Double-wind-wheel wind-driven generator for realizing maximum wind energy capture

Abstract

The application discloses a double-wind-wheel wind power generator for realizing maximum wind energy capture, wherein front and rear wind wheels rotate asynchronously; the front wind wheel is connected to a sun gear of a planetary differential device through an electric control variable speed device; the rear wind wheel is connected to a planetary gear system of the planetary differential device through a gear reverse device; power of the front and rear wind wheels is integrated through the planetary differential device and then transmitted to a generator.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wind power generation equipment, in particular to a double-wind-wheel wind turbine for realizing maximum wind energy capture. BACKGROUND

[0002] With the rapid growth of social demand for energy and the decreasing reserves of traditional energy, environmental pollution is becoming increasingly serious, which has promoted the rapid development of green energy technology. However, in field engineering, the existing power supply mode still has many shortcomings. At present, although the common portable diesel generator is convenient, it is noisy and polluting, and fuel transportation and storage are difficult, especially in remote areas, fuel supply costs are high and complex. Although the solar power generation device is environmentally friendly, it is heavily dependent on weather and light conditions, and it is difficult to provide stable power in rainy weather or at night.

[0003] At the same time, although the single-wind-wheel wind turbine is widely used, its efficiency is significantly reduced in an unstable wind environment due to design limitations. This type of generator is more suitable for scenes with constant wind, such as large wind farms, and is difficult to work efficiently in field environments with frequent changes in wind speed. In addition, its wind speed adaptation range is limited, and it is difficult to meet the continuous power supply demand under low wind speed conditions. In contrast, double-impeller wind turbines show higher adaptability, not only better adapting to random wind direction, but also showing higher power generation efficiency in low wind speed or unstable wind conditions, especially suitable for complex field environments, providing a better solution for the application of green energy in field operations.

[0004] Compared with traditional wind turbines, double-wind-wheel wind turbines have more applicable environments and larger swept areas. In a single-wind-field scene, obtaining higher efficiency of wind energy conversion is the primary consideration for field operation power supply systems. At present, how to realize the structure and control mechanism of a double-impeller wind turbine with higher efficiency is a key problem to be studied. SUMMARY

[0005] It is a new topic to consider how to further improve the efficiency of double-wind-wheel wind turbines and ensure safety during operation. Therefore, the present application provides a design of a double-wind-wheel wind turbine for realizing maximum wind energy capture. By adopting different front and rear wind wheels, combined with an electrically controlled transmission and a planetary differential device, the wind energy utilization rate can be improved while minimizing energy loss caused by asynchronous rotation of the front and rear wind wheels. In addition, the integrated wind energy capture control device and overload protection system effectively ensure the operating efficiency and safety of the system, overcoming the instability problem of the existing wind power generation system under high wind speed and variable wind speed conditions.

[0006] The present application specifically adopts the following technical solutions:

[0007] A double rotor wind turbine for maximum wind energy capture, the front rotor and the rear rotor rotate asynchronously: the front rotor is connected to the sun gear of the planetary differential device through the electrically controlled variable speed device; the rear rotor is connected to the planetary gear system of the planetary differential device through the gear reverse device, and the power of the front rotor and the rear rotor is integrated through the planetary differential device and then transmitted to the generator. The generator is preferably a permanent magnet synchronous generator.

[0008] Further, the controller monitors the current external wind speed in real time through the wind speed sensor, and adjusts the electrically controlled variable speed device within the maximum limit speed according to the current external wind speed, so that the speed ratio of the front rotor output speed to the rear rotor output speed is kept within the optimal range, to achieve maximum wind energy capture.

[0009] Further, when the current external wind speed or the load of the double rotor wind turbine is greater than the corresponding set threshold, the controller adjusts the speed and torque of the generator through the frequency converter of the generator, to ensure that the front rotor and the rear rotor operate within a safe range.

[0010] Here, the controller directly controls the frequency converter, because at this time it belongs to a relatively dangerous working condition, and further adjustment through the electrically controlled variable speed device is not safe enough, so the frequency converter of the generator is directly adjusted to run at a reduced speed.

