefore, there is an urgent need for a power dispatch optimization method that can quantify the fatigue damage of wind turbines and aim at minimizing it to solve the above problems in wind farm operation.

[0061] This embodiment provides a wind turbine power dispatch optimization method aiming at minimizing the fatigue damage of wind turbines. The specific method process is as follows: based on the operation characteristics and load response of wind turbines, a fatigue damage accumulation model of key components of wind turbines is constructed, and with the minimization of wind turbine fatigue damage as the optimization objective, while taking into account the overall power output demand of the wind farm and the operation constraints of the power grid, a wind turbine power dispatch optimization model is constructed to generate a wind turbine power dispatch strategy.

[0062] The present invention is applicable to the quantification of fatigue damage and power dispatch optimization of wind turbines, mainly including three parts: the force model of key components of wind turbines, the modeling of wind turbine fatigue damage, power dispatch optimization and fatigue damage assessment, aiming to realize the quantification and minimization of wind turbine fatigue damage, extend the service life of key components of wind turbines, and improve the economy and reliability of wind power generation.

[0063] The first part is based on the operating characteristics of the wind turbine and environmental information such as wind speed. Using force analysis and the law of conservation of momentum, a force model of the key components of the wind turbine is constructed, such as the thrust on the tower and the torque of the main shaft.

[0064] Specifically, it includes: obtaining geographical information such as the wind speed distribution and wind shear characteristics at the geographical location of the wind farm, as well as the operating condition data of the wind turbine, to form the basic data for wind turbine fatigue damage modeling;

[0065] Construct a calculation model for the torque of the main shaft of the wind turbine, and analyze the influence of wind speed fluctuations on the fatigue damage of the main shaft;

[0066] Construct a calculation model for the thrust of the tower, and analyze the influence of wind speed fluctuations on the fatigue damage of the tower.

[0067] The second part is based on the force model and fatigue damage mechanism of the key components of the wind turbine to construct a fatigue damage model of the key components of the wind turbine.

[0068] Specifically, it includes: combining the four-point rain flow counting method and the residual wave series method to identify and correct the stress cycle; using the Goodman curve to correct the S-N curve, and adopting the Palmgren-Miner linear cumulative damage theory to calculate the equivalent fatigue damage and cumulative fatigue damage of the main shaft and the tower.

[0069] The third part is based on the fatigue damage model to construct a power dispatch optimization model with the goal of minimizing the fatigue damage of the wind turbine.

[0070] Specifically, it includes: defining the objective function as the weighted minimum of the fatigue damage of the main shaft and the tower, and using the analytic hierarchy process to determine the weights of the fatigue damage of the main shaft and the tower;

[0071] Construct a power dispatch optimization model, considering the constraints of wind farm output power, grid dispatch requirements, wind turbine operating characteristics, and fatigue damage;

[0072] Use a solver to perform optimization and generate a wind turbine power dispatch strategy to reasonably allocate the wind turbine power and achieve the goal of minimizing the fatigue damage of the wind turbine under the premise of meeting the grid power demand.

[0073] The following is a specific description of the embodiment scheme in conjunction with the accompanying drawings.

[0074] As Figure 1 shown, the method includes the following steps. Specifically: First, based on the operating characteristics of the key components of the wind turbine and external environmental factors, a force characteristic model of the key components is constructed to accurately describe the load influence of factors such as wind speed fluctuations on components such as the main shaft and the tower.

[0075] Subsequently, combining the four-point rainflow counting method with the improved residual wave series method, the stress cycles during the operation of the wind turbine are identified and corrected, and based on the fatigue damage theory, the cumulative fatigue damage of the key components of the wind turbine is quantified in real time.

[0076] A power dispatch model with the optimization goal of minimizing the fatigue damage of the wind turbine is constructed. The objective function is defined as the weighted minimum value of the fatigue damage of the main shaft and the tower. At the same time, the output power constraint of the wind farm, the grid dispatch requirement constraint, the wind turbine operation characteristic constraint, and the fatigue damage constraint are comprehensively considered.

