Complex terrain wind power plant power and load collaborative optimization control method and system
By using lidar to construct a wake effect model and establish a multi-objective optimization function in wind farms, the control strategy of wind turbines was optimized, solving the wake effect problem of wind farms in complex terrain, maximizing power generation and minimizing structural load, and improving the operational safety and economy of wind farms.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- HUANENG JIANGXI CLEAN ENERGY GENERATION CO LTD
- Filing Date
- 2026-01-06
- Publication Date
- 2026-05-01
AI Technical Summary
Existing simulations of wake effects in complex terrain are inaccurate, leading to inaccurate power predictions and increased structural loads. Traditional control strategies ignore the negative impacts of wake effects, lack real-time dynamic calibration mechanisms, and have poor robustness.
Data was acquired using lidar, and a multi-wind turbine wake impact assessment model was constructed. A multi-objective optimization function was established to maximize the total power generation of the wind farm and minimize the structural load of the wind turbine units. The optimization control strategy was solved using a genetic algorithm, and the pitch angle and yaw angle were adjusted to achieve synergistic optimization.
It improves the accuracy of power prediction and load assessment, extends the life of wind turbine components, reduces operation and maintenance costs, enhances the safety and economy of wind farms, and the control strategy is adaptive to the dynamic wind farm environment.
Smart Images
Figure CN121965795A_ABST
Abstract
Description
A method and system for coordinated optimization control of power and load in wind farms with complex terrain Technical Field
[0001] This invention relates to the field of wind power generation, and specifically to a method and system for coordinated optimization control of power and load in wind farms with complex terrain. Background Technology
[0002] Wind power, as an important clean and renewable energy source, plays a crucial role in the global energy structure transformation. With the expansion of wind farm scale and the diversification of construction sites, the wake effect of wind turbine clusters and its negative impacts are becoming increasingly prominent.
[0003] Currently, the operation and control of wind farms mainly face the following problems: 1. Existing mainstream wind farm wake models are mostly based on the assumption of flat terrain, which has poor applicability in complex terrain. They are difficult to accurately simulate phenomena such as wind shear and turbulence aggravation caused by terrain. The prediction of the superposition effect of wakes of multiple wind turbines downstream has a large deviation, resulting in inaccurate wind farm power prediction.
[0004] 2. Traditional wind farm control strategies mostly aim to maximize the power generation of a single unit or the total power generation of the entire farm. They pursue power generation by optimizing yaw, pitch and other controls, but ignore the increased structural load caused by the wake effect. When the downstream wind turbine is in the wake of the upstream wind turbine, not only will the power decrease due to the reduced wind speed, but the fatigue load on components such as towers and blades will also be aggravated. In the long run, this will seriously threaten the structural safety of the wind turbine, shorten the life of key components, and increase maintenance costs and safety risks.
[0005] 3. Most optimization controls rely on preset fixed model parameters and lack a closed-loop mechanism for dynamic calibration and feedback adjustment using real-time measurement data. This can lead to the actual effect of the control strategy deviating from expectations and exhibiting poor robustness. Summary of the Invention
[0006] To address the problems mentioned in the prior art, this invention proposes a method and system for coordinated optimization control of power and load in wind farms with complex terrain. Data is acquired using lidar, and the optimization objectives are to maximize the total power generation of the wind farm and minimize the load on key structural components of each wind turbine. A multi-objective optimization function is established and solved to achieve optimized control.
[0007] To achieve the above objectives, the present invention adopts the following technical solution: A method for coordinated optimization control of power and load in wind farms with complex terrain, comprising the following steps: S1, acquiring real-time operating data and environmental data of the target wind farm; S2, constructing a multi-turbine wake impact assessment model of the target wind farm based on the environmental data; S3, establishing a multi-objective optimization function with maximizing the total power generation of the wind farm as the first optimization objective and minimizing the load of key structural components of each wind turbine as the second optimization objective; predicting the equivalent input wind speed of each wind turbine under different control strategies based on the multi-turbine wake impact assessment model; S4, substituting the predicted equivalent input wind speed into the constructed wind turbine aerodynamic performance model and structural load model to calculate the corresponding power generation estimate and load estimate; solving the multi-objective optimization function using an optimization algorithm to obtain the optimal coordinated control strategy at the current moment; S5, controlling each wind turbine to adjust to the corresponding pitch angle and yaw angle based on the optimal coordinated control strategy to achieve optimized control.
[0008] As a further improvement of the present invention, the operating data includes at least the real-time power, pitch angle, yaw angle and rotational speed of the wind turbine; the environmental data includes at least the free-flow wind speed and direction collected by the lidar system and the wind speed distribution data of the wake region collected.
