A fan power scheduling optimization method and system

Abstract

The application provides a kind of fan power scheduling optimization method and system, based on the operating characteristics of fan key components 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 four-point rain flow counting method and residual wave series method, the stress cycle in the operation of fan is identified and corrected, based on fatigue damage theory, the cumulative fatigue damage of fan key components is calculated;A power scheduling model is constructed to minimize the fatigue damage of the fan, the objective function is defined as the weighted minimum value of the main shaft and tower fatigue damage, and all aspects of constraints are considered comprehensively;The power scheduling model is solved by nonlinear programming method, and the optimal power scheduling strategy of each wind turbine generator is generated.The application can effectively reduce the cumulative speed of fan fatigue damage, prolong the service life of fan key components, and realize the comprehensive improvement of wind farm operation economy and equipment reliability through optimizing power scheduling strategy.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of wind power generation operation and dispatch optimization, and particularly relates to a wind turbine power dispatch optimization method and system, and more particularly relates to a wind turbine power dispatch optimization method and system aiming at minimizing wind turbine fatigue damage. BACKGROUND

[0002] The statements in this section merely provide background information related to the present application 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 form of energy, has been widely applied and promoted. However, the operating environment of wind turbine generators (hereinafter referred to as wind turbines) is complex and variable, and factors such as wind speed fluctuations and wind direction changes cause wind turbines to bear unstable loads, thereby causing fatigue damage problems. This fatigue damage mainly occurs on key components, and long-term accumulation can 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-capacity large-scale wind turbines are put into use. For large-scale wind power mechanical components, because they are more flexible, they accumulate fatigue damage faster and are more prone to damage. Therefore, optimization methods are needed to reduce the cumulative fatigue damage of wind turbines during operation, thereby reducing the loss of power generation caused by abnormal shutdown due to wind turbine failure, reducing the cumulative fatigue damage of wind turbines during operation, reducing the maintenance frequency of mechanical components to save operation and maintenance personnel and material costs, thereby improving the economic and social benefits brought by wind farm operators.

[0005] During the operation of the wind farm, active power distribution will be performed. The specific process is as follows: the power grid issues a dispatch instruction to the control end of the wind farm station, and then the Automatic Generation Control (AGC) of the station automatically responds to the dispatch instruction issued by the power grid with a sampling rate of once per second. Subsequently, the AGC calculates and distributes the required power generation capacity 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 capacity 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, in the whole life cycle of the wind turbine, the management and control of fatigue damage are of great significance to reduce maintenance costs and prolong equipment life. In recent years, researchers have gradually recognized the close relationship between wind turbine power output strategy and fatigue damage, and have proposed some optimization methods based on load control. However, these methods still have the following problems in practical application:

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

[0008] Balancing power dispatch 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 are insufficient in terms of computational complexity and real-time performance, making it difficult to meet the actual operational needs of large-scale wind farms. Summary of the Invention

[0010] This invention proposes a wind turbine power scheduling optimization method and system. Addressing the problem of accumulated fatigue damage caused by wind speed fluctuations in existing wind turbine operation, and the fact that traditional power scheduling methods do not fully consider the impact of wind turbine fatigue damage on equipment lifespan and operational economy, this invention optimizes power scheduling with the goal of minimizing wind turbine fatigue damage. This effectively reduces the accumulation rate of wind turbine fatigue damage, extends the service life of key wind turbine components, and comprehensively improves the operational economy and equipment reliability of wind farms through optimized power scheduling strategies.

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

[0012] A wind turbine power scheduling optimization method includes the following steps:

[0013] Based on the operating characteristics of key wind turbine components 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, and combining the four-point rainflow counting method and the residual wave series method, the stress cycle in the operation of the wind turbine is identified and corrected. Based on the fatigue damage theory, the cumulative fatigue damage of the key components of the wind turbine is calculated.

[0015] A power dispatch model is constructed with the goal of minimizing wind turbine fatigue damage. The objective function is defined as the weighted minimum value of fatigue damage of the main shaft and tower, taking into account the constraints of wind farm output power, grid dispatch demand, wind turbine operating characteristics and fatigue damage.

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

[0017] As an alternative implementation method, the process of constructing a force characteristic model of key components based on the operating characteristics of key components of the wind turbine and external environmental factors includes: acquiring the power scheduling command, wind speed and wind turbine status data of the wind turbine; estimating the torque of the main shaft of the wind turbine tower based on the acquired data; decomposing the force of the wind speed directly acting on the blade surface into two forces, one perpendicular to the blade surface and the other parallel to the wind turbine rotation surface, to obtain the tangential component of the main shaft along the blade rotation surface as the effective force, thereby realizing the estimation of the wind turbine tower thrust.

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

[0019] Preprocess a series of tower thrust and spindle torque data in a time series, retaining only the peaks and troughs, and the reconstruction process must start from the highest peak or lowest trough in the series;

[0020] The peak-valley sequence is counted according to a predetermined rule, and the counted peak-valley values ​​are combined to form a residual wave.

