Fan system finite time control method based on load prediction

Abstract

The invention provides a fan system finite time control method based on load prediction, and relates to the field of fan motor control, in the method, a fan system is driven by a BLDC motor, a torque control mode is adopted, and a needed torque control signal is obtained through calculation of a microprocessor; according to the control method, the air duct load is estimated in real time through the established air duct model on the basis of the air volume change detected at the air outlet and the rotating speed condition of the motor, so that high-performance air volume control is carried out, and the fan motor system can dynamically adapt to various environments; and quick response and control precision of the fan can be ensured in different air volume intervals and external environments. According to the fan system finite time control method based on load prediction, dynamic adaptation is achieved under different environment static pressure conditions, and accurate air volume control under multiple scenes is achieved by adjusting parameters of the static pressure identification model and the load prediction model in the microprocessor.

Description

Technical Field

[0001] The invention relates to the field of fan motor control, and in particular to a finite time control method for a fan system based on load prediction. Background Art

[0002] In traditional fan motor systems, simple negative feedback control is usually used to adjust the motor speed. This control method lacks understanding of the duct load. When the duct load changes, the motor speed will be unevenly accelerated and decelerated, resulting in fluctuating output air volume, a long time to reach stability, and high energy consumption for motor speed regulation. In addition, the accuracy of control is very dependent on parameter adjustment. When the external static pressure environment changes, the duct load will also change synchronously. At this time, the control effect of the same set of parameters cannot be guaranteed to be consistent, resulting in poor control performance.

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

[0004] In view of this, the purpose of the present invention is to propose a finite time control method for a fan system based on load prediction. The method models the duct load, designs an adaptive algorithm to estimate the ambient static pressure, and combines the finite time control method to design a load compensation control method for the fan motor. The control method can adaptively estimate environmental information, dynamically estimate the duct load, and achieve smooth and rapid control of the output air volume; at the same time, the designed finite time controller can quickly make the air volume reach the expected target in a short time without overshoot, and can effectively avoid energy consumption caused by motor speed fluctuations.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] Based on 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 wind speed sensor detects the real-time wind speed signal of the air outlet, and the motor speed is obtained in real time through the built-in speed sensor of the motor;

[0008] According to the real-time wind speed signal and the cross-sectional area of ​​the air duct, the current air volume value is calculated, and according to the set target air volume value, the output air volume difference is calculated;

[0009] The static pressure identification model is used to estimate the static pressure in the current air duct according to the current air volume value and the motor speed;

[0010] Based on the estimated static pressure value and the motor speed, a load prediction model is used to predict the load prediction result of the current 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 the present invention is based on the air volume changes detected at the air outlet and the speed of the motor. The duct load is estimated in real time through the established duct model to perform high-performance air volume control, so that the fan motor system can 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 solution 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 solution 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 calculation formula of the current air volume value is:

[0015] Q=Sv

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

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

[0018] As a further solution of the present invention, the predicted air volume error is obtained by subtracting the actual signal from the estimated air volume signal output by the reference model under the estimated static pressure, so as to realize static pressure identification; the actual air volume error is obtained by subtracting the target air volume signal from the actual air volume signal, so as to calculate the control amount.

[0019] As a further solution of the present invention, the torque control signal required by the fan system is calculated by a microprocessor, which updates the static pressure estimate through an adaptive algorithm and generates a load compensation signal based on the static pressure estimate and the air volume 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 solution of the present invention, the static pressure identification model and the load prediction model constitute an air duct prediction model, and the air duct prediction model parameters are obtained from air duct experiments and stored in a parameter memory for reading by a microprocessor.

[0021] As a further solution of the present invention, the static pressure identification model data is obtained through wind duct experiments. Tests are performed under different static pressure environments to obtain data. The current air volume value Q, motor speed n and static pressure value P are established through data fitting. s The relationship between:

[0022]

[0023] In the formula, f Q (·) is the fitting polynomial; c q is the set of polynomial coefficients fitted by the data, k is the highest number of times of the fitted polynomial; i is the power index of the fan speed n; j is the static pressure value P s The power index of qij is the specific coefficient in the polynomial, indicating the weight of each power term; n i is the i-th power term of wind speed n; is the static pressure value P s The j-th power term.

