High-degree-of-freedom self-detection wind energy intelligent self-adjusting system and method

Abstract

The invention discloses a high-degree-of-freedom self-detection wind energy intelligent self-adjustment system and method. The system comprises a sensor module, a central control module and a signal transmission module. The method comprises the following steps of: acquiring data, preprocessing the data, constructing a'tornado 'model from two angles of wind speed and track through an obtained data model, calculating necessary variables according to a tornado attraction mechanism, attraction, air density, contact area and drag force, finally simulating a maximum power value, and outputting the obtained maximum power. The high-degree-of-freedom self-detection wind energy intelligent self-adjusting system is provided by analyzing the power generation efficiency of high-altitude wind power generation, factors influencing the high-degree-of-freedom self-detection wind energy intelligent self-adjusting system are analyzed through the central system, the maximum power generation efficiency can be achieved, the electric quantity is large, the efficiency is high, and the power generation efficiency is high. And the predicted annual energy output can reach more than 6500 hours.

Description

Technical Field

[0001] The present invention relates to the intersection of new energy technology and intelligent equipment, and in particular to a high-degree-of-freedom self-detection wind energy intelligent self-regulation system and method. Background Art

[0002] Existing wind power generation technologies face three major technical bottlenecks: low efficiency of low-altitude wind energy utilization: Traditional wind turbines are limited to an area 30-100 meters above the ground, where wind speeds are low and turbulence is high, resulting in energy capture efficiencies of less than 40%; poor equipment adaptability: existing high-altitude wind power generation devices lack multi-dimensional real-time sensing capabilities and are unable to dynamically respond to changes in environmental parameters such as wind direction and wind shear, leading to system downtime; and imperfect mathematical models: Traditional control algorithms using PID or fuzzy control struggle to establish nonlinear dynamic models of high-altitude wind fields, resulting in maximum power point tracking (MPPT) accuracy below 85%. Electricity resources are an essential component of modern human life, crucial for daily life, industrial development, and technological advancement. However, electricity consumption is extremely tight, and a severe shortage of electricity supply has become a major problem. Traditional thermal power generation is highly polluting and inefficient, while more advanced nuclear power generation also carries significant safety risks. Therefore, developing a new, pollution-free, high-capacity, and efficient power generation technology has become an urgent issue. Therefore, in response to the above problems, the present invention proposes an intelligent control system based on a tornado cyclone model, which realizes the efficient capture of high-altitude wind energy by constructing a three-dimensional wind field sensing network and a Coriolis force compensation mechanism. Summary of the Invention

[0003] Purpose of the invention: The purpose of the present invention is to provide a high degree of freedom self-detection wind energy intelligent self-regulation system and method.

[0004] Technical solution: The high-degree-of-freedom self-detection wind energy intelligent self-regulation system described in the present invention includes a sensor module, a central control module and a signal transmission module; the central control module is connected to the sensor module and the signal transmission module respectively, and the central control module constructs a function equation for converting wind energy into electrical energy based on the collected environmental parameters, and uses the tornado algorithm to transmit the calculation results to the signal transmission module and the sensor module, thereby realizing signal reception, transmission, and equipment operation and regulation.

[0005] Furthermore, the sensor module includes a wind force sensor, a wind shear direction sensor, and a position sensor of the high-altitude intelligent monitor; the high-altitude intelligent monitor position sensor and the wind shear direction sensor are respectively located on the left and right sides of the high-altitude intelligent monitor, the wings of the high-altitude intelligent monitor, and the top of the high-altitude intelligent monitor; the position sensor of the high-altitude intelligent monitor is located at the ground base station and is used to detect the operating trajectory of the high-altitude intelligent monitor.

[0006] Furthermore, the signal transmission module includes a signal receiver and a signal transmitter. The signal receiver and the signal transmitter are both located at the tail of the high-altitude intelligent monitor and the top of the base station, and realize signal transmission by receiving or sending ultrasonic signals.

