Frequency conversion control method, device and equipment of circulating water system

Abstract

The invention discloses a frequency conversion control method, device and equipment of a circulating water system and a storage medium, relates to the technical field of control of the circulating water system, and can enable a parameter prediction model of the circulating water system to output control parameters with higher adaptability to external disturbance. The method comprises the following steps: performing flow field modeling according to multi-source characteristic parameters acquired by a circulating water system in real time, and extracting a critical air turbidity margin and an efficiency flow inflection point when a variable frequency pump and a constant speed pump run in parallel according to a simulation result of a flow field model; performing global optimization on set control parameters in the circulating water system by taking the critical air turbidity allowance as a first objective function for realizing energy consumption minimization and the efficiency flow inflection point as a second objective function for realizing equipment service life maximization, and generating a constraint condition set of the set control parameters; and controlling the parameter prediction model to output target control parameters meeting the constraint condition set in a future time period by adopting a model prediction control method.

Description

Technical Field

[0001] This application relates to the technical field of the control of circulating water systems, and particularly to a variable-frequency control method, device, equipment and storage medium for a circulating water system. Background Art

[0002] With the increasing demand for deep peak shaving in the power system, the variable-frequency control technology of circulating water systems has become an important means to achieve energy conservation and consumption reduction and improve the flexibility of units. Currently, the variable-frequency control strategies of circulating water systems are mainly implemented through methods such as PID regulation, segmented control, constant temperature difference / constant pressure difference control, and multi-pump collaborative control.

[0003] Among them, PID control adjusts the output frequency of the frequency converter by feedback of the temperature, pressure or flow deviation of the circulating water. Although it can maintain the steady-state operation of the system, its algorithm has poor adaptability in multi-variable coupling scenarios (such as load fluctuations and environmental temperature changes), and is prone to response lag and adjustment oscillation, resulting in insufficient control accuracy under dynamic conditions. Segmented control divides the operation range based on the unit load and environmental temperature, and realizes regulation by switching the combination mode of variable-frequency pumps and constant-speed pumps. However, this strategy relies on manual experience to set the interval threshold, and it is difficult to respond to the continuous changes of complex working conditions in real time, resulting in low energy efficiency in some load intervals. Constant temperature difference / constant pressure difference control dynamically adjusts the speed of the variable-frequency pump to maintain the target temperature difference or pressure difference. Although it can optimize energy consumption, it lacks the modeling of the dynamic characteristics of the pipeline flow field and is prone to deviate from the optimal operating point under external disturbances (such as sudden changes in wind speed). Although multi-pump collaborative control coordinates the start-stop and frequency allocation of multiple pumps through a logic controller, when variable-frequency pumps and constant-speed pumps operate in parallel, uneven flow distribution is likely to cause cavitation, vibration and efficiency reduction problems, and it requires manual intervention to adjust, and the automation level of the system is limited. Generally speaking, the existing variable-frequency control process of circulating water systems still has significant technical limitations in practical applications and is difficult to meet the requirements of efficient and stable operation under complex working conditions. Summary of the Invention

[0004] In view of this, this application provides a variable-frequency control method, device, equipment and storage medium for a circulating water system, mainly aiming to solve the problems of insufficient adaptability of the existing variable-frequency control strategy to multi-variable coupling systems and poor dynamic response lag, resulting in poor regulation stability.

[0005] According to the first aspect of this application, a variable-frequency control method for a circulating water system is provided, including:

[0006] Performing a flow field modeling based on multi-source characteristic parameters collected in real time by the circulating water system, so as to simulate the fluid flow state in the circulating water system through the flow field model;

[0007] According to the simulation results of the flow field model, extract the critical cavitation margin and the efficiency flow inflection point when the variable-frequency pump and the constant-speed pump operate in parallel;

[0008] Take the critical cavitation margin as the first objective function to minimize energy consumption and the efficiency flow inflection point as the second objective function to maximize the equipment life, and globally optimize the set control parameters in the circulating water system to generate a set of constraint conditions for the set control parameters;

[0009] Pre-construct a parameter prediction model for the circulating water system, and use the model predictive control method to control the parameter prediction model to output the target control parameters that meet the set of constraint conditions in the future time period.

[0010] Further, perform flow field modeling based on the multi-source characteristic parameters collected in real time from the circulating water system to simulate the fluid flow state in the circulating water system through the flow field model, including:

[0011] Perform flow field modeling using a preset physical model according to the flow characteristics of each component in the circulating water system to establish a control equation describing fluid flow through the preset physical model;

[0012] Apply the multi-source characteristic parameters collected in real time from the circulating water system to the control equation to dynamically update the boundary conditions of the control equation;

[0013] Use the finite volume method to solve and iterate the control equation with dynamically updated boundary conditions to simulate the fluid flow state in the circulating water system through the output parameters of the control equation.