[0021] Connect the residuals in chronological order, process the connection points using the PV principle, and delete the connection points. Reapply the rainflow counting method to the cascade sequence until all full cycles are determined and deleted in sequence.

[0022] As an alternative implementation, based on the stress characteristic model of the key components, and combining the four-point rainflow counting method and the residual wave series method, during the process of identifying and correcting stress cycles in wind turbine operation, the four-point rainflow 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 waves at the previous set moment, and the four-point rainflow counting method is performed to obtain new cycle data and residual waves. The sum of the two cycle data is the stress cycle data up to the target moment.

[0023] As an alternative implementation method, the process of calculating the cumulative fatigue damage of key components of the wind turbine based on fatigue damage theory includes: using Goodman correction to combine the actual stress amplitude and mean stress of the material to correct the equivalent stress; calculating the equivalent fatigue load based on 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 cycles; calculating the loss caused by each cycle; and then summing them up according to the Palmgren-Miner linear cumulative damage theory to obtain the cumulative fatigue damage value.

[0024] As an alternative implementation method, the process of constructing a power dispatch model with the optimization objective of minimizing wind turbine fatigue damage includes: solving the wind turbine power dispatch optimization problem, with the decision variable being the power dispatch command of the wind turbine, and the objective function being to minimize the fatigue damage of key components of the wind turbine, while satisfying the constraints of wind farm output power, grid dispatch demand, wind turbine operating characteristics, and fatigue damage in system operation.

[0025] As an alternative implementation method, the objective function of minimizing the fatigue damage of key components of the wind turbine is specifically defined as follows: the cumulative fatigue damage degree index corresponding to the main shaft torque and the tower top thrust is defined as two objectives for the fatigue damage of the two components of the main shaft and the tower. The analytic hierarchy process is used to assign certain weights to the fatigue damage of the two components of the main shaft and the tower, and the sum of the product of the fatigue damage of the two components and the weight is taken 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, the spindle and the tower, includes a higher fatigue damage weight for the spindle, with a value range of [0.7, 0.8], and a lower fatigue damage weight for the tower, with a value range of [0.2, 0.3]. The sum of the weights of the spindle and the tower under each criterion is 1.

[0027] A wind turbine power scheduling optimization system, comprising:

[0028] The wind turbine key component modeling module is configured to construct a stress characteristic model of the key components based on their operating characteristics and external environmental factors.

[0029] The 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 the four-point rainflow counting method and the residual wave series method, and calculate the cumulative fatigue damage of the key components of the wind turbine based on fatigue damage theory.

[0030] The power dispatch model construction module is configured to build a power dispatch model with the optimization objective of minimizing wind turbine fatigue damage. The objective function is defined as the weighted minimum of the main axis and tower fatigue damage, taking into account the wind farm output power constraints, grid dispatch demand constraints, wind turbine operating characteristic constraints and fatigue damage constraints.

[0031] The optimization solution module is configured to solve the power scheduling model using a nonlinear programming method to generate the optimal power scheduling strategy for each wind turbine.

[0032] An electronic device includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor, wherein the computer instructions, when executed by the processor, perform the steps in the method described above.

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

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

[0035] This invention combines the four-point rainflow counting method with the improved residual wave series method to identify and correct stress cycles during wind turbine operation. Based on fatigue damage theory, it quantifies the cumulative fatigue damage of key wind turbine components in real time, which helps to ensure the accuracy of the solution results.

[0036] This paper constructs a power dispatch model with the optimization objective of minimizing wind turbine fatigue damage. The objective function is defined as the weighted minimum value of fatigue damage to the main shaft and tower. Simultaneously, it comprehensively considers the constraints of wind farm output power, grid dispatch demand, wind turbine operating characteristics, and fatigue damage. The model is solved using a nonlinear programming method to generate the optimal power dispatch strategy for each wind turbine. This effectively reduces the fatigue damage accumulation rate of key wind turbine components while meeting grid power demand, extends the service life of key wind turbine components, and improves the economic efficiency and reliability of wind farm operation. This provides important technical support for wind power operation optimization and equipment life management, and realizes the extension of wind turbine operating life and the improvement of wind farm economic benefits.

[0037] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0038] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0039] Figure 1 This is a flowchart of a wind turbine power scheduling optimization process aimed at minimizing fatigue damage, as one embodiment.

[0040] Figure 2 This is a flowchart illustrating the estimation of wind turbine tower thrust and main shaft torque in one embodiment.

[0041] Figure 3 This is a force analysis diagram of a fan blade according to one embodiment;

[0042] Figure 4 This is a flowchart illustrating the calculation of fatigue damage quantification indicators in one embodiment.