[0024] As a further solution of the present invention, the load prediction model is established by the motor load data in the wind volume experiment, and the load torque T f The relationship between static pressure and speed is fitted by a bivariate polynomial function:

[0025]

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

[0027] As a further solution of the present invention, the updating direction of the static pressure estimation value is calculated by calculating the difference between the target air volume value of the reference model and the actual output air volume. When calculating the output air volume difference, it is calculated by the difference between the current air volume value and the target air volume value. The calculation formula is:

[0028]

[0029] In the formula, Q err is the output air volume difference, Q is the current output air volume, is 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; represents the estimated value of static pressure; c q Indicates the polynomial coefficients of the data fitting, the target air volume value By fitting the polynomial model f Q (·) and estimated static pressure Calculated.

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

[0033]

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

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

[0036]

[0037] in, is the estimated value of static pressure at the kth moment; is the estimated static pressure at the k+1th moment; T s is the sampling time.

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

[0039] The static pressure estimation value mentioned above needs to be set with upper and lower limits to ensure that it is within the normal operating range of the motor system to avoid abnormal control output caused by error accumulation.

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

[0041]

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

[0043] As a further solution of the present invention, the finite time control method of the fan system based on load prediction also includes a finite time control mechanism, adopts a finite time control strategy, calculates the control signal output air volume according to the current air volume error and load prediction information, and adjusts the fan output torque through the controller within a predetermined time, wherein the output torque adjusted by the controller is calculated by the difference between the target air volume and the current air volume, and the difference is expressed as: ΔQ = Q d -Q; where ΔQ is the current error of the output air volume; Q d is the target output air volume; Q is the current output air volume.

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

[0045]

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

[0047] The load prediction-based finite-time control method for the fan system dynamically adapts to different environmental static pressure conditions and achieves precise air volume control in multiple scenarios by adjusting the static pressure identification model and load prediction model parameters in the microprocessor.

[0048] In another aspect of the present invention, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, any one of the above-mentioned methods for finite-time control of a wind turbine system based on load prediction according to the present invention is executed.

[0049] In another aspect of the present invention, a computer-readable storage medium is provided, storing computer program instructions, which, when executed, implement any of the above-mentioned methods for finite-time control of a wind turbine system based on load prediction according to the present invention.

[0050] Compared with the prior art, the finite time control method for a fan system based on load prediction proposed in the present invention has the following beneficial effects:

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

[0052] 2. The adaptive algorithm of the present invention can dynamically adjust the static pressure estimation value according to the change of the predicted air volume error, can adapt to the highly dynamic external environment, and ensure the consistency of the control performance.

[0053] 3. The present invention adopts a load torque prediction control method. Compared with speed control, torque control can accurately control the acceleration and deceleration process of the motor and improve the energy efficiency of the fan.

[0054] 4. The present invention is suitable for various scenarios that require precise air volume control, such as industrial air ducts, air conditioning systems, etc., and can demonstrate excellent adaptability and reliability in complex environments.

[0055] In summary, the present invention realizes rapid response and high-precision control of the fan system under various load conditions through an innovative control algorithm, and has broad application prospects and economic benefits.

[0056] These and other aspects of the present application will be more concise and understandable in the following description of the embodiments. It should be understood that the above general description and the following detailed description are only exemplary and explanatory and cannot limit the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or related technologies, the following briefly introduces the drawings required for use in the exemplary embodiments or related technical descriptions. The drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation to the present invention. In the drawings:

[0058] Figure 1 The present invention is a control flow chart of a finite time control method for a fan system based on load prediction according to an embodiment of the present invention.

[0059] Figure 2 The figure is a physical structure diagram of a finite time control method for a fan system based on load prediction according to an embodiment of the present invention.

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

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

[0062] Figure 5 This is a curve diagram of air duct load prediction results of a finite time control method for a fan system based on load prediction according to an embodiment of the present invention. DETAILED DESCRIPTION

[0063] Below, the present application is further described in conjunction with the accompanying drawings and specific implementation methods. It should be noted that, under the premise of no conflict, the various embodiments or technical features described below can be arbitrarily combined to form a new embodiment.

[0064] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the embodiments of the present invention are further described in detail below in combination with specific embodiments and with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0065] It should be noted that all expressions using "first" and "second" in the embodiments of the present invention are intended to distinguish two non-identical entities or non-identical parameters with the same name. It can be seen that "first" and "second" are only for the convenience of expression and should not be understood as limitations on the embodiments of the present invention. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, other steps or units inherent to a process, method, system, product or device that includes a series of steps or units.

