A power optimization method and device for an umbrella ladder type land-based high-altitude wind power generation system

Abstract

The present application relates to the technical field of high-altitude wind energy, and particularly relates to a power optimization method and device for an umbrella-ladder type land-based high-altitude wind power generation system. The method comprises: obtaining a wind speed prediction value and current state information of the umbrella-ladder type land-based high-altitude wind power generation system; based on a cable winding and unwinding speed sequence in a prediction time domain, the wind speed prediction value and the current state information, calculating cable mechanical power in each control period in the prediction time domain through a preset dynamic simulation model; taking the winding and unwinding speed sequence as a decision variable, constructing an objective function, the objective function comprising a reward item of the cable mechanical power and a penalty item of system operation stability; solving the objective function to obtain a target winding and unwinding speed sequence that optimizes the objective function; and outputting a first target speed in the target winding and unwinding speed sequence for execution by a hoist actuator of the system in a current control period. The above technical solution can reduce power fluctuation, improve mechanical power and take into account long-term stable operation of the system.

Description

Technical Field

[0001] This invention relates to the field of high-altitude wind energy technology, and in particular to a power optimization method and apparatus for a ladder-type land-based high-altitude wind power generation system. Background Technology

[0002] The parachute-type land-based high-altitude wind power generation system consists of devices such as a power parachute, a balance parachute, a helium balloon, a main cable, a drum, and a winch actuator. Through the traction of the high-altitude flying body in a strong wind field, it drives the ground winch actuator and the generator to work, realizing the cyclical operation of generating electricity during the cable laying stage and resetting during the cable retrieval stage.

[0003] In existing technologies, the control strategies for cable deployment and take-up speeds typically employ fixed speed rules, empirical threshold judgments, or single-objective optimization under local operating conditions. Such single control strategies are prone to tension shocks and drastic power fluctuations when encountering sudden changes in wind speed or changes in operating conditions, resulting in large fluctuations in the system's mechanical power and making it difficult to maintain stable output over a long period.

[0004] Therefore, there is an urgent need to provide a new technical solution to address the aforementioned technical problems. Summary of the Invention

[0005] This invention provides a power optimization method and apparatus for a ladder-type land-based high-altitude wind power generation system, which can reduce power fluctuations, increase mechanical power, and ensure the long-term stable operation of the system.

[0006] In a first aspect, embodiments of the present invention provide a power optimization method for a ladder-type land-based high-altitude wind power generation system, comprising: Obtain wind speed forecasts and current status information of the umbrella ladder-type land-based high-altitude wind power generation system; Based on the cable's release and take-up speed sequence, wind speed prediction, and current state information within the predicted time domain, the cable's mechanical power for each control cycle within the predicted time domain is calculated using a preset dynamic simulation model. The release and take-up speed sequence includes the release and take-up speeds corresponding to each control cycle within the predicted time domain. Using the release and take-up speed sequence as decision variables, an objective function is constructed. The objective function includes a benefit term characterizing the mechanical power of the cable in the prediction time domain and a penalty term characterizing the system's operational stability. Under preset constraints, the objective function is solved to obtain the target release and take-off speed sequence that optimizes the objective function; The first target speed in the target retraction speed sequence is output for the system's hoisting actuator to execute in the current control cycle, so as to optimize the mechanical power of the system in the current control cycle.

[0007] Secondly, embodiments of the present invention also provide a power optimization device for a ladder-type land-based high-altitude wind power generation system, comprising: The information acquisition module acquires wind speed forecasts and current status information of the umbrella ladder-type land-based high-altitude wind power generation system; The dynamics simulation module, connected to the information acquisition module, calculates the cable mechanical power for each control cycle in the prediction time domain based on the cable's release and take-up speed sequence, wind speed prediction, and current state information, using a preset dynamics simulation model. The release and take-up speed sequence includes the release and take-up speeds corresponding to each control cycle in the prediction time domain. The function construction module, connected to the dynamics simulation module, constructs an objective function using the release and take-up speed sequence as the decision variable. The objective function includes a benefit term characterizing the mechanical power of the cable in the prediction time domain and a penalty term characterizing the system's operational stability. The speed optimization module, connected to the function construction module, solves the objective function under preset constraints to obtain the target release and take-off speed sequence that optimizes the objective function. The power optimization module, connected to the speed optimization module, outputs the first target speed in the target retraction speed sequence, which is then executed by the hoisting actuator of the system in the current control cycle to optimize the mechanical power of the system in the current control cycle.

[0008] Thirdly, embodiments of the present invention also provide an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the method described in any embodiment of the present invention.

[0009] Fourthly, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to perform the methods described in any embodiment of the present invention.