[0007] The high-degree-of-freedom self-detection wind energy intelligent self-regulation method of the present invention comprises the following steps:

[0008] (1) Data acquisition: various data during the operation of the high-degree-of-freedom self-detection wind energy intelligent self-regulation system are obtained through sensors, and converted into various signals and transmitted to the central system;

[0009] (2) Data preprocessing: The central system processes the signal to obtain data that meets the tornado input requirements, and accurately determines the tornado population size, dimensional position, and trajectory status, continuously updating the current data and predicting the data in the near future;

[0010] (3) Using the data model obtained in step (2), a “tornado” model is constructed from the perspectives of wind speed and trajectory;

[0011] (4) According to the tornado attraction mechanism, the necessary variables are calculated, including attraction, air density, contact area, and drag force. Strong updrafts are generated from atmospheric instability, and the vertical wind shear is enhanced by the high-altitude jet stream, causing the airflow to rotate horizontally, forming a mesoscale cyclone that extends downward and accelerates its rotation, forming a visible funnel cloud vortex that touches the ground, causing a sudden drop in air pressure and a sharp increase in wind speed to more than 100 meters per second. Finally, the maximum power value is simulated;

[0012] (5) Re-regulate the equipment angle, height, and trajectory according to step (4);

[0013] (6) If the objective function reaches a certain range threshold, the iterative update is terminated, and the steering and angle of the high-altitude intelligent monitor are adjusted, and the high-altitude intelligent monitor is raised and lowered to the optimal position, and the maximum power output is obtained.

[0014] Furthermore, the initialization formula of step (1) is:

[0015] y i，j =l j +Rand×(u j -l j )j

[0016] Among them, is y i，j The initial position of the i-th individual in the j-th dimension, l j and u j are the lower and upper bounds of the j-th dimension, and random numbers between Rand[0, 1].

[0017] Furthermore, the data required in step (2) include wind force, wind tangent direction and tornado position.

[0018] Furthermore, the "tornado" model constructed in step (3) is expressed as:

[0019]

[0020] Among them, Ω is the angular velocity of the earth, φ is the latitude, R is the radius of curvature, V is the wind speed, and f represents the wind force parameter. is the normal component of the pressure gradient force.

[0021] Furthermore, the value of the maximum power simulated in step (4) is expressed as follows:

[0022]

[0023] Where is the pressure AP difference, ρ is the gas density, A is the contact area, C d is the drag force, which is related to the shape of the high-altitude intelligent monitor, V is the wind speed, and F is the wind force at maximum power.

[0024] Furthermore, the optimization formula in step (4) considering the influence of Coriolis force is:

[0025]

[0026] in:

[0027] Rand is a random number between [0, 1];

[0028] is the speed of the ith storm at the t+1th iteration;

[0029] is the speed of the ith storm at the tth iteration;

[0030] η is the shrinkage factor, which is used to control the convergence speed of the storm. Its value is:

[0031]

[0032] Where χ is the acceleration factor, which is 4.10;

[0033] μ is the fuzzy adaptive kinetic energy factor, which is the curvature radius of the northern and southern hemispheres respectively. Its value is:

[0034]

[0035] R l and R r The values are the curvature radii of the northern and southern hemispheres respectively, and their values are:

[0036]

[0037] Where t is the current iteration number and T is the maximum iteration number;

[0038] c is a random factor, and its value range is:

[0039] c=br×δ1×wr

[0040] Where br is a constant with a value of 100,000; δ1 is a sign change factor, and wr is the product of angular velocity and radius.

[0041] Furthermore, the angle control formula in step (5) is as follows:

[0042]

[0043] in:

[0044] Pa: heading angle deviation;

[0045] a ideal =arctan(v / y);

[0046] θ a : Heading angle sensor error factor, position trajectory control formula is as follows:

[0047]

[0048] Pv: cable retraction and release rate deviation term; v opt : The optimal torque of the ground generator corresponds to the cable speed;

[0049] θ v : Cable rate sensor error factor.

[0050] Beneficial effects: Compared with the existing technology, the present invention has the following significant advantages: Through the analysis of the power generation efficiency of high-altitude wind power generation, the present invention proposes a high-freedom self-detection wind energy intelligent self-regulating system. Through the central system, the factors affecting the high-freedom self-detection wind energy intelligent self-regulating system are analyzed, which helps to achieve maximum power generation efficiency, large power generation and high efficiency. It is expected that the annual power generation can reach more than 6,500 hours. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 :System overall architecture diagram

