Large water pump cooperative control method and system based on axial flux motor

Abstract

The invention discloses a large-scale water pump cooperative control method and system based on an axial flux motor, and relates to the technical field of large-scale water pump cooperative control based on the axial flux motor, and the method comprises the steps: selecting the number of running motors and the distribution torque of each motor at different moments based on the maximum water lifting amount and a high-efficiency optimization operation target; establishing an objective function and constraint conditions for describing an optimization operation problem according to requirements; and optimizing a solution algorithm of the operation objective function, and solving the complex nonlinear objective function with constraints by adopting a simulated annealing intelligent optimization algorithm. According to the method, the water lifting amount of the pumping system can be maximized, and the photovoltaic utilization rate can be effectively improved. By optimizing operation of the pumping system, energy output of photovoltaic power generation can be more effectively utilized, and the overall energy utilization efficiency and economical efficiency are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of axial flux motors, and in particular to a large-scale water pump coordinated control method and system based on axial flux motors. Background Art

[0002] At present, my country is accelerating the construction of a new energy structure that is safe, efficient, clean and low-carbon. Photovoltaic water pump technology, as one of the most promising technologies in the field of solar energy applications, has always been a hot topic of research at home and abroad. At present, the research on photovoltaic water pump systems is mainly based on a photovoltaic array driving a pump, with a simple control method. The focus of the research has always been on the optimization of photovoltaic water pump systems with a single structure, including the capacity selection of photovoltaic arrays, the optimization of maximum power point tracking algorithms, and the design of efficient water pump motors. Although the optimization of single-machine systems has been very effective, there are still many problems for large-scale photovoltaic water pumping systems: when the light is weak, the speed of the water pump often cannot reach the minimum speed for water discharge. Even if the energy is consumed, the water cannot be pumped out, resulting in a waste of array power; when the light is strong, the energy provided by the array exceeds the rated power of the motor, resulting in the motor being unable to fully utilize the excess array energy. Under different light intensity and temperature conditions, the electric power provided by the photovoltaic array varies greatly. In addition, since the start-up of the photovoltaic water pump requires a minimum sunlight intensity, i.e., the water pumping threshold, it is inevitable that the single machine will not fully utilize energy when light load or overloaded, thus reducing the overall efficiency of the system.

[0003] In order to solve the problem of insufficient energy utilization of a single pump in a single system under both light load and overload, the same array energy can be distributed to multiple motors, and the number of running motors can be reasonably allocated. When the array power is small, a smaller number of motors are operated. When the array power increases, the number of running motors is gradually increased, so that energy can be reasonably distributed among multiple motors, which is conducive to increasing the system's water pumping capacity and thus improving the overall efficiency of the system. In order to meet high-power requirements, the present invention selects an axial flux permanent magnet synchronous motor, also known as a disc motor, in which both the stator and rotor are disc-shaped flat structures. The stator and rotor are arranged equidistantly so that the stator winding has better heat dissipation space. Multiple sets of stators and rotors are cascaded to form a modular multi-disc motor, which can not only reduce the power level and withstand voltage value of the switching devices used, but also reduce the current level of a single module. Existing research is only aimed at optimizing the distribution of single-stage torque between multiple motors. Modular motors have higher structural complexity and more control freedom. There are multiple multi-level considerations such as efficiency optimization control of each stator unit in the module, coordinated efficiency optimization control between different stator units in the module and different modules. At the same time, it is necessary to take into account the different working conditions of high-power water pumps for comprehensive torque distribution. These are the key technologies of the current high-power modular axial flux permanent magnet synchronous motor drive system. Therefore, proposing and studying the photovoltaic water pump system with multiple motors and its coordinated control strategy is of vital significance to the application and promotion of photovoltaic water pump systems. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

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

[0006] Therefore, the technical problem solved by the present invention is that under different light intensity and temperature conditions, the electric power provided by the photovoltaic array varies greatly, and since the start-up of the photovoltaic water pump requires the minimum sunlight intensity, i.e., the water pumping threshold, it will inevitably cause insufficient energy utilization of the single machine when light load and overload, thereby reducing the overall efficiency.

[0007] To solve the above technical problems, the present invention provides the following technical solutions: a large-scale water pump collaborative control method based on axial flux motors, which includes selecting the number of motors in operation and the allocated torque of each motor at different times based on the optimized operation goals of maximum water pumping and high efficiency; establishing the objective function and constraints describing the optimized operation problem according to the needs; optimizing the solution algorithm of the operation objective function, and using the simulated annealing intelligent optimization algorithm to solve the complex nonlinear objective function with constraints.

