A method and system for controlling a blower unit of a multi-disk axial flux motor

Abstract

The present invention discloses a blower control method and system for a multi-disk axial flux motor, the method comprising: taking the blower output air volume and wind pressure efficiency as the optimal control target, obtaining the blower required output power, and converting the required output power into the required output torque; according to the required output torque, constructing a prediction optimization model by introducing different operation modes and mode switching constraints, dynamically adjusting the control variables and outputting the switching mode according to the current state and predicted state of the prediction optimization model; based on the corresponding module driven after the mode switching, obtaining the output torque and output power of the corresponding module, and the total required torque of the blower system and inputting them into the neural network, so as to obtain the optimal output torque of each disc motor unit. The system is ensured to be lightweight and efficient; the fault tolerance and safety of the system operation are improved; one-to-many output is achieved, and the output efficiency and economy of the motor control system are effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of automation control technology, and in particular to a blower unit control method and system of a multi-disk axial flux motor. Background Art

[0002] At present, all fields of the industry are actively building a new energy structure that is safe, efficient, clean and low-carbon. Blowers are widely used in many industrial fields, such as providing sufficient air when blast furnaces are smelting steel to achieve the required high temperature in the furnace. Compared with the traditional radial flux motors used in blast furnace blower units, the use of axial flux motors in blower systems has great advantages, which will bring higher power density and efficiency, smaller space volume and weight, better heat dissipation performance, less noise and vibration, and more flexible integrated control systems.

[0003] Traditional radial flux motors have problems such as large size and weight, and low power at the same size and weight. When applied to blower control systems, on the one hand, it may limit its installation space in the blower and increase the overall weight of the blower. In particular, for blast furnace blower products that require compact design, additional space design and structural support are required; on the other hand, in high-power applications, radial flux motors require more sophisticated control strategies during the start and stop process. Although they are more efficient under high-speed operating conditions, they often perform poorly under low-speed conditions. A single disc motor is used in a blower drive system, which has a small output power range and a single drive motor failure. High risk, complex subsequent maintenance and repair, etc.; when a single disc motor is used as the blower drive motor, on the one hand, when it is applied to high-power production conditions, a single drive motor cannot fully meet the production needs, on the other hand, when a single motor fails, the blower unit as a whole cannot continue to work, causing production interruptions and further causing serious economic losses. At present, research on blast furnace blowers is mostly based on improving the performance of a single blower. There is no more systematic research on the control system of a blower unit composed of multiple blowers, which cannot meet the regulation efficiency requirements in various situations, and the output power cannot meet the production air volume requirements of the corresponding blast furnace, causing production interruptions and other problems.

[0004] Therefore, a control method with a multi-layer and multi-level control structure is needed to improve the coordination of blowers, increase output power and reasonable distribution. Summary of the invention

[0005] In view of the above existing problems, the present invention is proposed.

[0006] Therefore, the present invention provides a blower unit control method and system for a multi-disk axial flux motor to solve the problems of existing methods, such as the existing blower structure system often performs poorly under low-speed conditions, the output power range is small, the risk of failure of a single drive motor is high, a single drive motor cannot fully meet production needs, and cannot meet the regulation efficiency requirements under various situations. The output power cannot meet the production air volume requirements of the corresponding blast furnace, causing production interruptions.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, the present invention provides a blower unit control method of a multi-disk axial flux motor, the method being applied to a blower unit device, the device comprising at least one blower and the blower comprising a multi-disk axial flux motor drive layer, the drive layer comprising at least one modular disc axial flux permanent magnet synchronous motor, the motor comprising four modules, each module comprising two units, each unit consisting of a rotor in the middle and a stator on one side;

[0009] The method comprises:

[0010] Taking the optimal output air volume and air pressure efficiency of the blower as the control target, the required output power of the blower is obtained and the required output power is converted into the required output torque;

[0011] According to the required output torque, a prediction optimization model is constructed by introducing different operating modes and switching constraints between modes, and whether the optimization target is met is judged according to the current state and the predicted state of the prediction optimization model, so as to dynamically adjust the control variables and output the switching mode;

[0012] Based on the corresponding modules driven after mode switching, the output torque and output power of the corresponding modules and the total required torque of the blower system are obtained and input into the neural network to obtain the optimal output torque of each disc motor unit.

[0013] As a preferred solution of the blower unit control method of the multi-disk axial flux motor of the present invention, the different operation modes include:

[0014] The first operating mode is single module operation, where a single module provides all the required torque;

[0015] The second operation mode is dual-module operation, where the two modules evenly share the torque required by the system;

[0016] The third operation mode is three-module operation, and the three modules evenly share the torque required by the system;

[0017] The fourth operation mode is four-module operation, where the four modules evenly share the torque required by the system;

[0018] The rated power and rated output torque of each module are consistent.