[0014] Further, after performing flow field modeling using a preset physical model according to the flow characteristics of each component in the circulating water system to establish a control equation describing fluid flow through the preset physical model, the method further includes:

[0015] According to the complexity of each component in the circulating water system, select a suitable grid type to mesh the preset physical model to obtain a model grid. For the complex components in the circulating water system, unstructured grids are used, and for the non-complex components, structured grids are used;

[0016] Locally encrypt the key areas in the grid model by locally refining the grid, where the key areas are the areas with complex fluid flow in the circulating water system.

[0017] Further, after performing flow field modeling based on the multi-source characteristic parameters collected in real time from the circulating water system to simulate the fluid flow state in the circulating water system through the flow field model, the method further includes:

[0018] Mark the damage-sensitive areas according to the simulation results of the flow field model, where the damage-sensitive areas include the areas in the circulating water system that are prone to local damage.

[0019] Collect the pressure loss data for the damage-sensitive areas to correct the boundary conditions of the flow field model based on the collected pressure loss data.

[0020] Further, the extraction of the critical cavitation margin and the efficiency flow inflection point when the variable-frequency pump and the constant-speed pump operate in parallel according to the simulation results of the flow field model includes:

[0021] Set the combined simulation conditions when the constant-speed pump and the variable-frequency pump operate in parallel according to the simulation results of the flow field model.

[0022] Extract the cavitation monitoring parameters of the key points based on the combined simulation conditions, and determine the critical cavitation margin when the variable-frequency pump and the constant-speed pump operate in parallel according to the cavitation monitoring parameters; and

[0023] Extract the flow monitoring parameters of the key points based on the combined simulation conditions, and determine the efficiency flow inflection point when the variable-frequency pump and the constant-speed pump operate in parallel according to the flow monitoring parameters.

[0024] Further, taking the critical cavitation margin as the first objective function to minimize energy consumption and the efficiency flow inflection point as the second objective function to maximize the equipment life, globally optimize the set control parameters in the circulating water system to generate a set of constraint conditions for the set control parameters, including:

[0025] Taking the critical cavitation margin as the first objective function to minimize energy consumption and the efficiency flow inflection point as the second objective function to maximize the equipment life, combine the first objective function and the second objective function using the fitness function to obtain a multi-objective function.

[0026] Use the genetic algorithm to map the set control parameters in the circulating water to chromosome individuals with variable constraint ranges, and iteratively adjust the variable constraint ranges corresponding to the chromosome individuals based on the multi-objective function to generate a set of constraint conditions for the set control parameters.

[0027] Further, pre-construct a parameter prediction model for the circulating water system, and use the model predictive control method to control the parameter prediction model to output the target control parameters that meet the set of constraint conditions in the future time period, including:

[0028] Pre-construct a parameter prediction model for the circulating water system with the ambient temperature, wind speed, and unit load as input variables and the set value of the variable-frequency pump frequency, the start / stop state of the constant-speed pump, and the valve opening as output variables.

[0029] On the basis of pre - defining a weighted evaluation function, iteratively solve the target control parameters output by the parameter prediction model in a future time period, where the weighted evaluation function has a weight coefficient for dynamically regulating the mapping relationship between input variables and output variables in the parameter prediction model;

[0030] If the target control parameters output by the parameter prediction model in a future time period are not within the numerical range of the condition constraint set, adaptively adjust the weight coefficient of the weighted evaluation function to correspondingly adjust the mapping relationship between input variables and output variables in the parameter prediction model;

[0031] If the target control parameters output by the parameter prediction model in a future time period are within the numerical range of the condition constraint set, use the target control parameters that meet the constraint condition set to perform variable - frequency control on the circulating water system.

[0032] According to the second aspect of the present application, a variable - frequency control device for a circulating water system is provided, including:

[0033] A modeling unit, configured to perform a flow field modeling according to multi - source characteristic parameters collected in real - time by the circulating water system, so as to simulate the fluid flow state in the circulating water system through a flow field model;

[0034] An extraction unit, configured to extract the critical cavitation margin and the efficiency - flow inflection point when a variable - frequency pump and a constant - speed pump operate in parallel according to the simulation result of the flow field model;

[0035] A generation unit, configured to use the critical cavitation margin as the first objective function for minimizing energy consumption and the efficiency - flow inflection point as the second objective function for maximizing equipment life, globally optimize the set control parameters in the circulating water system, and generate a constraint condition set for the set control parameters;

[0036] A control unit, configured to pre - construct a parameter prediction model of the circulating water system, and use a model predictive control method to control the parameter prediction model to output target control parameters that meet the constraint condition set in a future time period.

[0037] Furthermore, the modeling unit is specifically configured to:

[0038] Perform flow field modeling using a preset physical model according to the flow characteristics of each component in the circulating water system, so as to establish a control equation for describing fluid flow through the preset physical model;

[0039] Apply the multi - source characteristic parameters collected in real - time by the circulating water system to the control equation, and dynamically update the boundary conditions of the control equation;

[0040] The finite volume method is used to solve and iterate the control equation with dynamically updated boundary conditions, so as to simulate the fluid flow state in the circulating water system through the output parameters of the control equation.