[0008] As a preferred solution of the large-scale water pump coordinated control method based on axial flux motor of the present invention, wherein: the selection of the number of motors in operation and the allocated torque of each motor at different times includes:

[0009] Set switching boundaries, divide different working modes and expand the motor's high-efficiency operating range;

[0010] Assume that the minimum starting shaft power of group i modular motors is Pi,min (i=1,2,3), and the maximum shaft power of group i modular motors running at rated speed is P i,max (i=1,2,3), the power generation of the photovoltaic array is P pv , when working normally, there are three sets of axial flux permanent magnet synchronous motor switching principles:

[0011] When P pv <P 1,mi At n, the single motor does not start and the water pump does not start;

[0012] When P 1,min ≤P pv <P 2,min When the power generation of the photovoltaic array is consumed, only a single set of axial flux permanent magnet synchronous motors can be started;

[0013] When P 2,min ≤P pv <P 1,max When the power generation of the photovoltaic array reaches the minimum power of the two modular motors when starting, it does not reach the maximum power of a single motor when running at rated speed;

[0014] When P 1,max ≤P pv <P 3,min When the double motors are started, they consume P pv The same speed operation, that is, Ppv is evenly distributed on the two groups of motors;

[0015] When P 3,min ≤P pv <P 2,max When the Ppv is consumed by only two motors, the speed of the two motors can be run to the rated speed;

[0016] When P 2,max ≤P pv ≤P 3,max When the three motors are started, they consume P pv The same speed operation, that is, Ppv is evenly distributed on the three groups of motors;

[0017] When P 3,max <P pvWhen the light radiation intensity S continues to increase, the photovoltaic power generation system cannot output more power and can only run at P 3,max Output electrical energy.

[0018] As a preferred solution of the large-scale water pump coordinated control method based on axial flux motor described in the present invention, the objective function and constraint conditions describing the optimization operation problem established according to the requirements include:

[0019] Use simulated annealing optimization algorithm to automate the decision-making process and dynamically adjust the torque allocation coefficient according to real-time power and torque requirements;

[0020] Take a single modular motor as an example, assuming the power when running at rated speed is P N , the torque is T N , where the shaft powers of the two equivalent motors are P1 and P2, and the distributed torques are T1 and T2, respectively. a is the power proportion of equivalent motor 1, i.e., the torque distribution coefficient. The equivalent motor operating torque ratio is k. When the speed is constant, there exists:

[0021] P1=αP pv

[0022] P2=(1-α)P pv

[0023]

[0024] Taking the maximum water lifting capacity as the goal, the objective function of the design optimization operation is:

[0025]

[0026] Among them, E(Q,k) is the objective function, a i (i=1,2,3) and b i (i=1,2,3,4) is the fitting coefficient, Q is the water lift of the pump, H ST Expressed as the required net head of the pipeline, S P It represents the pipe resistance coefficient between the motor and the water pump, and m represents the number of equivalent motors;

[0027] In order to reduce the amount of solution calculation and avoid the situation where the pump is in a serious off-condition operation due to speed changes, the operating flow range of the pump must be limited. Therefore, the constraint conditions are set as follows:

[0028] m·k·Q min ≤Q≤m·k·Q max

[0029] 0<k<1.

[0030] As a preferred solution of the large-scale water pump coordinated control method based on axial flux motor described in the present invention, the algorithm for solving the optimization operation objective function includes:

[0031] An optimization intelligent algorithm based on simulated annealing is used to accurately solve the objective function;

[0032] Solid annealing is used to simulate combinatorial optimization problems, internal energy is simulated as the objective function value, and temperature is simulated as the control parameter, thus obtaining a simulated annealing algorithm for solving combinatorial optimization problems.

[0033] As a preferred solution of the large-scale water pump coordinated control method based on axial flux motor described in the present invention, the solving of the complex nonlinear objective function with constraints includes:

[0034] Combine empirical rules and historical data to ensure that the generated initial solution has certain advantages in the feasible solution space of the problem, effectively find a suitable starting point for the complex system for subsequent optimization and adjustment; use the following formula to generate the initial solution based on rules and data:

[0035] k=w1·g1+w2·g2

[0036] Among them, g1 represents trend analysis based on historical data, g2 represents a randomly generated initial solution, and w1 and w2 are weight coefficients used to adjust the importance and contribution of empirical rules and historical data;

[0037] Then a disturbance is generated. If the new solution found is closer to the optimal solution than the previous one, it will be replaced. If the solution found is farther from the optimal solution, in order to prevent the optimization algorithm from falling into the local optimum due to multiple extreme points of the function, this solution is accepted with a certain probability instead of being discarded directly. The probability is:

[0038]

[0039] Where n represents the number of iterations, E(n) represents the current objective function value, E(n+1) represents the next objective function value, and T represents the temperature. The temperature scheduling strategy in the simulated annealing algorithm determines the exploration ability and convergence of the search space. The adaptive temperature scheduling strategy dynamically adjusts the rate of temperature drop according to the current system state. If the rate of change of the objective function value is large, it indicates that the search is still in progress. At this time, the rate of temperature drop is reduced to increase the coverage of the search space. If the rate of change is small, the rate of temperature drop is accelerated to accelerate convergence to the local optimal solution.