[0077] Finally, the model is solved by the nonlinear programming method to generate the optimal power dispatch strategy for each wind turbine unit. Thus, on the premise of meeting the grid power demand, the cumulative fatigue damage rate of the key components of the wind turbine is effectively reduced, and the extension of the wind turbine operation life and the improvement of the economic benefits of the wind farm are realized.

[0078] The following is a detailed description in steps.

[0079] First is Step 1: Estimate the tower thrust and main shaft torque using wind speed and power

[0080] Obtain the operating characteristics of the key components of the wind turbine and external environmental factors. Starting from the operating characteristics and environmental factors of the wind turbine, estimate the tower thrust and main shaft torque of the wind turbine through the power dispatch command and wind speed of the wind turbine, as well as state variables such as pitch angle, low-speed shaft speed, and high-speed shaft speed. The specific process includes the acquisition of basic data and the estimation of the tower thrust and main shaft torque of the wind turbine. The specific process is as Figure 2 shown. Specifically as follows:

[0081] Step 1.1 The basic data includes two categories. One is the input data: the power dispatch command P ref of the wind turbine, the input wind speed V of the wind turbine; the second is the state data: the pitch angle β, the low-speed shaft speed ω r , and the high-speed shaft speed ω f . Specifically:

[0082] The power dispatch command P ref of the wind turbine is usually issued by the control system of the wind farm or the grid dispatch center.

[0083] The input wind speed V of the wind turbine is a key parameter for the operation of the wind turbine and can be measured in real time by an anemometer (such as an ultrasonic anemometer or a mechanical anemometer) installed on the wind turbine.

[0084] The pitch angle β is an important parameter for the operation state of the wind turbine and can be adjusted in real time through the pitch control system inside the wind turbine. The pitch angle data is recorded in real time by the sensors and controllers of the wind turbine.

[0085] The low-speed shaft speed ωr It is an important state parameter of the fan drive system and is measured in real time by a speed sensor installed on the low-speed shaft. The low-speed shaft speed directly reflects the rotational speed of the fan impeller.

[0086] High-speed shaft speed ω f It is an important parameter at the input end of the fan generator and is measured by a speed sensor installed on the high-speed shaft. The high-speed shaft speed is directly related to the power output of the fan.

[0087] Step 1.2 Estimate the main shaft torque of the fan tower

[0088] The main method used in this model is phase-difference torque measurement. Its principle is based on the rigid plane assumption. When the component shaft is subjected to torque, the radii on different cross-sections of the shaft will rotate relative to the center line of the shaft. Taking one of the radii as a reference object, the other radius will rotate a certain angle around the center of the circle, which is the relative torque angle. When the torque is constant, the shear strain γ per unit length can be obtained, and then the shear stress τ on the cross-section can be obtained according to Hooke's law.

[0089]

[0090] Among them In the formula, G is the shear modulus of the shaft and is related to the material properties.

[0091] Take a cross-section dA at a radius of ρ, and then integrate the shear stress moment on the cross-section

[0092]

[0093] Combining formulas (1) and (2), we can get:

[0094]

[0095] Substitute Denote it as J P , which is called the polar moment of inertia of the cross-section with respect to point O. Let the ratio of the inner and outer diameters of the rotating shaft be, then the result of its integration is:

[0096]

[0097] Because Combining, the angle of twist per unit length θ can be obtained.

[0098]

[0099] Furthermore, it can be deduced that:

[0100] M = GJ P θ(6)

[0101] GJ PIt is the torsional stiffness of the circular shaft cross-section, representing the ability of the circumference to resist torsional deformation.

[0102] From the formula and derivation, we can get:

[0103]

[0104] When ρ = R, the shear stress at the edge of the circle is the largest at this time.

[0105]

[0106] Let Then

[0107]

[0108] Furthermore, by deriving formulas (4) and (8), we can get:

[0109]

[0110] From Combining formulas (4) and (6), we can get:

[0111]

[0112] According to formula (11), the shaft torque M can be obtained from the angle of twist.