[0041] Further, the device further includes:

[0042] A mesh generation unit, which is used to perform flow field modeling using a preset physical model according to the flow characteristics of each component in the circulating water system. After establishing a control equation describing fluid flow through the preset physical model, a suitable mesh type is selected to perform mesh generation on the preset physical model according to the complexity of each component in the circulating water system, obtaining a model mesh. Unstructured meshes are used for complex components in the circulating water system, and structured meshes are used for non-complex components;

[0043] A local refinement unit, which is used to locally refine the key areas in the mesh model through local mesh refinement. The key areas are areas with complex fluidity in the circulating water system.

[0044] Further, the device further includes:

[0045] A marking unit, which is used to perform flow field modeling according to the multi-source characteristic parameters collected in real time by the circulating water system. After simulating the fluid flow state in the circulating water system through the flow field model, the damage-sensitive areas are marked according to the simulation results of the flow field model. The damage-sensitive areas include areas in the circulating water system that are prone to local damage;

[0046] A correction unit, which is used to collect pressure loss data for the damage-sensitive areas, so as to correct the boundary conditions of the flow field model through the collected pressure loss data.

[0047] Further, the extraction unit is specifically further used for:

[0048] Setting a combined simulation condition when a constant-speed pump and a variable-frequency pump operate in parallel according to the simulation results of the flow field model;

[0049] Extracting cavitation monitoring parameters of key points on the basis of the combined simulation condition, and determining the critical cavitation margin when the variable-frequency pump and the constant-speed pump operate in parallel according to the cavitation monitoring parameters; and

[0050] Extracting flow rate monitoring parameters of key points on the basis of the combined simulation condition, and determining the efficiency flow rate inflection point when the variable-frequency pump and the constant-speed pump operate in parallel according to the flow rate monitoring parameters.

[0051] Further, the generation unit is specifically further used for:

[0052] Taking the critical net positive suction head as the first objective function for minimizing energy consumption and the efficiency flow inflection point as the second objective function for maximizing equipment life, a multi-objective function is obtained by combining the first objective function and the second objective function using a fitness function.

[0053] The genetic algorithm is used to map the set control parameters in the circulating water into chromosome individuals with variable constraint ranges. Based on the multi-objective function, the variable constraint ranges corresponding to the chromosome individuals are iteratively adjusted to generate a set of constraint conditions for the set control parameters.

[0054] Further, the control unit is specifically configured to:

[0055] Taking the ambient temperature, wind speed, and unit load as input variables, and the frequency set value of the variable-frequency pump, the start-stop state of the constant-speed pump, and the valve opening as output variables, a parameter prediction model of the circulating water system is pre-constructed.

[0056] Based on a pre-defined weighted evaluation function, the target control parameters output by the parameter prediction model in a future time period are iteratively solved. The weighted evaluation function has a weight coefficient for dynamically regulating the mapping relationship between the input variables and the output variables in the parameter prediction model.

[0057] If the target control parameters output by the parameter prediction model in a future time period are not within the numerical range of the condition constraint set, the weight coefficient of the weighted evaluation function is adaptively adjusted to correspondingly adjust the mapping relationship between the input variables and the output variables in the parameter prediction model.

[0058] If the target control parameters output by the parameter prediction model in a future time period are within the numerical range of the condition constraint set, the variable-frequency control of the circulating water system is performed using the target control parameters that satisfy the constraint set.

[0059] According to the third aspect of the present application, a computer device is provided, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the method described in the first aspect above are implemented.

[0060] According to the fourth aspect of the present application, a readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method described in the first aspect above are implemented.

[0061] With the above technical solution, a variable-frequency control method, device, equipment, and storage medium for a circulating water system provided by this application, compared with the current prior art that performs variable-frequency control on the circulating water system through methods such as PID regulation, segmented control, constant temperature difference / constant pressure difference control, and multi-pump coordinated control, this application performs flow field modeling based on multi-source characteristic parameters collected in real time by the circulating water system to simulate the fluid flow state in the circulating water system through the flow field model. According to the simulation results of the flow field model, the critical cavitation margin and the efficiency flow inflection point when the variable-frequency pump and the constant-speed pump operate in parallel are extracted; using the critical cavitation margin as the first objective function to minimize energy consumption and the efficiency flow inflection point as the second objective function to maximize the equipment life, the set control parameters in the circulating water system are globally optimized to generate a constraint condition set for the set control parameters; a parameter prediction model of the circulating water system is pre-constructed, and the model predictive control method is used to control the parameter prediction model to output the target control parameters that meet the constraint condition set in the future time period. The entire process performs flow field modeling through the circulating water system, can incorporate multi-source characteristic parameters, improve the adaptability of the variable-frequency control strategy to the multi-variable coupling system, and combine the critical cavitation margin and the efficiency flow inflection point when the fixed-frequency pump and the constant-speed pump operate in parallel to increase the dynamic flow balance mechanism of the equipment operating in coordination, enabling the parameter prediction model of the circulating water system to output control parameters with stronger adaptability to external disturbances.