[0052] Figure 2 :Sensor layout diagram

[0053] Figure 3 : Signal transmission timing diagram

[0054] Figure 4 :Flowchart of tornado model construction

[0055] Figure 5 :Control algorithm iteration flow chart DETAILED DESCRIPTION

[0056] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0057] like Figure 1 As shown, the intelligent high-degree-of-freedom self-detection wind energy intelligent self-regulation system of the present invention includes a sensor module, a central control module and a signal transmission module;

[0058] Module 1: The sensor module includes a wind force sensor to obtain the parameter f, a wind tangential sensor to obtain the angle parameter q, and a high-altitude intelligent monitor position sensor to obtain the device height trajectory parameter s; the high-altitude intelligent monitor position sensor and wind tangential sensor are respectively located on the left and right sides of the high-altitude intelligent monitor, the wings of the high-altitude intelligent monitor, and the top of the high-altitude intelligent monitor. The position sensor of the high-altitude intelligent monitor is located at the ground base station to detect the running trajectory of the high-altitude intelligent monitor, such as Figure 2 As shown;

[0059] Module 2: Figure 3 As shown, the signal transmission module includes a signal receiver and a signal transmitter. The signal receiver and the signal transmitter are both located at the tail of the high-altitude intelligent monitor and the top of the base station, and realize signal transmission by receiving or sending ultrasonic signals.

[0060] Module 3: Central control module, which is connected to the sensor module and signal transmission module respectively. It constructs the function equation for converting wind energy into electrical energy based on the collected environmental parameters, and uses the tornado algorithm to realize signal reception, transmission, equipment operation and regulation.

[0061] The data in the module 1 include wind force, wind direction, and the position of the high-altitude intelligent monitor.

[0062] The objective function in module 2 is:

[0063]

[0064] Among them, f m is the maximum power converted from wind power to electrical energy,

[0065] p f The maximum power generation under the influence of wind force:

[0066]

[0067] p qThe maximum power generation under the influence of wind tangential direction:

[0068]

[0069] p s is the maximum power size affected by the position of the high-altitude intelligent monitor,

[0070]

[0071] λ is the power factor coefficient, cosθ f 、cosθ q 、cosθ s They are the error factors of the wind force sensor, wind tangential direction sensor, and high-altitude intelligent monitor position sensor devices, respectively. f represents the wind force parameter, q represents the device angle parameter, s represents the device height trajectory parameter, f0 represents the initial value of the wind force, q0 represents the initial value of the device angle, and s0 represents the initial value of the device height trajectory.

[0072] Therefore, the power generation efficiency η of the high-degree-of-freedom self-detection wind energy intelligent self-regulation system is:

[0073]

[0074] The intelligent high-degree-of-freedom self-detection wind energy intelligent self-regulation method of the present invention comprises the following specific steps:

[0075] Step 1: Data Collection: Sensors are used to capture various data from the high-degree-of-freedom self-detection wind energy intelligent self-regulation system during operation, and these data are converted into various signals and transmitted to the central system. The initialization process of the population TOC algorithm involves randomly generating a set of design variable (storm, thunderstorm, and tornado) locations. These locations are evenly distributed between the upper and lower bounds of the search space. The initialization formula is:

[0076] y i，j =l j +Rand×(u j -l j )j

[0077] Among them, is y i，j The initial position of the i-th individual in the j-th dimension, l j and u j are the lower and upper bounds of the j-th dimension, and random numbers between Rand[0, 1].

[0078] Step 2: Data preprocessing: The central system processes the signal to obtain data that meets the tornado input requirements, such as wind speed, wind direction, and tornado location. It accurately determines the tornado population size, dimensional position, and trajectory, and continuously updates the current data and predicts data in the near future.

[0079] Step 3: If Figure 4 As shown, the data model obtained in step 2 is used to construct a "tornado" model from wind speed and trajectory. The latitude at that time is obtained from the real-time geographical location. The higher the latitude φ (such as mid- and high-latitude areas), the larger the sinφ value, the more significant the Coriolis force, and the easier it is to form a cyclonic vortex. The angular velocity of the earth, tornadoes in the northern hemisphere usually rotate counterclockwise (clockwise in the southern hemisphere), which is directly related to the direction of the Coriolis force, the radius of curvature, and the curvature radius R. It is related to the duration of the vortex: a smaller R corresponds to a stronger centrifugal force, requiring a larger pressure gradient force balance (the central low-pressure area can reach 200-400hPa); if R is too small (such as <50 meters), the vortex dissipates quickly due to turbulent dissipation. Combining all the data to form a tornado, the construction formula is as follows:

[0080]

[0081] Among them, Ω is the angular velocity of the earth, φ is the latitude, R is the radius of curvature, V is the wind speed, and f represents the wind force parameter. is the normal component of the pressure gradient force.