[0043] Figure 5 A waveform diagram of one embodiment;

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

[0045] Figure 7 This is a schematic diagram of the residual waveform in one embodiment;

[0046] Figure 8 This is a schematic diagram of the residual waveform of another embodiment;

[0047] Figure 9 This is a schematic diagram of a residual series waveform in one embodiment;

[0048] Figure 10 This is a schematic diagram illustrating stress cycling in a series residual wave according to one embodiment;

[0049] Figure 11 A schematic diagram of residual wave in one embodiment;

[0050] Figure 12 This is a flowchart illustrating power scheduling optimization as one embodiment;

[0051] Figure 13 This is a flowchart of the hierarchical analysis method for fatigue damage of two components, the spindle and the tower, in one embodiment.

[0052] Figure 14 The average fatigue damage of the tower and spindle at different times before and after optimization in one embodiment.

[0053] Figure 15 This is a line graph showing the variation of the reference power variance between wind turbines before and after optimization in one embodiment over time. Detailed Implementation

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

[0055] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0056] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, 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] Where there is no conflict, the embodiments and features described in this application may be combined with each other.

[0058] Example 1

[0059] As described in the background section, wind power, as a representative of clean and renewable energy, plays a vital role in the global energy transition. However, with the continuous growth of installed wind power capacity, wind farm operation and management face increasing challenges. During long-term operation, wind turbine generators accumulate fatigue damage to key components due to factors such as wind speed fluctuations and power output variations, severely impacting the turbine's service life and economic efficiency. In particular, fatigue damage to critical components such as the turbine main shaft and tower has become a significant factor restricting the long-term stable operation of wind farms. How to effectively reduce the rate of fatigue damage accumulation in wind turbines while meeting grid power demands through scientific and reasonable power dispatch strategies is a key technical challenge currently facing wind farm operation optimization.

[0060] Currently, research on wind turbine power dispatch mainly focuses on optimization methods aimed at maximizing wind farm power output or minimizing power fluctuations, with less attention paid to the cumulative effects of wind turbine fatigue damage. Existing methods have several shortcomings: First, traditional power dispatch methods do not fully consider the fatigue damage characteristics of key wind turbine components, potentially leading to premature failure of some turbines due to over-operation, increasing the operation and maintenance costs of wind farms. Second, most existing optimization models are based on a single time scale, making it difficult to adapt to the dynamic changes in the wind farm operating environment and achieve an effective combination of short-term real-time optimization and long-term lifespan management. Furthermore, existing methods lack a comprehensive consideration of wind turbine operating characteristics, grid power demand, and fatigue damage constraints in the design of optimization objectives and constraints, making it difficult to balance economy, reliability, and accuracy. Therefore, there is an urgent need for a power dispatch optimization method that can quantify wind turbine fatigue damage and minimize it to address the aforementioned problems in wind farm operation.

[0061] This embodiment provides a wind turbine power scheduling optimization method with the goal of minimizing wind turbine fatigue damage. The specific method flow is as follows: based on the operating characteristics and load response of the wind turbine generator set, a fatigue damage accumulation model of key components of the wind turbine is constructed. With minimizing wind turbine fatigue damage as the optimization objective, while taking into account the overall power output demand of the wind farm and the grid operation constraints, a wind turbine power scheduling optimization model is constructed, and a wind turbine power scheduling strategy is generated.

[0062] This invention is applicable to the quantification of fatigue damage and optimization of power dispatch for wind turbine generator sets. It mainly includes three parts: stress model of key components of wind turbine, fatigue damage modeling of wind turbine, power dispatch optimization, and fatigue damage assessment. The aim is to quantify and minimize fatigue damage of wind turbine, extend the service life of key components of wind turbine, and improve the economy and reliability of wind power generation.

[0063] The first part uses force analysis and the law of conservation of momentum to construct force models of key components of the wind turbine, such as the thrust on the tower and the torque on the main shaft, based on the operating characteristics of the wind turbine and environmental information such as wind speed.

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

[0065] A calculation model for the main shaft torque of the wind turbine was constructed to analyze the impact of wind speed fluctuations on the fatigue damage of the main shaft.

[0066] A calculation model for tower thrust was constructed to analyze the impact of wind speed fluctuations on tower fatigue damage.

[0067] The second part constructs a fatigue damage model for key wind turbine components based on the stress model and fatigue damage mechanism of the key components.

[0068] Specifically, this includes: combining the four-point rainflow counting method with the residual wave series method to identify and correct stress cycles; using the Goodman curve to correct the SN curve; and using the Palmgren-Miner linear cumulative damage theory to calculate the equivalent fatigue damage and cumulative fatigue damage of the main shaft and tower.

[0069] The third part constructs a power scheduling optimization model based on the fatigue damage model, with the goal of minimizing wind turbine fatigue damage.

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

[0071] A power dispatch optimization model is constructed, taking into account the output power constraints of wind farms, grid dispatch demand constraints, wind turbine operating characteristics constraints, and fatigue damage constraints.

[0072] An optimization solution is used to generate a wind turbine power scheduling strategy, which rationally allocates wind turbine power and achieves the goal of minimizing wind turbine fatigue damage while meeting the power demand of the power grid.