[0010] This invention provides a power optimization method and apparatus for a ladder-type land-based high-altitude wind power generation system. It uses the cable deployment and retraction speed sequence as the decision variable and predicts the cable mechanical power for each control cycle in the future prediction time domain through a dynamic simulation model, constructing an objective function that includes a benefit term and a penalty term. This allows for real-time adjustment of the deployment and retraction speed commands based on wind speed changes and system status, overcoming the poor adaptability of existing technologies during sudden wind speed changes or operating condition switching, and reducing system power fluctuations. During the optimization process of the objective function, while pursuing mechanical power gains, the penalty term comprehensively suppresses factors affecting the stable operation of the system, ensuring long-term stable operation of the system while improving mechanical power. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention or related technologies, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a flowchart of the power optimization method for the umbrella-ladder type land-based high-altitude wind power generation system provided in the embodiments of the present invention; Figure 2 This is a hardware architecture diagram of the electronic device provided in an embodiment of the present invention; Figure 3 This is a structural diagram of the power optimization device for the umbrella-ladder type land-based high-altitude wind power generation system provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of the umbrella-ladder type land-based high-altitude wind power generation system provided in an embodiment of the present invention. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0014] Please refer to Figure 1 This invention provides a power optimization method for a ladder-type land-based high-altitude wind power generation system, the method comprising: Step 100: Obtain wind speed prediction values ​​and current status information of the umbrella ladder-type land-based high-altitude wind power generation system; Step 102: Based on the cable's release and take-up speed sequence, wind speed prediction, and current state information within the prediction time domain, calculate the cable's mechanical power for each control cycle within the prediction time domain using a preset dynamic simulation model. The take-up and release speed sequence includes the take-up and release speeds corresponding to each control cycle within the prediction time domain; Step 104: Construct the objective function using the release and take-off speed sequence as the decision variable; The objective function includes a benefit term characterizing the mechanical power of the cable in the prediction time domain and a penalty term characterizing the system's operational stability. Step 106: Under the preset constraints, solve the objective function to obtain the target release and take-off speed sequence that optimizes the objective function; Step 108: Output the first target speed in the target retraction speed sequence for the system's hoisting actuator to execute in the current control cycle, so as to optimize the mechanical power of the system in the current control cycle.

[0015] In this embodiment, as Figure 4 As shown, the parachute-type land-based high-altitude wind power generation system includes: a helium balloon 41, a power parachute 42, a balancing parachute 43, a cable (main cable) 44, casters, a tensioning device, a winch actuator 45, and a generator 46. The helium balloon 41 provides lift to the power parachute 42, enabling it to rise to a set height. The power parachute 42 captures wind energy at high altitudes and converts it into mechanical energy, which then drives the generator 46 located on the ground to generate electricity. The cable 44 connects the power parachute 42, helium balloon 41, and other aerial components to the ground devices (casters, tensioning device, winch actuator, and generator). The casters adapt to changes in wind direction at high altitudes, ensuring the cable direction adjusts accordingly to maximize wind energy capture. The tensioning device maintains cable tension, ensuring stability and efficient conversion of wind power into mechanical energy. The winch actuator is connected to the power parachute via the cable. Driven by the winch actuator, the generator produces electricity, which can be transmitted and distributed through the power grid. The system's power generation principle is as follows: the generator is located on the ground, and the power parachute's reciprocating motion achieves "kite-flying" power generation—airborne flight and ground-based power generation. Specifically, a helium balloon provides initial lift to the power parachute. Once the parachute reaches a certain height and opens, the parachute, attached to the cable, reciprocates under wind force, driving the ground generator's turntable to rotate, converting mechanical energy into electrical energy.

[0016] The predicted wind speed reflects the trend of wind speed changes in the upper-level wind field within the prediction time domain, while the system's current state information reflects the system's real-time operating conditions at the current moment. The current state information includes one or more of the following: the altitude of the designated aerial measurement node, the pitch angle to the ground (the angle between the cable and the ground), the cable length, the actual cable deployment and retrieval speeds, the cable tension, and the current operating stage of the system. The predicted wind speed and current state information serve as external environmental factors and internal state data, respectively. Based on these two pieces of information, a simulation dynamics model can be used to obtain the operating state within the prediction time domain. Furthermore, in the dynamics simulation model, the cable deployment and retrieval speed sequence also needs to be used as input. Combined with the predicted wind speed and current state information, the simulation yields information such as the cable mechanical power, cable tension, cable length, and the altitude of the designated aerial component node within the prediction time domain. A penalty term characterizing system operational stability is established based on information such as cable tension, cable length, and the height of designated nodes of aerial components. This is then combined with a benefit term representing cable mechanical power to construct an objective function. This objective function uses the cable's release and take-up speed sequence as decision variables. By solving the objective function, the target release and take-up speed sequence that optimizes the objective function is obtained. The first target speed in this sequence is selected as the command executed by the hoisting actuator in the current control cycle. By executing this command, the hoisting actuator achieves the target release and take-up speed, thereby optimizing the mechanical power in the current control cycle. This ensures that control commands are always generated based on the latest prediction results and only directly affect the execution behavior of the current cycle. This allows the system to instantly modulate mechanical power according to the optimization objective at every moment, unaffected by interference or delays from speed commands in subsequent cycles.