[0066] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0067] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0068] In conjunction with the accompanying drawings, some embodiments of the present application are described in detail below. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0069] Since in traditional fan motor systems, simple negative feedback control is usually used to adjust the motor speed, this control method lacks understanding of the duct load. When the duct load changes, it will cause uneven acceleration and deceleration of the motor speed, resulting in the output air volume fluctuating, a long time to reach stability, and high energy consumption of motor speed regulation. In addition, the accuracy of control is very dependent on parameter adjustment, and when the external static pressure environment changes, the duct load will also change synchronously. At this time, the control effect of the same set of parameters cannot be guaranteed to be consistent, resulting in poor control performance. The present invention proposes a finite time control method for a fan system based on load prediction. By modeling the duct load, designing an adaptive algorithm to estimate the ambient static pressure, and combining the finite time control method, a load compensation control method for the fan motor is designed. The control method can adaptively estimate environmental information, dynamically estimate the duct load, and realize smooth and rapid control of the output air volume; at the same time, the designed finite time controller can quickly make the air volume reach the expected target in a short time without overshoot, and can effectively avoid the energy consumption caused by motor speed fluctuations.

[0070] See also 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, detecting the real-time wind speed signal of the air outlet according to the wind speed sensor, and obtaining the motor speed in real time through the built-in speed sensor of the motor;

[0072] Step S20, calculating the current air volume value according to the real-time wind speed signal and the cross-sectional area of ​​the air duct, and calculating the output air volume difference according to the set target air volume value;

[0073] Step S30, using a static pressure identification model to estimate a static pressure estimation value in the current air duct according to the current air volume value and the motor speed;

[0074] Step S40: Based on the estimated static pressure value and the motor speed, a load prediction model is used to predict the load prediction result of the current air duct;

[0075] Step S50: perform load compensation control according to the output air volume difference and the load prediction result, calculate the control signal output air volume to adjust the fan output torque through the controller.

[0076] The control method of the present invention is based on the air volume changes detected at the air outlet and the speed of the motor. The duct load is estimated in real time through the established duct model to perform high-performance air volume control, so that the fan motor system can 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 obtained 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 calculation formula for the current air volume value is:

[0078] Q=Sv

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

[0080] In this embodiment, in the finite time control method of the fan system based on load prediction, the fan system is driven by a BLDC (Brushless Direct Current Motor) motor, the motor receives a torque control signal for torque control, the motor operates in a torque control mode, the motor feeds back a speed signal in real time through a built-in speed sensor, and an external air volume sensor feeds back an air volume signal in real time. The microprocessor calculates the predicted air volume error and the actual air volume error after receiving the feedback signal. Among them, the predicted air volume error is obtained by subtracting the actual signal from the estimated air volume signal output by the reference model under the estimated static pressure, so as to realize static pressure identification; the actual air volume error is obtained by subtracting the target air volume signal from the actual air volume signal, so as to calculate the control amount.

[0081] 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 the air volume 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 form a duct prediction model. The duct prediction model parameters are obtained by duct experiments and stored in a parameter memory for reading by the microprocessor. The static pressure identification model data is obtained by duct experiments, and the data is obtained by testing under different static pressure environments. The current air volume value Q, the motor speed n, and the static pressure value P are established through data fitting. s The relationship between:

[0083]

[0084] In the formula, f Q (·) is the fitting polynomial; c q is the set of polynomial coefficients fitted by the data, k is the highest number of times of the fitted polynomial; i is the power index of the fan speed n; j is the static pressure value P s The power index of qij is the specific coefficient in the polynomial, indicating the weight of each power term; ni is the i-th power term of wind speed n; P s j is the static pressure value P s The j-th power term.

[0085] In the above adaptive adjustment process, when the reference model output air volume is greater than the actual air volume, that is, Q err <0, the resistance in the duct is greater than the estimated one, which proves that the static pressure estimated by the reference model is less than the actual static pressure; when the output air volume is less than the actual air volume, the result is similar, so the estimated error value is adjusted in the opposite direction of the error. By updating the adaptive law, the reference model output and the actual duct output will eventually be consistent, and the static pressure estimate will approach the actual static pressure value.