[0040] For each temperature drop iteration, record the state changes at the current temperature, including the change of the current solution and the rate of change of the objective function value. Adjust the drop rate according to the rate of change of the objective function value and the system response:

[0041]

[0042] Among them, Var(f k ) represents the change rate of the objective function value in the current iteration; Var(f k ) is the initial change rate of the objective function value, which is estimated through the results of the previous several iterations; b is an adjustment parameter used to balance the adjustment amplitude of the temperature drop rate; according to the current temperature T(k) and the calculated drop rate, update the temperature T(k + 1) of the next iteration:

[0043] T(k + 1) = λ k T(k)

[0044] When E(n + 1) < E(n) no longer appears after several iterations or the temperature reaches the set value, the simulated annealing process ends, and the solution k of the finally obtained function is the optimal torque ratio that maximizes the water pumping volume of the water pump. The torque distribution coefficients of each equivalent motor in each group of modular motors are calculated through the torque ratio k.

[0045] Another object of the present invention is to provide a system for collaborative control of a large water pump based on an axial flux motor, which can select the number of operating motors and the distributed torque of each motor at different times based on the optimization operation objectives of maximum water pumping volume and high efficiency;

[0046] Establish the objective function and constraint conditions for describing the optimization operation problem according to the requirements;

[0047] For the solution algorithm of the optimization operation objective function, a simulated annealing intelligent optimization algorithm is used to solve the constrained complex nonlinear objective function, solving the problem of low efficiency of the current collaborative control of large water pumps based on axial flux motors.

[0048] As a preferred embodiment of the system for collaborative control of a large water pump based on an axial flux motor according to the present invention, it includes: a selection module, a establishment module, and a solution module;

[0049] The selection module is used to select the number of operating motors and the distributed torque of each motor at different times based on the optimization operation objectives of maximum water pumping volume and high efficiency;

[0050] The establishment module is used to establish the objective function and constraint conditions for describing the optimization operation problem according to the requirements;

[0051] The solution module is used for the solution algorithm of the optimization operation objective function, and a simulated annealing intelligent optimization algorithm is used to solve the constrained complex nonlinear objective function.

[0052] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement a step of a large water pump coordinated control method based on an axial flux motor.

[0053] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a large-scale water pump cooperative control method based on an axial flux motor.

[0054] Beneficial effects of the present invention: The present invention focuses on the use of high-power modular axial flux permanent magnet synchronous motors, and conducts in-depth research on the optimization of the synergistic efficiency within the modules and between different modules. This motor technology has the characteristics of high efficiency and precise control, and is particularly suitable for pumping systems requiring high performance. Emphasizing the optimization of the synergistic efficiency between different modules and stator units, this design allows the system to dynamically allocate driving force according to actual needs, ensuring that each motor module is in the best operating state, thereby improving the overall system efficiency and reliability. For this purpose, a double-layer collaborative optimization strategy is designed to optimize the operation strategy of the pumping system, focusing on improving the overall energy conversion efficiency of the system. Through precise control and simulated annealing optimization algorithms, it is ensured that the system can operate in the best state under different working conditions, and make full use of the power output of photovoltaic power generation, thereby increasing the water pumping capacity of the water pump. The optimization strategy proposed in the present invention can not only maximize the water pumping capacity of the pumping system, but also effectively improve the photovoltaic utilization rate. By optimizing the operation of the pumping system, the system can more effectively utilize the energy output of photovoltaic power generation, significantly improving the overall energy utilization efficiency and economy. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0056] Figure 1 This is a basic structural diagram of a multi-motor cascade photovoltaic water pumping system.

[0057] Figure 2 Schematic diagram of the simulated annealing algorithm flow.

[0058] Figure 3 The figure is a schematic diagram of the overall process.

[0059] Figure 4 A schematic diagram of a computer device. DETAILED DESCRIPTION

[0060] In order to make the above-mentioned objects, 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 accompanying drawings.