[0017] It should be noted that the current moment refers to the moment when the system is collecting state information and begins to predict relevant future data, usually expressed as... The time domain refers to the period from the current moment. The prediction time domain, however, refers to the time from the current moment... Beginning, including The future time period of a control cycle. For example, the current moment. Predicting the time domain Therefore, the prediction time domain covers five control cycles: 10, 11, 12, 13, and 14. That is, the time period of the prediction time domain can be represented as [ , ].

[0018] In one embodiment of the present invention, the objective function is expressed by the following formula: in, Let be the objective function. For the predicted first Within the first control cycle, the first The cable mechanical power of each simulated sub-step. For mechanical power gain weight, For cable tension safety penalty weight, For cable length safety penalty weight, For high security penalty weights, Weights for power fluctuation penalties The penalty weight for changes in target velocity, The penalty weight is the target velocity amplitude. For cable tension safety penalty function, For the cable length safety penalty function, For high-security penalty functions, To predict cable tension, To predict cable length, Specify the predicted altitude of the nodes for the aerial components. To predict the number of control cycles in the time domain, each control cycle includes... Number of simulation substeps, For simulating step size, To control the periodic index, For simulation substep index, For the first The prediction obtained at time i is used for the i-th time. The target release and retraction speed for each control cycle, when hour, Take the current actual cable deployment and retraction speed ,when hour, Take the target release / retraction speed from the previous control cycle.

[0019] Understandably, the designated node of the air component is a virtual marker point used for safety marking, usually referring to the designated marker point in the working umbrella.

[0020] Furthermore, the mechanical power of the cable It is calculated using the following formula: in, To predict cable tension, This represents the actual cable deployment and retraction speed. When... At this time, the system is in the direction of cable laying and traction, and the mechanical power is usually positive; when At this time, the system is in the cable recovery direction, and the mechanical power is usually negative, which is used to characterize the energy consumption during the recovery phase.

[0021] Because the hoist actuator has a maximum acceleration limit, the actual speed does not instantly equal the target speed, but rather tracks it step by step according to the following formula: in, For the maximum permissible tether acceleration, To dynamically simulate the step size, It is a saturation function.

[0022] In this embodiment, by constructing an objective function, the gains from predicting the mechanical power of the cable in the time domain and the penalties for system operational stability are quantified. This allows the magnitude of the objective function to reflect the comprehensive index resulting from the combination of gains and penalties. The penalty term is a multi-dimensional term including cable tension, cable length, aerial component height, power fluctuation, target speed change, and target speed amplitude. Data such as cable mechanical power, cable tension, cable length, and power fluctuation involved in both the gains and penalties can be obtained through a dynamic simulation model. Simultaneously, constraints are imposed on the continuous change and amplitude of the target deployment and retraction speed sequence. This ensures that the objective function optimization process maximizes mechanical power while simultaneously suppressing various unstable factors such as cable overload, height loss of control, drastic power fluctuations, and sudden speed changes. This achieves precise and coordinated control of system operational stability, improving the system's adaptability and operational reliability under complex wind conditions.

[0023] In one embodiment of the present invention, the cable tension safety penalty function is expressed by the following formula: The cable length safety penalty function is expressed by the following formula: The high-security penalty function is expressed by the following formula: in, The preset minimum allowable cable tension, The preset maximum allowable cable tension, The preset minimum allowable cable length, This is the preset maximum allowable cable length. This is the preset minimum safe height.

[0024] In this embodiment, constraints on cable length, cable tension, and height safety are set. This allows the optimization of the objective function to be based on preset minimum and maximum allowable cable tension, minimum and maximum allowable cable length, and minimum safe height. Nonlinear, square-gain penalties are applied to each simulation sub-step to determine whether the cable tension, cable length, and aerial component height exceed the limits. When any state parameter exceeds the safety boundary, the penalty value immediately becomes non-zero and increases quadratically with the degree of deviation. This automatically suppresses candidate deployment and take-up speed sequences that may lead to instability risks such as excessive tension, excessively short or long cable length, or insufficient height during the objective function optimization process. This achieves adaptive constraints on the system's operating state within the safety boundary, eliminating the need for manual threshold setting or external intervention. This effectively improves the system's operational stability and the synergistic optimization capability of mechanical power output under complex wind conditions.

[0025] In one embodiment of the present invention, the preset constraint conditions include one or more of the following: Boundary constraints on target deployment and recovery speeds; ,in, The preset maximum allowable cable winding speed, The preset maximum allowable cable laying speed; Target velocity variation constraints: , This is the preset maximum allowable rate of change of speed; Acceleration constraints of the hoist actuator: , The preset maximum permissible tether acceleration, This is for simulating step size; Cable tension safety constraints: ; Cable length safety constraints: ; Safety constraints for aerial components at high altitudes: .