[0086] The load prediction model is established based on the motor load data in the wind volume experiment, and the load torque T f The relationship between static pressure and speed is fitted by a bivariate polynomial function:

[0087]

[0088] In the formula, f T (·) is the fitting polynomial of the load prediction model; c T is the set of polynomial coefficients fitted to the data; c Tij is the specific coefficient in the polynomial, indicating the weight of each power term; P s i is the static pressure value P s The i-th power term; n j is the jth power of the motor speed n; according to the experimental results, when the number of fittings is k = 2, the model accuracy is sufficient to meet the control requirements. In this step, the fitting model needs to screen the collected data to reduce the impact of external disturbances. Multiple experiments are required for data collection under rated working conditions to ensure that the model is as accurate as possible.

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

[0090]

[0091] In the formula, Q err is the output air volume difference, Q is the current output air volume, is 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; represents the estimated value of static pressure; c q Indicates the polynomial coefficients of the data fitting, the target air volume value By fitting the polynomial model f Q (·) and estimated static pressure Calculated.

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

[0095]

[0096] in, is the updated value of the static pressure estimate, k p1 ,k p2 is the parameter update gain; Q err is the output air volume difference, p, q are positive constants and p <q, is a positive rational number greater than 0 and less than 1, and sgn(·) is a sign function.

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

[0098]

[0099] in, is the estimated value of static pressure at the kth moment; is the estimated static pressure at the k+1th moment; T s is the sampling time.

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

[0101] The static pressure estimation value mentioned above needs to be set with upper and lower limits to ensure that it is within the normal operating range of the motor system to avoid abnormal control output caused by error accumulation. During the adaptive update process, considering the slow change of static pressure in the environment, the calculation frequency of the adaptive algorithm can be reduced during the static pressure identification process compared to the controller to reduce the power consumption of the microprocessor.

[0102] In this embodiment, the predicted value of the air duct load is determined by the static pressure identification model and the load prediction model, and the calculation formula of the predicted value of the air duct load is:

[0103]

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

[0105] The load prediction-based finite time control method for a fan system also includes a finite time control mechanism, which adopts a finite time control strategy, calculates the control signal output air volume according to the current air volume error and load prediction information, and adjusts the fan output torque through the controller within a predetermined time, wherein the output torque adjusted by the controller is calculated by the difference between the target air volume and the current air volume, and the difference is expressed as: ΔQ = Q d -Q; where ΔQ is the current error of the output air volume; Q d is the target output air volume; Q is the current output air volume.

[0106] Among them, the output torque control signal is:

[0107]

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

[0109] The above control gain is generally controlled at 0.1k p2 <k p2 <0.5k p2 ,0.1k u2 <k u2 <0.5k u2 . For the convenience of calculation, the fractional power term in the above adaptive algorithm and controller calculation formula is generally taken as p = 1, q = 2 or p = 2, q = 3; its function is to accelerate the speed of the system converging to the stable area. When the above control process is close to the steady state, considering the positive correlation between the static pressure value and the duct load, when the estimated static pressure value is close to the actual static pressure value, the estimated duct load will be close to the actual duct load. After the controller offsets the duct load, the remaining control quantity can be used to achieve fast and stable speed regulation of the motor.

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

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

[0112] The load prediction-based finite-time control method for the fan system dynamically adapts to different environmental static pressure conditions and achieves precise air volume control in multiple scenarios by adjusting the static pressure identification model and load prediction model parameters in the microprocessor.

[0113] The present invention combines an adaptive static pressure identification method with a load prediction model, can estimate the duct load in real time, and generate an accurate torque control signal based on the estimated value, so as to achieve accurate control of the output air volume; the adaptive algorithm of the present invention can dynamically adjust the static pressure estimation value according to the change of the predicted air volume error, can adapt to the highly dynamic external environment, and ensure the consistency of the control performance; the present invention adopts a control method of load torque prediction, and compared with speed control, torque control can accurately control the acceleration and deceleration process of the motor and improve the energy efficiency of the fan; the present invention is suitable for various scenarios requiring precise air volume control, such as industrial air ducts, air-conditioning systems, etc., and can show excellent adaptability and reliability in complex environments.

[0114] In summary, the present invention realizes rapid response and high-precision control of the fan system under various load conditions through an innovative control algorithm, and has broad application prospects and economic benefits.

[0115] It should be noted that the above-mentioned figures are only schematic illustrations of the processes included in the method according to an exemplary embodiment of the present invention, and are not intended to be limiting. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0116] It should be understood that, although described in a certain order, these steps are not necessarily performed in sequence in the above order. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, a part of the steps of the present embodiment may include a plurality of steps or a plurality of stages, and these steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a part of the steps or stages in other steps or other steps.