[0019] As a preferred solution of the blower unit control method of the multi-disk axial flux motor of the present invention, wherein: constructing the prediction optimization model includes: defining state variables and control variables;

[0020] The state variable x k Includes: Single blower system total required torque T total ; Real-time output torque of each module motor T1, T2, T3, T4; Real-time output power of each module motor P1, P2, P3, P4-; Different modules have the same rated output torque T N , different modules have the same rated output power P N ; The control variable u k Including: real-time output torque T1, T2, T3, T4 of each module motor.

[0021] As a preferred solution of the blower unit control method of the multi-disk axial flux motor of the present invention, wherein: constructing the prediction optimization model further comprises: determining the optimization target;

[0022] Minimize the total system torque requirement T total The error between the output torque and the preset value is minimized, and the difference between the output torque of each modular motor and the rated torque is minimized.

[0023] As a preferred solution of the blower unit control method of the multi-disk axial flux motor described in the present invention, wherein: the mode switching constraint conditions in the constructed prediction optimization model include:

[0024] The rated constraint of each modular motor is expressed as:

[0025] T i ≤T N ,P i ≤P N ,

[0026] Among them, T i Indicates the real-time output torque of different modules i; T N Indicates the same rated output torque of different modules; P i Indicates the real-time output power of different modules i; P N Indicates the rated output power of different modules;

[0027] The constraints for switching modes include:

[0028] From the first mode to the second mode, when the output power of a single module exceeds its rated torque, it can be switched to dual modules for joint operation, which is expressed as:

[0029] T total ≥k1·T N

[0030] From the second mode to the third mode: When the total required torque exceeds twice the rated torque, it can be switched to three modules running together, expressed as:

[0031] T total ≥k2·T N

[0032] From the third mode to the fourth mode: when the total required torque exceeds three times the rated torque, the four modules are switched to operate together, which is expressed as:

[0033] T total ≥k3·T N

[0034] From the fourth mode to the first mode: when the total required torque is approximately kept near the single rated torque, it can be expressed as:

[0035] T total ≤k1·T N

[0036] Among them, k1, k2, and k3 are used to indicate that the module switches modes when the output approaches or exceeds the rated torque.

[0037] As a preferred solution of the blower unit control method of the multi-disk axial flux motor of the present invention, the prediction optimization model is expressed as:

[0038]

[0039] Among them, x k+j|k is the predicted state for time step j at time k; u k+j|k is the predicted control variable for time step j at time k; Q and R are weight matrices used to balance the importance of state variables and the weights of control variables; N is 4, indicating the number of modules.

[0040] As a preferred solution of the blower unit control method of the multi-disk axial flux motor of the present invention, the method further includes a fault response mechanism, and the fault response mechanism specifically includes:

[0041] One spare blower is set up, and one blast furnace corresponds to one regular blower;

[0042] When a conventional blower i′ corresponding to any blast furnace i fails, the electric valve of the blower i′ is closed, and the electric valve of the standby blower and the corresponding air valve are opened, and the remaining conventional blowers and the standby blowers input air volume to the blast furnace at the same time;

[0043] Air volume distribution includes:

[0044] Set the torque requirement T corresponding to the minimum air volume of each blast furnace i min_i (i=0,1,2......);

[0045] The blower in operation adjusts its output torque to meet the additional torque requirement of the blast furnace:

[0046] T extra_i =max(0,T output_i -T A )(i=1, 2,..., n; A=1, 2,..., n-1)

[0047] Among them, T extra_i is the required torque corresponding to the additional air volume required for the i-th blast furnace; T output_i is the total torque demand of the ith blast furnace; T A is the real-time output torque of the Ath normal conventional blower; T n Output torque to the running standby blower in real time;

[0048] If the required torque T corresponding to the additional air volume required by the blast furnace extra_i If the actual output torque TA of blower A is greater than that of blower A, blower A will output at full capacity to meet the torque demand of the i-th blast furnace; otherwise, the torque demand for the additional air volume required by the i-th blast furnace will be allocated to the A-th blower and the A+1-th blower in proportion.

[0049] Blower A idle capacity ratio X A , and the idle capacity ratio X of the next blower A+1 A+1 (i=A=1,2,3......n), expressed as:

[0050]

[0051] Finally, the torque corresponding to the required output air volume of the Ath blower and its adjacent A+1th blower is obtained:

[0052]

[0053] In a second aspect, the present invention provides a blower unit control system for a multi-disc axial flux motor, comprising: an acquisition module for acquiring the required output power of the blower with the blower output air volume and air pressure efficiency being the optimal control target, and converting the required output power into the required output torque;

[0054] A first control module is used to construct a prediction optimization model according to the required output torque by introducing different operating modes and switching constraints between modes, and to determine whether the optimization target is met according to the current state and predicted state of the prediction optimization model, so as to dynamically adjust the control variables and output the switching mode;

[0055] The second control module is used to obtain the output torque and output power of the corresponding module and the total required torque of the blower system according to the corresponding module driven after the mode switching, and input them into the neural network to obtain the optimal output torque of each disc motor unit.

