A maximum power generation power tracking control method for an air-based high-altitude wind power generation system

Abstract

The application provides an air-based high-altitude wind power generation system maximum power generation tracking control method, and a dynamic height optimization method of the air-based high-altitude wind power generation system, which can systematically consider multi-dimensional operation constraints such as a high-altitude wind speed profile, air density variation and height adjustment cost, significantly improves high-altitude wind energy capture efficiency and power generation power output stability, and is suitable for real-time power optimization and height scheduling strategy formulation of a floating wind power generation platform.

Description

Technical Field

[0001] This invention relates to the field of high-altitude wind power generation control technology, and in particular to a method for tracking and controlling the maximum power output of an airborne high-altitude wind power generation system. Background Technology

[0002] Floating high-altitude wind power systems (AWES), as a novel renewable energy technology, provide an important supplement to traditional ground-based wind power by capturing stronger and more stable wind energy resources at altitudes of 300-3000 meters. The core advantage of this technology lies in its ability to dynamically adjust operating altitude and actively track high-quality wind layers, thereby significantly improving power generation efficiency and output stability. However, the altitude adjustment process involves complex aerodynamic characteristics, system power consumption constraints, and operational safety limitations. Achieving maximum power output tracking under these multi-dimensional constraints has become a key bottleneck restricting the engineering application of AWES technology.

[0003] Existing technologies directly employ fixed-altitude operation strategies, neglecting the spatiotemporal variability of high-altitude wind speed profiles and the energy costs of altitude adjustment, potentially leading to significant power generation efficiency losses. AWES systems must simultaneously satisfy the objectives of maximizing power generation and ensuring operational safety, but existing methods struggle to achieve synergistic optimization of both under complex constraints. Specifically, handling coupled constraints across multiple altitude levels and time scales presents technical bottlenecks. Traditional heuristic or gradient optimization algorithms struggle to guarantee global optimality and real-time response capabilities when dealing with high-dimensional mixed-integer nonlinear programming problems. Consequently, existing technologies typically employ simplified steady-state models or single-step greedy strategies. However, due to the lack of forward-looking optimization mechanisms, their altitude scheduling decisions under time-varying wind field conditions suffer from suboptimal problems, thus impacting the energy capture efficiency and economic competitiveness of the AWES system. Summary of the Invention

[0004] The present invention aims to at least partially solve one of the technical problems in the related art.

[0005] Therefore, the first objective of this invention is to propose a method for tracking and controlling the maximum power output of an airborne high-altitude wind power generation system.

[0006] Another objective of this invention is to provide a maximum power generation tracking control device for an airborne high-altitude wind power generation system.

[0007] The third objective of this invention is to provide a computer device.

[0008] A fourth objective of this invention is to provide a non-transitory computer-readable storage medium.

[0009] To achieve the above objectives, a first aspect of the present invention provides a method for maximum power generation tracking control of an airborne high-altitude wind power generation system, comprising:

[0010] S1. Construct an optimization model for a floating wind power generation system with embedded height dynamic constraints. The model includes power generation calculation constraints, height maintenance power consumption constraints, height adjustment power consumption constraints, height state constraints, and height change rate constraints. Define the relationship between wind speed, air density, and power generation at different operating heights. S2, based on mixed integer nonlinear programming theory, the optimization model is transformed into a mixed integer linear programming (MILP) problem. The nonlinear wind speed-power relationship is linearized through high-level discretization and piecewise linear interpolation, and the absolute value term of the height change rate is eliminated by using auxiliary binary variables. S3, Solve the MILP problem to generate the optimal altitude trajectory and corresponding power output curve of the floating wind power generation system. The curve characterizes the precise relationship between time-series wind field changes and system net power generation through dynamic programming characteristics. It is used to evaluate the energy capture efficiency of different altitude scheduling strategies and assist in the formulation of real-time power tracking control strategies. S4. An adaptive model predictive control strategy is constructed based on the optimal height trajectory. The height decision sequence for the next T steps is updated in each control cycle through a rolling time-domain optimization algorithm. The height setpoint is dynamically adjusted according to real-time wind field prediction information. The rolling optimization algorithm adopts the warmstart technique, using the optimal solution of the previous cycle as the initial feasible solution of the solver in the current cycle, which significantly reduces the computation time of the MILP problem.