[0009] As a further improvement of the present invention, the multi-turbine wake impact assessment model for the target wind farm constructed in step S2 includes: using the Park wake model as the basic single-turbine wake model; and, based on the basic single-turbine wake model, applying the wake superposition formula to calculate the equivalent input wind speed of the downstream target wind turbine due to the combined influence of multiple upstream wind turbines. As a further improvement of the present invention, the expression of the Park wake model is as follows:
[0010] In the formula: k is the wake attenuation coefficient, which is determined according to the surface roughness of the wind farm, ranging from 0.04 to 0.075; r is the radius of the wind turbine rotor; r(s) is the wake radius of the wind turbine at a distance s downstream; v(s) is the wake wind speed at a distance downstream of the wind turbine; vo is the incoming wind speed at infinity; a is the axial induction factor; the wake superposition formula is as follows:
[0011] In the formula: vi is the wake wind speed of the i-th wind turbine at the i-th wind turbine; vj is the input wind speed assuming that the j-th wind turbine is not affected by the wake; A is the overlapping area of the wake region of the i-th wind turbine and the rotor region of the i-th wind turbine.
[0012] As a further improvement of the present invention, the load estimate in S4 includes the bending moment at the tower root and the bending moment at the blade root; the calculation formula for the tower bending moment is as follows:
[0013] In the formula: T is the thrust acting on the wind turbine; h is the tower height; the calculation formula for the bending moment at the blade root is as follows:
[0014] In the formula: m is the blade mass; g is the gravitational acceleration; D is the diameter of the wind turbine rotor.
[0015] As a further improvement to the present invention, the expression for the aerodynamic performance model of the wind turbine in S4 is as follows:
[0016] In the formula: a is the axial induction factor.
[0017] As a further improvement of the present invention, the multi-objective optimization function is solved by an optimization algorithm in S4, including: within each control cycle, based on the current and predicted short-term wind conditions, the multi-objective optimization function is solved within a rolling time window, and the optimization algorithm used in the solution process is a genetic algorithm.
[0018] This invention proposes a collaborative optimization control system for power and load in wind farms with complex terrain, comprising: an acquisition module for acquiring real-time operational and environmental data of the target wind farm; an evaluation module for constructing a multi-turbine wake impact assessment model of the target wind farm based on the environmental data; an optimization module for establishing a multi-objective optimization function with maximizing the total power generation of the wind farm as the first optimization objective and minimizing the load of key structural components of each wind turbine as the second optimization objective; predicting the equivalent input wind speed of each wind turbine under different control strategies based on the multi-turbine wake impact assessment model; a generation module for substituting the predicted equivalent input wind speed into the constructed wind turbine aerodynamic performance model and structural load model to calculate the corresponding power generation and load estimates; solving the multi-objective optimization function using an optimization algorithm to obtain the optimal collaborative control strategy at the current moment; and an execution module for controlling each wind turbine to adjust to the corresponding pitch and yaw angles based on the optimal collaborative control strategy to achieve optimized control.
[0019] This invention proposes a power and load co-optimization control device for wind farms in complex terrain, comprising a processor and a memory, wherein the processor executes a computer program stored in the memory to implement the power and load co-optimization control method for wind farms in complex terrain as described above.
[0020] This invention proposes a computer-readable storage medium for storing a computer program, wherein the computer program, when executed by a processor, implements the aforementioned method for coordinated optimization control of power and load in wind farms with complex terrain.
[0021] Compared with the prior art, the present invention has achieved the following technical effects: The present invention takes maximizing the total power generation of the wind farm and minimizing the load on the key structural components of each wind turbine as the optimization objectives, establishes a multi-objective optimization function, and obtains a control strategy by solving the function. This fundamentally solves the problems existing in traditional methods, ensuring that while increasing power generation, it can reduce the fatigue load on components such as towers and blades, thereby extending the service life of wind turbines, reducing operation and maintenance costs, and improving the safety and economy of wind farm operation.
[0022] The multi-wind turbine wake impact assessment model constructed in this invention is applicable to complex terrain scenarios. By fusing free flow and wake field data collected in real time by lidar and applying the Park wake model and wake superposition formula, it can calculate the equivalent input wind speed of downstream wind turbines under the superposition of multiple wakes and the influence of terrain, thereby providing reliable input for subsequent processes and significantly improving the accuracy of power prediction and load assessment.