[0062] The above description is only an overview of the technical solution of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features, and advantages of this application more obvious and understandable, the following specifically gives the specific implementation manners of this application. Brief Description of the Drawings

[0063] The drawings described herein are used to provide a further understanding of this application and constitute a part of this application. The schematic embodiments of this application and their descriptions are used to explain this application and do not constitute an improper limitation to this application. In the drawings:

[0064] Figure 1 is a schematic flowchart of the variable-frequency control method for the circulating water system in an embodiment of this application;

[0065] Figure 2 is Figure 1 a schematic flowchart of a specific implementation manner of step 101 in

[0066] Figure 3 is Figure 1 another schematic flowchart of a specific implementation manner of step 101 in

[0067] Figure 4 is a schematic flowchart of the variable-frequency control method for the circulating water system in another embodiment of this application;

[0068] Figure 5 is Figure 1 A schematic flow chart of a specific embodiment of step 102 in

[0069] Figure 6 is Figure 1 A schematic flow chart of a specific embodiment of step 103 in

[0070] Figure 7 is Figure 1 A schematic flow chart of a specific embodiment of step 104 in

[0071] Figure 8 A schematic structural diagram of a variable-frequency control device for a circulating water system in an embodiment of the present application;

[0072] Figure 9 A schematic structural diagram of a device of a computer device provided by an embodiment of the present invention. Specific embodiments

[0073] Now, the content of the present invention will be described with reference to several exemplary embodiments. It should be understood that these embodiments are described only to enable those of ordinary skill in the art to better understand and thus implement the content of the present invention, rather than to imply any limitation to the scope of the present invention.

[0074] As used herein, the term "comprising" and its variants are to be construed as open-ended terms meaning "including but not limited to". The term "based on" is to be construed as "at least partially based on". The term "one embodiment" and "an embodiment" are to be construed as "at least one embodiment". The term "another embodiment" is to be construed as "at least one other embodiment".

[0075] In the related art, the variable-frequency control strategy for the circulating water system is mainly achieved through methods such as PID regulation, segmented control, constant temperature difference / constant pressure difference control, and multi-pump coordinated control. Among them, PID control adjusts the output frequency of the frequency converter by feedback of the deviation of the circulating water temperature, pressure, or flow rate. Although it can maintain the steady-state operation of the system, its algorithm has poor adaptability in multi-variable coupling scenarios (such as load fluctuations and ambient temperature changes), and is prone to response lag and adjustment oscillation, resulting in insufficient control accuracy under dynamic conditions. Segmented control divides the operating range based on the unit load and ambient temperature, and realizes regulation by switching the combination mode of variable-frequency pumps and constant-speed pumps. However, this strategy relies on manual experience to set the interval threshold, and it is difficult to respond to the continuous changes of complex working conditions in real time, resulting in low energy efficiency in some load intervals. Constant temperature difference / constant pressure difference control dynamically adjusts the speed of the variable-frequency pump to maintain the target temperature difference or pressure difference. Although it can optimize energy consumption, it lacks the modeling of the dynamic characteristics of the pipeline flow field and is prone to deviate from the optimal operating point under external disturbances (such as sudden wind speed changes). Although multi-pump coordinated control coordinates the start-stop and frequency allocation of multiple pumps through a logic controller, when variable-frequency pumps and constant-speed pumps operate in parallel, uneven flow distribution is likely to cause problems such as cavitation, vibration, and efficiency decline, and it requires manual intervention to adjust, resulting in limited system automation level. Generally speaking, the variable-frequency control process of the existing circulating water system still has significant technical limitations in practical applications and is difficult to meet the requirements of efficient and stable operation under complex working conditions. Specifically, it is reflected in the following aspects: 1) The control algorithm has insufficient adaptability to multi-variable coupling systems, and the dynamic response is lagging, resulting in poor regulation stability; 2) Energy efficiency optimization relies on static interval division and cannot match the working condition changes in real time, causing energy waste; 3) The lack of a dynamic flow balance mechanism for the coordinated operation of equipment increases the risk of equipment wear; 4) The system has insufficient robustness, is sensitive to external disturbances, and reduces control reliability. These problems have become the key technical bottlenecks restricting the efficient operation of the variable-frequency circulating water system and urgently need to be broken through through control strategy innovation.

[0076] To solve this problem, this embodiment provides a variable-frequency control method for a circulating water system, as Figure 1 shown, including the following steps:

[0077] 101. Perform a flow field modeling based on the multi-source characteristic parameters collected in real time by the circulating water system, so as to simulate the fluid flow state in the circulating water system through the flow field model.

[0078] A circulating water system is a facility for industrial cooling or process water recycling. By continuously recycling water resources, it reduces energy consumption and waste. Its core function is to transfer the heat generated by the equipment to the cooling tower or other heat dissipation devices through the water cycle for cooling, and then re-transport the cooled water to the equipment to form a closed-loop cycle. The main components include but are not limited to: variable-frequency pumps and constant-frequency pumps that provide water flow power, cooling towers, heat exchangers, pipes and valves, water storage tanks / pools, etc.