[0011] In one embodiment of the present invention, S1 includes: S11, the relationship between power generation, maintenance power consumption and adjustment power consumption at different operating heights is defined by the first to fourth preset formulas. The power generation calculation constraint adopts the cubic wind speed model of the second preset formula. The cubic wind speed model obtains the wind speed and air density parameters at any height through the piecewise linear interpolation method of the eleventh and twelfth preset formulas. S12, the altitude adjustment is limited by a triple constraint mechanism through the eighth to tenth preset formulas. The eighth preset formula limits the rate of altitude change to not exceed the system's maximum climb / descent speed. The ninth and tenth preset formulas further limit the altitude adjustment operation to be performed at most once within D consecutive time steps to ensure system stability.

[0012] In one embodiment of the present invention, S2 includes: S21, the height decision variable is discretized through the fifteenth to eighteenth preset formulas. The fifteenth preset formula uses a multi-layer grid partitioning method to decompose the continuous height space into N×M discrete height points. The accuracy parameter M of the multi-layer grid partitioning method should be taken to ensure that the wind speed interpolation error between adjacent height points is less than 5% of the rated wind speed. S22, the nonlinear terms with high rate of change are eliminated by defining auxiliary variables through the twenty-second and twenty-third preset formulas. The twenty-second preset formula uses McCormick envelope relaxation technique to transform the binary variable product into a system of linear inequalities. The relaxation technique ensures the equivalence of the auxiliary variables and the original variables through the upper and lower bound constraints of the twenty-ninth and thirtieth preset formulas.

[0013] In one embodiment of the present invention, S3 includes: S31, the optimal height trajectory is generated by maximizing the objective function of the twenty-fifth preset formula, wherein the temporal resolution of the trajectory corresponds to the discretization step size of the system scheduling period T, and the spatial resolution corresponds to the number of height levels N defined by the binary selection variable constraint of the twenty-sixth preset formula. S32 uses the linearized power model of the nineteenth and twentieth preset formulas to evaluate net power generation. Net power generation is equal to the wind energy conversion power of the nineteenth preset formula minus the height maintenance power consumption of the twentieth preset formula and the height adjustment power consumption of the twenty-first preset formula, so as to characterize the comprehensive trade-off between system energy capture efficiency and operating cost under different height dispatch strategies.

[0014] To achieve the above objectives, a second aspect of the present invention provides a maximum power output tracking control device for an airborne high-altitude wind power generation system, comprising: A height dynamic constraint embedding module is used to construct an optimization model of a floating wind power generation system with embedded height dynamic constraints. The model includes power generation calculation constraints, height maintenance power consumption constraints, height adjustment power consumption constraints, height state constraints, and height change rate constraints, and defines the relationship between wind speed, air density, and power generation at different operating heights. The nonlinear wind speed-power relationship linearization module transforms the optimization model into a mixed integer linear programming (MILP) problem based on mixed integer nonlinear programming theory. It achieves linearization of the nonlinear wind speed-power relationship through height discretization and piecewise linear interpolation, and uses auxiliary binary variables to eliminate the absolute value term of the height change rate. The optimal altitude trajectory generation module solves the MILP problem to generate the optimal altitude trajectory and corresponding power output curve of the floating wind power generation system. The curve characterizes the precise relationship between time-series wind field changes and system net power generation through dynamic programming characteristics. It is used to evaluate the energy capture efficiency of different altitude scheduling strategies and assist in the formulation of real-time power tracking control strategies. The adaptive control strategy construction module builds an adaptive model predictive control strategy based on the optimal altitude trajectory. It updates the altitude decision sequence for the next T steps in each control cycle through a rolling time-domain optimization algorithm and dynamically adjusts the altitude setpoint according to real-time wind field prediction information. The rolling optimization algorithm adopts the warmstart technique, using the optimal solution of the previous cycle as the initial feasible solution of the solver in the current cycle, which significantly reduces the computation time of the MILP problem.