[0061] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0062] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0063] Example 1

[0064] Reference Figures 1 to 4 , which is an embodiment of the present invention, provides a large-scale water pump coordinated control method based on an axial flux motor, comprising:

[0065] The present invention provides a multi-disk axial flux permanent magnet synchronous motor efficiency optimization method for high-power water pumps. Three groups of cascaded modular axial flux permanent magnet synchronous motors are used to design a double-layer collaborative optimization strategy. The power saturation allocation strategy is adopted between modules. The allocation scheme is dynamically adjusted according to the actual operating conditions to cope with emergencies or changing working conditions, so that the system can maintain efficient operation under different working conditions. The simulated annealing algorithm is applied to the motor drive efficiency optimization calculation of the optimal torque allocation coefficient in the module, which can maximize the widening of the motor operation high efficiency range and increase the water pumping capacity of the water pump under the same solar photovoltaic conditions.

[0066] The research on the optimal operation of high-power water pumps with multi-motor cascade operation mainly includes the following three points:

[0067] (1) Study the optimal operation strategy of the pumping system with the goal of maximum water lift and high efficiency, and select the reasonable number of motors in operation and the allocated torque of each motor at different times;

[0068] (2) Establish the objective function and constraints that describe the optimization operation problem according to the requirements;

[0069] (3) Research the solution algorithm of the optimization operation objective function and use the simulated annealing intelligent optimization algorithm to solve the complex nonlinear objective function with constraints, in order to obtain the operating parameters of the pumping system with the maximum water lift under the same solar photovoltaic conditions.

[0070] like Figure 1 The figure shows the basic structure of a multi-motor cascaded photovoltaic water pumping system, which is mainly composed of a photovoltaic array, a photovoltaic water pumping dedicated inverter, three sets of cascaded modular axial flux permanent magnet synchronous motors, a high-power water pump, and a decision-making platform. The photovoltaic array is composed of multiple photovoltaic modules connected in series and parallel, and the energy of the entire system comes from the solar energy converted by the photovoltaic array; the output current of the photovoltaic array is connected to the photovoltaic water pump inverter after passing through the junction box, and the direct current generated by the solar module is converted into alternating current to drive the pump. With MPPT (maximum power point tracking), the photovoltaic water pump inverter can adjust the output frequency to control the speed of the pump; the motor converts the input electrical energy into mechanical energy to drive the water pump; the decision-making platform collects system operating parameters through the field bus, such as the array output voltage, current, number of motors put into operation and motor speed, and transmits them to the central controller to calculate the operating parameters of the pumping system. Finally, the calculated operating parameters, such as the number of motors put into operation and the torque distribution coefficient, are sent to each inverter through the field bus.

[0071] 1. Principles for determining the number of motors put into operation

[0072] Considering the torque demand caused by the change in the output power of the photovoltaic array, the modular axial flux permanent magnet synchronous motor can ensure that the system works in a high efficiency range by starting different numbers of modules. When the power is large, the torque demand is large, and two or three groups of axial flux permanent magnet synchronous motors can be started; when the power is small, the torque demand is small, and only a single group of axial flux permanent magnet synchronous motors can be started. By designing a reasonable switching boundary and dividing different working modes, the high-efficiency operation range of the motor can be widened, thereby increasing the water pumping capacity of the water pump.

[0073] S1: Based on the optimized operation goals of maximum pumping capacity and high efficiency, the number of motors in operation and the allocated torque of each motor are selected at different times.

[0074] Furthermore, the selecting the number of motors in operation and the allocated torque of each motor at different times includes:

[0075] Set switching boundaries, divide different working modes and expand the motor's high-efficiency operating range;

[0076] Assume that the minimum starting shaft power of group i modular motors is Pi,min (i=1,2,3), and the maximum shaft power of group i modular motors running at rated speed is P i,max (i=1,2,3), the power generation of the photovoltaic array is P pv , when working normally, there are three sets of axial flux permanent magnet synchronous motor switching principles:

[0077] When P pv <P 1,miAt n, the single motor does not start and the water pump does not start;

[0078] When P 1,min ≤P pv <P 2,min When the power generation of the photovoltaic array is consumed, only a single set of axial flux permanent magnet synchronous motors can be started;

[0079] When P 2,min ≤P pv <P 1,max When the power generation of the photovoltaic array reaches the minimum power of the two modular motors starting, it does not reach the maximum power of a single motor running at the rated speed. In order to ensure that each motor runs at its optimal working point and avoid overload or idleness of some motors, only a single motor runs at the speed of consuming Ppv to the rated speed, thereby improving overall efficiency and reliability.