[0082] Step 4: Based on the tornado attraction mechanism, the necessary variables are calculated, which are related to air density, contact area, and drag force. Strong updrafts are generated from atmospheric instability. The vertical wind shear is strengthened by the high-altitude jet stream, causing the airflow to rotate horizontally, forming a mesoscale cyclone that extends downward and accelerates its rotation, forming a visible funnel cloud vortex that touches the ground, causing a sudden drop in air pressure and a sharp increase in wind speed to over 100 meters per second. Finally, the maximum power value is simulated, which is expressed as follows:

[0083]

[0084] Where is the pressure AP difference, ρ is the gas density, A is the contact area, C d is the drag force (related to the shape of the high-altitude intelligent monitor), V is the wind speed, and F is the wind force at maximum power.

[0085] Taking into account the influence of Coriolis force, the optimization formula is:

[0086]

[0087] in:

[0088] Rand is a random number between [0, 1];

[0089] is the speed of the ith storm at the t+1th iteration;

[0090] is the speed of the ith storm at the tth iteration;

[0091] 77 is the shrinkage factor, which is used to control the convergence speed of the storm. Its value is:

[0092]

[0093] Where χ is the acceleration factor, which is 4.10;

[0094] μ is the fuzzy adaptive kinetic energy factor, which is the curvature radius of the northern and southern hemispheres respectively. Its value is:

[0095]

[0096] R l and R r The values are the curvature radii of the northern and southern hemispheres respectively, and their values are:

[0097]

[0098] Among them, is the current iteration t number, T is the maximum number of iterations;

[0099] c is a random factor, and its value range is:

[0100] c=br×δ1×wr

[0101] c=br×δ1×wr

[0102] Where br is a constant with a value of 100,000; δ1 is a sign change factor, and wr is the product of angular velocity and radius.

[0103] Step 5: Re-adjust the device angle, height, and trajectory according to step 4. The angle adjustment formula is as follows:

[0104]

[0105] in:

[0106] Pa: Heading angle deviation (angle)

[0107] a ideal =arctan(v / y) (theoretical optimal windward angle, which needs to be calculated in combination with wind speed V and wind force f)

[0108] θ a : Heading angle sensor error factor (needs to be calibrated through experiments)

[0109] The position trajectory control formula is as follows (cable retraction rate (v cable )):

[0110]

[0111] Pv: Cable retraction and release rate deviation term (position trajectory)

[0112] v opt : Optimal torque of the ground generator corresponds to the cable speed (the document mentions that 50-60km / h requires dynamic matching)

[0113] θ v : Cable rate sensor error factor

[0114] Step 6: If Figure 5 As shown, if the objective function value reaches a certain range threshold, the iterative update is terminated, and the steering of the high-altitude intelligent monitor, the opening angle of the high-altitude intelligent monitor, the position and trajectory of the high-altitude intelligent monitor are adjusted at the same time, and the maximum power output is obtained.

Claims

1. A high degree of freedom self-detection wind energy intelligent self-regulation system, characterized in that: It includes a sensor module, a central control module and a signal transmission module; the central control module is connected to the sensor module and the signal transmission module respectively. The central control module constructs a function equation for converting wind energy into electrical energy based on the collected environmental parameters, and uses the tornado algorithm to transmit the calculation results to the signal transmission module and the sensor module to realize signal reception, transmission, and equipment operation and regulation.

2. The high degree of freedom self-detection wind energy intelligent self-regulation system according to claim 1 is characterized in that: The sensor module includes a wind force sensor, a wind shear direction sensor, and a position sensor of the high-altitude intelligent monitor; the high-altitude intelligent monitor position sensor and the wind shear direction sensor are respectively located on the left and right sides of the high-altitude intelligent monitor, the wings of the high-altitude intelligent monitor, and the top of the high-altitude intelligent monitor. The position sensor of the high-altitude intelligent monitor is located at the ground base station and is used to detect the operating trajectory of the high-altitude intelligent monitor.