[0011] Further, the electrically controlled variable speed device is composed of an input sprocket, an output sprocket and a chain, the input sprocket is connected to the output sprocket through the chain, the front rotor is connected to the input sprocket, and the output sprocket is connected to the sun gear in the planetary differential device, so as to transmit the power of the front rotor to the sun gear in the planetary differential device.

[0012] Further, the planetary differential device includes a planetary gear system and a sleeve; the planetary gear system includes a sun gear and a plurality of planetary gears; the sun gear is engaged with the plurality of planetary gears; the output shaft of the rear rotor is connected to the second gear of the gear reverse device, the first gear is engaged with the second gear of the gear reverse device; the second gear is fixedly connected to the sleeve, and the sleeve is arranged on the outer circumferential part of the planetary gear system and the inner ring is engaged with the plurality of planetary gears.

[0013] Further, the controller obtains a preliminary range of the optimal speed ratio corresponding to the current external wind speed based on the maximum wind energy capture power estimation, and takes the preliminary range of the optimal speed ratio corresponding to the current external wind speed as a preliminary constraint; within the preliminary constraint, the real-time collected current external wind speed is input into the trained SVR model to fit and obtain the optimal range between the speed ratio of the front rotor output speed to the rear rotor output speed under the preliminary constraint.

[0014] Further, the wind speed sensor is specifically a wind direction and speed sensor; the maximum wind energy capture power estimation method, which obtains the preliminary range of the optimal rotation speed ratio corresponding to the current external wind speed, further comprises: on the basis of the maximum wind energy capture power estimation, the initial rotation speed ratio of the optimal wind energy capture under different wind directions on the basis of the current external wind speed is obtained according to the simulation test empirical formula and simulation results of the forward wind and the lateral wind, as the preliminary range of the optimal rotation speed ratio.

[0015] Within the preliminary constraint range, the real-time collected current external wind speed is input into the trained SVR model to fit the optimal range between the rotation speed of the front wind wheel and the rotation speed of the rear wind wheel under the preliminary constraint condition, comprising: inputting the meteorological features of the wind speed, the wind direction and the initial rotation speed ratio into the trained SVR model to fit the optimal range between the rotation speed of the front wind wheel and the rotation speed of the rear wind wheel under the preliminary constraint condition.

[0016] Further, the training process of the SVR model specifically comprises:

[0017] The historical wind condition data is classified using the K-means clustering algorithm, and a data set for training the SVR model is constructed according to the classification results;

[0018] The SVR model is trained using the data set, and the input features of the data in each category in the data set correspond to meteorological feature terms, the meteorological feature terms include wind speed, wind direction and initial rotation speed ratio, and the training target of the SVR model is to fit the nonlinear relationship between the meteorological feature terms including wind speed, wind direction and initial rotation speed ratio and the target rotation speed ratio by minimizing the following SVR model loss function:

[0019]

[0020] Wherein, ω is the model weight in vector form, according to the mathematical definition of support vector regression (SVR), it describes the mapping relationship of the SVR model to the input data in the feature space; y i is the real output, corresponding to the target rotation speed ratio, f(x i ) is the predicted output, corresponding to the meteorological feature term, ∈ is the tolerance error, and C is the penalty coefficient;

[0021] The kernel function of the SVR model adopts the radial basis kernel function RBF.

[0022] Further, the K-means clustering is used to classify the historical wind condition data to obtain each classification, and the classification labels of each classification correspond to the gear positions of the stepped transmission in the electric control variable speed device.

[0023] Further, the wind condition data, power generation and efficiency accumulated during the operation of the double wind wheel wind generator are added as incremental data to the training set of the SVR model to perform incremental learning.

[0024] Compared with the prior art, the outstanding advantages of the present application and the preferred schemes thereof at least include: the power output of the front wind wheel and the rear wind wheel is integrated through the planetary differential device, the maximum conversion of energy is realized, and the energy loss problem caused by the asynchronous rotation of the wind wheel in the traditional wind power generation system is solved.