[0056] In a third aspect, the present invention provides a computing device, comprising:

[0057] Memory and processor;

[0058] The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the blower group control method of the multi-disk axial flux motor are implemented.

[0059] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the blower unit control method of the multi-disk axial flux motor.

[0060] Compared with the prior art, the present invention has the following beneficial effects: the present invention is based on a multi-module axial flux motor as the core driving motor, takes the blower output air volume and wind pressure efficiency as the optimal control target, adopts a torque graded optimization allocation strategy, adopts a method of evenly dividing different modules between modules, studies and determines the boundary conditions for switching to different modules for control, and stipulates different operating modes for a single blower; a torque allocation strategy with the smallest total system loss is adopted within the module, and based on the neural network algorithm, it is converted into a target optimization problem to ensure the efficient operation of the motor system; for special moments such as failure of a single blower in the blower unit, the control target is to maintain stable operation of the system, and fault handling methods such as two-way air diversion and air supply between different blower units are designed to ensure the lightweight and high efficiency of the control system; the control system has a wide power range output and improves the fault tolerance and safety of the system operation; one-to-many output is achieved, effectively improving the output efficiency and economy of the motor control system. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0062] Figure 1 It is a schematic diagram of the overall process logic of the blower unit control method of the multi-disc axial flux motor according to the first embodiment of the present invention;

[0063] Figure 2 It is a schematic diagram of the basic structure of the blower unit in the blower unit control method of the multi-disc axial flux motor according to the first embodiment of the present invention;

[0064] Figure 3 It is a basic structural block diagram of a blower unit system in a blower unit control method of a multi-disk axial flux motor according to a first embodiment of the present invention;

[0065] Figure 4 It is a flow chart of the allocation strategy among modules in the blower unit control method of the multi-disk axial flux motor according to the second embodiment of the present invention;

[0066] Figure 5 This is an exemplary blower unit structure diagram of a fault response mechanism in the blower unit control method of a multi-disk axial flux motor described in the first embodiment of the present invention. DETAILED DESCRIPTION

[0067] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0068] Example 1, reference Figure 1-Figure 5 , is an embodiment of the present invention, and provides a blower unit control method of a multi-disc axial flux motor, the method being applied to a blower unit device, such as Figure 2 As shown, the device includes at least one blower and the blower includes a multi-disk axial flux motor drive layer, the drive layer includes at least one modular disc axial flux permanent magnet synchronous motor, the motor includes four modules, each module includes two units, and each unit is composed of a rotor in the middle and a stator on one side;

[0069] Specifically, the blower unit device also includes: Figure 3The first layer shown is the data transmitted back to the central controller through multiple sensors installed inside the blast furnace. The controller determines the required air volume and air pressure under different working conditions based on the input parameters; the second layer is the multi-blower control layer, which converts the air volume and air pressure instructions input from the previous layer into torque control instructions for the axial flux motor inside the blower; the third layer is the multi-disc axial flux motor drive layer inside the blower; the fourth layer is the bottom layer, that is, the four modules included in the motor, which input electrical energy, drive the multi-disc motor to rotate, drive the compressor to work, and output high-temperature hot air.

[0070] like Figure 1 As shown, the method includes:

[0071] S100: Taking the optimal output air volume and air pressure efficiency of the blower as the control target, obtaining the required output power of the blower, and converting the required output power into the required output torque;

[0072] S200: constructing a prediction optimization model based on the required output torque by introducing different operation modes and switching constraints between modes, and judging whether the optimization target is met based on the current state and the predicted state of the prediction optimization model, so as to dynamically adjust the control variables and output the switching mode;

[0073] In the embodiment of the present application, different operation modes include:

[0074] The first operating mode is single module operation, where a single module provides all the required torque;

[0075] The second operation mode is dual-module operation, where the two modules evenly share the torque required by the system;

[0076] The third operation mode is three-module operation, and the three modules evenly share the torque required by the system;

[0077] The fourth operation mode is four-module operation, where the four modules evenly share the torque required by the system;

[0078] The rated power and rated output torque of each module are consistent.

[0079] It should be noted that the torque distribution control strategy between modules is constructed based on the torque command size of the blast furnace system and the efficiency distribution characteristics of the modules. Its core lies in the selection of the startup module in the four independent modules and the torque configuration on different modules. On the whole, when the torque command output by the blast furnace system is small, only running a single module can meet the torque demand, so all the torque is allocated to one module; but when the torque command output by the blast furnace system is large, two, three or four modules need to be run to drive the blower to output the appropriate air volume and air pressure. At the same time, it is necessary to select the number of modules to be started and the specific modules to be started with the goal of optimizing system efficiency. Therefore, combined with the system operating conditions, the blower is divided into the above four operating modes.