[0015] This invention discloses a maximum power point tracking (MPPT) control technology and device for an airborne high-altitude wind power generation system. It proposes a height optimization solution method based on mixed-integer linear programming (MILP). By transforming the nonlinear height-power coupling model, the complex continuous height optimization problem is equivalent to a mixed-integer linear programming problem, achieving efficient and accurate tracking of the maximum power point of the floating wind power generation system. For the first time, a height-discrete wind energy capture optimization framework is proposed, which comprehensively considers multi-dimensional physical constraints such as high-altitude wind speed profiles, air density variations, and height maintenance costs. This overcomes the limitations of traditional fixed-height operation strategies in adapting to time-varying wind fields, significantly improving wind energy capture efficiency and power output stability. Subsequently, a power model linearization method based on piecewise linear interpolation is presented. Utilizing multi-layer grid partitioning and auxiliary variable techniques, the complex nonlinear wind speed-power relationship is transformed into a mixed-integer linear programming problem that can be directly solved by a commercial optimizer. Under conditions of large-scale height variation, the optimal height scheduling trajectory is quickly determined through optimization. Finally, an adaptive control strategy based on rolling time-domain optimization is proposed, and the warmstart technique is introduced to accelerate the MILP solution. The height setpoint can be dynamically adjusted according to real-time wind field prediction information to achieve closed-loop maximum power point tracking control, ensuring that the energy capture efficiency of the system is maximized under complex wind field conditions, and effectively improving the economy and reliability of the floating wind power generation system.

[0016] To achieve the above objectives, a third aspect of this application provides a computer device, including a processor and a memory; wherein the processor reads executable program code stored in the memory to run a program corresponding to the executable program code, for implementing a maximum power tracking control method for an airborne high-altitude wind power generation system as described in the first aspect embodiment.

[0017] To achieve the above objectives, the fourth aspect of this application proposes a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements a maximum power generation tracking control method for an airborne high-altitude wind power generation system as described in the first aspect embodiment.

[0018] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0019] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of a maximum power generation tracking control method for an airborne high-altitude wind power generation system according to an embodiment of the present invention; Figure 2 This is a structural diagram of a maximum power generation tracking control device for an airborne high-altitude wind power generation system according to an embodiment of the present invention; Figure 3 It is a computer device according to an embodiment of the present invention. Detailed Implementation

[0020] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0021] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0022] The following description, with reference to the accompanying drawings, describes a method and apparatus for tracking and controlling the maximum power output of an airborne high-altitude wind power generation system according to an embodiment of the present invention.

[0023] Figure 1 This is a flowchart of a maximum power output tracking control method for an airborne high-altitude wind power generation system according to an embodiment of the present invention, such as... Figure 1 As shown, it includes: Total power consumption constraints, altitude state constraints, and altitude change rate constraints define the relationship between wind speed, air density, and power generation at different operating altitudes; S2, based on mixed integer nonlinear programming theory, the optimization model is transformed into a mixed integer linear programming (MILP) problem. The nonlinear wind speed-power relationship is linearized through high-level discretization and piecewise linear interpolation, and the absolute value term of the height change rate is eliminated by using auxiliary binary variables. S3, Solve the MILP problem to generate the optimal altitude trajectory and corresponding power output curve of the floating wind power generation system. The curve characterizes the precise relationship between time-series wind field changes and system net power generation through dynamic programming characteristics. It is used to evaluate the energy capture efficiency of different altitude scheduling strategies and assist in the formulation of real-time power tracking control strategies. S4. An adaptive model predictive control strategy is constructed based on the optimal height trajectory. The height decision sequence for the next T steps is updated in each control cycle through a rolling time-domain optimization algorithm. The height setpoint is dynamically adjusted according to real-time wind field prediction information. The rolling optimization algorithm adopts the warmstart technique, using the optimal solution of the previous cycle as the initial feasible solution of the solver in the current cycle, which significantly reduces the computation time of the MILP problem.

[0024] The following is a detailed description of a maximum power generation tracking control technology and device for an airborne high-altitude wind power generation system according to an embodiment of the present invention.

[0025] This invention proposes a method for maximum power generation tracking control of an airborne high-altitude wind power generation system, comprising the following steps: S10. Construct an optimization model for a floating wind power generation system with embedded high dynamic constraints.

[0026] The first to fourth preset formulas define the relationship between power generation, maintenance power consumption, and adjustment power consumption at different operating altitudes. The power generation calculation constraint adopts the cubic wind speed model of the second preset formula. The cubic wind speed model obtains the wind speed and air density parameters at any altitude through the piecewise linear interpolation method of the eleventh and twelfth preset formulas. The altitude adjustment is limited by a triple constraint mechanism of the eighth to tenth preset formulas. The eighth preset formula limits the rate of altitude change to not exceed the maximum ascent / descent speed of the system. The ninth and tenth preset formulas further limit the altitude adjustment operation to be performed at most once within D consecutive time steps to ensure the stability of system operation.