[0025] The stability and safety of the system are effectively improved through the wind energy capture control system and the overload protection function, in addition, the function of calculating the optimal wind wheel speed ratio through capturing the real-time wind speed enables it to maintain efficient operation under different wind speed conditions, and is suitable for a wide range of wind power application scenarios. BRIEF DESCRIPTION OF DRAWINGS

[0026] The present application will be further described in detail below with reference to the accompanying drawings and specific embodiments:

[0027] Figure 1 It is a schematic view of the overall external structure of the wind generator and the local wind energy capture control device of the embodiment of the present application;

[0028] Figure 2 It is a front view of the inside of the storage compartment of the wind generator of the embodiment of the present application;

[0029] Figure 3 It is an oblique view of the inside of the storage compartment of the wind generator of the embodiment of the present application;

[0030] Figure 4 It is a control principle diagram of the embodiment of the present application. DETAILED DESCRIPTION

[0031] In the following, specific embodiments of the present application will be described in detail with reference to the accompanying drawings, and those skilled in the art can clearly understand the present application and implement the present application according to these detailed descriptions. The features in each different embodiment can be combined to obtain new implementation modes, or replace some features in certain embodiments to obtain other preferred implementation modes, without departing from the principles of the present application.

[0032] It should be noted that the terms used herein are only for describing specific embodiments, and are not intended to limit the exemplary embodiments according to the present application. As used herein, the singular form is intended to include the plural form, unless the context clearly indicates otherwise, and furthermore, it should be understood that when the terms "comprise" and / or "include" are used in the specification, there is a feature, step, operation, device, component and / or combination thereof.

[0033] To make the features and advantages of this patent more apparent and understandable, specific embodiments are provided below for detailed explanation:

[0034] like Figure 1 As shown, this embodiment provides a dual-rotor wind turbine system, including: a front rotor 2 and a rear rotor 3 with blades 1, a generator system 4, a tower 5, and a base 6. The main body of the wind turbine generator system is mounted on top of the tower 5. The front rotor 2 and the rear rotor 3 are respectively mounted on both sides of the generator system 4 and connected to the generator system 4 via a rotating shaft, forming a power transmission structure. The rotational power of the rotors is transmitted to the generator system 4 for wind energy conversion and power output. The generator system 4 is fixed to the top of the tower 5 and connected to the tower 5 via a support structure. The tower 5 supports the entire wind power generation device, mounting the generator system 4 and its upper components at a certain height to capture wind energy. The tower is fixed to the ground via the base 6, ensuring the stability and wind resistance of the system.

[0035] The dual-rotor wind turbine system also includes a wind energy capture and control device. This device integrates wind direction and speed sensors 8 and a controller 7. The wind direction and speed sensors 8 are used to monitor external wind speed in real time. The controller 7 is connected to the electronically controlled transmission device inside the generator system 4. After acquiring real-time data, the wind speed acquisition system processes the data and adjusts system parameters based on the wind speed acquisition and subsequent calculation results to regulate the speed ratio between the front and rear rotors.

[0036] like Figure 2 , Figure 3 As shown, in this embodiment, the rotation of the wind blades drives the front and rear wind turbines to rotate asynchronously. The front wind turbine is connected to the planetary differential device via an electronically controlled speed change device, while the rear wind turbine is connected to the sleeve of the planetary differential device via a gear reversal device. The planetary differential device integrates the power of the front and rear wind turbines and transmits it to the permanent magnet synchronous generator 10, achieving maximum wind energy capture and efficient power conversion.

[0037] Specifically, Figure 2 The electronically controlled speed change device shown is connected to the output shaft 15 of the front wind turbine and to the controller 7 to adjust the output speed of the front wind turbine 2, so that the speed ratio between the output speed of the front wind turbine 2 and the rear wind turbine 3 is kept within the optimal range, ensuring maximum wind energy capture under different wind speed conditions.

[0038] Specifically, the electronically controlled transmission device consists of an input sprocket 14, an output sprocket 16, and a chain 19. The input sprocket 14 is connected to the output sprocket 16 via the chain 19, transmitting the power of the front wind turbine to the sun gear 11 in the planetary differential device.