[0079] Among them, the multi-source characteristic parameters collected in real time by the circulating water system include but are not limited to environmental parameters, temperature parameters, unit load parameters, pressure parameters, and flow parameters, etc. Specifically, the environmental temperature T, wind speed V, unit load L, differential pressure ΔP between the inlet and outlet of the circulating water pipeline, and instantaneous flow rate Q can be collected in real time through temperature sensors, pressure transmitters, flow meters, anemometers, and unit load monitoring devices arranged in the circulating water system. It can be understood that the sensors arranged in the circulating water system cover key parameters such as temperature, pressure, flow rate, liquid level, water quality, and vibration, forming an all-round monitoring of the circulating water system, enabling the circulating water system to achieve efficient operation, fault prevention, and resource conservation.

[0080] In this embodiment, during the process of performing flow field modeling based on the multi-source characteristic parameters collected in real time by the circulating water system, preprocessing can be first performed on the multi-source characteristic parameters, including but not limited to data synchronization and fusion. This process can align the timestamps for multi-source characteristic parameters with different sampling frequencies, map the sensor positions to the geometric model coordinate system, and establish the association between the physical space and the data space. Then, a three-dimensional geometric model is constructed for the preprocessed multi-source characteristic parameters to retain the key features of the circulating water system. According to the characteristics of the circulating water system, a suitable physical model is selected, and the key features of the circulating water system are used as boundary conditions and dynamically mapped into the physical model. By solving the physical model, the field variable parameters of the circulating water system are obtained, and the fluid flow state in the circulating water system is simulated based on the field variable parameters.

[0081] It can be understood that the solution process of the flow field modeling is to solve the control equations of the physical model to obtain field variable parameters such as velocity, pressure, and turbulence. These simulation results can all be used to simulate the flow state through visualization and analysis tools (such as streamline diagrams, pressure contour maps, and vorticity diagrams).

[0082] The execution subject of this embodiment can be the variable-frequency control device or equipment of the circulating water system, and a server for the power plant circulating water system can be configured. By performing flow field modeling based on the multi-source characteristic parameters collected in real time by the circulating water system, multi-parameter coupling can be achieved. Combining dynamic visualization and quantitative analysis, the flow state of the circulating water system can be accurately simulated.

[0083] 102. Extract the critical cavitation margin and the efficiency flow inflection point when the variable-frequency pump and the constant-speed pump operate in parallel according to the simulation results of the flow field model.

[0084] It can be understood that in the simulation results of the flow field model, a flow field model including the variable-frequency pump, constant-speed pump, pipeline, and key equipment in the circulating water system is established, and the fixed speed of the constant-speed pump and the speed range of the variable-frequency pump are correspondingly defined. By adjusting the variable-frequency pump, the flow rate distribution of the constant-speed pump and the variable-frequency pump under different working conditions can be recorded, and different working condition combinations of the constant-speed pump and the variable-frequency pump in the circulating water system can be simulated.

[0085] For the critical cavitation margin during the parallel operation of a variable-frequency pump and a constant-speed pump, specifically, according to the simulation results of the flow field model, the operating parameters of the combined pump under different working conditions can be extracted. When the operating parameters of the combined pump meet the critical conditions, the critical cavitation margin during the parallel operation of the variable-frequency pump and the constant-speed pump is determined.

[0086] It should be noted that when the speed of the variable-frequency pump increases, its flow rate increases, and the flow rate of the constant-speed pump is suppressed, resulting in a decrease in the inlet flow rate of the constant-speed pump and a corresponding reduction in the effective cavitation margin, generating a flow-pressure coupling effect. Here, a relationship curve between the critical cavitation margin and the flow rate can be established, and the offset of the critical cavitation margin during parallel operation can be calibrated to achieve dynamic correction of the critical cavitation margin.

[0087] For the efficiency-flow inflection point during the parallel operation of a variable-frequency pump and a constant-speed pump, specifically, according to the simulation results of the flow field model, the operating parameters of the combined pump under different working conditions can be extracted. The comprehensive efficiency can be extracted based on the operating parameters of the combined pump. The flow rate parameters and the comprehensive efficiency of the combined pump under different working conditions are fitted to establish an efficiency-flow curve, and the derivative of the efficiency-flow curve is obtained to get the efficiency-flow inflection point during the parallel operation of the variable-frequency pump and the constant-speed pump.

[0088] 103. Taking the critical cavitation margin as the first objective function to minimize energy consumption and the efficiency-flow inflection point as the second objective function to maximize the equipment life, globally optimize the set control parameters in the circulating water system to generate a set of constraint conditions for the set control parameters.