[0026] In this embodiment, in addition to the penalty term in the objective function, hard constraints can also be set. The hard constraints include the target release and retraction speed boundary constraints, the target speed change constraints, the hoisting actuator acceleration constraints, the cable tension safety constraints, the cable length safety constraints, and the aerial component height safety constraints.

[0027] In the The complete predictive optimization problem for solving at each control time can be written as: in, , The only direct decision variable in this optimization problem is the sequence of future target cable deployment and retraction speeds. The future state, actual speed, tension, cable length, height, and mechanical power are all predicted by a preset dynamic simulation model.

[0028] In one embodiment of the present invention, solving the objective function includes: Using the cable winding and unwinding speed sequence as the optimization variable, a current sampling distribution is constructed. The current sampling distribution is a normal distribution. The initial mean of each winding and unwinding speed sequence in the current sampling distribution is obtained by shifting the optimal target winding and unwinding speed sequence of the previous control cycle. If there is no optimization result from the previous control cycle, the current actual winding and unwinding speed is used as the initial mean. Randomly sample from the current sampling distribution to generate multiple first candidate sequences. Perform constraint projection on each first candidate sequence based on preset constraints to obtain multiple second candidate sequences that satisfy the constraints. Based on each second candidate sequence and the current state information, the mechanical power of the cable in the time domain is predicted by the dynamic simulation model, and the objective function value corresponding to each second candidate sequence is calculated based on the mechanical power of the cable. The second candidate sequences are sorted according to the objective function value, and the top few second candidate sequences with the best objective function value are selected as elite samples. Update the mean and standard deviation of the current sampling distribution based on the elite samples, use the updated sampling distribution as the current sampling distribution for the next round of iteration, return to the step of randomly sampling from the current sampling distribution to generate multiple first candidate sequences and continue iterating until the preset convergence condition is met; The candidate take-up and take-down velocity sequence with the optimal objective function value during the iteration process is taken as the target take-up and take-down velocity sequence.

[0029] In this embodiment, as can be seen from the above steps, the prediction and optimization problem of the objective function exhibits obvious nonlinear, multi-constraint, and black-box simulation characteristics. Specifically, the cable mechanical power, cable tension, cable length, and aerial component height in the objective function are not explicit analytical functions of the target velocity, but rather need to be calculated step-by-step through a pre-defined dynamic simulation model. Therefore, this invention employs the cross-entropy optimization method to solve this problem. The cross-entropy optimization method does not require the objective function to be differentiable, nor does it require the dynamic model to have an analytical gradient, making it suitable for direct coupling with dynamic simulation programs for online searching of the optimal target cable deployment and retrieval speed sequence.

[0030] In the The cross-entropy optimization solution process at each control time point is as follows: Get Current Input: Collect the current system status Current actual cable deployment and retrieval speed Cable tension Main cable length Current operational phase and spatial wind speed field If the system has a wind speed prediction model, the predicted wind speed field is used; if it does not have a wind speed prediction model, the frozen wind field assumption is adopted in the prediction time domain, and the future wind speed prediction value is obtained based on the wind speed field collected at the current moment.

[0031] Determine the optimization variables: The basic variables for cross-entropy optimization are the future cable release and take-up speed sequences. When the prediction time domain is long, piecewise preservation or linear interpolation can be used to reduce the optimization dimensionality. In this case, the low-dimensional parameters are optimized first: Then, the complete take-up and take-down speed sequence is obtained from the mapping relationship: in, , This indicates a piecewise preservation or interpolation mapping. When dimensionality reduction is not required, it can be directly taken. .

[0032] Initialize the sampling distribution, and let the cross-entropy sampling distribution be: in, The initial mean, This is the initial covariance matrix. The initial mean is obtained by shifting the optimal release / recovery velocity sequence from the previous control cycle: If no optimization result from the previous control cycle exists, initialize using the current actual cable deployment / retraction speed: .

[0033] Generate candidate velocity sequences and perform constrained projection: in the first... In the next iteration, generate from the current sampling distribution Candidate control parameters: in, , For a standard normally distributed random vector, The standard deviation vector of the samples. This represents element-wise multiplication. Each candidate parameter is processed... After mapping, candidate take-up and take-down velocity sequences are obtained. (First candidate sequence).

[0034] To ensure that the candidate velocity sequence meets the basic executability constraints, the target velocity boundary is first projected: Then, target velocity variation constraint projection is performed: Among them, when hour, Take the current actual cable deployment and retraction speed After the above constraint projection, a second candidate sequence that satisfies the constraint conditions is obtained.

[0035] The dynamic simulation method is invoked to evaluate the candidate sequences: for each second candidate sequence From the current state The system then invokes a pre-defined dynamic simulation model to predict the future system response. During the prediction process, the actual cable deployment and retraction speeds are updated according to the actuator acceleration limits. The future state is obtained recursively by the dynamic simulation method: The corresponding mechanical power is: Substitute the predicted mechanical power, tension, cable length, height, power fluctuation, and speed change into... The objective function value of the candidate sequence is obtained. .