[0039] The planetary differential device provided by the embodiment includes a sun gear 11, a plurality of planetary gears 13, and a sleeve 12. The power of the front wind wheel 2 is transmitted to the sun gear 11 through the output shaft 15 of the front wind wheel and the electric control transmission device. The sun gear 11 is engaged with the plurality of planetary gears 13 through the internal structure and is connected with the output sprocket 16, forming a power transmission path. The planetary gears 13 complete power integration under the support of the sleeve 12, realizing efficient transmission and energy distribution. The power of the rear wind wheel 3 is transmitted to the sleeve 12 through the gear reversing device 18 directly connected with the output shaft 9 of the rear wind wheel and the gear reversing device 17 engaged and connected. The gear reversing device 17 is closely connected with the sleeve 12, ensuring the effective transmission of the power of the rear wind wheel. The planetary gears 13 transmit the power of the two to the main shaft of the permanent magnet synchronous generator 10 after integrating the power, ensuring efficient transmission and conversion of the power.

[0040] In the planetary transmission device, a plurality of planetary gears rotate around a central gear (sun gear), and at the same time, these planetary gears can also rotate in the internal ring gear or sleeve. The planetary transmission device is compact and efficient, and can provide multiple gear ratios, suitable for high-torque demand scenarios.

[0041] In the embodiment, the controller 7 adjusts the speed ratio of the electric control transmission device according to the wind speed, ensuring that the wind wheel is always in the best operating state. The controller 7 is connected with the generator 10, the wind direction and speed sensor 8, and the electric control transmission device 14, receives data from the wind direction and speed sensor 8, and adjusts the speed ratio of the electric control transmission device 14 and the speed and torque of the generator 10 according to the system requirements, ensuring efficient and stable operation of the system.

[0042] As a preferred, the controller 7 also integrates an overload protection function. When the wind speed is too high or the generator load is too large, the controller 7 automatically adjusts the speed and torque of the generator, ensuring that the wind wheel operates within a safe range, avoiding equipment damage and ensuring system safety.

[0043] The application innovatively applies the planetary transmission device to the double-wind-wheel wind turbine generator system, and the front wind wheel and the rear wind wheel are installed at different positions of the planetary transmission device. The front wind wheel is connected to the sun gear through the electric control transmission device, and the rear wind wheel is connected to the planetary gear system through the gear reversing device. The planetary gears can rotate around the sun gear while rotating by themselves, thereby integrating and transmitting the power of the front wind wheel and the rear wind wheel to the generator.

[0044] Through the structural design of the planetary transmission device, the application effectively solves the problem of different speeds of the front wind wheel and the rear wind wheel caused by different wind speeds and wind pressures, reduces the loss of internal energy, and maximizes the conversion of wind energy into mechanical energy. This innovative design improves the overall efficiency of the generator set and ensures that the wind wheel can operate efficiently under different wind conditions.

[0045] The fan is connected coaxially and in different directions by the front and rear wind wheels. The mechanical structure is stable, and the stress area is increased by the stable structure of the front and rear three-blade impellers, thereby improving the wind energy capture rate. The design of the front and rear impellers can significantly improve the output power of the double-impeller generator compared to the single-impeller generator under low wind speed conditions, ensuring efficient power generation in single-wind field conditions.

[0046] In this example, as shown in Figure 1 , the internal structure of the wind turbine generator set is shown in Figure 2 , Figure 3 , including a generator, a planetary differential device, a speed-changing device, a wind energy capture control device, and a sensor. The control method is shown in Figure 4 , the control board is connected to the generator, the speed changer, and the sensor, and comprehensively analyzes the actual wind conditions and makes corresponding adjustments. The basic algorithm of the above controller 7 is as follows:

[0047] The formula for maximum wind energy capture power is as follows:

[0048] P = 0.5 x A x p x C p

[0049] Where p is an objective physical quantity related to air density, A is the swept area of the fan wind wheel, and C p is the wind energy utilization coefficient, which is the size of the wind turbine's energy absorption from natural wind energy. The wind energy utilization coefficient represents the conversion efficiency of the wind turbine generator from wind energy to electrical energy. The characteristics of the fan are often represented by the dimensionless performance curve of the power coefficient C p , and the wind energy utilization coefficient is a function of the tip speed ratio φ and the pitch angle β, i.e. Therefore, the wind energy utilization coefficient C p can be expressed by the approximate formula as follows:

[0050]

[0051] For a double-wind-wheel fan, the front and rear wind wheels have different wind energy capture capabilities in the case of positive wind. After simulation testing, the approximate empirical formula for the wind speed region during wind energy capture of the front and rear wind wheels is as follows:

[0052] V b = V a (1-a) 2

[0053] Where V a is the wind speed at the front wind wheel, and V b is the wind speed at the rear wind wheel.