[0073] The following is a detailed description of the embodiments with reference to the accompanying drawings.

[0074] like Figure 1 As shown, this method includes the following steps: First, 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 to accurately describe the load influence of factors such as wind speed fluctuations on components such as the main shaft and tower.

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

[0076] A power dispatch model is constructed with the goal of minimizing wind turbine fatigue damage. The objective function is defined as the weighted minimum value of fatigue damage of the main shaft and tower, while comprehensively considering the constraints of wind farm output power, grid dispatch demand, wind turbine operating characteristics, and fatigue damage.

[0077] Finally, the model is solved using nonlinear programming to generate the optimal power scheduling strategy for each wind turbine. This effectively reduces the fatigue damage accumulation rate of key wind turbine components while meeting the power demand of the power grid, thereby extending the service life of the wind turbines and improving the economic benefits of the wind farm.

[0078] The following is a detailed description of each step.

[0079] The first step is to estimate the tower thrust and main shaft torque using wind speed and power.

[0080] The process involves acquiring the operating characteristics of key wind turbine components and external environmental factors. Starting from these characteristics, the tower thrust and main shaft torque are estimated using power dispatch commands, wind speed, and state variables such as pitch angle, low-speed shaft speed, and high-speed shaft speed. The specific process includes acquiring basic data and estimating the tower thrust and main shaft torque. The detailed steps are as follows: Figure 2 As shown. Specifically:

[0081] Step 1.1 The basic data includes two types: one is input data, namely the power dispatch command P of the wind turbine. ref The first category is the input wind speed V of the wind turbine; the second category is state data: blade pitch angle β, low-speed shaft speed ω. r High-speed shaft speed ω f Specifically:

[0082] Power dispatching command P of the wind turbine ref It is usually issued by the wind farm's control system or the power grid dispatch center.

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

[0084] The pitch angle β is a crucial parameter for wind turbine operation and can be adjusted in real time via the internal pitch control system. Pitch angle data is recorded in real-time by the wind turbine's sensors and controllers.

[0085] Low-speed shaft speed ωr This is a crucial status parameter of the wind turbine drive system, measured in real time by a speed sensor mounted on the low-speed shaft. The speed of the low-speed shaft directly reflects the speed of the wind turbine impeller.

[0086] High-speed shaft speed ω f This is a crucial parameter at the input of the wind turbine generator, measured by a speed sensor mounted on the high-speed shaft. The high-speed shaft speed is directly related to the wind turbine's power output.

[0087] Step 1.2 Estimate the main shaft torque of the wind turbine 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 shaft cross sections will rotate relative to each other around the center line of the shaft. Taking one radius as a reference, the other radius will rotate around the center by a certain angle, 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 of the cross section can be obtained according to Hooke's law.

[0089]

[0090] in In the formula, G is the shear modulus of the shaft, which is related to the material properties.

[0091] Take a section dA at radius ρ, and then integrate the shear stress moment over the cross section.

[0092]

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

[0094]

[0095] Will Let J be the denoted J P , is called the polar moment of inertia of the cross section about point O. Let the ratio of the inner and outer diameters of the axis of rotation be denoted by , then the result of its integration is:

[0096]

[0097] because Combining these, we can obtain the angle of twist per unit length θ.

[0098]

[0099] Furthermore, it can be deduced that:

[0100] M = GJ P θ(6)

[0101] GJ Pis the torsional stiffness of a circular shaft section, representing the ability of the circumference to resist torsional deformation.

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

[0103]

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

[0105]

[0106] set up but

[0107]

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

[0109]

[0110] Depend on Combining formulas (4) and (6), we can obtain:

[0111]

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

[0113] The wind power obtained from the wind turbine's low-speed shaft via the rotor is:

[0114]

[0115] Where ρ is air density, U is wind speed (m / s), A is the swept area of ​​the wind turbine blades, and is the wind power absorption coefficient. The low-speed shaft of the wind turbine rotates under the drive of the impeller. The relationship between the active power, shaft torque, and shaft rotation speed of the low-speed shaft is as follows:

[0116]

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

[0118]

[0119] Simplified to:

[0120]

[0121] The final relationship between shaft torque and shaft power can be obtained as follows:

[0122]

[0123] According to formula (16), using the power scheduling index Pref The low-speed shaft torque M can be obtained from the low-speed shaft rotation speed. l This refers to the spindle torque.

[0124] Step 1.3 Estimate the wind turbine tower thrust

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

[0126] First, a force analysis is performed on the fan blades. Since the fan blade surface is not a flat horizontal plane, this force analysis assumes 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 of the force perpendicular to the plane of the fan blades is F. 推1 The tangential component of the force along the principal axis of the fan blade's rotation plane is F. t This model posits that the thrust directly exerted by wind speed on the fan blades is not entirely sufficient to drive the fan's rotation; only the force acting along the fan's rotational plane and tangential to the main shaft surface can effectively drive the fan's rotation. For example... Figure 3 As shown, the blue line represents the fan blade plane, and the red line represents the fan rotation plane. Therefore, this model decomposes the force of wind speed acting directly on the fan blade surface into two forces: perpendicular to the fan blade surface and parallel to the fan rotation plane. Finally, the tangential component along the main axis of the fan blade rotation plane is obtained as the effective force.

