Fan system finite time control method based on load prediction

Abstract

The application provides a fan system finite time control method based on load prediction, and relates to the field of fan motor control. In the method, the fan system is driven by a BLDC motor, a torque control mode is adopted, and a required torque control signal is obtained by a microprocessor. The control method is based on the air volume change detected at an air outlet and the speed of the motor, and the load of an air duct is estimated in real time through an established air duct model to perform high-performance air volume control. The fan motor system can dynamically adapt to various environments, and the rapid response and control precision of the fan can be ensured under different air volume intervals and external environments. The fan system finite time control method based on load prediction dynamically adapts to different environmental static pressure conditions, the parameters of a static pressure identification model and a load prediction model in the microprocessor are adjusted, and accurate air volume control in multiple scenes is realized.

Description

Technical Field

[0001] This invention relates to the field of wind turbine motor control, and more specifically to a finite-time control method for wind turbine systems based on load prediction. Background Technology

[0002] Traditional fan motor systems typically employ simple negative feedback control to regulate motor speed. This method lacks an understanding of the duct load, leading to uneven acceleration and deceleration of the motor speed when the duct load changes. This results in fluctuating output airflow, a long time to reach stability, and high energy consumption for motor speed regulation. Furthermore, control accuracy is highly dependent on parameter adjustments. When the external static pressure changes, the duct load also changes synchronously. In such cases, the control effect of the same set of parameters cannot be guaranteed to be consistent, leading to poor control performance.

[0003] Therefore, how to estimate the duct load and design a fan control system with environmental adaptive characteristics has become an urgent technical problem to be solved. Summary of the Invention

[0004] In view of this, the purpose of this invention is to propose a finite-time control method for a fan system based on load prediction. This method models the duct load, designs an adaptive algorithm to estimate the ambient static pressure, and combines it with a finite-time control method to design a load compensation control method for the fan motor. This control method can adaptively estimate environmental information and dynamically estimate the duct load, achieving stable and rapid control of the output air volume. Simultaneously, the designed finite-time controller can quickly bring the air volume to the expected target within a short time without overshoot, effectively avoiding energy consumption caused by motor speed fluctuations.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] To achieve the above objectives, in a first aspect, the present invention provides a finite-time control method for a wind turbine system based on load prediction, comprising the following steps:

[0007] The motor speed is obtained in real time by detecting the real-time wind speed signal at the air outlet through the wind speed sensor and the motor speed is obtained in real time through the motor's built-in speed sensor.

[0008] Based on the real-time wind speed signal and the cross-sectional area of ​​the duct, the current air volume value is calculated, and the output air volume difference is calculated based on the set target air volume value.

[0009] The static pressure in the duct is estimated based on the current air volume and motor speed using a static pressure identification model.

[0010] Based on the static pressure estimate and motor speed, the load prediction model is used to predict the current load of the air duct.

[0011] Load compensation control is performed based on the output air volume difference and load prediction results, and the control signal output air volume is calculated to adjust the fan output torque through the controller.

[0012] The control method of this invention is based on the air volume changes detected at the air outlet and the motor speed. It estimates the air duct load in real time through the established air duct model to perform high-performance air volume control. This enables the fan motor system to dynamically adapt to various environments and ensure the fan's rapid response and control accuracy in different air volume ranges and external environments.

[0013] As a further aspect of the present invention, the real-time wind speed signal acquired by the wind speed sensor is the wind speed detected by the wind speed sensor at the air outlet in real time.

[0014] As a further aspect of the present invention, when calculating the current air volume value, the cross-sectional area of ​​the air duct is the average cross-sectional area of ​​the fan duct, and the formula for calculating the current air volume value is:

[0015] Q = Sv

[0016] In the formula, Q is the current output air volume, S is the average cross-sectional area of ​​the duct, and v is the wind speed obtained by the wind speed sensor.

[0017] As a further aspect of the present invention, in the finite-time control method for a fan system based on load prediction, the fan system is driven by a BLDC (Brushless Direct Current Motor). The motor receives a torque control signal for torque control and operates in torque control mode. The motor provides real-time feedback of rotational speed signal through a built-in speed sensor and real-time feedback of air volume signal from an external air volume sensor. After receiving the feedback signal, the microprocessor calculates the predicted air volume error and the actual air volume error.