[0023] After the control strategy is executed, the method of this invention uses lidar to collect actual wake data again, compares and calibrates it with the model prediction results, so that the wake model and optimization strategy can adapt to the complex and dynamically changing wind field environment and be continuously corrected, thus ensuring the long-term effectiveness and robustness of the control method. Attached Figure Description
[0024] Figure 1 is a schematic diagram of the overall process of the present invention. Detailed Implementation
[0025] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present invention, and not all of the structures.
[0026] Referring to Figure 1, this embodiment proposes a method for coordinated optimization control of power and load in wind farms with complex terrain, including the following steps: S1, real-time acquisition of operational and environmental data of the target wind farm; S2, construction of a multi-turbine wake impact assessment model of the target wind farm based on the environmental data; S3, establishment of a multi-objective optimization function with maximizing the total power generation of the wind farm as the first optimization objective and minimizing the load of key structural components of each wind turbine as the second optimization objective; prediction of the equivalent input wind speed of each wind turbine under different control strategies based on the multi-turbine wake impact assessment model; S4, substituting the predicted equivalent input wind speed into the constructed wind turbine aerodynamic performance model and structural load model to calculate the corresponding power generation estimate and load estimate; solving the multi-objective optimization function using an optimization algorithm to obtain the optimal coordinated control strategy at the current moment; S5, control each wind turbine to adjust to the corresponding pitch angle and yaw angle based on the optimal coordinated control strategy to achieve optimized control.
[0027] The invention will be further explained below with reference to the accompanying drawings and specific embodiments: Since traditional wind farm operation heavily relies on anemometers and wind vanes on the top of the wind turbine nacelles, this embodiment first deploys two types of lidar in the wind farm. One type is installed on the top of the wind turbine nacelle, which can detect the wind speed, direction, and turbulence intensity of the free flow hundreds of meters in front of the turbine. The other type of lidar is fixedly installed on the ground or a tower base, and performs a 360° rotational scan. Its emitted laser pulses can detect wind speeds at different distances and directions. This scanning can generate a real-time wind speed contour map covering most of the wind farm area, showing the wake region extending from behind the upstream turbines where the wind speed significantly decreases. The environmental data acquired by the lidar, along with real-time operating status data such as power, pitch angle, yaw angle, and rotational speed read from the SCADA system of each wind turbine, forms the data foundation for subsequent analysis and decision-making.
[0028] Based on the real-time environmental data obtained through the above steps, this method uses the Park wake model as its foundation, and its expression is as follows:
[0029] In the formula: k is the wake attenuation coefficient, which is determined according to the surface roughness of the wind farm, ranging from 0.04 to 0.075; r is the radius of the wind turbine rotor; r(s) is the wake radius of the wind turbine at a distance s downstream; v(s) is the wake wind speed at a distance downstream of the wind turbine; vo is the incoming wind speed at infinity; a is the axial induction factor; in this embodiment, since the wind farm is a whole, downstream wind turbines are often simultaneously under the wake shadows of multiple upstream wind turbines; therefore, wake superposition calculation is introduced, and its expression is as follows:
[0030] In the formula: vi is the wake wind speed of the i-th wind turbine at the i-th wind turbine; vj is the input wind speed assuming that the j-th wind turbine is not affected by the wake; A is the overlapping area of the wake region of the i-th wind turbine and the rotor region of the i-th wind turbine.
[0031] For the target wind turbine, the system will identify all possible upstream influencing wind turbines. For each upstream wind turbine, the system will first use the Park model to calculate the wind speed attenuation value V caused at its location. _i The system then calculates the overlap area A between the wake cone of each upstream wind turbine and the circular surface of the target wind turbine impeller. _ij Finally, the equivalent input wind speed V of the target wind turbine at point J was calculated. _j This allows for the prediction of wind speed distribution under any combination of turbine layout and yaw conditions within a given timeframe.
[0032] Based on the calculated wind speed distribution, the estimated power generation and load can be calculated; specifically, the estimated power generation is calculated by using the predicted equivalent input wind speed V. _j By substituting the aerodynamic performance model of the fan into the equation, we can obtain the following expression for the aerodynamic performance model of the fan in this embodiment:
[0033] In the formula: a is the axial induction factor.
[0034] The mechanical load estimate is calculated using a mechanical model, based on the aerodynamic thrust T acting on the impeller, and derived from wind speed and turbine characteristics. The bending moment M at the base of the tower... r It is mainly obtained by multiplying the thrust T by the tower height H (lever arm), that is... The bending moment at the root of the leaf; It consists of two parts: one part is the torque generated by aerodynamic thrust at the blade root (T * R, where R is the impeller radius), and the other part is the periodic alternating torque generated by the blade's own weight during rotation, expressed as: .