3. The high degree of freedom self-detection wind energy intelligent self-regulation system according to claim 1 is characterized in that: The signal transmission module includes a signal receiver and a signal transmitter. The signal receiver and the signal transmitter are both located at the tail of the high-altitude intelligent monitor and the top of the base station, and realize signal transmission by receiving or sending ultrasonic signals.

4. A high degree of freedom self-detection wind energy intelligent self-regulation method, characterized in that: The steps include: (1) Data acquisition: various data during the operation of the high-degree-of-freedom self-detection wind energy intelligent self-regulation system are obtained through sensors, and converted into various signals and transmitted to the central system; (2) Data preprocessing: The central system processes the signal to obtain data that meets the tornado input requirements, and accurately determines the tornado population size, dimensional position, and trajectory status, continuously updating the current data and predicting the data in the near future; (3) Using the data model obtained in step (2), a "tornado" model is constructed from the perspectives of wind speed and trajectory; (4) According to the tornado attraction mechanism, the necessary variables are calculated, including attraction, air density, contact area, and drag force. Strong updrafts are generated from atmospheric instability, and the vertical wind shear is enhanced by the high-altitude jet stream, causing the airflow to rotate horizontally, forming a mesoscale cyclone that extends downward and accelerates its rotation, forming a visible funnel cloud vortex that touches the ground, causing a sudden drop in air pressure and a sharp increase in wind speed to more than 100 meters per second. Finally, the maximum power value is simulated; (5) Re-regulate the equipment angle, height, and trajectory according to step (4); (6) If the objective function reaches a certain range threshold, the iterative update is terminated, and the steering and angle of the high-altitude intelligent monitor are adjusted, and the high-altitude intelligent monitor is raised and lowered to the optimal position, and the maximum power output is obtained.

5. The high degree of freedom self-detection wind energy intelligent self-regulation method according to claim 1 is characterized in that: The initialization formula of step (1) is: y i，j =l j +Rand×(u j -L j )j Among them, y i，j is the initial position of the i-th individual in the j-th dimension, l j and u j are the lower and upper bounds of the j-th dimension, and random numbers between Rand[0, 1].

6. The high degree of freedom self-detection wind energy intelligent self-regulation method according to claim 1, characterized in that: The data required in step (2) include wind force, wind tangent direction and tornado position.

7. The high degree of freedom self-detection wind energy intelligent self-regulation method according to claim 1, characterized in that: The "tornado" model constructed in step (3) is expressed as: Among them, Ω is the angular velocity of the earth, φ is the latitude, R is the radius of curvature, V is the wind speed, and f represents the wind force parameter. is the normal component of the pressure gradient force.

8. The high degree of freedom self-detection wind energy intelligent self-regulation method according to claim 1 is characterized in that: The maximum power value simulated in step (4) is expressed as follows: Where is the pressure AP difference, ρ is the gas density, A is the contact area, C d is the drag force, which is related to the shape of the high-altitude intelligent monitor, V is the wind speed, and F is the wind force at maximum power.

9. The high degree of freedom self-detection wind energy intelligent self-regulation method according to claim 1, characterized in that: The optimization formula in step (4) considering the influence of Coriolis force is: in: Rand is a random number between [0, 1]; is the speed of the ith storm at the t+1th iteration; is the speed of the ith storm at the tth iteration; η is the shrinkage factor, which is used to control the convergence speed of the storm. Its value is: Where χ is the acceleration factor, which is 4.10; μ is the fuzzy adaptive kinetic energy factor, which is the curvature radius of the northern and southern hemispheres respectively. Its value is: R l and R r The values are the curvature radii of the northern and southern hemispheres respectively, and their values are: Where t is the current iteration number and T is the maximum iteration number; c is a random factor, and its value range is: c=br×δ1×wr Where br is a constant with a value of 100,000; δ1 is a sign change factor, and wr is the product of angular velocity and radius.

10. The high degree of freedom self-detection wind energy intelligent self-regulation method according to claim 1, characterized in that: The angle control formula in step (5) is as follows: in: Pa: heading angle deviation; to ideal =arctan(v / y). θ a : Heading angle sensor error factor, position trajectory control formula is as follows: Pv: cable retraction and release rate deviation term; v opt : The optimal torque of the ground generator corresponds to the cable speed; θ v : Cable rate sensor error factor.

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