[0080] When P 1,max ≤P pv <P 3,min When the double motors are started, they consume P pv The two motors can run at the same speed, that is, PPV is evenly distributed on the two groups of motors, which can avoid overuse of some motors and shorten their lifespan, and help improve the reliability and durability of the system;

[0081] When P 3,min ≤P pv <P 2,max In order to ensure the best working point and avoid overuse, only two motors are used to consume Ppv, so that the speed of both motors can reach the rated speed.

[0082] When P 2,max ≤P pv ≤P 3,max When the three motors are started, they consume P pv The same speed operation, that is, Ppv is evenly distributed on the three groups of motors;

[0083] When P 3,max <P pv When the light radiation intensity S continues to increase, the photovoltaic power generation system cannot output more power and can only run at P 3,max Output electrical energy.

[0084] It should be noted that the above steps are mainly used to select the working mode of modular motors in large water pump systems, which is the premise for the subsequent calculation of the torque distribution coefficient. Only when the working mode is determined can we know how many groups of motor modules are running and the power allocated to each group of motors, laying the foundation for the subsequent calculation of the equivalent motor distribution torque in the group. This part can dynamically select the number of motors to be started according to the actual load demand to avoid unnecessary motor operation and overload of a single motor. Compared with simple average distribution, it can only start the necessary motors at low loads, thereby improving the energy efficiency of the system, reducing heat loss and extending the life of the motor.

[0085] 2. Motor torque distribution principle

[0086] Modular motors have higher structural complexity and more control freedom, so there are multiple and multi-level considerations, such as efficiency optimization control of each stator unit within the module, and coordinated efficiency optimization control between different stator units within the module and between different modules. Each set of modular motors can be equivalent to two single-stator-single-rotor axial flux motors coaxially connected, with each set of stator windings independently controlled.

[0087] S2: Establish the objective function and constraints that describe the optimization operation problem according to the requirements.

[0088] Furthermore, the objective function and constraint conditions for describing the optimization operation problem are established according to the requirements, including:

[0089] Use simulated annealing optimization algorithm to automate the decision-making process and dynamically adjust the torque allocation coefficient according to the real-time power and torque demand to achieve the best system efficiency and pumping capacity;

[0090] Take a single modular motor as an example, assuming the power when running at rated speed is P N , the torque is T N , where the shaft powers of the two equivalent motors are P1 and P2, and the distributed torques are T1 and T2, respectively. a is the power proportion of equivalent motor 1, i.e., the torque distribution coefficient. The equivalent motor operating torque ratio is k. When the speed is constant, there exists:

[0091] P1=αP pv

[0092] P2=(1-α)P pv

[0093]

[0094] The design of the objective function is the core of the optimization problem. In the present invention, the maximum water lifting capacity is taken as the goal, and the objective function of the design optimization operation is:

[0095]

[0096] Among them, E(Q,k) is the objective function, a i (i=1,2,3) and b i (i=1,2,3,4) is the fitting coefficient, Q is the water lift of the pump, H ST Expressed as the required net head of the pipeline, S P It represents the pipe resistance coefficient between the motor and the water pump, and m represents the number of equivalent motors;

[0097] In order to reduce the amount of calculation and avoid the situation where the pump is in a serious deviation from the working condition due to the speed change, the operating flow range of the pump must be limited to ensure that the pump is always in a high-reliability operating state. At the same time, it is necessary to protect the motor to operate safely and stably. Therefore, the constraints are set as follows:

[0098] m·k·Q min ≤Q≤m·k·Q max

[0099] 0<k<1

[0100] It should be noted that, as can be seen from the above formula, the objective function is relatively complex and difficult to solve in a forward direction. It is difficult to solve it using traditional solution methods. Therefore, an optimization intelligent algorithm based on simulated annealing is used to accurately solve the objective function in the present invention.

[0101] S3: Optimize the solution algorithm of the running objective function and use the simulated annealing intelligent optimization algorithm to solve the complex nonlinear objective function with constraints.

[0102] Furthermore, the solution algorithm of the optimization operation objective function includes:

[0103] An optimization intelligent algorithm based on simulated annealing is used to accurately solve the objective function;

[0104] Solid annealing is used to simulate combinatorial optimization problems, internal energy is simulated as the objective function value, and temperature is simulated as the control parameter, thus obtaining a simulated annealing algorithm for solving combinatorial optimization problems.