[0018] As a further aspect of the present invention, the predicted air volume error is obtained by subtracting the estimated air volume signal output by the reference model under the estimated static pressure from the actual signal, in order to achieve static pressure identification; the actual air volume error is obtained by subtracting the target air volume signal from the actual air volume signal, in order to calculate the control quantity.

[0019] As a further aspect of the present invention, the torque control signal required by the fan system is calculated by a microprocessor. The microprocessor updates the static pressure estimate through an adaptive algorithm and generates a load compensation signal based on the static pressure estimate and airflow error. The microprocessor combines the duct load prediction model and the static pressure identification model to calculate the duct load estimate and the control torque signal required by the system, and sends the signal to the motor to achieve precise torque control.

[0020] As a further aspect of the present invention, the static pressure identification model and the load prediction model constitute the duct prediction model. The parameters of the duct prediction model are obtained from duct experiments and stored in the parameter memory for the microprocessor to read.

[0021] As a further aspect of the present invention, the static pressure identification model data is obtained through duct experiments. Data is acquired by testing under different static pressure environments, and a relationship between the current airflow value Q and the motor speed n and static pressure value P is established through data fitting. s The relationship between them:

[0022]

[0023] In the formula, f Q (·) represents the fitting polynomial; c q The set of polynomial coefficients fitted to the data, where k is the highest degree of the fitted polynomial; i is the power exponent of the fan speed n; and j is the static pressure value P. s The power exponent; c qij n represents the specific coefficients in the polynomial, indicating the weight of each power term; i Let i be the term representing the wind speed n raised to the power of n; Static pressure value P s The j-th power term.

[0024] As a further aspect of the present invention, the load prediction model is established based on motor load data from airflow experiments, with load torque T... f The relationship between static pressure and rotational speed is fitted by a bivariate polynomial function as follows:

[0025]

[0026] In the formula, f T (·) represents the fitting polynomial of the load forecasting model; c T c is the set of polynomial coefficients fitted to the data. Tij P represents the specific coefficients in the polynomial, indicating the weight of each power term; s i Static pressure value P s The i-th power term; n j Let n be the j-th power of the motor speed n; according to experimental results, when the fitting order is k=2, the model accuracy is sufficient to meet the control requirements.

[0027] As a further aspect of the present invention, the update direction of the static pressure estimate is calculated by the difference between the target air volume value and the actual output air volume of the calculation reference model. When calculating the output air volume difference, it is obtained by the difference between the current air volume value and the target air volume value, and the calculation formula is as follows:

[0028]

[0029] In the formula, Q err Q represents the difference in output airflow, where Q is the current output airflow. The target air volume value; where:

[0030]

[0031] In the formula, f Q (·) represents the polynomial fitting model of air volume; n represents the motor speed; Indicates the estimated static pressure; c q Represents the polynomial coefficients fitted from the data, and the target air volume value. By fitting the polynomial model f Q (·) and static pressure estimate The calculation yielded the result.

[0032] As a further aspect of the present invention, the real-time calculation method for the static pressure estimate is obtained by an adaptive algorithm, and the adaptive update calculation formula is as follows:

[0033]

[0034] in, k is the updated value of the static pressure estimate. p1 ,k p2 Update the gain for the parameters; Q err The output airflow difference is given, where p and q are positive constants and p... <q, sgn(·) is a positive rational number greater than 0 and less than 1, and sgn(·) is a sign function.

[0035] As a further aspect of the present invention, the update formula is calculated in discrete form in the microprocessor as follows:

[0036]

[0037] in, This is the estimated static pressure at time k; T is the estimated static pressure at time k+1; s Sampling time.

[0038] The control gain mentioned above is generally controlled at 0.1k. p2 <k p2 <0.5k p2 .

[0039] The aforementioned static pressure estimate needs to have upper and lower limits set to ensure it is within the normal operating range of the motor system, thus avoiding error accumulation that could lead to abnormal control output.