[0127] F 推 It can be expressed in the following form, where ρ is the air density, R is the fan radius, and C... t β 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 ,but

[0130] F 推 =C t F 风 (18)

[0131] Let P be momentum, v 转 The linear velocity of the principal axis surface, according to the momentum theorem, is:

[0132] F t v 转 =P (19)

[0133] We perform momentum theorem analysis and integration at every point on the blade surface, let P m As for the mechanical energy converted from wind energy, we can obtain:

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

[0135] Since the previous text assumes that the fan blades are horizontal, equation (20) can be simplified as follows, where A is a constant.

[0136] P m =AF 推 sinβcosβv 转 (twenty one)

[0137] The formulas for the aerodynamic system of wind turbine generators are as follows:

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

[0139] Where C p The wind energy capture factor can be obtained through computational fluid dynamics simulation, and the calculation formula is as follows:

[0140]

[0141] By applying formulas (21) and (22), we can obtain the following solution:

[0142]

[0143] And because of v 转 =ωR, we can get:

[0144]

[0145] From the definition of 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, based on formula (27), the tower top thrust F under the corresponding input and state parameters can be obtained. t .

[0150] Step 2: Low-complexity model of fatigue damage to wind turbine main shaft and tower

[0151] The calculation method for wind turbine fatigue damage primarily utilizes the rainflow counting method to extract effective stress cycles from complex loads. This is combined with the material's Sn-S curve and Miner's damage accumulation theory to assess the fatigue life of key wind turbine components under long-term alternating loads. The rainflow counting method identifies stress amplitude and cycle number, the Sn-S curve provides fatigue life data for the material at different stress levels, and Miner's theory predicts fatigue failure through cumulative damage values. This method is widely used in the fatigue design and life assessment of wind turbine blades, towers, main shafts, and other components. The specific calculation process is as follows: Figure 4 As shown.

[0152] Step 2.1 Stress Cycle Identification

[0153] Actual stress signals are typically complex, requiring decomposition into cycles of varying stress amplitudes. Several methods exist, with rainflow counting being the most common for fatigue life calculation. Based on this, a novel real-time fatigue damage prediction method using rainflow counting is proposed. This method concatenates residual stress waves that did not form complete cycles with newly measured stress data to predict current fatigue damage in real time. Simultaneously, it stores new residual stress waves and combines them with subsequent stress measurements to continuously calculate the fatigue damage of the wind turbine.

[0154] Step 2.1.1 Four-point rainflow counting method

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

[0156] First, the time series of tower thrust and spindle torque data is preprocessed, retaining only peaks and troughs. This reconstruction process must begin at the highest peak or lowest trough in the sequence. Then, the peak-trough value sequence is counted according to the following rules:

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

[0158] S a =|BC| / 2 (28)

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

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

[0161] S a =|FG| / 2 (30)

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

[0163] The AH values ​​mentioned above represent the peak / valley values ​​of each waveform. After repeating the above method to count, the remaining peak and valley values ​​form the residual waveform.

[0164] Step 2.1.2 Improved residual series method

[0165] In actual calculations, due to the limitations of the calculation time window, residual waves (stress points that have not formed a complete cycle) are often generated. According to the residual wave cascading method, we can continue to identify cycles by connecting the residual data points in each time period and combining them with new stress data. This method can combine previously incomplete cycles with new data, ensuring accurate assessment of fatigue damage. The idea behind the residual wave cascading method is as follows:

[0166] First, the two sets of residual waves after performing the four-point rainflow counting method on waveforms 1 and 2 are as follows: Figure 7 and Figure 8 As shown, the sequences are connected in chronological order, and the PV principle is used to process the connection points. Connection point E is then deleted, as shown. The four-point rainflow counting method is then reapplied to the cascaded sequences until all full cycles are determined and deleted in sequence, as shown. Figure 9 , Figure 10 and Figure 11 As shown.

[0167] In addition, based on the four-point rainflow counting method and the residual wave series method, this embodiment optimizes the mathematical model for stress cycle identification: First, at each integer t in all time series... s The four-point rainflow counting method is executed once every second, recording and storing the calculated cyclic data and residuals. In subsequent stress cyclic calculations at any given time, the new time-series stress values ​​are compared with the previous integer values. s The residual waves at each moment are connected in series, and the four-point rainflow counting method is used to obtain new cyclic data and residual waves. The sum of the two cyclic data is the stress cyclic data up to the target time.