[0080] In an embodiment of the present application, constructing a prediction optimization model includes: defining state variables and control variables;

[0081] State variable x k Includes: Single blower system total required torque T total ; Real-time output torque T1, T2, T3, T4 of each module motor; Real-time output power P1, P2, P3, P4 of each module motor-; Different modules have the same rated output torque T N , different modules have the same rated output power P N ; Control variables u k includes: the real-time output torque T1, T2, T3, T4 of each module motor.

[0082] In the embodiment of the present application, constructing the prediction optimization model further includes: determining the optimization target;

[0083] Minimize the total system torque requirement T total The error between the output torque and the preset value is minimized, and the difference between the output torque of each modular motor and the rated torque is minimized.

[0084] It should be noted that determining the optimization target means finding the optimal control variable according to the current state variable in each control cycle to achieve the specific optimization target.

[0085] In the embodiment of the present application, the switching constraints between modes in the prediction optimization model are constructed including:

[0086] The rated constraint of each modular motor is expressed as:

[0087] T i ≤T N ,P i ≤P N ,

[0088] Among them, T i Indicates the real-time output torque of different modules i; T N Indicates the same rated output torque of different modules; P i Indicates the real-time output power of different modules i; P N Indicates the rated output power of different modules;

[0089] The constraints for switching modes include:

[0090] From the first mode to the second mode, when the output power of a single module exceeds its rated torque, it can be switched to dual modules for joint operation, which is expressed as:

[0091] T total ≥k1·TN

[0092] From the second mode to the third mode: When the total required torque exceeds twice the rated torque, it can be switched to three modules running together, expressed as:

[0093] T total ≥k2·T N

[0094] From the third mode to the fourth mode: when the total required torque exceeds three times the rated torque, the four modules are switched to operate together, which is expressed as:

[0095] T total ≥k3·T N

[0096] From the fourth mode to the first mode: when the total required torque is approximately kept near the single rated torque, it can be expressed as:

[0097] T total ≤k1·T N

[0098] Among them, k1, k2, and k3 are used to indicate that the module switches modes when the output approaches or exceeds the rated torque.

[0099] Specifically, k1, k2, and k3 are values ​​close to 1, 2, and 3, respectively, and their upper and lower fluctuation ranges are close to 5%. The specific values ​​need to be determined based on the performance curve of the motor and the requirements of the system design, and can be fine-tuned based on experience and experimental data.

[0100] In the embodiment of the present application, the prediction optimization model is expressed as:

[0101]

[0102] Among them, x k+j|k is the predicted state for time step j at time k; u k+j|k is the predicted control variable for time step j at time k; Q and R are weight matrices used to balance the importance of state variables and the weights of control variables; N is 4, indicating the number of modules.

[0103] It should be noted that in each control cycle, the current state and predicted state of the prediction optimization model are used to determine whether the conditions for switching modes are met. When the switching conditions are met, the control variables are dynamically adjusted to trigger the switch of different modes of the blower. The overall process is as follows: Figure 4 shown.

[0104] S300: Based on the corresponding modules driven after the mode switching, the output torque and output power of the corresponding modules and the total required torque of the blower system are obtained and input into the neural network to obtain the optimal output torque of each disc motor unit.

[0105] Specifically, the parameters are first defined, including the total required torque T of the single blower system mentioned above. total ; Real-time output torque T1, T2, T3, T4 of each module motor; Real-time output power P1, P2, P3, P4 of each module motor-; Different modules have the same rated output torque T N , different modules have the same rated output power P N ; and, P total is the output power requirement of the single blower system, T j The real-time output torque of each unit j (j=1,2) in different modules, P j --Real-time output power of each unit j (j=1,2) in different modules.

[0106] This is followed by the selection of the neural network architecture, including:

[0107] Input layer: receives the output torque T of the module i , output power P i , and the total required torque of the blower system T total The basic idea of ​​torque distribution within the module is to distribute the torque required by each module unit to the two units in a single module with different proportional coefficients, which is directly related to the output torque T of the defined variable receiving module. i Related;

[0108] The other two input layer parameters are determined by the defined loss function.

[0109] Hidden layer: contains multiple neurons. Each neuron receives the input variables from the previous layer, and after weighting and adding bias items, it is sent to the activation function (Sigmoid function) and outputs a value. The main function of this layer is to learn the nonlinear relationship and complex function characteristics of the input data.

[0110] Output layer: The optimal output torque T of each disc motor unit j_opt The output layer does not need to go through the activation function and directly outputs the predicted result, which is the optimal output torque T of each disc motor unit. j_opt , where the value range of j is (j=1,2).

[0111] Among them, exemplary, the loss function is expanded in mean square error:

[0112]

[0113] Among them, w1 and w2 are the weights of each term in the loss function, which are used to balance the error of the total demand rotation of the blower system and the output power of the system.

[0114] Next, the data set used to train the neural network is obtained, which mainly includes the output torque T of each disc motor unit under different blast furnace operating conditions. j , output power P j , and the total system torque requirement.