[0027] Specifically, with the goal of maximizing the net power generation of the floating wind power generation system, an optimization model with height dynamic constraints is constructed. The objective function is to maximize the cumulative net power generation of the system over T time steps: (1) (2) (3) (4) In the formula, This represents the power generation at time t. and These represent high-level sustaining power consumption and high-level adjusting power consumption, respectively. Indicates the operating altitude. Indicates wind speed. Indicates air density; , , The system aerodynamic characteristic coefficient; To highly adjust the indicator variable, Choose a variable for the height range.

[0028] Formula (1) defines the time-series cumulative target of the system's net power generation; Formula (2) is the power generation calculation based on the cubic law of wind speed; Formulas (3) and (4) are the power consumption models for height maintenance and adjustment, respectively.

[0029] S101. When describing the altitude dispatch capability of a floating wind power generation system, altitude state constraints and altitude change rate constraints must be considered. Altitude state constraints ensure that the system operating altitude remains within a safe range, while altitude change rate constraints limit the system's ascent / descent speed to ensure operational stability. The altitude state constraints and altitude change rate constraints are respectively expressed as follows: (5) (6) (7) (8) (9) (10) in, and These represent the lower and upper bounds of the i-th height interval, respectively. This is a binary variable indicating whether the system is in the i-th altitude interval at time t; Initial height ; The maximum rate of change of height; This is a binary variable indicating whether a height adjustment is performed at time t. A sufficiently large constant; This is the minimum height adjustment interval.

[0030] Formulas (5) to (7) define the height state constraints to ensure that the system operating height is within the preset safe range; Formula (8) limits the height change rate; Formulas (9) and (10) together constrain the height adjustment frequency to avoid frequent adjustments that could cause system oscillations.

[0031] S102. When acquiring wind speed and air density data at different altitudes, since the actual measurement data is only available at discrete altitude points, a piecewise linear interpolation method must be used to obtain parameter values ​​at arbitrary altitudes. The piecewise linear interpolation formula for wind speed and air density is as follows: (11) (12) in, and They represent heights respectively. and Measured wind speed at the location; and These represent the measured air density values ​​at the corresponding altitudes.

[0032] Formulas (11) and (12) achieve piecewise linear interpolation through a weighted summation method, where the weight coefficients are... Ensure that the interpolation only takes effect within the height range of the system.

[0033] S20. Model linearization based on mixed-integer nonlinear programming theory.

[0034] The height decision variables are discretized using preset formulas 15 to 18. Preset formula 15 uses a multi-layer grid partitioning method to decompose the continuous height space into N×M discrete height points. The accuracy parameter M of the multi-layer grid partitioning method should be set to ensure that the wind speed interpolation error between adjacent height points is less than 5% of the rated wind speed. The nonlinear terms of the height change rate are eliminated by defining auxiliary variables using preset formulas 22 and 23. Preset formula 22 uses the McCormick envelope relaxation technique to transform the binary variable product into a system of linear inequalities. The relaxation technique ensures the equivalence of the auxiliary variables with the original variables through the upper and lower bound constraints of preset formulas 29 and 30.

[0035] Specifically, this technology proposes a highly discretized mixed-integer linear programming (MILP) solution method for efficiently and accurately obtaining the optimal altitude scheduling trajectory of a floating wind power generation system. Compared with traditional altitude control methods based on heuristic algorithms, this method can comprehensively consider the multi-dimensional physical constraints of the system, achieve fine tracking of the maximum power point, and avoid the suboptimal problem caused by neglecting the altitude adjustment cost in traditional methods.

[0036] S201, Discretization of highly variable decision processes.

[0037] To transform the continuous height optimization problem into a solvable MILP problem, the height variable is... Discretize the data. Within each height interval... Within, M representative height points are selected evenly. ,in ,satisfy: (13) Introducing binary variables Indicates whether the system is at an altitude point at time t. Then the continuous height variable can be approximated as: (14) Accordingly, we define the discretized wind speed square term, wind speed-density product term, and wind speed cube-density product term: (15) (16) (17) in, and Each is a height point The wind speed and air density values ​​are obtained through interpolation.

[0038] S202, Linearized expression of the power calculation model.