[0113] The wind power obtained by the low-speed shaft of the wind turbine from the wind wheel side is:

[0114]

[0115] Among them, ρ is the air density, U is the wind speed in m / s, A is the swept area of the fan blades, and is the absorption coefficient of wind power. The low-speed shaft of the fan rotates driven by the wind wheel. The relationship between the active power, shaft torque and shaft rotational speed of the low-speed shaft of the fan is:

[0116]

[0117] According to formulas (11) and (13), the shaft power of the rotating shaft can be obtained as follows:

[0118]

[0119] Simplified to:

[0120]

[0121] Finally, the relationship between the shaft torque and shaft power can be obtained as:

[0122]

[0123] According to formula (16), using the power scheduling index Pref The low-speed shaft torque M can be obtained from the rotational speed state variables of the rotating shaft and the low-speed shaft. l , that is, the main shaft torque.

[0124] Step 1.3 Estimate the thrust of the wind turbine tower

[0125] The model is established by using methods such as force analysis and energy conservation. The specific derivation process is as follows:

[0126] First, perform a force analysis on the fan blades of the wind turbine. Since the fan blade surface is not a flat horizontal plane, in this force analysis, it is assumed that the fan blade surface is an ideal horizontal plane. Let the force generated by the wind speed acting on the fan blade surface be F 推 , F 推 The component force perpendicular to the fan blade plane is F 推1 , and the tangential component force along the main shaft of the fan blade rotation plane is F t . This model believes that the thrust directly acting on the fan blades by the wind speed cannot all effectively drive the rotation of the wind turbine. Only the force acting along the fan blade rotation plane and tangent to the main shaft surface can effectively drive the rotation of the wind turbine. As Figure 3 shown, where the blue line represents the fan blade plane and the red line represents the wind turbine rotation plane. Therefore, in this model, the force directly acting on the fan blade surface by the wind speed is decomposed into two force decompositions perpendicular to the fan blade surface and parallel to the wind turbine rotation plane, and finally the tangential component force along the main shaft of the fan blade rotation plane is obtained as the effective force.

[0127] F 推 can be expressed in the following form, where ρ is the air density, R is the radius of the wind turbine, C t is the thrust coefficient, and β is the pitch angle (°).

[0128] F 推 = 0.5πρR 2 C t (λ,β)v 2 (17)

[0129] Let F 风 = 0.5πρR 2 (λ,β)v 2 , then

[0130] F 推 = C t F 风 (18)

[0131] Let P be the momentum and v 转 be the linear velocity on the main shaft surface. According to the momentum theorem:

[0132] F t v 转 = P (19)

[0133] Perform momentum theorem analysis and integration for each point on the fan blade surface. Let P m be the mechanical energy converted from wind energy, and we can obtain:

[0134] ∫F t v = ∫F 推 sinβcosβv 转 = P m (20)

[0135] Also, because it was previously assumed that the fan blade surface of the wind turbine is a horizontal plane, equation (20) can be simplified as follows, where A is a constant.

[0136] P m = AF 推 sinβcosβv 转 (21)

[0137] The formula for the aerodynamic system of a wind turbine is as follows:

[0138] P m = 0.5πρR 2 C P (λ,β)v 3 (22)

[0139] where C p is the wind energy capture coefficient, which can be obtained through computational fluid dynamics simulation. The calculation formula is as follows:

[0140]

[0141] Connect equations (21) and (22) and solve to obtain:

[0142]

[0143] Also, since v 转 = ωR, we can obtain:

[0144]

[0145] From the definition of the tower thrust, we know that:

[0146] F t = F 风 cosβ(26)

[0147] Combining equations (18), (24), and (25), we can obtain:

[0148]

[0149] Finally, according to equation (27), from the input parameters and state parameters, the tower top thrust F t .

[0150] Step 2. Low-complexity model of fatigue damage degree of the fan main shaft and tower

[0151] The calculation method of fan fatigue damage mainly extracts the effective stress cycles in complex loads through the rain-flow counting method, and combines the S-N curve of the material and the Miner damage accumulation theory to evaluate the fatigue life of key components of the fan under long-term alternating loads. The rain-flow counting method is used to identify the stress amplitude and the number of cycles, the S-N curve provides the fatigue life data of the material under different stress levels, and the Miner theory predicts fatigue failure by accumulating the damage value. This method is widely used in the fatigue design and life assessment of components such as fan blades, towers, and main shafts. The specific calculation process is as Figure 4 shown.