[0168] Compared to traditional methods that directly calculate the full stress cycle at any given time, this optimized method significantly reduces the amount of data required for subsequent calculations by pre-storing cycle data and residuals at key moments. This greatly shortens the time required to calculate the quantitative index of accumulated fatigue damage over any given period. The introduction of this method effectively improves the model's real-time performance, ensuring a rapid and accurate assessment of fatigue damage in wind turbine components.

[0169] Step 2.2 Calculation of quantitative indicators for cumulative fatigue damage

[0170] The quantitative indicators of cumulative fatigue damage include equivalent fatigue load and cumulative fatigue damage value, and their corresponding calculation formulas are as follows:

[0171] (1)Goodman correction

[0172] The Goodman correction is a commonly used fatigue life prediction method that considers the effect of mean stress on fatigue strength. This method combines the actual stress amplitude of the material with the mean stress to correct for equivalent stress, thus providing a more accurate assessment of fatigue life. The Goodman correction assumes that the fatigue limit of the material decreases linearly with increasing mean stress, and its expression is:

[0173]

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

[0175] (2) Equivalent fatigue load

[0176]

[0177] In the formula, L i These are the load amplitudes at each level after Goodman correction.

[0178] (3) Cumulative fatigue damage value

[0179] Substituting the Goodman-corrected load amplitudes of each level into the SN curve formula (33), the corresponding maximum number of cyclic loads can be obtained.

[0180] S m ×N max =C(34)

[0181] Then, using equation (34), the loss caused by each cycle is calculated, and the cumulative fatigue damage value is obtained by summing them up according to the Palmgren-Miner linear cumulative damage theory.

[0182]

[0183] Step 3: Optimize wind turbine power scheduling

[0184] This paper addresses the wind turbine power scheduling optimization problem, where the decision variable is the power scheduling command of the wind turbine. The objective is to minimize fatigue damage to key components of the wind turbine while satisfying various system operation constraints. It is a multi-objective optimization problem, and the main process is as follows: Figure 12 As shown.

[0185] Step 3.1 Objective Function

[0186] Define the cumulative fatigue damage index corresponding to spindle torque and tower thrust. and The two objectives are the fatigue damage of the main spindle and the tower components. Using the analytic hierarchy process (AHP), certain weights are assigned to the fatigue damage of the two components, and the objective function for the overall fatigue damage level is the minimum sum of the products of the fatigue damage of each component and its weight. Its form is:

[0187]

[0188] Step 3.1.2 Calculation of weights

[0189] The weights w can be determined using the analytic hierarchy process (AHP), entropy weighting, or data-driven methods. main and w tower It should be noted that the tower thrust and spindle torque are of different orders of magnitude and need to be normalized.

[0190] Literature review reveals that fatigue damage in both the spindle and the tower is influenced by load conditions, cyclic stress, material properties, and structural complexity. Therefore, the following hierarchical structure is proposed for hierarchical analysis: Figure 13 As shown.

[0191] The following is an analysis of the weighting of fatigue damage to the spindle and tower based on four criteria: load condition, cyclic stress, material properties, and structural complexity.

[0192] Analyzing the load conditions, firstly, the types of loads borne by the spindle and the tower are different, therefore their fatigue sources are also different. The fatigue sources of the spindle include: cyclic loads, non-uniform loads, instantaneous high loads, and vibration. The fatigue sources of the tower include: static loads, lateral loads, and resonance effects.

[0193] Analysis of the load type criteria shows that the main shaft experiences more severe loads, especially during periods of large wind speed changes and rotor acceleration / deceleration. The stress on the main shaft is more pronounced during these periods, resulting in a larger stress cycle amplitude and a faster rate of cumulative fatigue damage. In contrast, the tower's primary fatigue originates from wind loads, which are more uniform and exhibit lower stress variation frequencies. Therefore, the cumulative fatigue damage increases more slowly.

[0194] Analysis of the stress cycle count criterion reveals that the rotor and main shaft rotate at relatively high speeds, while the tower primarily bears low-frequency, high-replication wind loads. Therefore, the stress cycle count of the main shaft is significantly higher than that of the tower, and the cumulative fatigue damage of the main shaft increases much faster.

[0195] Analysis of material properties reveals that the spindle is typically made of high-strength steel, while the tower is usually constructed from thicker steel plates and composite materials, both exhibiting relatively high fatigue limits. However, due to the larger stress fluctuations experienced by the spindle, its material fatigue life is relatively short.

[0196] Analysis based on the structural complexity criterion shows that the connection points and bearings of Zhuzhou are stress concentration areas, while the structure of other companies is simpler and the stress is more evenly distributed, resulting in a relatively higher fatigue damage rate of the spindle.

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

[0198] Because the main shaft has a large stress cycle amplitude and a large number of stress cycles, its fatigue life is relatively short and the stress is concentrated, the fatigue damage weight of the main shaft is higher, which should be between 0.7 and 0.8. In contrast, the fatigue damage weight of the tower is smaller, around 0.2 to 0.3. The sum of the weights of the main shaft and the tower under each criterion is 1.