[0035] Therefore, for any set of predicted wind speeds, the system can simultaneously output two evaluation values: the estimated power generation and the estimated load.
[0036] This embodiment focuses on the decision-making phase. The decision problem is formalized as a mathematical optimization problem: finding a set of yaw and pitch angle settings for all wind turbines such that, within a short time window (e.g., the next 10 minutes), the predicted total power generation of the entire wind farm is maximized, while the predicted cumulative load of key components is minimized. Therefore, a genetic algorithm is preferred to find the optimal solution. The genetic algorithm randomly generates a large number of possible control strategy combinations, and then uses the process described above to evaluate the comprehensive score of power generation and load for each strategy. After several generations of iterative evolution, the algorithm converges to the optimal cooperative control strategy under the current weather conditions. The cooperative control strategy indicates whether the yaw angle of each wind turbine needs fine-tuning to reduce shading of downstream turbines, and whether the pitch angle needs fine-tuning to achieve balance.
[0037] After the decision is made, the control command is quickly transmitted to the local controller of each wind turbine through the fiber optic network within the wind farm, causing the turbines to perform pitch and yaw maneuvers, and the entire wind farm enters a new steady state. In this preferred embodiment, the ground-based scanning lidar will be restarted after the new control strategy has been executed for a period of time (e.g., 15 minutes) to acquire a measured map of the actual wake distribution of the wind farm at this time. The average error is calculated by comparing this measured map with the predicted map predicted by the model based on the executed strategy.
[0038] If the error remains high, it indicates that the current wind field physical model parameters deviate from the actual situation. The system will then initiate a calibration procedure to fine-tune the model parameters, bringing the predicted values closer to the measured values. Through continuous learning, the model will become increasingly accurate, and the reliability of the optimized decisions will be enhanced.
[0039] Based on the same inventive concept, this embodiment of the invention also provides a power and load co-optimization control system for wind farms in complex terrain. Since the principle of this power and load co-optimization control system for wind farms in complex terrain is similar to that of the aforementioned power and load co-optimization control method for wind farms in complex terrain, the implementation of this power and load co-optimization control system for wind farms in complex terrain can refer to the implementation of the power and load co-optimization control method for wind farms in complex terrain, and the repeated parts will not be described again.
[0040] In specific implementation, the power and load collaborative optimization control system for wind farms in complex terrain provided by this invention includes: an acquisition module for acquiring real-time operating data and environmental data of the target wind farm; an evaluation module for constructing a multi-turbine wake impact assessment model of the target wind farm based on the environmental data; an optimization module for establishing a multi-objective optimization function with maximizing the total power generation of the wind farm as the first optimization objective and minimizing the load of key structural components of each wind turbine as the second optimization objective; predicting the equivalent input wind speed of each wind turbine under different control strategies based on the multi-turbine wake impact assessment model; a generation module for substituting the predicted equivalent input wind speed into the constructed wind turbine aerodynamic performance model and structural load model to calculate the corresponding power generation estimate and load estimate; solving the multi-objective optimization function using an optimization algorithm to obtain the optimal collaborative control strategy at the current moment; and an execution module for controlling each wind turbine to adjust to the corresponding pitch angle and yaw angle based on the optimal collaborative control strategy to achieve optimized control.
[0041] Accordingly, this embodiment of the invention also provides a power and load co-optimization control device for wind farms in complex terrain, including a processor and a memory, wherein the processor executes a computer program stored in the memory to implement the power and load co-optimization control method for wind farms in complex terrain as provided in this embodiment of the invention.
[0042] For more detailed information on the above methods, please refer to the relevant content disclosed in the foregoing embodiments, which will not be repeated here.
[0043] Accordingly, embodiments of the present invention also provide a computer-readable storage medium for storing a computer program, wherein the computer program, when executed by a processor, implements the above-described method for coordinated optimization control of power and load in wind farms with complex terrain as provided in embodiments of the present invention.
[0044] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems, devices, and storage media disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.
[0045] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.
[0046] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.
[0047] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.
[0048] The foregoing has provided a detailed description of the method, system, equipment, and storage medium for the coordinated optimization control of power and load in wind farms with complex terrain provided by this invention. Specific examples have been used to illustrate the principles and implementation methods of this invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.