[0117] According to a second aspect of an embodiment of the present invention, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the method of any one of the above embodiments is implemented.

[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 may be connected via a bus or other means, and the input system may receive input digital or character information, and generate a finite time control method for a fan system based on load prediction. The output system may include display devices such as a display screen.

[0119] As a non-volatile computer-readable storage medium, the memory 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-forecasting-based wind turbine system finite time control method in the embodiment of the present application. The memory may include a program storage area and a data storage area, wherein the program storage area may store an operating system and at least one application required for a function; the data storage area may store data created by the use of the load-forecasting-based wind turbine system finite time control method, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the local module via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, 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. The processor is generally used to control the overall operation of the computer device. In this embodiment, the processor is used to run the program code stored in the memory or process data. The processors of the multiple computer devices of the computer device of this embodiment execute various functional applications and data processing of the server by running non-volatile software programs, instructions and modules stored in the memory, that is, implementing the steps of the finite time control method of the fan system based on load prediction of the above method embodiment.

[0121] It should be understood that, to the extent that they do not conflict with each other, all the embodiments, features and advantages described above for the load prediction-based finite-time control method for a fan system according to the present invention are also applicable to the load prediction-based finite-time control method and storage medium for a fan system according to the present invention.

[0122] It will also be appreciated by those skilled in the art that 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 the two. In order to clearly illustrate this interchangeability of hardware and software, a general description has been given to the functions of various schematic components, blocks, modules, circuits and steps. Whether this function is implemented as software or hardware depends on specific applications and the design constraints imposed on the entire system. Those skilled in the art can implement the function in various ways for each specific application, but this implementation decision should not be interpreted as causing a departure from the disclosed scope of the embodiments of the present invention.

[0123] Finally, it should be noted that the computer-readable storage medium (e.g., memory) herein may be a volatile memory or a non-volatile memory, or may include both a volatile memory and a non-volatile memory. As an example and not by way of limitation, a non-volatile memory may include a read-only memory (ROM), a programmable ROM (PROM), an electrically programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), or a flash memory. A volatile memory may include a random access memory (RAM), which may act as an external cache memory. As an example and not by way of limitation, RAM may be obtained in a variety of forms, such as synchronous RAM (DRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct Rambus RAM (DRRAM). The storage devices of the disclosed aspects 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 in conjunction with the disclosure herein may be implemented or executed using the following components designed to perform the functions herein: a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, 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, for example, a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP, and / or any other such configuration.

[0125] The above are exemplary embodiments disclosed in the present invention, but it should be noted that various changes and modifications may be made without departing from the scope disclosed in the embodiments of the present invention as defined in the claims. The functions, steps and / or actions of the method claims according to the disclosed embodiments described herein do not need to be performed in any particular order. In addition, although the elements disclosed in the embodiments of the present invention may be described or required in individual form, they may also be understood as multiple unless explicitly limited to the singular.

[0126] It should be understood that, as used herein, the singular form "a" or "an" 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 items listed in association. The serial numbers of the embodiments disclosed in the above embodiments of the present invention are for description only and do not represent the advantages and disadvantages of the embodiments.

[0127] A person skilled in the art should understand that the discussion of any of the above embodiments is only exemplary and is not intended to imply that the scope of the disclosure of the embodiments of the present invention (including the claims) is limited to these examples; under the concept of the embodiments of the present invention, the technical features in the above embodiments or different embodiments can also be combined, and there are many other changes in different aspects of the embodiments of the present invention as above, which are not provided in detail for the sake of simplicity. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present invention should be included in the protection scope of the embodiments of the present invention.

Claims

1. A finite time control method for a fan system based on load prediction, characterized in that: The method comprises the following steps: The wind speed sensor detects the real-time wind speed signal of the air outlet, and the motor speed is obtained in real time through the built-in speed sensor of the motor; According to the real-time wind speed signal and the cross-sectional area of ​​the air duct, the current air volume value is calculated, and according to the set target air volume value, the output air volume difference is calculated; The static pressure identification model is used to estimate the static pressure in the current air duct according to the current air volume value and the motor speed; Based on the estimated static pressure value and the motor speed, a load prediction model is used to predict the load prediction result of the current 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 according to claim 1, characterized in that: 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 calculation formula for the current air volume value is: Q=Sv Where Q is the current output air volume, S is the average cross-sectional area of ​​the air duct, and v is the wind speed obtained by the wind speed sensor.