[0105] Furthermore, the solving of the complex nonlinear objective function with constraints includes:

[0106] Combine empirical rules and historical data to ensure that the generated initial solution has certain advantages in the feasible solution space of the problem, effectively find a suitable starting point for the complex system for subsequent optimization and adjustment; use the following formula to generate the initial solution based on rules and data:

[0107] k=w1·g1+w2·g2

[0108] Among them, g1 represents the trend analysis based on historical data, g2 represents an initial solution randomly generated, and w1, w2 are weight coefficients used to adjust the importance and contribution of empirical rules and historical data;

[0109] Next, a perturbation is generated. If the newly found solution is closer to the optimal solution than the previous one, it is replaced; if the found solution is farther from the optimal solution, to prevent the optimization algorithm from falling into a local optimum due to the function having multiple extreme points, this solution is accepted with a certain probability instead of being directly discarded. This probability is:

[0110]

[0111] Among them, n represents the number of iterations, E(n) represents the current objective function value, E(n + 1) represents the next objective function value, T represents the temperature. The temperature scheduling strategy in the simulated annealing algorithm determines the exploration ability and convergence of the search space. The adaptive temperature scheduling strategy dynamically adjusts the rate of temperature decrease according to the current system state; if the change rate of the objective function value is large, it indicates that the search is still in progress. At this time, the rate of temperature decrease is reduced to increase the coverage of the search space; if the change rate is small, the rate of temperature decrease is accelerated to accelerate convergence to the local optimal solution;

[0112] For each iteration of temperature decrease, record the state changes at the current temperature, including the changes in the current solution and the change rate of the objective function value, and adjust the decrease rate according to the change rate of the objective function value and the system response:

[0113]

[0114] Among them, Var(f k ) represents the change rate of the objective function value in the current iteration; Var(f k ) is the initial change rate of the objective function value, estimated through the results of the previous several iterations; b is an adjustment parameter used to balance the adjustment amplitude of the temperature decrease rate; according to the current temperature T(k) and the calculated decrease rate, update the temperature T(k + 1) for the next iteration:

[0115] T(k + 1) = λ k T(k)

[0116] After several iterations, when E(n + 1) < E(n) no longer appears or the temperature reaches the set value, the simulated annealing process ends. The solution k of the finally obtained function is the optimal torque ratio that maximizes the water pumping volume of the water pump. The torque distribution coefficients of each equivalent motor in each group of modular motors are calculated through the torque ratio k.

[0117] It should be noted that the overall scheme of the present invention is as Figure 3As shown in the figure, in the decision-making platform, the power generation of the photovoltaic array is first simulated in the software according to the system operating parameters transmitted by the field bus, and the working mode of the system is determined according to the power distribution principle: mode 1, mode 2 or mode 3. The mode with the highest system operating efficiency is selected. Different working modes start different numbers of modules and determine the power distribution value of each group; then, taking the maximum pumping capacity as the optimization goal, the optimal parameters of the equivalent motors in each group of modular motors are calculated, and the optimization intelligent algorithm based on simulated annealing is used to accurately solve the objective function, and the torque distribution coefficient of each equivalent motor is obtained quickly and accurately.

[0118] In summary, the present invention focuses on the use of high-power modular axial flux permanent magnet synchronous motors, and conducts in-depth research on the optimization of the synergistic efficiency within the modules and between different modules. This motor technology has the characteristics of high efficiency and precise control, and is particularly suitable for pumping systems requiring high performance. Emphasizing the optimization of the synergistic efficiency between different modules and stator units, this design allows the system to dynamically allocate driving force according to actual needs, ensuring that each motor module is in the best operating state, thereby improving the overall system efficiency and reliability. For this purpose, a double-layer collaborative optimization strategy is designed to optimize the operation strategy of the pumping system, focusing on improving the overall energy conversion efficiency of the system. Through precise control and simulated annealing optimization algorithms, it is ensured that the system can operate in the best state under different working conditions, and make full use of the power output of photovoltaic power generation, thereby increasing the water pumping capacity of the water pump. The optimization strategy proposed in the present invention can not only maximize the water pumping capacity of the pumping system, but also effectively improve the photovoltaic utilization rate. By optimizing the operation of the pumping system, the system can more effectively utilize the energy output of photovoltaic power generation, significantly improving the overall energy utilization efficiency and economy.

[0119] The above is a schematic scheme of a large-scale water pump cooperative control method based on an axial flux motor in this embodiment. It should be noted that the technical scheme of the system for cooperative control of large-scale water pumps based on an axial flux motor and the technical scheme of the above-mentioned method for cooperative control of large-scale water pumps based on an axial flux motor belong to the same concept. The details not described in detail in the technical scheme of the large-scale water pump cooperative control system based on an axial flux motor in this embodiment can be referred to the description of the technical scheme of the above-mentioned method for cooperative control of large-scale water pumps based on an axial flux motor.