[0089] In this example, the set control parameters include but are not limited to the frequency range of the variable-frequency pump, the start-stop threshold of the constant-speed pump, and the flow distribution balance coefficient during the parallel operation of multiple pumps. Here, the frequency adjustment of the variable-frequency pump will affect its speed, thereby affecting the flow rate and head, and also affecting the energy consumption and equipment wear. The start-stop threshold of the constant-speed pump determines when to start or stop the constant-speed pump, which will affect the energy consumption of the entire system and the switching frequency of the pump, thus affecting the life. The flow distribution balance coefficient may be used to control the load distribution during the parallel operation of multiple pumps. Balanced distribution may reduce the wear of a single pump, but may affect the overall efficiency.

[0090] It can be understood that when combining the set control parameters with the objective functions, minimizing energy consumption can be modeled by calculating the total power consumption of the pump, while maximizing the life requires considering factors such as the operating time, start-stop times, and load rate of the pump.

[0091] Specifically, a genetic algorithm is used to globally optimize the set control parameters in the circulating water system. The design of this genetic algorithm includes a coding scheme, a fitness function, selection, crossover, and mutation operations. For coding, the above-mentioned set control parameters need to be encoded into chromosomes through variable encoding. For example, the frequency range of the variable-frequency pump consists of two variables, f_min and f_max. The start-stop thresholds of the constant-speed pump are Q_start and Q_stop. The flow distribution balance coefficient β is a value between 0 and 1. Here, it is necessary to determine the feasible range and coding method of each variable. Then, the two objective functions are combined through the fitness function, and the method of multi-objective optimization can be used here. At the same time, the penalty function method or the constraint domination method can be used for the constraint conditions. The dynamic constraints need to be adjusted according to the current system state. For example, under different flow demands, the constraint conditions may be different, and this requires dynamically generating a set of constraint conditions for the set control parameters in the algorithm.

[0092] 104. A parameter prediction model of the circulating water system is pre-constructed, and a model predictive control method is used to control the parameter prediction model to output target control parameters that meet the set of constraint conditions in a future time period.

[0093] In this embodiment, for the parameter prediction model of the circulating water system, the input variables are the environmental temperature T, the wind speed V, and the unit load L, and the output variables are the variable-frequency pump frequency f_set, the start-stop state S_on / S_off of the constant-speed pump, and the valve opening θ_valve. Specifically, the state-space modeling method can be used to construct the parameter prediction model.

[0094] It can be understood that the core of the model predictive control method is that in each control period, based on the parameter prediction model, the system behavior in the future period of time is predicted, the optimal control sequence is solved by optimizing the weighted evaluation function, and then the corresponding control sequence is executed. Here, the constraint conditions of the circulating water system need to be considered, such as the frequency range of the variable-frequency pump, the limit of the valve opening, and the start-stop state of the constant-speed pump.

[0095] Specifically, in the process of using the model predictive control method to control the output variables of the parameter prediction model in a future time period, an adaptive weighted evaluation function can be pre-set. Different parameters in this weighted evaluation function have weight coefficients. By jumping the weight coefficients, the mapping relationship between the input variables and the output variables in the parameter prediction model can be changed. The parameter prediction model is embedded into the method framework of model predictive control. In each control period, the optimization problem of the weighted evaluation function is solved. The optimization goal is to control the parameter prediction model to output target control parameters that meet the set of constraint conditions in a future time period. If the optimization goal is not achieved, the mapping relationship between the input variables and the output variables in the parameter prediction model is adaptively adjusted by adjusting the weight coefficients of the weighted evaluation function to achieve variable-frequency control of the circulating water system.

[0096] The variable-frequency control method for the circulating water system provided by the embodiment of the present application, compared with the current prior art that performs variable-frequency control on the circulating water system through methods such as PID regulation, segmented control, constant temperature difference / constant pressure difference control, and multi-pump collaborative control, the present application performs flow field modeling based on multi-source characteristic parameters collected in real time by the circulating water system to simulate the fluid flow state in the circulating water system through the flow field model. According to the simulation results of the flow field model, the critical cavitation margin and the efficiency flow inflection point when the variable-frequency pump and the constant-speed pump operate in parallel are extracted; using the critical cavitation margin as the first objective function to minimize energy consumption and the efficiency flow inflection point as the second objective function to maximize the equipment life, the set control parameters in the circulating water system are globally optimized to generate a set of constraint conditions for the set control parameters; a parameter prediction model of the circulating water system is pre-constructed, and the model predictive control method is used to control the parameter prediction model to output the target control parameters that meet the set of constraint conditions in the future time period. The entire process performs flow field modeling through the circulating water system, can incorporate multi-source characteristic parameters, improve the adaptability of the variable-frequency control strategy to the multi-variable coupling system, and combine the critical cavitation margin and the efficiency flow inflection point when the fixed-frequency pump and the constant-speed pump operate in parallel to increase the dynamic flow balance mechanism of the equipment in collaborative operation, enabling the parameter prediction model of the circulating water system to output control parameters with stronger adaptability to external disturbances.