[0054]

[0055] According to the empirical formula and the simulation results, the rotation speed ratio of the front wind wheel and the rear wind wheel for maximum wind energy capture under stable wind speed is concentrated in R=0.6-0.7. For the side wind, due to the uneven wind speed distribution of the front wind wheel and the rear wind wheel in the side wind, the front wind wheel is subjected to a larger direct wind pressure, while the rear wind wheel is partially blocked and the wind speed is significantly attenuated. According to the empirical formula and the simulation results, the rotation speed ratio of the front wind wheel and the rear wind wheel for optimal wind energy capture under the side wind is concentrated in the range of R=0.6-0.8. However, the optimal rotation speed ratio for each specific scenario needs to be determined in combination with specific wind speed information, etc.

[0056] To further improve the control accuracy of the system, especially the optimization of the rotation speed ratio of the front wind wheel and the rear wind wheel under complex wind conditions, the present application introduces a machine learning model as a supplement based on the above formula calculation. The system collects wind speed, wind direction, front wind wheel and rear wind wheel rotation speed, wind energy capture efficiency and other data in real time through wind direction and speed sensors and other devices. The data not only comes from the monitoring in actual operation, but also includes the results obtained through simulation testing. In order to ensure the accuracy and consistency of the input data, all data need to be preprocessed before entering the system, including removing outliers and standardizing processing, so that the subsequent machine learning model can make full use of these data.

[0057] One of the innovations of the present application is to combine machine learning models with traditional formula calculations. The system first estimates the preliminary range of the optimal rotation speed ratio corresponding to the current wind speed through traditional formula calculation, and then optimizes the calculation results using the machine learning model. When the wind conditions change, the machine learning model can quickly analyze the new wind speed and wind direction data and provide more accurate adjustment suggestions to ensure that the system is always in an efficient operating state. The calculated rotation speed ratio range is used as a constraint and input into the SVR model to further fit the wind wheel rotation speed ratio for maximum wind energy capture under specific conditions.

[0058] The machine learning model system first uses the K-means clustering algorithm to classify historical wind condition data to construct a data set for SVR model training. K-means clustering discretizes continuous meteorological data and generates feature entries, making the structure of the feature set more concise and facilitating analysis and optimization. The system inputs the discretized features as labels into the SVR model after each sampling to ensure that the system can quickly output the optimal rotation speed ratio of the front wind wheel and the rear wind wheel after detecting new wind conditions.

[0059] The wind speed interval is divided by 4 meters / second, starting from 0 meters / second, mapped as 0, 1, 2, and so on (such as 0-4 meters / second, 4-8 meters / second, 8-12 meters / second, the former contains the latter), and the wind speed data of each sampling is rounded to reduce the complexity of the feature space.

[0060] Horizontal wind direction component is mapped as 0, 1, 2, corresponding to "radial positive 0°", "radial lateral 15°-45°", "radial lateral 45°-75°" respectively.

[0061] Vertical wind direction component is mapped as 0, 1, corresponding to "longitudinal 15°-75°" and "longitudinal positive 90°" respectively.

[0062] Temperature interval is discretized with 5℃ as a unit, starting from -20℃, mapped as 0, 1, 2, and so on (e.g. -20℃ to -15℃, -15℃ to -10℃).

[0063] Weather variability is represented by 0 for stable weather conditions and 1 for significant changes within the next 1 hour.

[0064] In one design example, the system employs a stepped transmission with 0.9, 0.8, 0.7, 0.6 four gears. During operation, the K-means clustering labels correspond to the gear positions of the stepped transmission. Whenever new weather data is detected, the system fits the SVR model to this class and directly outputs the optimal speed ratio of the front and rear wind wheels. This structured strategy avoids the need to recalculate complex models every time the operating conditions change, ensuring the efficiency and accuracy of the system's response.