[0152] Step 2.1 Stress cycle identification

[0153] The actual stress signal is usually complex, so it needs to be decomposed into the number of cycles of different stress amplitudes. There are various commonly used methods at present, and the rain-flow counting method is most often used in fatigue life calculation. On this basis, a new real-time fatigue damage prediction method based on the rain-flow counting method is adopted. This method predicts the current fatigue damage in real time by concatenating the residual stress waves that have not formed a complete cycle in the past with the newly measured stress data, and at the same time stores the new residual stress waves and combines them with the stress data measured subsequently to continuously calculate the fatigue damage of the wind turbine.

[0154] Step 2.1.1 Four-point rain-flow counting method

[0155] The four-point rain-flow counting method determines whether a cycle is formed by two adjacent wave peaks and two wave troughs. The basic process of the four-point rain-flow counting method is as follows:

[0156] First, preprocess a series of tower thrust and main shaft torque data in the time series, only retain the wave peak and wave trough points, and this reconstruction process needs to start from the highest wave peak or the lowest wave trough in the sequence. Then, perform counting according to the following rules for the peak-valley value sequence:

[0157] If A > B; B ≥ D; C ≤ A, record a cycle BCB′ and remove it. As Figure 5 shown, the expressions (28) and (29) of the load amplitude and the load average value can be obtained.

[0158] S a = |B - C| / 2 (28)

[0159] S m = (B + C) / 2 (29)

[0160] If E < F; H ≤ F; G ≥ E, record a cycle BCB′ and remove it, asFigure 6 As shown, the expressions (30) and (31) for the load amplitude and the average load can be obtained.

[0161] S a = |F - G| / 2 (30)

[0162] S m = (F + G) / 2 (31)

[0163] The above A - H are the peak / trough values of each waveform respectively. After counting by repeating the above method, the remaining peak - trough values form the residual wave.

[0164] Step 2.1.2 Improved method of connecting residual waves

[0165] In actual calculation, due to the limitation of the calculation time window, residual waves (stress points that do not form a complete cycle) usually occur. According to the method of connecting residual waves, we can connect the residual data points in each time period and combine them with new stress data to continue to identify cycles. This method can combine the previously unfinished cycles with new data to ensure the accurate assessment of fatigue damage. The idea of the method of connecting residual waves is as follows:

[0166] First, the two groups of residual waves after the four - point rain - flow counting method for the above waveform 1 and waveform 2 are as Figure 7 and Figure 8 shown. Connect them in chronological order, then process the connection points with the PV principle and delete the connection point E, as shown. Then, re - apply the four - point rain - flow counting method to the connected sequence until all the full cycles are determined and deleted in turn, as Figure 9 、 Figure 10 and Figure 11 shown.

[0167] In addition, on the basis of the basic processes of the four - point rain - flow counting method and the method of connecting residual waves, this embodiment optimizes the mathematical model for stress cycle identification: First, perform the four - point rain - flow counting method once at each whole t s second moment of all time series, record and store the calculated cycle data and residual waves. In the stress cycle calculation at any subsequent moment, connect the new time - series stress value with the residual wave at the previous whole t s moment and perform the four - point rain - flow counting method to obtain new cycle data and residual waves. The sum of the two segments of cycle data is the stress cycle data up to the target moment.

[0168] Compared with the traditional method of calculating stress cycles in full at any time, this optimization method significantly reduces the amount of data in subsequent calculations by storing cyclic data and residual waves at critical moments in advance, thus greatly shortening the time required to calculate the quantification index of cumulative fatigue damage degree in any time period. The introduction of this method effectively improves the real-time performance of the model and provides guarantee for quickly and accurately evaluating the fatigue damage of wind turbine components.