[0199] Based on the above analysis, the four criteria are compared in pairs, and their relative importance is assigned. The weights can then be obtained using the analytic hierarchy process (AHP).

[0200] Step 3.1.3 and Calculation

[0201] First, using formulas (16) and (27), the power scheduling instruction P of the decision variable is used. i Using other input and status data, the spindle torque and tower thrust are estimated. Then, using the low-complexity model of the fatigue damage degree of the wind turbine spindle and tower from step 2, the cumulative fatigue damage of the wind turbine i spindle at this moment is calculated. Cumulative fatigue damage to tower

[0202] Step 3.2 Constraints

[0203] To meet the needs of wind turbine power dispatching, the following constraints need to be established:

[0204] (1) Ensure that the sum of all wind turbine power dispatches equals the grid dispatch command.

[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 allocation value of each wind turbine and the result of the average allocation method does not exceed 1MW.

[0209]

[0210] (4) Ensure that the fatigue damage of each key component of the fan does not exceed the limit (i.e., does not exceed the threshold), that is, fatigue damage does not occur.

[0211] D i <D imax (40)

[0212] In addition, new constraints can be added based on actual needs.

[0213] The effectiveness of this patented method is verified by taking a wind farm as an example. The wind farm has 100 wind turbines, all of which are of the same model, with a rated output power of 5MW and a rotor radius of 63m.

[0214] First, following step 1, acquire the input and status data for each wind turbine. Input data includes real-time wind speed, and status data includes blade pitch angle, low-speed shaft speed, and high-speed shaft speed. The table below shows the relevant data for a specific wind turbine over the first 20 seconds.

[0215] Table 1. Input and status data of a certain fan in the first 20 seconds.

[0216]

[0217]

[0218] Then, based on formulas (16) and (27) in step 1, the functional relationship between tower thrust / main shaft torque and decision variables can be established. Next, using the fatigue damage model from step 2, the degree of fatigue damage D is obtained. i With 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, and the objective function expression for the total cumulative fatigue damage at this point is:

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

[0220] The constraints are:

[0221] 0≤P i ≤P max =5MW (42)

[0222]

[0223] Subsequently, the objective function and constraints are input into the optimizer for solution. During the solution process, initial values ​​and upper and lower bounds for the decision variables are required. The optimizer will iteratively calculate and ultimately obtain the optimal solution that satisfies the constraints. Finally, the optimization result is substituted back into the objective function and constraints for verification, ensuring that the objective function value is minimized and all constraints are satisfied.

[0224] Table 2. Scheduling power P of a certain fan before and after optimization (0-20s) i and cumulative fatigue damage value

[0225]

[0226]

[0227] Depend on Figure 14 It can be seen that the growth trend of the average fatigue value has slowed down, increasing component life and indicating that the model optimization is effective. Next, through quantitative analysis, we will more intuitively demonstrate the degree of optimization of fatigue damage.

[0228] Since the wind energy carried by the wind is completely absorbed and converted into electrical energy by the wind turbine, the thrust (the thrust generated by the wind pushing the wind turbine plane) and torque (the torque caused by the mismatch between the actual speed of the wind turbine and the speed that should be reached under the current wind speed) borne by the wind turbine are minimized. Therefore, in the results analysis, the variance of the reference power between the wind turbines before and after optimization is used for comparison.

[0229] like Figure 15 As shown, the difference between the variance of the wind turbine reference power before optimization and the variance of the wind turbine reference power after optimization is significantly reduced, indicating that the optimization model is effective.

[0230] Example 2

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

[0232] The wind turbine key component modeling module is configured to construct a stress characteristic model of the key components based on their operating characteristics and external environmental factors.

[0233] The 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 the four-point rainflow counting method and the residual wave series method, and calculate the cumulative fatigue damage of the key components of the wind turbine based on fatigue damage theory.

[0234] The power dispatch model construction module is configured to build a power dispatch model with the optimization objective of minimizing wind turbine fatigue damage. The objective function is defined as the weighted minimum of the main axis and tower fatigue damage, taking into account the wind farm output power constraints, grid dispatch demand constraints, wind turbine operating characteristic constraints and fatigue damage constraints.

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

[0236] Example 3

[0237] An electronic device includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor, wherein the computer instructions, when executed by the processor, perform the steps in the method provided in Embodiment 1.