[0065] The logic of constructing the feature set is to convert complex weather data into simplified class labels and correspond to the gear positions of the stepped transmission, reducing computational overhead and optimizing system response time. The dataset generated by K-means clustering is the input basis for the SVR model, enabling the system to quickly predict the optimal control strategy under different operating conditions.

[0066] The solution of the SVR model includes the following steps:

[0067] Model training: using the dataset after K-means clustering for training. The data input features of each class correspond to weather feature items, including wind speed, wind direction, temperature, weather variability, and initial speed ratio. Objective function: the SVR model fits the nonlinear relationship between wind speed and speed ratio by minimizing the following loss function:

[0068]

[0069] where y i is the true output, f(x i ) is the predicted output, ∈ is the tolerance error, and C is the penalty coefficient, used to control the complexity of the model.

[0070] Kernel function selection: the system uses a radial basis kernel function (RBF) to handle complex nonlinear relationships. The form of the radial basis kernel function is:

[0071] K(x i ,y i )=exp(-γ||x i -x j || 2 )

[0072] Where γ controls the learning ability and generalization performance of the model.

[0073] According to the joint software simulation analysis of OpenFast and Simulink, the system tests the following two typical wind condition scenarios. For each scenario, the system identifies the corresponding category according to the K-means clustering terms and selects the optimal front and rear wind wheel speed ratio to achieve maximum power output. In this system, the front and rear wind path speed ratio of the stepped transmission is set to 0.9, 0.8, 0.7, 0.6, and the optimal front and rear wind wheel speed ratio is selected to obtain the maximum wind energy capture rate. Since the bag of words summarizes the typical features of the meteorological environment state, various wind speed and direction terms are discretized, and the wind path speed ratio that can achieve maximum wind wheel capture under two scenarios is analyzed.

[0074] As shown in Figure 4 , based on the above preferred design, a preferred control scheme provided by an embodiment of the present application is:

[0075] First, the controller 7 collects real-time wind direction and speed through the wind direction and speed sensor 8, and obtains the corresponding data of the weather station through wireless communication for evaluating the meteorological stability. The data acquisition and subsequent judgment and calculation process of the latter can be completed locally according to the calculation ability of the hardware itself or realized through edge computing and the like;

[0076] And according to the meteorological stability, the interval of collecting real-time wind direction and speed and evaluating and adjusting the speed ratio is determined; and the judgment and calculation of whether the speed ratio needs to be adjusted and its value are performed in turn, and then the judgment of whether the maximum speed needs to be limited is performed to control the electric control variable speed device and the generator 10.

[0077] It should be particularly noted that, under the condition that the conditions permit, the system can obtain meteorological environmental state information through the addition of sensors (such as temperature sensors) and wireless transmission (such as meteorological variability) to perform data acquisition and updating, but according to the above design of the present application, as long as the wind speed related information can be obtained, the fitting of the optimal speed ratio can be realized, and other parameters can adopt default values. If accurate values are used, the fitting result will be more accurate, but it is not an absolutely necessary condition.

[0078] Scenario 1 - K-means terms under stable wind speed scenario:

[0079] Wind speed: 12-16 m / s; Horizontal wind direction: radial lateral 15°-45°; Vertical wind direction: longitudinal 15°-75°; Temperature: 20-25℃; Weather variability: 0 (weather stable).

[0080] Real data collected in the field:

[0081] Wind speed (m / s): 13.2, 14.0, 15.8, 12.5, 14.9

[0082] Horizontal wind direction (degrees): 20°, 22°, 35°, 28°, 42°

[0083] Vertical wind direction (degrees): 25°, 25°, 35°, 20°, 25°

[0084] Temperature (℃): 21.5, 22.0, 23.0, 22.5, 22.5

[0085] Weather variability: 0 (weather conditions stable)

[0086] In this scenario, the wind speed is high and the wind direction changes are limited, and the weather conditions are stable. The system selects the best front and rear wind wheel speed ratio in the current wind category according to the clustering results of K-means. Through simulation analysis, under this condition, the front and rear wheel speed ratio of 0.7 can realize the maximum power output, and ensure the optimal wind energy capture rate. Since the "weather variability: 0", it is necessary to re-collect weather information after 1H and determine whether to adjust the speed ratio according to the clustering results.