[0040] As a further aspect of the present invention, the predicted value of the duct load is determined by the static pressure identification model and the load prediction model, and the calculation formula for the predicted value of the duct load is as follows:

[0041]

[0042] In the formula, For the estimated load torque; f T (·) represents the fitting polynomial of the load forecasting model; c is the estimated static pressure value. T To fit the set of coefficients of the polynomial.

[0043] As a further aspect of the present invention, the finite-time control method for a wind turbine system based on load prediction also includes a finite-time control mechanism. This mechanism employs a finite-time control strategy, calculating the output airflow of the control signal based on the current airflow error and load prediction information. Within a predetermined time, the controller adjusts the output torque of the wind turbine. The output torque adjusted by the controller is calculated from the difference between the target airflow and the current airflow, expressed as: ΔQ = Q d -Q; where ΔQ is the current error of the output air volume; Q d Q represents the target output air volume; Q represents the current output air volume.

[0044] As a further aspect of the present invention, the output torque control signal is:

[0045]

[0046] Where, k u1 ,k u2 The control gain is u; the output control torque is u. is the estimated load torque; sgn(·) is the sign function.

[0047] This load prediction-based finite-time control method for wind turbine systems dynamically adapts to different environmental static pressure conditions. By adjusting the parameters of the static pressure identification model and load prediction model within the microprocessor, it achieves precise airflow control in multiple scenarios.

[0048] In another aspect, the present invention provides a computer device including a memory and a processor, the memory storing a computer program which, when executed by the processor, performs any of the above-described load-prediction-based finite-time control methods for wind turbine systems according to the present invention.

[0049] In another aspect, the present invention provides a computer-readable storage medium storing computer program instructions that, when executed, implement any of the above-described load-prediction-based finite-time control methods for wind turbine systems according to the present invention.

[0050] Compared with existing technologies, the finite-time control method for wind turbine systems based on load prediction proposed in this invention has the following advantages:

[0051] 1. This invention combines an adaptive static pressure identification method with a load prediction model to estimate the duct load in real time and generate a precise torque control signal based on the estimated value, thereby enabling precise control of the output air volume.

[0052] 2. The adaptive algorithm of this invention can dynamically adjust the static pressure estimate based on the changes in the predicted air volume error, adapt to highly dynamic external environments, and ensure the consistency of control performance.

[0053] 3. This invention employs a load torque prediction control method. Compared to speed control, torque control can accurately control the acceleration and deceleration process of the motor, thereby improving the energy efficiency of the fan.

[0054] 4. This invention is applicable to various scenarios requiring precise airflow control, such as industrial air ducts and air conditioning systems, and exhibits excellent adaptability and reliability in complex environments.

[0055] In summary, this invention, through its innovative control algorithm, achieves rapid response and high-precision control of the wind turbine system under various load conditions, and has broad application prospects and economic benefits.

[0056] These or other aspects of this application will become more apparent from the following description of embodiments. It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the application. Attached Figure Description

[0057] To more clearly illustrate the technical solutions in the embodiments of the present invention or related technologies, the accompanying drawings used in the description of the exemplary embodiments or related technologies will be briefly introduced below. The drawings are used to provide a further understanding of the present invention and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the present invention and do not constitute a limitation thereof. In the drawings:

[0058] Figure 1 This is a control flowchart of a finite-time control method for a wind turbine system based on load prediction, according to an embodiment of the present invention.

[0059] Figure 2 This is a schematic diagram of the physical structure of a finite-time control method for a wind turbine system based on load prediction, according to an embodiment of the present invention.

[0060] Figure 3 The output air volume curve is a measured value of a load prediction-based finite-time control method for a fan system according to an embodiment of the present invention, under a fixed target output.

[0061] Figure 4This is a static pressure curve fitting result graph of a finite-time control method for a wind turbine system based on load prediction, according to an embodiment of the present invention.

[0062] Figure 5 This is a curve showing the duct load prediction result of a finite-time control method for a wind turbine system based on load prediction, according to an embodiment of the present invention. Detailed Implementation

[0063] The present application will now be further described in conjunction with the accompanying drawings and specific embodiments. It should be noted that, without conflict, the various embodiments or technical features described below can be arbitrarily combined to form new embodiments.

[0064] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to specific examples and the accompanying drawings. It should be understood that the specific embodiments described herein are merely illustrative and are not intended to limit the scope of this application.