Claims
1. A method for coordinated optimization control of power and load in wind farms with complex terrain, characterized in that, Includes the following steps: S1. Acquire real-time operational and environmental data of the target wind farm; S2. Construct a multi-turbine wake impact assessment model for the target wind farm based on the environmental data; S3. Establish a multi-objective optimization function with maximizing the total power generation of the wind farm as the first optimization objective and minimizing the load on the key structural components of each wind turbine as the second optimization objective. Based on the multi-wind turbine wake impact assessment model, the equivalent input wind speed of each wind turbine is predicted under different control strategies. S4. Substitute the predicted equivalent input wind speed into the constructed wind turbine aerodynamic performance model and structural load model to calculate the corresponding power generation estimate and load estimate; use optimization algorithms to solve the multi-objective optimization function to obtain the optimal cooperative control strategy at the current moment; S5. Based on the optimal cooperative control strategy, control each wind turbine to adjust to the corresponding pitch angle and yaw angle to achieve optimized control.
2. The method for coordinated optimization control of power and load in wind farms with complex terrain according to claim 1, characterized in that, The operational data includes at least the real-time power, pitch angle, yaw angle, and rotational speed of the wind turbine; the environmental data includes at least the free-flow wind speed and direction collected by the lidar system, as well as the wind speed distribution data in the wake region.
3. The method for coordinated optimization control of power and load in wind farms with complex terrain according to claim 1, characterized in that, The S2 section describes the construction of a multi-turbine wake impact assessment model for the target wind farm, which includes: using the Park wake model as the basic single-turbine wake model; and, based on the basic single-turbine wake model, applying the wake superposition formula to calculate the equivalent input wind speed of the downstream target wind turbine due to the combined influence of multiple upstream wind turbines.
4. The method for coordinated optimization control of power and load in wind farms with complex terrain according to claim 3, characterized in that, The expression for the Park wake model is as follows: In the formula: k is the wake attenuation coefficient, which is determined according to the surface roughness of the wind farm, ranging from 0.04 to 0.075; r is the radius of the wind turbine rotor; r(s) is the wake radius of the wind turbine at a distance s downstream; v(s) is the wake wind speed at a distance downstream of the wind turbine; vo is the incoming wind speed at infinity; a is the axial induction factor; the wake superposition formula is as follows: In the formula: vi is the wake wind speed of the i-th wind turbine at the i-th wind turbine; vj is the input wind speed assuming that the j-th wind turbine is not affected by the wake; A is the overlapping area of the wake region of the i-th wind turbine and the rotor region of the i-th wind turbine.
5. The method for coordinated optimization control of power and load in wind farms with complex terrain according to claim 1, characterized in that, The load estimate in S4 includes the bending moment at the tower root and the bending moment at the blade root; the formula for calculating the tower bending moment is as follows: In the formula: T is the thrust acting on the wind turbine; h is the tower height; the calculation formula for the bending moment at the blade root is as follows: In the formula: m is the blade mass; g is the gravitational acceleration; D is the diameter of the wind turbine rotor.
6. The method for coordinated optimization control of power and load in wind farms with complex terrain according to claim 1, characterized in that, The expression for the aerodynamic performance model of the wind turbine in S4 is as follows: In the formula: a is the axial induction factor.
7. The method for coordinated optimization control of power and load in wind farms with complex terrain according to claim 1, characterized in that, The S4 process utilizes an optimization algorithm to solve a multi-objective optimization function, including: within each control cycle, based on the current and predicted short-term wind conditions, solving the multi-objective optimization function within a rolling time window, using a genetic algorithm in the solution process.
8. A power and load collaborative optimization control system for wind farms in complex terrain, characterized in that, include: The acquisition module is used to acquire the operation data and environmental data of the target wind farm in real time; the evaluation module is used to construct a multi-wind turbine wake impact evaluation model of the target wind farm based on the environmental data; the optimization module is used to establish a multi-objective optimization function with maximizing the total power generation of the wind farm as the first optimization objective and minimizing the load of the key structural components of each wind turbine as the second optimization objective. Based on the multi-wind turbine wake impact assessment model, the equivalent input wind speed of each wind turbine is predicted under different control strategies. The generation module is used to substitute the predicted equivalent input wind speed into the constructed wind turbine aerodynamic performance model and structural load model to calculate the corresponding power generation estimate and load estimate; the optimization algorithm is used to solve the multi-objective optimization function to obtain the optimal cooperative control strategy at the current moment; the execution module is used to control each wind turbine to adjust to the corresponding pitch angle and yaw angle based on the optimal cooperative control strategy to achieve optimized control.
9. A power and load collaborative optimization control device for wind farms in complex terrain, characterized in that, It includes a processor and a memory, wherein the processor executes a computer program stored in the memory to implement the power and load co-optimization control method for wind farms in complex terrain as described in any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that, Used to store a computer program, wherein the computer program, when executed by a processor, implements the power and load co-optimization control method for wind farms in complex terrain as described in any one of claims 1 to 7.