[0238] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made by those skilled in the art without creative effort within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A wind turbine power scheduling optimization method, characterized in that, Includes the following steps: Based on the operating characteristics of key wind turbine components and external environmental factors, a force characteristic model of key components is constructed, including: acquiring the power dispatch command, wind speed and wind turbine status data of the wind turbine; estimating the main shaft torque of the wind turbine tower based on the acquired data; decomposing the force of the wind speed directly acting on the blade surface into two forces, perpendicular to the blade surface and parallel to the wind turbine rotation surface, to obtain the tangential component of the main shaft along the blade rotation surface as the effective force, thereby realizing the estimation of the wind turbine tower thrust; Based on the stress characteristic model of the key components, and combining the four-point rainflow counting method and the residual wave series method, the stress cycle in the operation of the wind turbine is identified and corrected. Based on the fatigue damage theory, the cumulative fatigue damage of the key components of the wind turbine is calculated. A power dispatch model is constructed with the goal of minimizing wind turbine fatigue damage. The objective function is defined as the weighted minimum value of fatigue damage of the main shaft and tower, taking into account the constraints of wind farm output power, grid dispatch demand, wind turbine operating characteristics and fatigue damage. Define the cumulative fatigue damage index corresponding to spindle torque and tower thrust. and To address the fatigue damage of two components—the main spindle and the tower—an analytic hierarchy process (AHP) is employed. Weights are assigned to the fatigue damage of each component, and the objective function for determining the overall fatigue damage level is minimized by taking the minimum sum of the products of the fatigue damage and their respective weights. The objective function takes the following form: The weights are determined by the analytic hierarchy process (AHP), entropy weight method, or data-driven methods. and ; The power scheduling model is solved using a nonlinear programming method to generate the optimal power scheduling strategy for each wind turbine.

2. The wind turbine power scheduling optimization method as described in claim 1, characterized in that, Based on the stress characteristic model of the key components, and combining the four-point rainflow counting method and the residual wave series method, the process of identifying and correcting stress cycles during wind turbine operation includes: Preprocess a series of tower thrust and spindle torque data in time series, retaining only the peaks and troughs, and the reconstruction process must start from the highest peak or lowest trough in the series; The peak-valley sequence is counted according to a predetermined rule, and the counted peak-valley values ​​are combined to form a residual wave. Connect the residuals in chronological order, process the connection points using the PV principle, and delete the connection points. Reapply the rainflow counting method to the cascade sequence until all full cycles are determined and deleted in sequence.

3. The wind turbine power scheduling optimization method as described in claim 1, characterized in that, Based on the stress characteristic model of the key components, and combining the four-point rainflow counting method and the residual wave series method, in the process of identifying and correcting the stress cycle during wind turbine operation, the four-point rainflow 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 waves at the previous set moment, and the four-point rainflow counting method is performed to obtain new cycle data and residual waves. The sum of the two cycle data is the stress cycle data up to the target moment.

4. The wind turbine power scheduling optimization method as described in claim 1, characterized in that, Based on fatigue damage theory, the process of calculating the cumulative fatigue damage of key components of a wind turbine includes: using Goodman correction to combine the actual stress amplitude and mean stress of the material to correct the equivalent stress; calculating the equivalent fatigue load based on 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 cycles; 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.

5. The wind turbine power scheduling optimization method as described in claim 1, characterized in that, The process of constructing a power dispatch model with the optimization objective of minimizing wind turbine fatigue damage includes: solving the wind turbine power dispatch optimization problem, with the decision variable being the power dispatch command of the wind turbine, and the objective function being to minimize the fatigue damage of key components of the wind turbine, while satisfying the constraints of wind farm output power, grid dispatch demand, wind turbine operating characteristics, and fatigue damage in system operation.

6. The wind turbine power scheduling optimization method as described 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 index corresponding to the main shaft torque and the tower top thrust is defined as two objectives for the fatigue damage of the two components, the main shaft and the tower. The analytic hierarchy process is used to assign certain weights to the fatigue damage of the two components, the main shaft and the tower, and the sum of the products of the fatigue damage of the two components and the weights is taken as the objective function for the overall fatigue damage.

7. The wind turbine power scheduling optimization method as described in claim 6, characterized in that, The process of assigning certain weights to the fatigue damage of the two components, the spindle and the tower, includes a higher weight for the fatigue damage of the spindle, with a value range of [0.7, 0.8], and a lower weight for the fatigue damage of the tower, with a value range of [0.2, 0.3]. The sum of the weights of the spindle and the tower under each criterion is 1.

8. A wind turbine power scheduling optimization system, employing the wind turbine power scheduling optimization method according to any one of claims 1-7, characterized in that, include: The wind turbine key component modeling module is configured to construct a stress characteristic model of the key components based on their operating characteristics and external environmental factors. The 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 the four-point rainflow counting method and the residual wave series method, and calculate the cumulative fatigue damage of the key components of the wind turbine based on fatigue damage theory. The power dispatch model construction module is configured to build a power dispatch model with the optimization objective of minimizing wind turbine fatigue damage. The objective function is defined as the weighted minimum of the main axis and tower fatigue damage, taking into account the wind farm output power constraints, grid dispatch demand constraints, wind turbine operating characteristic constraints and fatigue damage constraints. The optimization solution module is configured to solve the power scheduling model using a nonlinear programming method to generate the optimal power scheduling strategy for each wind turbine.

9. An electronic device, characterized in that, It includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor, which, when executed by the processor, perform the steps of the method according to any one of claims 1-7.

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