[0115] Training a neural network includes forward propagation, loss calculation, backpropagation, and parameter update, and repeating multiple training iterations until the loss function converges or reaches a preset stopping condition.

[0116] Finally, the optimal output torque T of each disc motor unit is obtained. j_opt , which can be used in the torque distribution strategy of the actual system to minimize the system loss.

[0117] In the embodiment of the present application, the method further includes a fault response mechanism, which specifically includes:

[0118] One spare blower is set up, and one blast furnace corresponds to one regular blower;

[0119] When a conventional blower i′ corresponding to any blast furnace i fails, the electric valve of the blower i′ is closed, and the electric valve of the standby blower and the corresponding air valve are opened, and the remaining conventional blowers and the standby blowers input air volume to the blast furnace at the same time;

[0120] Air volume distribution includes:

[0121] Set the torque requirement T corresponding to the minimum air volume of each blast furnace i min_i (i=0,1,2......);

[0122] The blower in operation adjusts its output torque to meet the additional torque requirement of the blast furnace:

[0123] T extra_i =max(0,T output_i -T A )(i=1, 2,..., n; A=1, 2,..., n-1)

[0124] Among them, T extra_i is the required torque corresponding to the additional air volume required for the i-th blast furnace; T output_i is the total torque demand of the ith blast furnace; T A is the real-time output torque of the Ath normal conventional blower; T n Output torque to the running standby blower in real time;

[0125] If the required torque T corresponding to the additional air volume required by the blast furnace extra_i Greater than the actual output torque T of blower A A , then blower A will output at full capacity to meet the torque demand of the i-th blast furnace; otherwise, the torque demand for the additional air volume required by the i-th blast furnace will be allocated to the A-th blower and the A+1-th blower in proportion.

[0126] Blower A idle capacity ratio X A , and the idle capacity ratio X of the next blower A+1 A+1 (i=A=1,2,3......n), expressed as:

[0127]

[0128] Finally, the torque corresponding to the required output air volume of the Ath blower and its adjacent A+1th blower is obtained:

[0129]

[0130] For example, based on the above general fault response mechanism, three blowers (A, B, C) are used as an example to illustrate the fault response mechanism. Figure 2 and Figure 5 As shown:

[0131] Three blowers (A, B, C) can be used for the production of two blast furnaces. Once a blower of a blast furnace fails and stops running, the blowers of other normally operating blast furnaces will immediately deliver part of the air volume to the tuyere of the faulty blast furnace through the laid air ducts. The purpose is to ensure that the faulty blast furnace will not stop blowing suddenly, thereby avoiding large-scale slag filling at the tuyere. The partial air volume delivered is sufficient to maintain the basic production of the faulty blast furnace, and at the same time provide the necessary conditions for starting the standby blower or stopping the blower normally after the molten iron.

[0132] During normal operation, blower A corresponds to blast furnace No. 1, blower B corresponds to blast furnace No. 2, and delivers hot air that meets the wind pressure and wind pressure requirements, and blower C is used as a backup blower. By laying corresponding sensors in the air ducts, the hot air in different air ducts is digitally monitored to ensure the smooth operation of different blowers and deliver hot air that meets the requirements to the blast furnace. Different emergencies that may occur are classified and a fault response mechanism is established as follows:

[0133] When both blast furnaces are running smoothly, blower C is turned off, electric valve 3 is always kept closed, and the electric valves of blowers A and B are opened. Hot air is directly input to each blast furnace by the corresponding blower, and air valves 1 and 2 are kept closed. Then, according to the above torque optimization allocation strategy, the total torque demand of the system is allocated to each motor unit accordingly.

[0134] When the air inlet sensor of No. 1 blast furnace detects a decrease in air pressure, indicating a fault in the corresponding blower A, the electric valve 1 is closed, and the fault of blower A is immediately checked. At the same time, the electric valve 3 of blower C and the air valves 1 and 2 are opened, and the air volume is input to the two blast furnaces by blowers B and C at the same time;

[0135] When the air inlet sensor of No. 2 blast furnace detects a decrease in air pressure, indicating a fault in the corresponding blower B, the electric valve 2 is closed, and the fault of blower B is immediately checked. At the same time, the electric valve 3 of blower C and the air valves 1 and 2 are opened, and the air volume is input to the two blast furnaces by blowers A and C at the same time;

[0136] When two blowers are used to input air into the blast furnace, a certain air volume distribution strategy is involved. The air volume distribution can be mainly divided into three steps:

[0137] Basic air volume allocation: A basic minimum air volume corresponding to the torque requirement T is specified for each blast furnace. min to meet the basic needs of maintaining production.