[0065] It should be noted that all uses of "first" and "second" in the embodiments of the present invention are for the purpose of distinguishing two different entities or different parameters with the same name. Therefore, "first" and "second" are merely for convenience of expression and should not be construed as limiting the embodiments of the present invention. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion, such as other steps or units inherent in a process, method, system, product, or device that includes a series of steps or units.

[0066] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0067] The flowchart shown in the attached diagram is for illustrative purposes only and does not necessarily include all content and operations / steps, nor does it necessarily have to be performed in the order described. For example, some operations / steps can be broken down, combined, or partially merged, so the actual execution order may change depending on the actual situation.

[0068] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0069] Traditional fan motor systems typically employ simple negative feedback control to regulate motor speed. This method lacks an understanding of the duct load, leading to uneven acceleration and deceleration of the motor speed when the duct load changes. This results in fluctuating output airflow, prolonged stabilization time, and high energy consumption during motor speed regulation. Furthermore, control accuracy heavily relies on parameter adjustments. When the external static pressure changes, the duct load also changes synchronously, making it impossible to guarantee consistent control performance with the same set of parameters, leading to poor control quality. This invention proposes a load prediction-based finite-time control method for fan systems. By modeling the duct load and designing an adaptive algorithm to estimate the environmental static pressure, combined with the finite-time control method, a load compensation control method for the fan motor is designed. This control method can adaptively estimate environmental information and dynamically estimate the duct load, achieving stable and rapid control of the output airflow. Simultaneously, the designed finite-time controller can quickly bring the airflow to the expected target without overshoot, effectively avoiding energy consumption caused by motor speed fluctuations.

[0070] See Figures 1 to 5 As shown, an embodiment of the present invention provides a finite-time control method for a wind turbine system based on load prediction, the method comprising the following steps:

[0071] Step S10: Based on the real-time wind speed signal detected by the wind speed sensor at the air outlet, the motor speed is obtained in real time through the motor's built-in speed sensor.

[0072] Step S20: Calculate the current air volume value based on the real-time wind speed signal and the cross-sectional area of ​​the air duct, and calculate the output air volume difference based on the set target air volume value.

[0073] Step S30: Use the static pressure identification model to estimate the static pressure in the current duct based on the current air volume and motor speed;

[0074] Step S40: Based on the static pressure estimate and motor speed, use the load prediction model to predict the load prediction result of the current air duct;

[0075] Step S50: Perform load compensation control based on the output air volume difference and load prediction results, calculate the control signal output air volume to adjust the fan output torque through the controller.

[0076] The control method of this invention is based on the air volume changes detected at the air outlet and the motor speed. It estimates the air duct load in real time through the established air duct model to perform high-performance air volume control. This enables the fan motor system to dynamically adapt to various environments and ensure the fan's rapid response and control accuracy in different air volume ranges and external environments.

[0077] In this embodiment, the real-time wind speed signal acquired by the wind speed sensor is the wind speed detected by the wind speed sensor at the air outlet in real time. When calculating the current air volume value, the cross-sectional area of ​​the air duct is the average cross-sectional area of ​​the fan duct, and the formula for calculating the current air volume value is:

[0078] Q = Sv

[0079] In the formula, Q is the current output air volume, S is the average cross-sectional area of ​​the duct, and v is the wind speed obtained by the wind speed sensor.

[0080] In this embodiment, the finite-time control method for a wind turbine system based on load prediction uses a BLDC (Brushless Direct Current Motor) for driving. The motor receives a torque control signal for torque control and operates in torque control mode. The motor provides real-time feedback of its rotational speed via a built-in speed sensor, and real-time feedback of its airflow signal via an external airflow sensor. After receiving the feedback signals, the microprocessor calculates the predicted airflow error and the actual airflow error. The predicted airflow error is obtained by subtracting the estimated airflow signal from the reference model under the estimated static pressure, used for static pressure identification. The actual airflow error is obtained by subtracting the target airflow signal from the actual airflow signal, used for calculating the control quantity.

[0081] The torque control signal required by the fan system is calculated by the microprocessor. The microprocessor updates the static pressure estimate through an adaptive algorithm and generates a load compensation signal based on the static pressure estimate and airflow error. The microprocessor combines the duct load prediction model and the static pressure identification model to calculate the duct load estimate and the control torque signal required by the system, and sends the signal to the motor to achieve precise torque control.