[0097] In the actual application scenario, considering the influence of the flow field characteristics on the performance of the circulating water system, by performing flow field modeling on the circulating water system, the optimal design of the components in the circulating water system can be realized. Specifically, as Figure 2 shown, step 101 includes the following steps:

[0098] 201. Use a preset physical model to perform flow field modeling according to the flow characteristics of each component in the circulating water system, so as to establish a control equation describing fluid flow through the preset physical model.

[0099] 202. Apply the multi-source characteristic parameters collected in real time by the circulating water system to the control equation to dynamically update the boundary conditions of the control equation.

[0100] 203. Use the finite volume method to solve and iterate the control equation with dynamically updated boundary conditions, so as to simulate the fluid flow state in the circulating water system through the output parameters of the control equation.

[0101] In this embodiment, each component in the circulating water system may include but is not limited to pipelines, centrifugal pumps, cooling towers, etc. Considering the differences between different components, different components may require different models. Specifically, different preset physical models can be used according to the flow characteristics of each component in the circulating water system. Here, the preset physical models can include turbulence models, multiphase flow models, etc. For example, a turbulence model is used for pipelines and centrifugal pumps, and a multiphase flow model is used for cooling towers. Then, control equations describing fluid flow are established according to the corresponding preset physical models.

[0102] It can be understood that considering the relationship between the multi-source characteristic parameters collected in real time by the circulating water system and the variables in the control equations. For example, the flow rate parameter may be related to the velocity term in the momentum conservation equation, and the temperature parameter may be related to the heat transfer term in the energy conservation equation. Specifically, the multi-source characteristic parameters and the variables of the control equations can be analyzed for correlation to determine the specific application of the multi-source characteristic parameters in the control equations. Further, the obtained correlation relationship is substituted into the control equations to dynamically update the boundary conditions of the control equations.

[0103] For example, if the inlet flow rate collected in real time changes, the new flow rate value is updated into the inlet boundary condition; if there is a new measured value of the outlet temperature, this value is used to replace the original outlet temperature boundary condition value.

[0104] In this embodiment, the circulating water system used to describe fluid flow usually involves continuity equations, momentum equations, energy equations, etc. Specifically, in the process of solving and iterating the control equations with dynamically updated boundary conditions using the finite volume method, the space where the circulating water system is located can be divided into a series of non-overlapping grids, and the grids are discretized to obtain discrete equations. The variable values of each grid in the discrete equations are updated in a certain order. In each iteration, the currently updated values of adjacent grids are used to calculate the variables of the current grid, and the discrete equations are repeatedly solved to update the variable values of each grid. After each iteration, the convergence condition is checked. When the maximum residual of all control equations is less than a preset threshold, the iteration is considered to converge. After the iteration converges, the control equations output the required physical quantities to simulate the fluid flow state in the circulating water system, such as velocity field, pressure field, temperature field, etc.

[0105] In actual application scenarios, considering the different complexities of each component in the circulating water system, for components with relatively high flow velocities, turbulence may be formed. Correspondingly, at parts such as elbows and valves in pipelines, local pressure losses and flow separation phenomena will occur. Further, as Figure 3 shown, after step 201, the method further includes the following steps:

[0106] 301. Select a suitable mesh type to mesh the preset physical model according to the complexity of each component in the circulating water system, and obtain a model mesh.

[0107] 302. Locally encrypt the key areas in the mesh model by locally refining the mesh.

[0108] Among them, unstructured meshes are used for complex components in the circulating water system, such as triangular and tetrahedral meshes. Structured meshes are used for non-complex components, such as Cartesian meshes. Here, the complexity can be determined according to the system geometry and flow characteristics.

[0109] It can be understood that the mesh units corresponding to the structured mesh are arranged regularly, with high computational accuracy and efficiency, and are suitable for simple geometries, such as straight pipes. Unstructured meshes can adapt to complex geometries, such as valves and pump bodies, but the computational efficiency is relatively low. For the circulating water system, a combination of structured and unstructured meshes is usually adopted, with structured meshes used in the straight pipe section and unstructured meshes used in the complex component section. Specifically, a mesh generation software can be used to mesh the preset physical model. When meshing, the size and quality of the mesh need to be considered. Generally speaking, the mesh size should be reasonably selected according to the flow characteristics and computational accuracy requirements.

[0110] In this embodiment, the key areas are the areas with complex fluidity in the circulating water system, usually including pipe elbows, valves, pump inlets and outlets, pipe branches, etc. The flow characteristics in these areas are complex, and phenomena such as flow separation and vortices are likely to occur, which have a great impact on the accuracy of the simulation results. Therefore, in the key areas, the mesh needs to be encrypted to improve the simulation accuracy.

[0111] Specifically, the method of locally refining the mesh can be used to reduce the mesh size in the key areas. For example, unstructured meshes are used to locally encrypt the pipe elbow and valve necking areas, and the mesh size is not greater than 1 / 10 of the pipe diameter.

[0112] In the actual application scenario, considering that each component in the circulating water system has different complexities, for components with relatively high flow velocities, turbulence may be formed, and correspondingly, local pressure losses and flow separation phenomena will occur at pipe elbows, valves, etc. Further, as Figure 4 shown, after step 101, the method further includes the following steps:

[0113] 401. Mark the damage-sensitive areas according to the simulation results of the flow field model.