[0138] Additional air distribution, including:

[0139] The remaining normal blowers need to adjust their output torque to meet the additional required torque of the blast furnace:

[0140] T extra1 =max(0,T output1 -T A )

[0141] T extra2 =max(0,T output2 -T B )

[0142] Among them, T extra1 , T extra2 --Required torque corresponding to the additional air volume required for blast furnaces 1 and 2; T output1 , T output2 --Total torque demand of blast furnaces 1 and 2; T A , T B --The blower outputs torque in real time. After removing the faulty blower, set the blower that was still running at the previous moment to A and the standby blower to B.

[0143] Additional air volume allocation strategy for blast furnace 1: If T extra1 Greater than the actual output torque T of blower A A , then blower A will output at full capacity to meet the torque demand of blast furnace 1; otherwise, the torque demand will be allocated to blowers A and B in proportion; for the additional air volume allocation strategy of blast furnace 2: if T extra2Greater than the actual output torque T of blower B B , then blower B will output at full capacity to meet the torque demand of blast furnace 2; otherwise, the torque demand will be distributed to blowers A and B in proportion.

[0144] Adjust output torque

[0145] Calculate the idle capacity ratio x of each blower A 、x B :

[0146]

[0147] Calculate the torque T corresponding to the output air volume required by the two blowers extra_A , T extra_B :

[0148] T extra_A =x A ×T extra1

[0149] T extra_B =x B ×T extra2

[0150] Therefore, the air volume distribution strategy for the other two blowers when a single blower fails was finally determined. This strategy can not only ensure the smooth operation of the system when a failure occurs, but also maintain the optimal system efficiency.

[0151] When both blowers fail, hot air is directly supplied to the two blast furnaces by a single standby blower.

[0152] In summary, the present invention is based on a high-power blower unit system of a multi-disc axial flux motor, and aims to minimize the overall system loss. A multi-layer and multi-level control structure is established. The first layer is the field sensor and the central decision processor, which is responsible for feeding back the total air volume required in the blast furnace based on the real-time data in the air duct and the furnace, and monitoring the system for faults. When the blower fails, the smooth operation of the system is guaranteed; the second layer is the execution layer of three different blowers, which is responsible for converting the air volume demand into the torque demand of the motor module; the third layer is the superposition of four motor modules in the blower, which involves the torque optimization allocation strategy between modules; the fourth layer is the two basic components inside the module, which involves the torque collaborative optimization strategy within the module.

[0153] The problem of collaborative efficiency optimization was studied in depth within and between modules of the overall system. For the torque distribution optimization strategy within the module, due to the relatively complex nonlinear relationship between the output torque of the disc motor unit and the total torque demand of the system, a neural network algorithm was adopted in this design invention. Through the nonlinear activation function of the hidden layer, it can flexibly adapt to the torque distribution requirements under different working conditions and improve the accuracy and adaptability of the model. For the problem of torque distribution optimization between modules, combined with the model predictive control algorithm, in each control cycle, according to the current state and predicted state of the prediction model, it is judged whether the conditions for switching modes are met. When the switching conditions are met, the control variables are dynamically adjusted to trigger the switch of different modes of the blower. Overall, collaborative efficiency optimization can accurately control the operation of the blower, so that it can operate in the best working state, thereby reducing energy waste and reducing production costs.

[0154] The high-power blower unit optimization method and system based on the multi-disc axial flux motor proposed in the present invention can not only effectively improve the motor power density, but also minimize the system loss. At the same time, the constructed blower unit system has a certain self-fault handling capability when dealing with sudden faults, reducing the risk of sudden shutdown caused by faults, making the system more stable and reliable for continuous operation.

[0155] The above is a schematic scheme of a method for controlling a blower unit of a multi-disk axial flux motor in this embodiment. It should be noted that the technical scheme of the system for controlling the blower unit of the multi-disk axial flux motor and the technical scheme of the method for controlling the blower unit of the multi-disk axial flux motor described above belong to the same concept, and the details of the technical scheme of the control system of the blower unit of the multi-disk axial flux motor in this embodiment that are not described in detail can all be referred to the description of the technical scheme of the method for controlling the blower unit of the multi-disk axial flux motor described above.

[0156] The blower unit control system of the multi-disk axial flux motor in this embodiment includes:

[0157] An acquisition module is used to obtain the required output power of the blower with the optimal output air volume and air pressure efficiency of the blower as the control target, and convert the required output power into the required output torque;

[0158] The first control module is used to construct a prediction optimization model according to the required output torque by introducing different operating modes and switching constraints between modes, and judge whether the optimization target is met according to the current state and predicted state of the prediction optimization model, so as to dynamically adjust the control variables and output the switching mode;

[0159] The second control module is used to obtain the output torque and output power of the corresponding module and the total required torque of the blower system according to the corresponding module driven after the mode switching, and input them into the neural network to obtain the optimal output torque of each disc motor unit.

[0160] This embodiment further provides a computing device, which is applicable to the case of controlling a blower unit of a multi-disk axial flux motor, and includes:

[0161] Memory and processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the blower unit control method of the multi-disk axial flux motor as proposed in the above embodiment.