[0082] In this embodiment, the static pressure identification model and the load prediction model constitute the duct prediction model. The parameters of the duct prediction model are obtained from duct experiments and stored in a parameter memory for the microprocessor to read. Specifically, the static pressure identification model data is obtained through duct experiments, with tests conducted under different static pressure environments to acquire data. Data fitting is used to establish a relationship between the current airflow value Q and the motor speed n and static pressure value P. s The relationship between them:

[0083]

[0084] In the formula, f Q (·) represents the fitting polynomial; c q The set of polynomial coefficients fitted to the data, where k is the highest degree of the fitted polynomial; i is the power exponent of the fan speed n; and j is the static pressure value P. s The power exponent; c qij n represents the specific coefficients in the polynomial, indicating the weight of each power term;i P is the i-th power of the wind speed n; s j Static pressure value P s The j-th power term.

[0085] In the above adaptive adjustment process, when the output air volume of the reference model is greater than the actual air volume, i.e., Q... err If the static pressure is less than 0, the resistance within the duct is greater than estimated, indicating that the reference model's estimated static pressure is lower than the actual static pressure. A similar result occurs when the output airflow is less than the actual airflow; therefore, the estimated error value is adjusted in the opposite direction of the error. Through the adaptive law update, the reference model output will eventually match the actual duct output, at which point the static pressure estimate will approximate the true static pressure value.

[0086] The load prediction model is established based on motor load data from airflow experiments, with load torque T... f The relationship between static pressure and rotational speed is fitted by a bivariate polynomial function as follows:

[0087]

[0088] In the formula, f T (·) represents the fitting polynomial of the load forecasting model; c T c is the set of polynomial coefficients fitted to the data. Tij P represents the specific coefficients in the polynomial, indicating the weight of each power term; s i Static pressure value P s The i-th power term; n j Let be the j-th power of the motor speed n. According to experimental results, when the fitting iteration k=2, the model accuracy is sufficient to meet the control requirements. In this step, the fitted model needs to screen the collected data to reduce the influence of external disturbances. Multiple experiments are required to collect data under rated operating conditions to ensure that the model is as accurate as possible.

[0089] In this embodiment, the update direction of the static pressure estimate is calculated by the difference between the target air volume value and the actual output air volume of the calculation reference model. The output air volume difference is calculated using the difference between the current air volume value and the target air volume value, and the calculation formula is as follows:

[0090]

[0091] In the formula, Q err Q represents the difference in output airflow, where Q is the current output airflow. The target air volume value; where:

[0092]

[0093] In the formula, f Q(·) represents the polynomial fitting model of air volume; n represents the motor speed; Indicates the estimated static pressure; c q Represents the polynomial coefficients fitted from the data, and the target air volume value. By fitting the polynomial model f Q (·) and static pressure estimate The calculation yielded the result.

[0094] The real-time calculation method for the static pressure estimate is obtained by an adaptive algorithm, and the adaptive update calculation formula is as follows:

[0095]

[0096] in, k is the updated value of the static pressure estimate. p1 ,k p2 Update the gain for the parameters; Q err The output airflow difference is given, where p and q are positive constants and p... <q, sgn(·) is a positive rational number greater than 0 and less than 1, and sgn(·) is a symbolic function.

[0097] In this embodiment, the update formula is calculated in discrete form within the microprocessor:

[0098]

[0099] in, This is the estimated static pressure at time k; T is the estimated static pressure at time k+1; s Sampling time.

[0100] The control gain mentioned above is generally controlled at 0.1k. p2 <k p2 <0.5k p2 .

[0101] The aforementioned static pressure estimate needs to have upper and lower bounds set to ensure it is within the normal operating range of the motor system, thus avoiding error accumulation that could lead to abnormal control output. During the adaptive update process, considering the slow change in static pressure in the environment, the calculation frequency of the adaptive algorithm can be reduced compared to the controller during the static pressure identification process to decrease microprocessor power consumption.