[0036] If further penalties are needed for hard constraint violations, the total constraint violation amount can be constructed: And define the evaluation function after penalty: in, To constrain the penalty coefficient for violations, subsequent sorting and filtering will all be based on... Based on.

[0037] Select elite samples: The candidate velocity sequences are evaluated according to the penalty-post evaluation function. Sort by largest to smallest, then select the top... The sample is an elite set: Update the sampling distribution: Calculate the new sampling mean and standard deviation based on the elite sample. To improve the stability of online solutions, a smooth update method is adopted: in, Update the step size for the cross-entropy distribution.

[0038] Termination condition: Repeat the above steps from "generating candidate velocity sequences and performing constrained projection" to "updating sampling distribution" until the maximum number of iterations is reached. Or, the sampling standard deviation satisfies the convergence condition: in, The convergence threshold is optimized for cross-entropy.

[0039] Output the current control input and update it continuously: Select the candidate release / recovery velocity sequence with the largest value after penalty as the optimal sequence at the current control moment: Based on the rolling predictive control concept, only the first target velocity in the optimal sequence is actually output: Hoist drive mechanism receives Then, within the current control cycle, the actual cable deployment and take-up speeds are gradually brought closer to the target speed according to the maximum acceleration limit. After one control cycle ends, the system enters the next... At each control moment, the system status and wind speed information are re-acquired, and the above cross-entropy prediction optimization process is executed again.

[0040] Through the above solution method, the present invention can directly evaluate candidate control sequences using dynamic operation simulation methods without requiring the dynamic model to be analytically differentiable, and obtain the optimal cable release and take-up speed command that satisfies multiple constraints such as speed, acceleration, tension, cable length and height online. This improves the mechanical power of the cable release traction stage, reduces unnecessary energy consumption in the cable take-up stage, and suppresses power fluctuations, speed abrupt changes and tension shocks.

[0041] In one embodiment of the present invention, it further includes: After the current control cycle ends, the current status information of the umbrella ladder-type land-based high-altitude wind power generation system will be reacquired. Based on the updated current state information, repeat the steps from obtaining the wind speed forecast value to outputting the target speed.

[0042] In this embodiment, after the current control cycle ends, the current state information of the umbrella ladder-type land-based high-altitude wind power generation system is reacquired, and the complete optimization process from acquiring the wind speed prediction value to outputting the target speed is repeated based on the updated current state information. In this way, the latest wind speed prediction value and real-time operating status can be used as inputs in each control cycle to dynamically reconstruct the optimal sequence of cable deployment and retraction speeds. This effectively offsets the drift of the optimization benchmark caused by sudden wind speed changes, cable tension fluctuations, or system inertial disturbances, ensuring that the synergistic optimization of mechanical power enhancement and operational stability remains effective in dynamic environments. This enhances the system's adaptability to complex wind field conditions and the real-time responsiveness of the control closed loop.

[0043] In one embodiment of the present invention, the dynamic simulation model is constructed in the following manner: The aerial components and main cable of the parachute-type land-based high-altitude wind power generation system are discretized into multiple units, and the motion differential equations of each unit are established. The motion differential equations include force terms, which include one or more of the following: gravity term, cable tension term, aerodynamic term, and buoyancy term. The aerodynamic term is calculated based on the predicted wind speed and the current motion speed of each unit. The cable tension term is calculated based on the cable deformation and the current motion speed. The buoyancy term is based on the material properties and dimensions of the helium balloon. Based on the equations of motion, the time domain is recursively solved using numerical integration to obtain the cable mechanical power, predicted cable tension, predicted cable length, and predicted height of the specified node of the aerial component for each control cycle in the predicted time domain.

[0044] In this embodiment, the aerial components and main cable of the umbrella ladder-type land-based high-altitude wind power generation system are discretized into multiple units, and motion differential equations are established for each unit. These equations include gravity terms, cable tension terms, aerodynamic terms, and buoyancy terms. The aerodynamic terms are calculated based on predicted wind speed and the real-time velocity of the unit; the cable tension term is determined based on the constitutive relationship between cable deformation and velocity; and the buoyancy term is calculated based on the material properties and geometric dimensions of the helium balloon. The motion differential equations are recursively solved in the time domain using numerical integration methods, outputting the cable mechanical power, cable tension, cable length, and aerial component height for each control cycle within the predicted time domain.

[0045] like Figure 2 , Figure 3 As shown, this invention provides a power optimization device for a ladder-type land-based high-altitude wind power generation system. The device can be implemented through software, hardware, or a combination of both. From a hardware perspective, as... Figure 2 The diagram shown is a hardware architecture diagram of an electronic device housing a power optimization device in a ladder-type land-based high-altitude wind power generation system according to an embodiment of the present invention. (Except for...) Figure 2In addition to the processor, memory, network interface, and non-volatile memory shown, the electronic device in the embodiment may also include other hardware, such as a forwarding chip responsible for processing packets. Taking software implementation as an example, such as... Figure 3 As shown, a device in a logical sense is formed by the CPU of the electronic device in which it is located reading the corresponding computer program from the non-volatile memory into the memory for execution.