[0169] Step 2.2 Calculation of the quantification index of cumulative fatigue damage degree

[0170] The quantification index of cumulative fatigue damage degree includes equivalent fatigue load and cumulative fatigue damage value, and their corresponding calculation formulas are as follows:

[0171] (1) Goodman correction

[0172] Goodman correction is a commonly used fatigue life prediction method for considering the influence of mean stress on fatigue strength. This method combines the actual stress amplitude of the material with the mean stress to correct the equivalent stress, so as to more accurately evaluate the fatigue life. Goodman correction assumes that the fatigue limit of the material decreases linearly with the increase of mean stress, and its expression is:

[0173]

[0174] In the formula, S ai is the load amplitude of the i-th cycle, S i is the load amplitude when the equivalent mean value is 0, S mi is the load average value of the i-th cycle, σ b is the maximum load value of the material at tensile fracture.

[0175] (2) Equivalent fatigue load

[0176]

[0177] In the formula, L i is the load amplitude of each level after Goodman correction.

[0178] (3) Cumulative fatigue damage value

[0179] Substitute the load amplitudes of each level after Goodman correction into the S-N curve formula (33) to obtain the corresponding maximum cyclic load times.

[0180] S m ×N max =C(34)

[0181] Then, using Equation (34), calculate the losses caused by each cycle, and then sum them up according to the Palmgren-Miner linear cumulative damage theory to obtain the cumulative fatigue damage value.

[0182]

[0183] Step 3: Optimization of wind turbine power scheduling

[0184] Solve the problem of wind turbine power scheduling optimization. The decision variable is the power scheduling instruction of the wind turbine, and the goal is to minimize the fatigue damage of the key components of the wind turbine while meeting various constraints of the system operation. It is a multi-objective optimization problem, and the main process is as Figure 12 shown.

[0185] Step 3.1 Objective function

[0186] Define the cumulative fatigue damage degree indicators corresponding to the main shaft torque and tower top thrust and as the two objectives of the fatigue damage of two components of the main shaft and the tower. Using the analytic hierarchy process, assign certain weights to the fatigue damage of two components of the main shaft and the tower, and take the minimum value of the sum of the products of the fatigue damage of both and the weights as the objective function of the overall fatigue damage degree. Its form is:

[0187]

[0188] Step 3.1.2 Calculation of weights

[0189] The weights w main and w tower can be determined by the analytic hierarchy process, entropy weight method or data-driven method. It should be noted that the data orders of magnitude of the tower thrust and the main shaft torque are different and need to be normalized.

[0190] By consulting the literature, it can be known that the fatigue damage of two components of the main shaft and the tower is affected by factors such as load conditions, cyclic stress, material properties, and structural complexity. Therefore, the following hierarchical structure is proposed for hierarchical analysis, as Figure 13 shown.

[0191] The following is the analysis of the weight values of the fatigue damage of the main shaft and the tower for four criteria of load conditions, cyclic stress, material properties, and structural complexity.

[0192] Analyze the criterion of load conditions. First, the load types borne by the main shaft and the tower are different, so their fatigue sources are also different. The fatigue sources of the main shaft include: cyclic load, uneven load, instantaneous high load, and vibration. The fatigue sources of the tower include: static load, lateral load, and resonance effect.

[0193] Analyzing the load type criterion, the load on the main shaft is more intense. Especially when the wind speed changes greatly and the wind turbine rotates and accelerates or decelerates, the stress on the main shaft is more obvious. Therefore, the amplitude of the stress cycle on the main shaft is large, resulting in a faster cumulative fatigue damage rate. For the tower, its main fatigue comes from wind loads, the load is relatively uniform and the stress change frequency is low. Therefore, the cumulative fatigue damage grows slower.

[0194] Analyzing the stress cycle number criterion, the rotation speeds of the wind turbine and the main shaft are relatively fast, while the tower mainly bears low-frequency and large-amplitude wind loads. Therefore, the stress cycle number of the main shaft is significantly higher than that of the tower, and the cumulative fatigue damage of the main shaft will also increase faster.

[0195] Analyzing the material property criterion, the main shaft usually uses high-strength steel materials, while the tower is usually composed of relatively thick steel plates and composite materials. The fatigue limits of both are relatively high. However, due to the large stress fluctuations on the main shaft, the fatigue life of its material is relatively short.