[0102] In this embodiment, the predicted value of the duct load is determined by the static pressure identification model and the load prediction model. The formula for calculating the predicted value of the duct load is as follows:

[0103]

[0104] In the formula, For the estimated load torque; f T(·) represents the fitting polynomial of the load forecasting model; c is the estimated static pressure value. T To fit the set of coefficients of the polynomial.

[0105] The load prediction-based finite-time control method for wind turbine systems also includes a finite-time control mechanism. Employing a finite-time control strategy, it calculates the output airflow of the control signal based on the current airflow error and load prediction information. Within a predetermined time, the controller adjusts the wind turbine output torque. The output torque adjusted by the controller is calculated from the difference between the target airflow and the current airflow, expressed as: ΔQ = Q d -Q; where ΔQ is the current error of the output air volume; Q d Q represents the target output air volume; Q represents the current output air volume.

[0106] The output torque control signal is:

[0107]

[0108] Where, k u1 ,k u2 The control gain is u; the output control torque is u. is the estimated load torque; sgn(·) is the sign function.

[0109] The control gain mentioned above is generally controlled at 0.1k. p2 <k p2 <0.5k p2 0.1k u2 <k u2 <0.5k u2 For ease of calculation, the fractional power terms in the above adaptive algorithm and controller calculation formula are generally taken as p=1, q=2 or p=2, q=3; their function is to accelerate the convergence speed of the system towards the stable region. When the above control process approaches steady state, considering the positive correlation between static pressure and duct load, once the estimated static pressure value is close to the actual static pressure value, the estimated duct load will also be close to the actual duct load. After the controller offsets the duct load, the remaining control quantities can be used to achieve rapid and stable speed regulation of the motor.

[0110] During the state saving process of the above steps, the output air volume is designed with a maximum saturation level. If the expected air volume output cannot be met under the current environmental conditions, the motor will output the maximum load torque.

[0111] During the system hold process described above, the error window between the output air volume and the reference model output air volume is set to within 1% to 2% of the target air volume. When the error enters the window and remains there for a certain period of time, the system enters the hold state, stops the calculation, and maintains the output result until the error leaves the error window.

[0112] This load prediction-based finite-time control method for wind turbine systems dynamically adapts to different environmental static pressure conditions. By adjusting the parameters of the static pressure identification model and load prediction model within the microprocessor, it achieves precise airflow control in multiple scenarios.

[0113] This invention combines an adaptive static pressure identification method with a load prediction model to estimate the duct load in real time and generate a precise torque control signal based on the estimate, enabling accurate control of the output airflow. The adaptive algorithm dynamically adjusts the static pressure estimate based on changes in the predicted airflow error, adapting to highly dynamic external environments and ensuring consistent control performance. The invention employs a load torque prediction control method; compared to speed control, torque control can accurately control the acceleration and deceleration of the motor, improving the fan's energy efficiency. This invention is applicable to various scenarios requiring precise airflow control, such as industrial ducts and air conditioning systems, and exhibits excellent adaptability and reliability in complex environments.

[0114] In summary, this invention, through its innovative control algorithm, achieves rapid response and high-precision control of the wind turbine system under various load conditions, and has broad application prospects and economic benefits.

[0115] It should be noted that the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Furthermore, it is readily understood that these processes may, for example, be executed synchronously or asynchronously in multiple modules.

[0116] It should be understood that although the above description follows a certain order, these steps are not necessarily executed in that order. Unless otherwise expressly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, some steps in this embodiment may include multiple steps or multiple stages, which are not necessarily completed at the same time, but may be executed at different times. The execution order of these steps or stages is not necessarily sequential, but may be performed alternately or in turn with other steps or at least a portion of steps or stages in other steps.

[0117] A second aspect of the present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program that, when executed by the processor, implements the method of any of the above embodiments.

[0118] The computer device includes a processor and a memory, and may also include an input system and an output system. The processor, memory, input system, and output system can be connected via a bus or other means. The input system can receive input digital or character information and generate finite-time control methods for wind turbine systems based on load forecasting. The output system may include display devices such as a screen.