[0039] Based on discretization, the nonlinear power models of equations (2) to (4) can be linearized as follows: (18) (19) For highly adjustable power consumption, due to the high rate of change involved... The absolute value term needs further processing. First, the rate of change of altitude is approximated as the ratio of the altitude difference between adjacent moments to the time step size: (20) The power consumption for height adjustment can then be expressed as: (twenty one) S203, Linearization of the absolute value of the rate of change of height.

[0040] Bilinear term in formula (21) Since this is a non-linear term, an auxiliary variable needs to be introduced for linearization. Define an auxiliary binary variable. This indicates that the system is located at altitude point t at time t. And at time t-1, the point is at altitude. The joint state: (twenty two) Then for The power consumption of height adjustment can be accurately expressed as: (twenty three) For the initial time t=1, we have: (twenty four) S30. Construction and solution of mixed-integer linear programming problems.

[0041] The optimal altitude trajectory is generated by maximizing the objective function of the twenty-fifth preset formula, where the temporal resolution of the trajectory corresponds to the discretization step size of the system scheduling period T, and the spatial resolution corresponds to the number of altitude levels N defined by the binary selection variable constraint of the twenty-sixth preset formula. The net power generation is evaluated through the linearized power model of the nineteenth and twentieth preset formulas, where the net power generation is equal to the wind energy conversion power of the nineteenth preset formula minus the altitude maintenance power consumption of the twentieth preset formula and the altitude adjustment power consumption of the twenty-first preset formula, so as to characterize the comprehensive trade-off between system energy capture efficiency and operating cost under different altitude scheduling strategies.

[0042] Specifically, taking into account the above linearization processes, the complete MILP model can be expressed as: (25) (26) (27) (28) (29) Solution process for S301, MILP problem.

[0043] The specific solution process is as follows: Step 1: Input system parameters (rated power) Rated wind speed Aerodynamic coefficient (etc.) and operational constraints (height range) Maximum height change rate Adjusting the interval wait).

[0044] Step 2: Input time-series wind field data, including discrete height points. wind speed at the location With air density Time series.

[0045] Step 3: Perform spatial discretization of altitude, uniformly generate M representative altitude points in each altitude interval, and calculate the wind speed and air density at each altitude point through linear interpolation.

[0046] Step 4: Construct the MILP model (25)-(30) and call a commercial optimization solver (such as Gurobi, CPLEX) to solve it.

[0047] Step 5: Output the optimal height trajectory Corresponding power output curve and maximum net power generation.

[0048] S302, Performance evaluation metrics based on MILP solutions The energy capture efficiency of different altitude scheduling strategies can be intuitively evaluated by using the objective function value of the MILP model. The following key performance indicators are defined: Cumulative net power generation: (30) Average net power generation: (31) Energy capture efficiency is relatively improved: (32) in, To achieve dynamically optimized net power generation, Net power generation when operating at a fixed altitude.

[0049] S40. Adaptive Model Predictive Control Based on Rolling Time Domain Optimization An adaptive model predictive control strategy is constructed based on the optimal altitude trajectory. The altitude decision sequence for the next T steps is updated in each control cycle through a rolling time-domain optimization algorithm. The altitude setpoint is dynamically adjusted according to real-time wind field prediction information. The rolling optimization algorithm adopts the warmstart technique, using the optimal solution of the previous cycle as the initial feasible solution of the solver in the current cycle, which significantly reduces the computation time of the MILP problem.

[0050] S401, Rolling Time Domain Optimization Framework.

[0051] To achieve closed-loop maximum power point tracking (MPPT) control, a retceding horizontal optimization strategy is employed. In each control cycle... (generally Perform the following steps: Step 1: Obtain the current system status (current height) Current moment (and wind field forecast data for the next T steps)

[0052] Step 2: Using the current state as the initial condition, solve the MILP problem (25)-(30) to obtain the optimal height trajectory for the next T steps. .

[0053] Step 3: Only execute the first step of high-level decision-making. This will be used as the height setpoint for the next control cycle.

[0054] Step 4: Update the system status and enter the next control cycle. Repeat steps 1 to 3.

[0055] S402 and Warmstart techniques accelerate the solution.

[0056] Since the MILP problem needs to be solved again in each control cycle, computational efficiency becomes a key bottleneck restricting real-time control. The warmstart technique significantly improves the solution speed. In the Each control cycle will control the previous cycle ( The optimal solution This serves as the initial feasible solution for the current periodic solver. Specifically, the initial solution is constructed through a time-shift operation: (33) (34) For the final step The solution from the last step of the previous cycle is used to fill the gap.