[0196] Analyzing the structural complexity criterion, the connection points and bearings of the main shaft are stress concentration areas, while the structure of the tower is simple and the force is more uniform. Therefore, the fatigue damage rate of the main shaft is relatively higher.

[0197] Based on the above analysis, we estimate the fatigue damage frequencies of the main shaft and the tower under the four criteria as follows:

[0198] Due to the large stress cycle amplitude, many stress cycles, relatively short fatigue life and stress concentration on the main shaft, the fatigue damage weight of the main shaft is higher, which should be between 0.7 and 0.8. Relatively speaking, the fatigue damage weight of the tower is smaller, about 0.2 - 0.3, and the sum of the weights of the main shaft and the tower under each criterion is 1.

[0199] Based on the above analysis results, pairwise comparisons are made for the four criteria, and values are assigned to their relative importance. Finally, the weights can be obtained through the analytic hierarchy process.

[0200] Step 3.1.3 and Calculation

[0201] First, through formulas (16) and (27), using the decision variable power dispatch instruction P i and other input data and state data, estimate the main shaft torque and tower thrust. Subsequently, through the low-complexity model of the fatigue damage degree of the wind turbine main shaft and tower in Step 2, calculate the cumulative fatigue damage of the main shaft of wind turbine i at this moment and the cumulative fatigue damage of the tower

[0202] Step 3.2 Constraints

[0203] To meet the needs of fan power scheduling, the following constraints need to be established:

[0204] (1) Ensure that the sum of all fan power scheduling is equal to the grid scheduling instruction.

[0205] 0 ≤ P i ≤ P max = 5MW (37)

[0206] (2) Ensure that the active power reference value of each fan does not exceed its rated power.

[0207]

[0208] (3) Ensure that the difference between the optimized active power distribution value of each fan and the result of the average distribution method does not exceed 1MW.

[0209]

[0210] (4) Ensure that the fatigue damage of the key components of each fan does not exceed the limit (i.e., not greater than the threshold), that is, no fatigue damage occurs.

[0211] D i < D imax (40)

[0212] In addition, new constraints can be added according to actual needs.

[0213] Taking a wind farm as an example to verify the effect of the method of this patent, this wind farm has a total of 100 fans, the models of the 100 fans are the same, the rated output power is 5MW, and the rotor radius is 63m.

[0214] First, according to Step 1, obtain the input data and status data of each fan. The input data includes the real-time wind speed, and the status data includes the pitch angle, low-speed shaft speed, and high-speed shaft speed. The following table shows the relevant data of a certain fan in the first 20s.

[0215] Table 1 Input Data and Status Data of a Certain Fan in the First 20s

[0216]

[0217]

[0218] Then, according to Formula (16) and Formula (27) in Step 1, the functional relationship between the tower thrust / main shaft torque and the decision variables can be established. Then, through the fatigue damage model in Step 2, the fatigue damage degree D i and the decision variable P iThe functional relationship between them. Finally, construct the objective function according to Step 3, where the weights are w main = 0.28754, w tower = 0.71246. At this time, the expression of the total cumulative fatigue damage objective function is:

[0219] min(0.28754·∑D main + 0.71246∑D tower ) (41)

[0220] The constraint conditions are:

[0221] 0 ≤ P i ≤ P max = 5MW (42)

[0222]

[0223] Subsequently, input the objective function and constraint conditions into the optimizer for solution. During the solution process, initial values and the upper and lower bounds of decision variables need to be provided, and the optimizer will perform iterative calculations to finally obtain the optimal solution that satisfies the constraint conditions. Finally, substitute the optimization results into the objective function and constraint conditions for verification to ensure that the objective function value reaches the minimum and all constraint conditions are satisfied.

[0224] Table 2 Scheduled power P i of a certain wind turbine from 0 to 20s before and after optimization

[0225]

[0226]

[0227] It can be seen from Figure 14 that the growth trend of the fatigue average value slows down, the life of the growth element increases, indicating that the optimization of the model is effective. Next, through quantitative analysis, the optimization degree of fatigue damage is given more intuitively.