[0046] The information acquisition module 300 acquires wind speed prediction values ​​and current status information of the umbrella ladder-type land-based high-altitude wind power generation system. The dynamics simulation module 302 is connected to the information acquisition module. Based on the cable winding and unwinding speed sequence, wind speed prediction value and current state information in the prediction time domain, it calculates the cable mechanical power of each control cycle in the prediction time domain through a preset dynamics simulation model. The winding and unwinding speed sequence includes the winding and unwinding speed corresponding to each control cycle in the prediction time domain. The function construction module 304 is connected to the dynamics simulation module. It constructs an objective function with the release and take-off speed sequence as the decision variable. The objective function includes a benefit term that characterizes the mechanical power of the cable in the prediction time domain and a penalty term that characterizes the stability of the system operation. The speed optimization module 306 is connected to the function construction module. Under preset constraints, it solves the objective function to obtain the target release and take-off speed sequence that optimizes the objective function. The power optimization module 308, connected to the speed optimization module, outputs the first target speed in the target retraction speed sequence for the system's hoisting actuator to execute in the current control cycle, so as to optimize the mechanical power of the system in the current control cycle.

[0047] In this embodiment of the invention, the information acquisition module 300 can be used to execute step 100 in the above method embodiment, the dynamics simulation module 302 can be used to execute step 102 in the above method embodiment, the function construction module 304 can be used to execute step 104 in the above method embodiment, the speed optimization module 306 can be used to execute step 106 in the above method embodiment, and the power optimization module 308 can be used to execute step 108 in the above method embodiment.

[0048] In one embodiment of the present invention, the objective function is represented by the following formula: in, Let be the objective function. For the predicted first Within the first control cycle, the first The cable mechanical power of each simulated sub-step. For mechanical power gain weight, For cable tension safety penalty weight, For cable length safety penalty weight, For high security penalty weights, Weights for power fluctuation penalties The penalty weight for changes in target velocity, The penalty weight is the target velocity amplitude. For cable tension safety penalty function, For the cable length safety penalty function, For high-security penalty functions, To predict cable tension, To predict cable length, Specify the predicted altitude of the nodes for the aerial components. To predict the number of control cycles in the time domain, each control cycle includes... Number of simulation substeps, For simulating step size, To control the periodic index, For simulation substep index, For the first The prediction obtained at time i is used for the i-th time. The target release and retraction speed for each control cycle, when hour, Take the current actual cable deployment and retraction speed ,when hour, Take the target release / retraction speed from the previous control cycle.

[0049] In one embodiment of the present invention, the cable tension safety penalty function is expressed by the following formula: The cable length safety penalty function is expressed by the following formula: The high-security penalty function is expressed by the following formula: in, The preset minimum allowable cable tension, The preset maximum allowable cable tension, The preset minimum allowable cable length, This is the preset maximum allowable cable length. This is the preset minimum safe height.

[0050] In one embodiment of the present invention, the preset constraint conditions include one or more of the following: Boundary constraints on target deployment and recovery speeds; ,in, The preset maximum allowable cable winding speed, The preset maximum allowable cable laying speed; Target velocity variation constraints: , This is the preset maximum allowable rate of change of speed; Acceleration constraints of the hoist actuator: , The preset maximum permissible tether acceleration, This is for simulating step size; Cable tension safety constraints: ; Cable length safety constraints: ; Safety constraints for aerial components at high altitudes: .

[0051] In one embodiment of the present invention, the speed optimization module is configured to perform the following operations: Using the cable winding and unwinding speed sequence as the optimization variable, a current sampling distribution is constructed. The current sampling distribution is a normal distribution. The initial mean of each winding and unwinding speed sequence in the current sampling distribution is obtained by shifting the optimal target winding and unwinding speed sequence of the previous control cycle. If there is no optimization result from the previous control cycle, the current actual winding and unwinding speed is used as the initial mean. Randomly sample from the current sampling distribution to generate multiple first candidate sequences. Perform constraint projection on each first candidate sequence based on preset constraints to obtain multiple second candidate sequences that satisfy the constraints. Based on each second candidate sequence and the current state information, the mechanical power of the cable in the time domain is predicted through the dynamic simulation model, and the objective function value corresponding to each second candidate sequence is calculated based on the mechanical power of the cable. The second candidate sequences are sorted according to the objective function value, and the top few second candidate sequences with the best objective function value are selected as elite samples. The mean and standard deviation of the current sampling distribution are updated based on the elite samples. The updated sampling distribution is used as the current sampling distribution for the next round of iteration. The process returns to the step of randomly sampling from the current sampling distribution to generate multiple first candidate sequences and continues to iterate until the preset convergence condition is met. The candidate take-up and release speed sequence with the optimal objective function value during the iteration process is taken as the target take-up and release speed sequence.