[0162] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for controlling a blower unit of a multi-disk axial flux motor as proposed in the above embodiment is implemented.

[0163] The storage medium proposed in this embodiment and the blower unit control method for realizing a multi-disk axial flux motor proposed in the above embodiment belong to the same inventive concept. The technical details not described in detail in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0164] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (Random Access Memory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments of the present invention.

[0165] Example 2

[0166] Referring to Tables 1 to 3, based on the previous embodiment, this embodiment provides an application case of a blower unit control method for a multi-disc axial flux motor to illustrate the feasibility of our solution.

[0167] The present invention chooses to build a simulation model of DSSR-AFPMSM in MATLAB / Simulink.

[0168] The four different modules of DSSR-AFPMSM in a single group in this paper are the same as each other, but the parameters of the two units of DSSR-AFPMSM in the module are different. The specific parameter differences are shown in Table 1. The simulation parameters of the DSSR-AFPMSM designed in this paper are shown in Table 1.

[0169] Table 1 DSSR-AFPMSM simulation parameter settings

[0170]

[0171]

[0172] In MATLAB / Simulink, the Model Predictive Controller module in the control system toolbox is used, and the MPC controller is selected. First, the control target is set, specifically defining the objective function of the MPC controller, including the tracking error and the optimal energy consumption; secondly, the constraints are defined, specifically setting the constraints of the control input and output; finally, the prediction and control time domains are adjusted, and the prediction step and control step are configured. The main simulation parameters of the algorithm in the simulation model are shown in Table 2.

[0173] Table 2 Main simulation parameters of MPC model

[0174]

[0175] In MATLAB / Simulink, open the Neural Network Toolbox and enter nnstart in the MATLAB command window to start the neural network toolbox. First, you can create a neural network. Use the Pattern Recognition or Regression tool to create and train the neural network, and use the motor operation data to train the neural network to predict the behavior of the motor, including speed, torque, etc.; secondly, integrate the neural network in Simulink, use the MATLAB Function module, save the trained neural network as a MATLAB function, and integrate the function into the Simulink model; finally, connect the neural network, connect the input of the neural network module to the state variable of the motor model, and connect the output to the input of the controller. The main simulation parameters of the algorithm in the simulation model are shown in Table 3.

[0176] Table 3 Main parameters of neural network algorithm training

[0177]

[0178] The simulation results show that compared with the traditional torque equalization strategy, the new torque distribution strategy invented and designed this time has obvious improvements in different aspects.

[0179] First, the torque cooperative distribution strategy can accurately adjust the dynamic characteristics and steady-state error of the system by optimizing the control matrix, and improves the control accuracy by about 10% compared with the traditional torque equal distribution method;

[0180] Second, the torque cooperative distribution strategy can optimize the dynamic response of the system, reduce overshoot and response time. The simulation results show that it can effectively reduce the system overshoot by about 6.8%, effectively improving the system stability.

[0181] Third, the efficiency of the control system under the torque cooperative distribution strategy is significantly higher than the efficiency of the average torque distribution, which is improved by about 2.1%. This is because when the torque value is small, the average distribution strategy makes the working torque of different motors much smaller than their rated torque, and the motor works in a low efficiency range.

[0182] Overall, the torque coordination distribution strategy based on the multi-disk axial flux motor ensures that the system works in a high-efficiency space by coordinating the torque distribution within and between modules. This verifies the effectiveness and superiority of the double-layer torque coordination distribution strategy designed in this invention in reducing the loss of the drive system, improving the efficiency of the drive system, and expanding the high-efficiency operation space of the drive system within the full torque range.

[0183] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for controlling a blower unit of a multi-disk axial flux motor, characterized in that: The method is applied to a blower assembly device, the device comprising at least one blower and the blower comprising a multi-disk axial flux motor drive layer, the drive layer comprising at least one modular disc axial flux permanent magnet synchronous motor, the motor comprising four modules, each module comprising two units, each unit consisting of a rotor in the middle and a stator on one side; The method comprises: Taking the optimal output air volume and air pressure efficiency of the blower as the control target, the required output power of the blower is obtained and the required output power is converted into the required output torque; According to the required output torque, a prediction optimization model is constructed by introducing different operating modes and switching constraints between modes, and whether the optimization target is met is judged according to the current state and the predicted state of the prediction optimization model, so as to dynamically adjust the control variables and output the switching mode; Based on the corresponding modules driven after mode switching, the output torque and output power of the corresponding modules and the total required torque of the blower system are obtained and input into the neural network to obtain the optimal output torque of each disc motor unit.

2. The blower unit control method of the multi-disc axial flux motor according to claim 1, characterized in that: The different operating modes include: The first operating mode is single module operation, where a single module provides all the required torque; The second operation mode is dual-module operation, where the two modules evenly share the torque required by the system; The third operation mode is three-module operation, and the three modules evenly share the torque required by the system; The fourth operation mode is four-module operation, where the four modules evenly share the torque required by the system; The rated power and rated output torque of each module are consistent.