[0057] S403, Fusion of wind field prediction information.

[0058] The key advantage of the rolling optimization framework lies in its ability to integrate real-time updated wind field forecast information. In each control cycle, wind speed and air density forecasts for the next T steps are obtained based on numerical weather prediction (NWP) or statistical prediction models, and the parameters of the MILP model are updated accordingly. .

[0059] Considering prediction uncertainty, robust optimization or stochastic programming methods can be used to enhance the robustness of the control strategy. For example, a prediction error boundary can be introduced. Construct a robust MILP model: (35) Ensure that the system still achieves acceptable performance even under the worst-case prediction error conditions.

[0060] Therefore, this method, through a hybrid integer linear programming and rolling time-domain optimization framework, significantly improves the maximum power point tracking accuracy and real-time response capability of the floating wind power generation system, and has broad engineering application value.

[0061] To achieve the above embodiments, such as Figure 2 As shown, this embodiment also provides a maximum power output tracking control device 10 for an airborne high-altitude wind power generation system, comprising: The height dynamic constraint embedding module 100 is used to construct an optimization model of a floating wind power generation system with embedded height dynamic constraints. The model includes power generation calculation constraints, height maintenance power consumption constraints, height adjustment power consumption constraints, height state constraints, and height change rate constraints, and defines the relationship between wind speed, air density, and power generation at different operating heights. The nonlinear wind speed-power relationship linearization module 200 is used to transform the optimization model into a mixed integer linear programming (MILP) problem based on mixed integer nonlinear programming theory. It achieves linearization of the nonlinear wind speed-power relationship through height discretization and piecewise linear interpolation, and uses auxiliary binary variables to eliminate the absolute value term of the height change rate. The optimal altitude trajectory generation module 300 is used to solve the MILP problem to generate the optimal altitude trajectory and corresponding power output curve of the floating wind power generation system. The curve characterizes the precise relationship between time-series wind field changes and system net power generation through dynamic programming characteristics. It is used to evaluate the energy capture efficiency of different altitude scheduling strategies and assist in the formulation of real-time power tracking control strategies. The adaptive control strategy construction module 400 is used to construct an adaptive model predictive control strategy based on the optimal height trajectory. It updates the height decision sequence for the next T steps in each control cycle through a rolling time-domain optimization algorithm and dynamically adjusts the height setpoint according to real-time wind field prediction information. The rolling optimization algorithm adopts the warmstart technique, using the optimal solution of the previous cycle as the initial feasible solution of the solver in the current cycle, which significantly reduces the computation time of the MILP problem.

[0062] Furthermore, the highly dynamic constraint embedding module 100 is also used for: The first to fourth preset formulas define the relationship between power generation, maintenance power consumption and adjustment power consumption at different operating heights. The power generation calculation constraint adopts the cubic wind speed model of the second preset formula. The cubic wind speed model obtains the wind speed and air density parameters at any height through the piecewise linear interpolation method of the eleventh and twelfth preset formulas. The reserve capacity constraint is achieved through a dual constraint mechanism of the fourth and fifth preset formulas and the thirteenth preset formula. The fourth preset formula limits the total reserve capacity of the generator to no more than the difference between its maximum and minimum output. The thirteenth preset formula further limits the allocation of reserve capacity to meet the ramp limit constraint of each generator.

[0063] Furthermore, the nonlinear wind speed-power relationship linearization module 200 is also used for: The height decision variables are discretized using the fifteenth to eighteenth preset formulas. The fifteenth preset formula uses a multi-layer grid partitioning method to decompose the continuous height space into N×M discrete height points. The accuracy parameter M of the multi-layer grid partitioning method should be selected to ensure that the wind speed interpolation error between adjacent height points is less than 5% of the rated wind speed. The nonlinear terms with high rate of change are eliminated by defining auxiliary variables through the twenty-second and twenty-third preset formulas. The twenty-second preset formula uses McCormick envelope relaxation technique to transform the binary variable product into a system of linear inequalities. The relaxation technique ensures the equivalence of the auxiliary variables and the original variables through the upper and lower bound constraints of the twenty-ninth and thirtieth preset formulas.

[0064] This invention discloses a maximum power tracking control device for an airborne high-altitude wind power generation system. It aims to improve the energy capture efficiency and operational stability of the floating wind power generation system through optimized algorithms and intelligent control. This technology is applied to the development of high-altitude wind energy resources, realizing the leap from "passive adaptation" to "active optimization" in wind power generation, and providing support for the intelligent operation of new renewable energy systems.