[0052] In one embodiment of the present invention, it is also used to perform the following operations: After the current control cycle ends, the current status information of the umbrella ladder-type land-based high-altitude wind power generation system is reacquired. Based on the updated current state information, repeat the steps from obtaining the wind speed prediction value to outputting the target speed.

[0053] In one embodiment of the present invention, the dynamic simulation model is constructed in the following manner: The aerial components and main cable of the parachute-type land-based high-altitude wind power generation system are discretized into multiple units, and the motion differential equations of each unit are established. The motion differential equations include force terms, which include one or more of the following: gravity term, cable tension term, aerodynamic term, and buoyancy term. The aerodynamic term is calculated based on the predicted wind speed and the current motion speed of each unit. The cable tension term is calculated based on the cable deformation and the current motion speed. The buoyancy term is based on the material properties and dimensions of the helium balloon. Based on the aforementioned motion differential equations, a recursive solution is performed in the time domain using numerical integration to obtain the cable mechanical power, predicted cable tension, predicted cable length, and predicted height of the specified node of the aerial component for each control cycle in the predicted time domain.

[0054] It is understood that the structures illustrated in the embodiments of the present invention do not constitute a specific limitation on the power optimization device for a ladder-type land-based high-altitude wind power generation system. In other embodiments of the present invention, a power optimization device for a ladder-type land-based high-altitude wind power generation system may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0055] The information interaction and execution process between the modules in the above-mentioned device are based on the same concept as the method embodiment of the present invention, and the specific details can be found in the description of the method embodiment of the present invention, and will not be repeated here.

[0056] This invention also provides an electronic device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements a power optimization method for a ladder-type land-based high-altitude wind power generation system according to any embodiment of this invention.

[0057] This invention also provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program causes the processor to perform a power optimization method for a ladder-type land-based high-altitude wind power generation system according to any embodiment of this invention.

[0058] Specifically, a system or apparatus equipped with a storage medium may be provided, on which software program code implementing the functions of any of the embodiments described above is stored, and the computer (or CPU or MPU) of the system or apparatus may read and execute the program code stored in the storage medium.

[0059] In this case, the program code read from the storage medium can itself implement the function of any of the above embodiments, and therefore the program code and the storage medium storing the program code constitute part of the present invention.

[0060] Storage media embodiments for providing program code include floppy disks, hard disks, magneto-optical disks, optical disks (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), magnetic tapes, non-volatile memory cards, and ROMs. Alternatively, program code can be downloaded from a server computer via a communication network.

[0061] Furthermore, it should be clear that not only can the program code read by the computer be executed, but also the operating system or other components operating on the computer can be instructed based on the program code to perform some or all of the actual operations, thereby realizing the function of any of the embodiments described above.

[0062] Furthermore, it is understood that the program code read from the storage medium is written to the memory set in the expansion board inserted into the computer or to the memory set in the expansion module connected to the computer. Then, based on the instructions of the program code, the CPU or other components installed on the expansion board or expansion module execute some and all of the actual operations, thereby realizing the function of any of the above embodiments.

[0063] 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 electronic device 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 electronic device. 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 electronic device that includes said element.

[0064] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various storage media that can store program code, such as ROM, RAM, magnetic disk, or optical disk.

[0065] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A power optimization method for a ladder-type land-based high-altitude wind power generation system, characterized in that, include: Obtain wind speed forecasts and current status information of the umbrella ladder-type land-based high-altitude wind power generation system; Based on the cable's release and take-up speed sequence, wind speed prediction, and current state information within the predicted time domain, the cable's mechanical power for each control cycle within the predicted time domain is calculated using a preset dynamic simulation model. The release and take-up speed sequence includes the release and take-up speeds corresponding to each control cycle within the predicted time domain. Using the release and take-up speed sequence as decision variables, an objective function is constructed. The objective function includes a benefit term characterizing the mechanical power of the cable in the prediction time domain and a penalty term characterizing the system's operational stability. Under preset constraints, the objective function is solved to obtain the target release and take-off speed sequence that optimizes the objective function; The first target speed in the target retraction speed sequence is output for the system's hoisting actuator to execute in the current control cycle, so as to optimize the mechanical power of the system in the current control cycle.

2. The method according to claim 1, characterized in that, The objective function is expressed by the following formula: in, Let be the objective function. For the predicted first Within the first control cycle, the first The cable mechanical power of each simulated sub-step. For mechanical power gain weight, For cable tension safety penalty weight, For cable length safety penalty weight, For high security penalty weights, Weights for power fluctuation penalties The penalty weight for changes in target velocity, The penalty weight is the target velocity amplitude. For cable tension safety penalty function, For the cable length safety penalty function, For high-security penalty functions, To predict cable tension, To predict cable length, Specify the predicted altitude of the nodes for the aerial components. To predict the number of control cycles in the time domain, each control cycle includes... Number of simulation substeps, For simulating step size, To control the periodic index, For simulation substep index, For the first The prediction obtained at time i is used for the i-th time. The target release and retraction speed for each control cycle, when hour, Take the current actual cable deployment and retraction speed ,when hour, Take the target release / retraction speed from the previous control cycle.