[0119] Memory, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules, such as the program instructions / modules corresponding to the load-prediction-based finite-time control method for wind turbine systems in this application embodiment. The memory may include a program storage area and a data storage area, wherein the program storage area may store the operating system and application programs required for at least one function; the data storage area may store data created by using the load-prediction-based finite-time control method for wind turbine systems, etc. Furthermore, the memory may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the local module via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0120] In some embodiments, the processor may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip. This processor is typically used to control the overall operation of a computer device. In this embodiment, the processor is used to run program code stored in memory or process data. In this embodiment, the processors of multiple computer devices execute various server functions and data processing by running non-volatile software programs, instructions, and modules stored in memory, thereby implementing the steps of the finite-time control method for a wind turbine system based on load prediction described in the above method embodiment.

[0121] It should be understood that, where there is no conflict, all the embodiments, features and advantages described above for the load prediction-based finite-time control method for wind turbine systems according to the present invention are equally applicable to the load prediction-based finite-time control method for wind turbine systems and the storage medium according to the present invention.

[0122] Those skilled in the art will also understand that the various exemplary logic blocks, modules, circuits, and algorithm steps described in conjunction with the disclosure herein can be implemented as electronic hardware, computer software, or a combination of both. To clearly illustrate this interchangeability between hardware and software, the functionality of various illustrative components, blocks, modules, circuits, and steps has been generally described. Whether this functionality is implemented as software or as hardware depends on the specific application and the design constraints imposed on the system as a whole. Those skilled in the art can implement the functionality in various ways for each specific application, but such implementation decisions should not be construed as departing from the scope of the embodiments disclosed herein.

[0123] Finally, it should be noted that the computer-readable storage medium (e.g., memory) described herein can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. By way of example, and not limitation, non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM), which can act as external cache memory. By way of example, and not limitation, RAM can be obtained in various forms, such as synchronous RAM (DRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct Rambus RAM (DRRAM). The storage devices disclosed herein are intended to include, but are not limited to, these and other suitable types of memory.

[0124] The various exemplary logic blocks, modules, and circuits described herein can be implemented or performed using the following components designed to perform the functions herein: general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic, discrete hardware components, or any combination of these components. A general-purpose processor may be a microprocessor, but alternatively, the processor may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors combined with a DSP, and / or any other such configuration.

[0125] The above are exemplary embodiments disclosed in this invention. However, it should be noted that various changes and modifications can be made without departing from the scope of the embodiments of this invention as defined by the claims. The functions, steps, and / or actions of the methods according to the disclosed embodiments described herein do not need to be performed in any particular order. Furthermore, although the elements disclosed in the embodiments of this invention may be described or claimed individually, they may be understood as multiple unless explicitly limited to a singular number.

[0126] It should be understood that, as used herein, the singular form "a" is intended to include the plural form as well, unless the context clearly supports an exception. It should also be understood that, as used herein, "and / or" refers to any and all possible combinations of one or more of the associatedly listed items. The embodiment numbers disclosed above are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0127] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of the invention (including the claims) is limited to these examples. Within the framework of the invention, technical features of the above embodiments or different embodiments can be combined, and many other variations of different aspects of the invention exist, which are not provided in the details for the sake of brevity. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the invention should be included within the protection scope of the invention.

Claims

1. A finite-time control method for a wind turbine system based on load prediction, characterized in that, The method includes the following steps: The motor speed is obtained in real time by detecting the real-time wind speed signal at the air outlet through the wind speed sensor and the motor speed is obtained in real time through the motor's built-in speed sensor. Based on the real-time wind speed signal and the cross-sectional area of ​​the duct, the current air volume value is calculated, and the output air volume difference is calculated based on the set target air volume value. The static pressure in the duct is estimated based on the current air volume and motor speed using a static pressure identification model. Based on the static pressure estimate and motor speed, the load prediction model is used to predict the current load of the air duct. Load compensation control is performed based on the output air volume difference and load prediction results, and the control signal output air volume is calculated to adjust the fan output torque through the controller.

2. The finite-time control method for a wind turbine system based on load prediction as described in claim 1, characterized in that, When calculating the current airflow value, the cross-sectional area of ​​the duct is the average cross-sectional area of ​​the fan duct. The formula for calculating the current airflow value is: ； In the formula, This is the current output air volume. This represents the average cross-sectional area of ​​the air duct. The wind speed is obtained from the wind speed sensor.