[0120] This embodiment also proposes a large-scale water pump cooperative control system based on an axial flux motor, including:

[0121] Select module, build module, solve module;

[0122] The selection module is used to select the number of motors in operation and the allocated torque of each motor at different times based on the optimized operation goals of maximum water lifting capacity and high efficiency;

[0123] The establishment module is used to establish the objective function and constraint conditions describing the optimization operation problem according to the requirements;

[0124] The solution module is used to optimize the solution algorithm of the running objective function, and adopts the simulated annealing intelligent optimization algorithm to solve the complex nonlinear objective function with constraints.

[0125] This embodiment further provides a computing device, which is applicable to the coordinated control of a large water pump based on an axial flux motor, and includes:

[0126] 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 large water pump collaborative control method based on axial flux motor as proposed in the above embodiment.

[0127] 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 realizing the coordinated control of a large water pump based on an axial flux motor as proposed in the above embodiment is implemented.

[0128] The storage medium proposed in this embodiment and the method for realizing coordinated control of a large water pump based on an axial flux motor proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0129] 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, but in many cases the former is a better implementation method. 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 (RandomAccess 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.

[0130] Example 2

[0131] Reference Figure 2 , which is the second embodiment of the present invention, provides a large water pump collaborative control method based on an axial flux motor, which is verified through experiments.

[0132] In order to verify the effectiveness of this scheme, the total flow and efficiency based on the power average distribution strategy before optimization are compared to ensure that the total power of the water pump is 3.0kW and the rated power of each modular motor is 1.5kW. Table 1 shows the experimental results of the water pump under different control strategies. The comparison shows that the control system based on the proposed optimization strategy has a larger total flow and higher efficiency, which verifies the superiority of the optimization strategy in maximizing the water lift of the pumping system and improving efficiency.

[0133] Table 1 Experimental results

[0134]

[0135] Due to its higher structural complexity and more control freedom, modular motors have multiple and multi-level considerations, such as efficiency optimization control of each stator unit within the module, coordinated efficiency optimization control between different stator units within the module and between different modules. At the same time, it is necessary to take into account the different working conditions of high-power water pumps for comprehensive torque distribution. These are the key technologies of the current high-power modular axial flux permanent magnet synchronous motor drive system.

[0136] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0137] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0138] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0139] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

[0140] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. 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 large-scale water pump coordinated control method based on an axial flux motor, characterized in that: include, Based on the optimized operation goals of maximum water lifting capacity and high efficiency, the number of motors in operation and the allocated torque of each motor are selected at different times; Establish the objective function and constraints that describe the optimization operation problem according to the requirements; The solution algorithm of the optimized running objective function adopts the simulated annealing intelligent optimization algorithm to solve the complex nonlinear objective function with constraints.

2. The large-scale water pump coordinated control method based on the axial flux motor according to claim 1 is characterized in that: The selecting the number of motors in operation and the allocated torque of each motor at different times includes: Set switching boundaries, divide different working modes and expand the motor's high-efficiency operating range; Assume that the minimum starting shaft power of group i modular motors is Pi,min (i=1,2,3), and the maximum shaft power of group i modular motors running at rated speed is P i,max (i=1,2,3), the power generation of the photovoltaic array is P pv , when working normally, there are three sets of axial flux permanent magnet synchronous motor switching principles: When P pv <P 1,mi At n, the single motor does not start and the water pump does not start; When P 1,min ≤P pv <P 2,min When the power generation of the photovoltaic array is consumed, only a single set of axial flux permanent magnet synchronous motors can be started; When P 2,min ≤P pv <P 1,max When the power generation of the photovoltaic array reaches the minimum power of the two modular motors when starting, it does not reach the maximum power of a single motor when running at rated speed; When P 1,max ≤P pv <P 3,min When the double motors are started, they consume P pv The same speed operation, that is, Ppv is evenly distributed on the two groups of motors; When P 3,min ≤P pv <P 2,max When the Ppv is consumed by only two motors, the speed of the two motors can be run to the rated speed; When P 2,max ≤P pv ≤P 3,max When the three motors are started, they consume P pv The same speed operation, that is, Ppv is evenly distributed on the three groups of motors; When P 3,max <P pv When the light radiation intensity S continues to increase, the photovoltaic power generation system cannot output more power and can only run at P 3,max Output electrical energy.