3. The finite time control method for a wind turbine system based on load prediction according to claim 2, characterized in that: In the finite time control method of the fan system based on load prediction, the fan system is driven by a brushless DC motor, the motor receives a torque control signal for torque control, the motor operates in a torque control mode, the motor feeds back a speed signal in real time through a built-in speed sensor, and an external air volume sensor feeds back an air volume signal in real time. After receiving the feedback signal, the microprocessor calculates the predicted air volume error and the actual air volume error; the predicted air volume error is obtained by subtracting the actual signal from the estimated air volume signal output by the reference model under the estimated static pressure, so as to realize static pressure identification; the actual air volume error is obtained by subtracting the target air volume signal from the actual air volume signal, so as to calculate the control amount.

4. The finite time control method for a wind turbine system based on load prediction according to claim 2, characterized in that: The static pressure identification model and the load prediction model constitute the duct prediction model. The duct prediction model parameters are obtained from the duct experiment and stored in the parameter memory for the microprocessor to read. The static pressure identification model data is obtained through the duct experiment. The data is obtained by testing under different static pressure environments. The current air volume value Q, motor speed n and static pressure value P are established through data fitting. s The relationship between: In the formula, f Q (·) is the fitting polynomial; c q is the polynomial coefficient fitted by the data, k is the highest degree of the fitted polynomial; i is the power index of the fan speed n; j is the static pressure value P s The power index of qij is the specific coefficient in the polynomial, indicating the weight of each power term; n i is the i-th power term of wind speed n; P s j is the static pressure value P s The j-th power term.

5. The finite time control method for a wind turbine system based on load prediction according to claim 4, characterized in that: The load prediction model is established based on the motor load data in the air volume experiment. The load torque T f The relationship between static pressure and speed is fitted by a bivariate polynomial function: In the formula, f T (·) is the fitting polynomial of the load prediction model; c T is the set of polynomial coefficients fitted to the data; c Tij is the specific coefficient in the polynomial, indicating the weight of each power term; P s i is the static pressure value P s The i-th power term; n j It is the jth power of the motor speed n.

6. The finite time control method for a wind turbine system based on load prediction according to claim 5, characterized in that: The update direction of the static pressure estimate is calculated by calculating the difference between the target air volume value of the reference model and the actual output air volume. When calculating the output air volume difference, it is calculated by the difference between the current air volume value and the target air volume value. The calculation formula is: In the formula, Q err is the output air volume difference, Q is the current output air volume, is the target air volume value; where: In the formula, f Q (·) represents the polynomial fitting model of air volume; n represents the motor speed; represents the estimated value of static pressure; c q Indicates the polynomial coefficients of the data fitting, the target air volume value By fitting the polynomial model f Q (·) and estimated static pressure Calculated.

7. The finite time control method for a wind turbine system based on load prediction according to claim 1, characterized in that: The real-time calculation method of the static pressure estimation value is obtained by the adaptive algorithm, and the calculation formula for adaptive update is: in, is the updated value of the static pressure estimate, k p1 , k p2 is the parameter update gain; Q err is the output air volume difference, p, q are positive constants and p<q, is a positive rational number greater than 0 and less than 1, and sgn(·) is the sign function.

8. The finite time control method for a wind turbine system based on load prediction according to claim 7, characterized in that: Calculated in discrete form in a microprocessor, the update formula is: in, is the estimated value of static pressure at the kth moment; is the estimated static pressure at the k+1th moment; T s is the sampling time.

9. The method for finite-time control of a wind turbine system based on load prediction according to 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 calculation formula for the predicted value of the duct load is: In the formula, is the estimated load torque; f T (·) is the fitting polynomial of the load prediction model; is the estimated value of static pressure; c T is the set of fitted polynomial coefficients.

10. The finite time control method for a wind turbine system based on load prediction according to claim 9, characterized in that: The load prediction-based finite time control method for the fan system also includes a finite time control mechanism, adopts a finite time control strategy, calculates the control signal output air volume according to the current air volume error and load prediction information, and adjusts the fan output torque through the controller within a predetermined time; the output torque control signal is: Among them, k u1 ,k u2 is the control gain; u is the output control torque; is the estimated load torque; sgn(·) is the sign function.

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