[0087] Scenario 2 - K-means entry in wind speed fluctuation scenario: wind speed: 8-12 m / s; Horizontal wind direction: radial positive direction (0°); Vertical wind direction: longitudinal 15°-75°; Temperature: 15-20℃; Weather variability: 1 (weather changes significantly)

[0088] Real data collected in the field:

[0089] Wind speed (m / s): 8.5, 9.2, 10.8, 11.3, 9.7

[0090] Horizontal wind direction (degrees): 5°, 0°, 10°, 3°, 0°

[0091] Vertical wind direction (degrees): 20°, 30°, 60°, 55°, 50°

[0092] Temperature (℃): 16.5, 18.0, 17.5, 19.0, 15.8

[0093] Weather variability: 1 (weather changes significantly)

[0094] Under this scenario, the wind speed fluctuates within the range of 8-12 m / s, and the weather conditions are changeable. The system identifies this wind condition category using the K-means clustering results and dynamically selects the appropriate speed ratio. Simulation results show that under these conditions, a speed ratio of 0.8 for the front and rear wind wheels can effectively improve the wind energy capture rate of the system. Due to the "weather variability: 1", it is necessary to re-collect weather information after 0.5H and determine whether to adjust the speed ratio based on the clustering results. The speed ratio obtained by SVR analysis can not only ensure the stability of power generation in wind speed fluctuations, but also avoid energy loss caused by frequent gear shifting.

[0095] In addition, the system has online learning and incremental updating functions. With the accumulation of operation data, the system regularly adds new wind condition data and corresponding power generation power and converted efficiency to the training set of the model and performs incremental learning, ensuring that the model is continuously optimized to adapt to new weather conditions. When a new wind condition combination is detected, the system can update the model online, ensuring that in a complex and dynamic wind condition environment, accurate optimization strategies can still be provided.

[0096] Through the combination of K-means clustering and SVR optimization, the system realizes efficient regulation and control of the double-wind-wheel wind turbine. Even in rapidly changing wind conditions, the system can immediately adjust the speed ratio of the front and rear wind wheels to ensure maximum wind energy capture and reduce energy loss during mechanical transmission, providing a stable guarantee for the efficient operation of the generator.

[0097] After determining the speed ratio, the controller 7 adjusts the generator speed through the connected frequency converter (VFD) on the generator. After determining the generator speed, the system can further feedback adjust the speed input of the front and rear wind wheel wind turbines. Such a design not only ensures that the wind wheel maximizes wind energy capture, but also avoids energy loss during mechanical transmission.

[0098] It should be noted that, unless otherwise defined, technical terms or scientific terms used in the present application should be understood as their ordinary meanings to those skilled in the art to which the present application belongs. The terms "first", "second" and similar words used in the present application do not represent any order, number or importance, but are only used to distinguish different components. The terms "include" or "contain" and similar words mean that the elements or objects before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. The terms "connect" or "connect" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to represent relative positional relationships, which may change accordingly when the absolute position of the described object changes.

[0099] The above is the preferred embodiment of the present application, it should be pointed out that, for those skilled in the art, without departing from the principles of the present application, can also make several improvements and refinements, these improvements and refinements are also considered to be within the scope of the present application.

[0100] The patent is not limited to the above best mode, anyone under the inspiration of the patent can derive other various forms of a maximum wind energy capture of double wind wheel wind turbine, any changes and modifications made in accordance with the scope of the patent application of the present application, all should belong to the scope of the present patent.