3. The large-scale water pump coordinated control method based on the axial flux motor according to claim 2 is characterized in that: The objective function and constraint conditions for describing the optimization operation problem according to the requirements include: Use simulated annealing optimization algorithm to automate the decision-making process and dynamically adjust the torque allocation coefficient according to real-time power and torque requirements; Take a single modular motor as an example, assuming the power when running at rated speed is P N , the torque is T N , where the shaft powers of the two equivalent motors are P1 and P2, and the distributed torques are T1 and T2, respectively. a is the power proportion of equivalent motor 1, i.e., the torque distribution coefficient. The equivalent motor operating torque ratio is k. When the speed is constant, there exists: P1=αP pv P2=(1-α)P pv Taking the maximum water lifting capacity as the goal, the objective function of the design optimization operation is: Among them, E(Q,k) is the objective function, a i (i=1,2,3) and b i (i=1,2,3,4) is the fitting coefficient, Q is the water lift of the pump, H ST Expressed as the required net head of the pipeline, S P It represents the pipe resistance coefficient between the motor and the water pump, and m represents the number of equivalent motors; In order to reduce the amount of solution calculation and avoid the situation where the pump is in a serious off-condition operation due to speed changes, the operating flow range of the pump must be limited. Therefore, the constraint conditions are set as follows: m·k·Q min ≤Q≤m·k·Q max 0＜k＜1。 4. The large-scale water pump coordinated control method based on the axial flux motor according to claim 3 is characterized in that: The algorithm for solving the objective function of the optimization operation includes: An optimization intelligent algorithm based on simulated annealing is used to accurately solve the objective function; Solid annealing is used to simulate combinatorial optimization problems, internal energy is simulated as the objective function value, and temperature is simulated as the control parameter, thus obtaining a simulated annealing algorithm for solving combinatorial optimization problems.

5. The large-scale water pump coordinated control method based on the axial flux motor according to claim 4 is characterized in that: The solving of the complex nonlinear objective function with constraints includes: Combining empirical rules and historical data to ensure that the generated initial solution has a certain advantage in the feasible solution space of the problem, effectively finding a suitable starting point for the complex system for subsequent optimization and adjustment; Use the following formula to generate an initial solution based on rules and data: k=w1·g1+w2·g2 Among them, g1 represents the trend analysis based on historical data, g2 represents an initial solution generated randomly, and w1 and w2 are weight coefficients used to adjust the importance and contribution of empirical rules and historical data; Then a disturbance is generated. If the new solution found is closer to the optimal solution than the previous one, it will be replaced. If the solution found is farther from the optimal solution, in order to prevent the optimization algorithm from falling into the local optimum due to multiple extreme points of the function, this solution is accepted with a certain probability instead of being discarded directly. The probability is: Where n represents the number of iterations, E(n) represents the current objective function value, E(n+1) represents the next objective function value, and T represents the temperature. The temperature scheduling strategy in the simulated annealing algorithm determines the exploration ability and convergence of the search space. The adaptive temperature scheduling strategy dynamically adjusts the rate of temperature drop according to the current system state. If the rate of change of the objective function value is large, it indicates that the search is still in progress. At this time, the rate of temperature drop is reduced to increase the coverage of the search space. If the rate of change is small, the rate of temperature drop is accelerated to accelerate convergence to the local optimal solution. For each temperature drop iteration, record the state changes at the current temperature, including the change of the current solution and the rate of change of the objective function value. Adjust the drop rate according to the rate of change of the objective function value and the system response: Among them, Var(f k ) represents the rate of change of the objective function value in the current iteration; Var(f k ) is the initial rate of change of the objective function value, which is estimated by the results of the previous iterations; b is an adjustment parameter used to balance the adjustment amplitude of the temperature drop rate; according to the current temperature T(k) and the calculated drop rate, the temperature T(k+1) of the next iteration is updated: T(k+1)=λ k T(k) After several iterations, when E(n + 1) < E(n) no longer appears or the temperature reaches the set value, the simulated annealing process ends. The solution k of the finally obtained function is the optimal torque ratio that maximizes the water pumping volume of the water pump. The torque distribution coefficients of each equivalent motor in each group of modular motors are calculated through the torque ratio k.

6. A system using the large-scale water pump coordinated control method based on an axial flux motor as claimed in any one of claims 1 to 5, characterized in that: It includes a selection module, a construction module, and a solution module; The selection module is used to select the number of operating motors and the allocated torque of each motor at different times based on the optimization operation objectives of maximum water pumping volume and high efficiency; The construction module is used to establish the objective function and constraint conditions for describing the optimization operation problem according to requirements; The solution module is used to optimize the solution algorithm of the operation objective function and solve the complex non-linear objective function with constraints by using the simulated annealing intelligent optimization algorithm.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the large water pump collaborative control method based on the axial flux motor according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the large water pump collaborative control method based on the axial flux motor according to any one of claims 1 to 5.

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