3. The method according to claim 2, characterized in that, The cable tension safety penalty function is expressed by the following formula: The cable length safety penalty function is expressed by the following formula: The high-security penalty function is expressed by the following formula: in, The preset minimum allowable cable tension, The preset maximum allowable cable tension, The preset minimum allowable cable length, This is the preset maximum allowable cable length. This is the preset minimum safe height.

4. The method according to claim 3, characterized in that, The preset constraints include one or more of the following: Boundary constraints on target deployment and recovery speeds; ,in, The preset maximum allowable cable winding speed, The preset maximum allowable cable laying speed; Target velocity variation constraints: , This is the preset maximum allowable rate of change of speed; Acceleration constraints of the hoist actuator: , The preset maximum permissible tether acceleration, This is for simulating step size; Cable tension safety constraints: ; Cable length safety constraints: ; Safety constraints for aerial components at high altitudes: .

5. The method according to claim 1, characterized in that, Solving the objective function includes: Using the cable winding and unwinding speed sequence as the optimization variable, a current sampling distribution is constructed. The current sampling distribution is a normal distribution. The initial mean of each winding and unwinding speed sequence in the current sampling distribution is obtained by shifting the optimal target winding and unwinding speed sequence of the previous control cycle. If there is no optimization result from the previous control cycle, the current actual winding and unwinding speed is used as the initial mean. Randomly sample from the current sampling distribution to generate multiple first candidate sequences. Perform constraint projection on each first candidate sequence based on preset constraints to obtain multiple second candidate sequences that satisfy the constraints. Based on each second candidate sequence and the current state information, the mechanical power of the cable in the time domain is predicted through the dynamic simulation model, and the objective function value corresponding to each second candidate sequence is calculated based on the mechanical power of the cable. The second candidate sequences are sorted according to the objective function value, and the top few second candidate sequences with the best objective function value are selected as elite samples. The mean and standard deviation of the current sampling distribution are updated based on the elite samples. The updated sampling distribution is used as the current sampling distribution for the next round of iteration. The process returns to the step of randomly sampling from the current sampling distribution to generate multiple first candidate sequences and continues to iterate until the preset convergence condition is met. The candidate take-up and release speed sequence with the optimal objective function value during the iteration process is taken as the target take-up and release speed sequence.

6. The method according to claim 1, characterized in that, Also includes: After the current control cycle ends, the current status information of the umbrella ladder-type land-based high-altitude wind power generation system is reacquired. Based on the updated current state information, repeat the steps from obtaining the wind speed prediction value to outputting the target speed.

7. The method according to claim 1, characterized in that, The dynamic simulation model is constructed in the following manner: The aerial components and main cable of the parachute-type land-based high-altitude wind power generation system are discretized into multiple units, and the motion differential equations of each unit are established. The motion differential equations include force terms, which include one or more of the following: gravity term, cable tension term, aerodynamic term, and buoyancy term. The aerodynamic term is calculated based on the predicted wind speed and the current motion speed of each unit. The cable tension term is calculated based on the cable deformation and the current motion speed. The buoyancy term is based on the material properties and dimensions of the helium balloon. Based on the aforementioned motion differential equations, a recursive solution is performed in the time domain using numerical integration to obtain the cable mechanical power, predicted cable tension, predicted cable length, and predicted height of the specified node of the aerial component for each control cycle in the predicted time domain.

8. A power optimization device for an umbrella-ladder type land-based high-altitude wind power generation system, characterized in that, include: The information acquisition module acquires wind speed forecasts and current status information of the umbrella ladder-type land-based high-altitude wind power generation system; The dynamics simulation module, connected to the information acquisition module, calculates the cable mechanical power for each control cycle in the prediction time domain based on the cable's release and take-up speed sequence, wind speed prediction, and current state information, using a preset dynamics simulation model. The release and take-up speed sequence includes the release and take-up speeds corresponding to each control cycle in the prediction time domain. The function construction module, connected to the dynamics simulation module, constructs an objective function using the release and take-up speed sequence as the decision variable. The objective function includes a benefit term characterizing the mechanical power of the cable in the prediction time domain and a penalty term characterizing the system's operational stability. The speed optimization module, connected to the function construction module, solves the objective function under preset constraints to obtain the target release and take-off speed sequence that optimizes the objective function. The power optimization module, connected to the speed optimization module, outputs the first target speed in the target retraction speed sequence, which is then executed by the hoisting actuator of the system in the current control cycle to optimize the mechanical power of the system in the current control cycle.

9. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed in a computer, causes the computer to perform the method described in any one of claims 1-7.

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