Claims

1. A double rotor wind driven generator for maximum wind energy capture, characterized in that: The front wind wheel and the rear wind wheel rotate asynchronously: the front wind wheel is connected to the sun gear of the planetary differential device through the electrically controlled variable speed device; the rear wind wheel is connected to the planetary gear system of the planetary differential device through the gear reverse device, and the power of the front wind wheel and the rear wind wheel is integrated through the planetary differential device and then transmitted to the generator; The controller monitors the current external wind speed in real time through the wind speed sensor, and adjusts the electrically controlled variable speed device within the maximum limited rotating speed according to the current external wind speed, so that the rotating speed ratio of the front wind wheel output and the rear wind wheel output is kept within the optimal range, to realize the maximum wind energy capture; The electrically controlled variable speed device is composed of an input sprocket, an output sprocket and a chain, the input sprocket is connected to the output sprocket through the chain, the front wind wheel is connected to the input sprocket, and the output sprocket is connected to the sun gear in the planetary differential device, to transmit the power of the front wind wheel to the sun gear in the planetary differential device; The planetary differential device includes a planetary gear system and a sleeve; the planetary gear system includes a sun gear and a plurality of planetary gears; the sun gear is engaged with the plurality of planetary gears; the output shaft of the rear wind wheel is connected to the second gear of the gear reverse device, the first gear is engaged with the second gear of the gear reverse device; the second gear is fixedly connected to the sleeve, and the sleeve is arranged at the outer circumferential part of the planetary gear system and the inner ring is engaged with the plurality of planetary gears.

2. The dual rotor wind driven generator for maximum wind energy capture of claim 1, wherein: When the current external wind speed or the load of the double-wind-wheel wind turbine generator is greater than the corresponding set threshold value, the controller adjusts the rotating speed and the torque of the generator through the frequency converter of the generator, to ensure that the front wind wheel and the rear wind wheel operate within the safety range.

3. The dual rotor wind driven generator for maximum wind energy capture of claim 1, wherein: The controller obtains the preliminary range of the optimal rotating speed ratio corresponding to the current external wind speed based on the maximum wind energy capture power estimation, and takes the preliminary range of the optimal rotating speed ratio corresponding to the current external wind speed as the preliminary constraint; within the preliminary constraint range, the optimal range between the rotating speed ratio of the front wind wheel output and the rear wind wheel output under the preliminary constraint is obtained by fitting the SVR model trained by inputting the real-time collected current external wind speed.

4. The dual rotor wind driven generator for maximum wind energy capture of claim 3, wherein: The wind speed sensor is specifically a wind direction and wind speed sensor; the maximum wind energy capture power estimation method for obtaining the preliminary range of the optimal rotating speed ratio corresponding to the current external wind speed includes: on the basis of the maximum wind energy capture power estimation, the initial rotating speed ratio of the optimal wind energy capture under different wind directions on the basis of the current external wind speed is obtained according to the simulation test empirical formula and the simulation simulation result of the forward wind and the lateral wind, as the preliminary range of the optimal rotating speed ratio; Within the preliminary constraint range, the optimal range between the rotating speed ratio of the front wind wheel output and the rear wind wheel output under the preliminary constraint is obtained by fitting the SVR model trained by inputting the real-time collected current external wind speed, which includes: taking the meteorological characteristics of the wind speed, the wind direction and the initial rotating speed ratio as the input, and inputting the discretized feature label into the trained SVR model to fit the optimal range between the rotating speed ratio of the front wind wheel output and the rear wind wheel output under the preliminary constraint.

5. The double-wind-wheel wind turbine generator for realizing the maximum wind energy capture according to claim 4, characterized in that: The training process of the SVR model is specifically: The historical wind condition data is classified by using a K-means clustering algorithm, and a data set for training the SVR model is constructed according to the classification result; The SVR model is trained by using the data set, and each category of data in the data set corresponds to a meteorological feature term, the meteorological feature term includes wind speed, wind direction and initial speed ratio, and the training target of the SVR model is to fit the nonlinear relationship between the meteorological feature term including wind speed, wind direction and initial speed ratio and the target speed ratio by minimizing the following loss function of the SVR model: wherein, is a model weight in vector form, is a true output, corresponding to a target speed ratio, is a predicted output, corresponding to a weather feature, is a tolerance error, is a penalty coefficient; The kernel function of the SVR model adopts a radial basis kernel function RBF.

6. A dual rotor wind driven generator for maximum wind energy capture according to claim 5 wherein: The historical wind condition data is classified by using a K-means clustering algorithm, and a data set for training the SVR model is constructed according to the classification result; 7. The dual rotor wind driven generator for maximum wind energy capture of claim 5, wherein: The wind condition data, power generation and efficiency accumulated during the operation of the double-wind-rotor wind turbine are taken as incremental data, and are added to the training set of the SVR model to perform incremental learning.

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