[0065] To implement the methods of the above embodiments, the present invention also provides a computer device, such as... Figure 3 As shown, the computer device 600 includes a memory 601 and a processor 602; wherein, the processor 602 reads executable program code stored in the memory 601 to run a program corresponding to the executable program code, so as to implement the various steps of the method described above.

[0066] To implement the above embodiments, this application also proposes a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the method described in the foregoing embodiments.

[0067] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0068] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

Claims

1. A method for maximum power generation tracking control of an airborne high-altitude wind power generation system, characterized in that, include: S1. Construct an optimization model for a floating wind power generation system with embedded height dynamic constraints. The model includes power generation calculation constraints, height maintenance power consumption constraints, height adjustment power consumption constraints, height state constraints, and height change rate constraints. Define the relationship between wind speed, air density, and power generation at different operating heights. S2, based on mixed integer nonlinear programming theory, the optimization model is transformed into a mixed integer linear programming (MILP) problem. The nonlinear wind speed-power relationship is linearized through high-level discretization and piecewise linear interpolation, and the absolute value term of the height change rate is eliminated by using auxiliary binary variables. S3, Solve the MILP problem to generate the optimal altitude trajectory and corresponding power output curve of the floating wind power generation system. The curve characterizes the precise relationship between time-series wind field changes and system net power generation through dynamic programming characteristics. It is used to evaluate the energy capture efficiency of different altitude scheduling strategies and assist in the formulation of real-time power tracking control strategies. S4. An adaptive model predictive control strategy is constructed based on the optimal height trajectory. The height decision sequence for the next T steps is updated in each control cycle through a rolling time-domain optimization algorithm. The height setpoint is dynamically adjusted according to real-time wind field prediction information. The rolling optimization algorithm adopts the warmstart technique, using the optimal solution of the previous cycle as the initial feasible solution of the solver in the current cycle, which significantly reduces the computation time of the MILP problem.

2. The method as described in claim 1, characterized in that, S1 includes: S11, the relationship between power generation, maintenance power consumption and adjustment power consumption at different operating heights is defined by the first to fourth preset formulas. The power generation calculation constraint adopts the cubic wind speed model of the second preset formula. The cubic wind speed model obtains the wind speed and air density parameters at any height through the piecewise linear interpolation method of the eleventh and twelfth preset formulas. S12, the altitude adjustment is limited by a triple constraint mechanism through the eighth to tenth preset formulas. The eighth preset formula limits the rate of altitude change to not exceed the system's maximum climb / descent speed. The ninth and tenth preset formulas further limit the altitude adjustment operation to be performed at most once within D consecutive time steps to ensure system stability.

3. The method as described in claim 1, characterized in that, S2 includes: S21, the height decision variable is discretized through the fifteenth to eighteenth preset formulas. The fifteenth preset formula uses a multi-layer grid partitioning method to decompose the continuous height space into N×M discrete height points. The accuracy parameter M of the multi-layer grid partitioning method should be taken to ensure that the wind speed interpolation error between adjacent height points is less than 5% of the rated wind speed. S22, the nonlinear terms with high rate of change are eliminated by defining auxiliary variables through the twenty-second and twenty-third preset formulas. The twenty-second preset formula uses McCormick envelope relaxation technique to transform the binary variable product into a system of linear inequalities. The relaxation technique ensures the equivalence of the auxiliary variables and the original variables through the upper and lower bound constraints of the twenty-ninth and thirtieth preset formulas.

4. The method as described in claim 1, characterized in that, The S3 further includes: S31, the optimal height trajectory is generated by maximizing the objective function of the twenty-fifth preset formula, wherein the temporal resolution of the trajectory corresponds to the discretization step size of the system scheduling period T, and the spatial resolution corresponds to the number of height levels N defined by the binary selection variable constraint of the twenty-sixth preset formula. S32 uses the linearized power model of the nineteenth and twentieth preset formulas to evaluate net power generation. Net power generation is equal to the wind energy conversion power of the nineteenth preset formula minus the height maintenance power consumption of the twentieth preset formula and the height adjustment power consumption of the twenty-first preset formula, so as to characterize the comprehensive trade-off between system energy capture efficiency and operating cost under different height dispatch strategies.