[0228] Since when the wind energy carried by the wind is completely digested and converted into electric energy by the wind turbine, the thrust (the thrust generated by the wind pushing the wind wheel plane) and torque (the torque brought by the mismatch between the actual rotational speed of the wind wheel and the rotational speed that should be reached under the current wind speed) borne by the wind turbine are the smallest, so in the result analysis, the variance value of the reference power between the wind turbines before and after optimization is used for comparison.

[0229] As Figure 15 shown, by calculating the variance value of the reference power of the wind turbine before optimization - the variance value of the reference power of the wind turbine after optimization, it is found that it decreases significantly, indicating the effectiveness of the optimization model.

[0230] Embodiment 2

[0231] A wind turbine power scheduling optimization system, comprising:

[0232] The key component modeling module of the fan is configured to construct a stress characteristic model of the key component based on the operating characteristics of the key component of the fan and external environmental factors;

[0233] The fatigue damage calculation module is configured to identify and correct the stress cycle during the operation of the fan based on the stress characteristic model of the key component, in combination with the four-point rain flow counting method and the residual wave series method, and calculate the cumulative fatigue damage of the key component of the fan based on the fatigue damage theory;

[0234] The power scheduling model construction module is configured to construct a power scheduling model with the optimization goal of minimizing the fatigue damage of the fan, define the objective function as the weighted minimum of the fatigue damage of the main shaft and the tower, and comprehensively consider the wind farm output power constraint, the power grid scheduling requirement constraint, the fan operating characteristic constraint, and the fatigue damage constraint;

[0235] The optimization solution module is configured to solve the power scheduling model by a nonlinear programming method and generate the optimal power scheduling strategy for each wind turbine.

[0236] Embodiment III

[0237] An electronic device includes a memory, a processor, and computer instructions stored on the memory and running on the processor. When the computer instructions are run by the processor, the steps in the method provided in Embodiment I are completed.

[0238] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, various changes and modifications can be made to the present invention. Any modifications, equivalent replacements, improvements, etc. made by those skilled in the art without creative efforts within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for optimizing wind turbine power scheduling, characterized in that: The following steps are involved: Based on the operating characteristics of key components of the wind turbine and external environmental factors, the stress characteristic model of key components is constructed; Based on the stress characteristic model of the key components, combined with the four-point rain flow counting method and the residual wave series method, the stress cycle in the operation of the wind turbine is identified and corrected, and based on the fatigue damage theory, the accumulated fatigue damage of the key components of the wind turbine is calculated; A power dispatch model with minimizing wind turbine fatigue damage as the optimization goal is constructed, and the objective function is defined as the weighted minimum value of the fatigue damage of the main shaft and tower, taking into account the wind farm output power constraints, grid dispatch demand constraints, wind turbine operation characteristics constraints and fatigue damage constraints; The power dispatch model is solved by a nonlinear programming method to generate an optimal power dispatch strategy for each wind turbine.

2. A method for optimizing wind turbine power scheduling as claimed in claim 1, characterized in that: Based on the operating characteristics of the key components of the wind turbine and external environmental factors, the process of constructing the force characteristic model of the key components includes: obtaining the power scheduling instructions, wind speed and status data of the wind turbine, estimating the main shaft torque of the wind turbine tower based on the acquired data, and decomposing the force directly acting on the fan blade surface by the wind speed twice, perpendicular to the fan blade surface and parallel to the fan rotation surface, obtaining the main shaft tangential component along the fan blade rotation surface as the effective force, and realizing the estimation of the thrust of the wind turbine tower.

3. A method for optimizing wind turbine power scheduling as claimed in claim 1, characterized in that: Based on the stress characteristic model of the key components, combined with the four-point rain flow counting method and the residual wave series method, the process of identifying and correcting the stress cycle in the operation of the wind turbine includes: Preprocess a series of tower thrust and main shaft torque data in time series, retain only the peaks and troughs, and the reconstruction process needs to start from the highest peak or lowest trough in the sequence; The peak-valley value sequence is counted according to a predetermined rule, and the peak-valley values ​​after counting are combined into residual waves; Connect the residual waves in chronological order, use the PV principle to process the connection points, delete the connection points, and reapply the rainflow counting method to the series sequence until all full cycles are determined and deleted in turn.