3. The finite-time control method for a wind turbine system based on load prediction as described in claim 2, characterized in that, In the finite-time control method for a wind turbine system based on load prediction, the wind turbine system is driven by a brushless DC motor. The motor receives a torque control signal for torque control and operates in torque control mode. The motor provides real-time feedback of its rotational speed signal through a built-in speed sensor and real-time feedback of its airflow signal from an external airflow sensor. After receiving the feedback signals, the microprocessor calculates the predicted airflow error and the actual airflow error. The predicted airflow error is obtained by subtracting the estimated airflow signal output by the reference model under the estimated static pressure from the actual signal, which is used to achieve static pressure identification. The actual airflow error is obtained by subtracting the target airflow signal from the actual airflow signal, which is used to calculate the control quantity.

4. The finite-time control method for a wind turbine system based on load prediction as described in claim 2, characterized in that, The static pressure identification model and the load prediction model together form the duct prediction model. The parameters of the duct prediction model are obtained from duct experiments and stored in a parameter memory for the microprocessor to read. The static pressure identification model data is obtained through duct experiments, and data is acquired by testing under different static pressure environments. The current airflow value is established through data fitting. With motor speed and static pressure value The relationship between them: ； In the formula, To fit a polynomial; The coefficients of the polynomial fitted to the data. The highest degree of the fitted polynomial; Fan speed The power exponent; Static pressure value The power exponent; represents the specific coefficients in the polynomial, indicating the weight of each power term; Wind speed of Power term; Static pressure value of Power term.

5. The finite-time control method for a wind turbine system based on load prediction as described in claim 4, characterized in that, The load prediction model is established based on motor load data from airflow experiments, including load torque. The relationship between static pressure and rotational speed is fitted by a bivariate polynomial function as follows: ； In the formula, This is the fitting polynomial for the load prediction model; The set of polynomial coefficients fitted to the data; represents the specific coefficients in the polynomial, indicating the weight of each power term; Static pressure value of Power term; Motor speed of Power term.

6. The finite-time control method for a wind turbine system based on load prediction as described in claim 5, characterized in that, The update direction of the static pressure estimate is calculated from the difference between the target air volume value and the actual output air volume of the calculation reference model. The output air volume difference is calculated using the difference between the current air volume value and the target air volume value, and the calculation formula is as follows: ； In the formula, The difference in output air volume. This is the current output air volume. The target air volume value; where: ； In the formula, A polynomial fitting model representing air volume; Indicates the motor speed; This represents the estimated static pressure value; Represents the polynomial coefficients fitted from the data, and the target air volume value. By fitting a polynomial model and static pressure estimate The calculation yielded the result.

7. The finite-time control method for a wind turbine system based on load prediction as described in claim 1, characterized in that, The real-time calculation method for static pressure estimates is obtained using an adaptive algorithm, and the adaptive update calculation formula is as follows: ； in, This is an updated value for the static pressure estimate. Update the gain for the parameters; The difference in output air volume. are positive numbers and , It is a positive rational number that is greater than 0 and less than 1. It is a symbolic function.

8. The finite-time control method for a wind turbine system based on load prediction as described in claim 7, characterized in that, In a microprocessor, the update formula is calculated in discrete form: ； in, For the first The estimated static pressure at time t; For the first The estimated static pressure at time t; Sampling time.

9. The finite-time control method for a wind turbine system based on load prediction as described in claim 8, characterized in that, The predicted value of the duct load is determined by the static pressure identification model and the load prediction model. The formula for calculating the predicted value of the duct load is as follows: ； In the formula, For the estimated load torque; This is the fitting polynomial for the load prediction model; This is an estimate of the static pressure. To fit the set of coefficients of the polynomial.

10. The finite-time control method for a wind turbine system based on load prediction as described in claim 9, characterized in that, The load prediction-based finite-time control method for the fan system also includes a finite-time control mechanism. Employing a finite-time control strategy, it calculates the output airflow of the control signal based on the current airflow error and load prediction information. Within a predetermined time, the controller adjusts the fan output torque. The output torque control signal is: ； in, To control the gain; The output control torque; For the estimated load torque; It is a symbolic function; This represents the current error in the output air volume.

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