5. A maximum power output tracking and control device for an airborne high-altitude wind power generation system, characterized in that, include: The height dynamic constraint embedding module is used to construct an optimization model of a floating wind power generation system with embedded height dynamic constraints. The model includes power generation calculation constraints, height maintenance power consumption constraints, height adjustment power consumption constraints, height state constraints, and height change rate constraints, and defines the relationship between wind speed, air density, and power generation at different operating heights. The nonlinear wind speed-power relationship linearization module is used to transform the optimization model into a mixed integer linear programming (MILP) problem based on mixed integer nonlinear programming theory. It achieves linearization of the nonlinear wind speed-power relationship through high-level discretization and piecewise linear interpolation, and uses auxiliary binary variables to eliminate the absolute value term of the height change rate. The optimal altitude trajectory generation module is used to solve the MILP problem to generate the optimal altitude trajectory and corresponding power output curve of the floating wind power generation system. The curve characterizes the precise relationship between time-series wind field changes and system net power generation through dynamic programming characteristics. It is used to evaluate the energy capture efficiency of different altitude scheduling strategies and assist in the formulation of real-time power tracking control strategies. The adaptive control strategy construction module is used to build an adaptive model predictive control strategy based on the optimal altitude trajectory. It updates the altitude decision sequence for the next T steps in each control cycle through a rolling time-domain optimization algorithm and dynamically adjusts the altitude setpoint according to real-time wind field prediction information. The rolling optimization algorithm adopts the warmstart technique, using the optimal solution of the previous cycle as the initial feasible solution of the solver in the current cycle, which significantly reduces the computation time of the MILP problem.

6. The apparatus as claimed in claim 5, characterized in that, The highly dynamic constraint embedding module is used for: The first to fourth preset formulas define the relationship between power generation, maintenance power consumption and adjustment power consumption at different operating heights. The power generation calculation constraint adopts the cubic wind speed model of the second preset formula. The cubic wind speed model obtains the wind speed and air density parameters at any height through the piecewise linear interpolation method of the eleventh and twelfth preset formulas. The reserve capacity constraint is achieved through a dual constraint mechanism of the fourth and fifth preset formulas and the thirteenth preset formula. The fourth preset formula limits the total reserve capacity of the generator to no more than the difference between its maximum and minimum output. The thirteenth preset formula further limits the allocation of reserve capacity to meet the ramp limit constraint of each generator.

7. The apparatus as claimed in claim 5, characterized in that, The nonlinear wind speed-power relationship linearization module is used for: The height decision variables are discretized using the fifteenth to eighteenth preset formulas. The fifteenth preset formula uses a multi-layer grid partitioning method to decompose the continuous height space into N×M discrete height points. The accuracy parameter M of the multi-layer grid partitioning method should be selected to ensure that the wind speed interpolation error between adjacent height points is less than 5% of the rated wind speed. The nonlinear terms with high rate of change are eliminated by defining auxiliary variables through the twenty-second and twenty-third preset formulas. The twenty-second preset formula uses McCormick envelope relaxation technique to transform the binary variable product into a system of linear inequalities. The relaxation technique ensures the equivalence of the auxiliary variables and the original variables through the upper and lower bound constraints of the twenty-ninth and thirtieth preset formulas.

8. The apparatus as claimed in claim 5, characterized in that, The optimal height trajectory generation module is used for: The optimal height trajectory is generated by maximizing the objective function of the twenty-fifth preset formula, wherein the temporal resolution of the trajectory corresponds to the discretization step size of the system scheduling period T, and the spatial resolution corresponds to the number of height levels N defined by the binary selection variable constraint of the twenty-sixth preset formula. Net power generation is assessed using a linearized power model based on the nineteenth and twentieth preset formulas. Net power generation is equal to the wind energy conversion power of the nineteenth preset formula minus the altitude maintenance power consumption of the twentieth preset formula and the altitude adjustment power consumption of the twenty-first preset formula, thus representing the comprehensive trade-off between system energy capture efficiency and operating costs under different altitude dispatch strategies.

9. A computer device, characterized in that, Including processor and memory; The processor reads executable program code stored in the memory to run a program corresponding to the executable program code, so as to implement the maximum power generation tracking control method for an airborne high-altitude wind power generation system as described in any one of claims 1-4.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements a maximum power generation tracking control method for an airborne high-altitude wind power generation system as described in any one of claims 1-4.

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