4. A method for optimizing wind turbine power scheduling as claimed in claim 1, characterized in that: Based on the stress characteristic model of the key components, combined with the four-point rain flow counting method and the residual wave series method, in the process of identifying and correcting the stress cycle in the operation of the wind turbine, the four-point rain flow counting method is executed once at each set moment in all time series, and the calculated cycle data and residual waves are recorded and stored; in the stress cycle calculation at any subsequent moment, the new time series stress value is connected in series with the residual wave at the previous set moment, and the four-point rain flow counting method is performed to obtain new cycle data and residual waves, and the sum of the two cycle data is the stress cycle data to the target moment.

5. A method for optimizing wind turbine power scheduling as claimed in claim 1, characterized in that: Based on fatigue damage theory, the process of calculating the cumulative fatigue damage of key components of wind turbines includes: using Goodman correction to combine the actual stress amplitude of the material with the average stress, correcting the equivalent stress, calculating the equivalent fatigue load according to the corrected load amplitudes at each level, substituting the Goodman-corrected load amplitudes at each level into the SN curve to obtain the corresponding maximum number of cyclic loads, and then calculating the loss caused by each cycle, and then adding them up according to the Palmgren-Miner linear cumulative damage theory to obtain the cumulative fatigue damage value.

6. A method for optimizing wind turbine power scheduling as claimed in claim 1, characterized in that: The process of constructing a power dispatch model with minimizing wind turbine fatigue damage as the optimization goal includes: solving the wind turbine power dispatch optimization problem, the decision variable is the wind turbine power dispatch instruction, the objective function is to minimize the fatigue damage of key components of the wind turbine, and to meet the wind farm output power constraints of the system operation, the grid dispatch demand constraints, the wind turbine operation characteristics constraints and the fatigue damage constraints.

7. A method for optimizing wind turbine power scheduling as claimed in claim 1, characterized in that: The objective function is to minimize the fatigue damage of key components of the wind turbine. Specifically, the cumulative fatigue damage degree indicators corresponding to the main shaft torque and the tower top thrust are defined as the two goals of fatigue damage of the two components of the main shaft and the tower. The hierarchical analysis method is used to assign certain weights to the fatigue damage of the two components of the main shaft and the tower, so that the sum of the product of the fatigue damage of the two and the weight takes the minimum value as the objective function of the overall fatigue damage degree.

8. A method for optimizing wind turbine power scheduling as claimed in claim 7, characterized in that: The process of assigning certain weights to the fatigue damage of the two components of the main shaft and the tower includes that the fatigue damage weight of the main shaft is high, ranging from [0.7, 0.8], the fatigue damage weight of the tower is small, ranging from [0.2, 0.3], and the sum of the weights of the main shaft and the tower under each criterion is 1.

9. A wind turbine power dispatch optimization system, characterized in that: include: A wind turbine key component modeling module is configured to construct a force characteristic model of the key components based on the operating characteristics of the wind turbine key components and external environmental factors; A fatigue damage calculation module is configured to identify and correct stress cycles in the operation of the wind turbine based on the stress characteristic model of the key components, combined with a four-point rain flow counting method and a residual wave series method, and calculate the accumulated fatigue damage of the key components of the wind turbine based on fatigue damage theory; The power dispatch model building module is configured to build a power dispatch model with minimizing wind turbine fatigue damage as the optimization goal, define the objective function as the weighted minimum value of the main shaft and tower fatigue damage, and comprehensively consider the wind farm output power constraint, grid dispatch demand constraint, wind turbine operation characteristic constraint and fatigue damage constraint; The optimization solution module is configured to solve the power dispatch model through a nonlinear programming method to generate an optimal power dispatch strategy for each wind turbine.

10. An electronic device, characterized in that: The method comprises a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the steps in the method according to any one of claims 1 to 8 are completed.

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