3. The blower unit control method of the multi-disc axial flux motor according to claim 2, characterized in that: Constructing a prediction optimization model includes: defining state variables and control variables; The state variable x k Includes: Single blower system total required torque T total ; Real-time output torque T1, T2, T3, T4 of each module motor; Real-time output power P1, P2, P3, P4 of each module motor-; Different modules have the same rated output torque T N , different modules have the same rated output power P N ; The control variable u k Including: real-time output torque T1, T2, T3, T4 of each module motor.

4. The blower unit control method of the multi-disc axial flux motor according to claim 3, characterized in that: Building a predictive optimization model also includes: determining the optimization goal; Minimize the total system torque requirement T total The error between the output torque and the preset value is minimized, and the difference between the output torque of each modular motor and the rated torque is minimized.

5. The blower unit control method of the multi-disc axial flux motor according to claim 4, characterized in that: The switching constraints between modes in the prediction optimization model include: The rated constraint of each modular motor is expressed as: Among them, T i Indicates the real-time output torque of different modules i; T N Indicates the same rated output torque of different modules; P i Indicates the real-time output power of different modules i; P N Indicates the rated output power of different modules; The constraints for switching modes include: From the first mode to the second mode, when the output power of a single module exceeds its rated torque, it can be switched to dual modules for joint operation, which is expressed as: T total ≥k1·T N From the second mode to the third mode: When the total required torque exceeds twice the rated torque, it can be switched to three modules running together, expressed as: T total ≥k2·T N From the third mode to the fourth mode: when the total required torque exceeds three times the rated torque, the four modules are switched to operate together, which is expressed as: T total ≥k3·T N From the fourth mode to the first mode: when the total required torque is approximately kept near the single rated torque, it can be expressed as: T total ≤k1·T N Among them, k1, k2, and k3 are used to indicate that the module switches modes when the output approaches or exceeds the rated torque.

6. The blower unit control method of the multi-disc axial flux motor according to claim 5, characterized in that: The prediction optimization model is expressed as: Among them, x k+j|k is the predicted state for time step j at time k; u k+j|k is the predicted control variable for time step j at time k; Q and R are weight matrices used to balance the importance of state variables and the weights of control variables; N is 4, indicating the number of modules.

7. The blower unit control method of the multi-disc axial flux motor according to claim 6, characterized in that: The method further includes a fault response mechanism, which specifically includes: One spare blower is set up, and one blast furnace corresponds to one regular blower; When a conventional blower i′ corresponding to any blast furnace i fails, the electric valve of the blower i′ is closed, and the electric valve of the standby blower and the corresponding air valve are opened, and the remaining conventional blowers and the standby blowers input air volume to the blast furnace at the same time; Air volume distribution includes: Set the torque requirement T corresponding to the minimum air volume of each blast furnace i min_i (i=0,1,2......); The blower in operation adjusts its output torque to meet the additional torque requirement of the blast furnace: T extra_i =max(0,T output_i -T A )(i=1、2、……、n;A=1、2、……、n-1) Among them, T extra_i is the required torque corresponding to the additional air volume required for the i-th blast furnace; T output_i is the total torque demand of the ith blast furnace; T A is the real-time output torque of the Ath normal conventional blower; T n Output torque to the running standby blower in real time; If the required torque T corresponding to the additional air volume required by the blast furnace extra_i Greater than the actual output torque T of blower A A , then blower A will output at full capacity to meet the torque demand of the i-th blast furnace; otherwise, the torque demand for the additional air volume required by the i-th blast furnace will be allocated to the A-th blower and the A+1-th blower in proportion. Blower A idle capacity ratio X A , and the idle capacity ratio X of the next blower A+1 A+1 (i=A=1,2,3......n), expressed as: Finally, the torque corresponding to the required output air volume of the Ath blower and its adjacent A+1th blower is obtained: T extraA =X A ×T extrai T extra_A+1 =X A+1 ×T extrai (i=A=1,2,3,......,n)。 8. A system using the blower unit control method of the multi-disk axial flux motor as claimed in claim 1, characterized in that: include: An acquisition module is used to obtain the required output power of the blower with the optimal output air volume and air pressure efficiency of the blower as the control target, and convert the required output power into the required output torque; A first control module is used to construct a prediction optimization model according to the required output torque by introducing different operating modes and switching constraints between modes, and to determine whether the optimization target is met according to the current state and predicted state of the prediction optimization model, so as to dynamically adjust the control variables and output the switching mode; The second control module is used to obtain the output torque and output power of the corresponding module and the total required torque of the blower system according to the corresponding module driven after the mode switching, and input them into the neural network to obtain the optimal output torque of each disc motor unit.

9. An electronic device, comprising: Memory and processor; The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the blower unit control method of the multi-disk axial flux motor described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the blower unit control method of the multi-disk axial flux motor according to any one of claims 1 to 7.

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