[0114] 402. Collect pressure loss data for the damage-sensitive area to correct the boundary conditions of the flow field model based on the collected pressure loss data.

[0115] In this embodiment, the damage-sensitive area includes the areas in the circulating water system that are prone to local damage. Specifically, the damage-sensitive area can be identified through the following methods. The first is the flow velocity distribution. For high-flow-velocity areas, especially where the flow velocity changes violently, such as the narrow section and elbow of the pipeline, the scouring effect of the fluid on the wall is stronger, which is likely to cause local damage and can be marked as a damage-sensitive area. The second is the pressure distribution. For areas with a large pressure gradient, there is a large pressure difference, which may cause cavitation and damage to the equipment, and these areas should also be marked. The third is the eddy current and turbulent flow areas. Eddy currents and turbulent flows will increase the energy loss of the fluid and the shear force on the wall, making the equipment more vulnerable to damage, and these areas are determined as damage-sensitive areas. Further, for the convenience of marking, in the visualization graph of the flow field model, the damage-sensitive area can be clearly identified through methods such as color marking and line outlining for subsequent targeted data collection.

[0116] Correspondingly, according to the actual situation of the circulating water system and the measurement accuracy requirements, select a suitable pressure measurement device to collect pressure loss data for the damage-sensitive area to ensure that the range and accuracy of the measurement device can meet the requirements for collecting pressure loss data in the damage-sensitive area. Specifically, the marked damage-sensitive area can be accurately installed with a pressure sensor or differential pressure transmitter according to the installation requirements of the measurement device. For example, install pressure sensors at different positions of the pipeline to measure the upstream and downstream pressure values to calculate the pressure loss in this area. When the circulating water system is operating normally, start the corresponding measurement device to collect the pressure loss data of the damage-sensitive area in real time.

[0117] Furthermore, the actual pressure loss value of the damage-sensitive area can be calculated through the pressure loss data collected from the damage-sensitive area. By comparing and analyzing the actual pressure loss value of the damage-sensitive area with the pressure loss result obtained from the simulation of the flow field model, the correction scheme for the boundary conditions of the flow field model can be determined. For example, if the actually measured pressure loss is greater than the simulation result, it may be necessary to adjust boundary conditions such as the inlet flow velocity, increase the fluid viscosity, or modify the outlet pressure to make the flow field model closer to the actual situation.

[0118] Specifically, as Figure 5 shown, step 102 includes the following steps:

[0119] 501. Set the combined simulation conditions when the constant-speed pump and variable-frequency pump operate in parallel according to the simulation results of the flow field model.

[0120] 502. Extract the cavitation monitoring parameters of key points based on the combined simulation conditions, and determine the critical cavitation margin when the variable-frequency pump and the constant-speed pump operate in parallel according to the cavitation monitoring parameters.

[0121] 503. Extract the flow monitoring parameters of key points based on the combined simulation conditions, and determine the efficiency flow inflection point when the variable-frequency pump and the constant-speed pump operate in parallel according to the flow monitoring parameters.

[0122] In this embodiment, based on the simulation results of the flow field model, the distribution of flow rate and pressure in the circulating water system can be obtained, and the flow rate requirements and pressure change trends in different regions of the circulating water system can be determined. For example, which parts require a large flow rate to ensure normal operation, and which places are more sensitive to pressure stability. According to the simulation results of the flow field model, the operating point positions of the constant-speed pump and the variable-frequency pump when operating alone can also be viewed, including parameters such as flow rate and head.

[0123] It can be understood that the speed of the constant-speed pump is usually fixed, but the adjustable range of its flow rate and head can be determined according to its performance curve. Considering parameters such as the rated flow rate, rated head, and allowable maximum and minimum operating flow rates of the constant-speed pump can be used as the basis for setting the combined conditions. The speed of the variable-frequency pump can be adjusted within a certain range. According to its variable-frequency adjustment ability and performance curve, the adjustment range of its speed is determined. At the same time, the corresponding flow rate and head change ranges at different speeds are determined. For example, the speed of the variable-frequency pump can be adjusted between the lowest speed (such as 30% of the rated speed) and the highest speed (100% of the rated speed), and the corresponding flow rate and head will also change within a certain range.

[0124] Specific combined simulation conditions when the constant-speed pump and the variable-frequency pump operate in parallel may include but are not limited to the following: The first is to fix the flow rate of the constant-speed pump and adjust the flow rate of the variable-frequency pump. First, set the constant-speed pump to operate at a certain fixed flow rate, and then gradually adjust the speed of the variable-frequency pump to change its flow rate from the lowest flow rate to the highest flow rate, and set a series of different operating condition points. The second is to fix the flow rate of the variable-frequency pump and adjust the flow rate of the constant-speed pump, which is the opposite of the first method. This method is to first set the variable-frequency pump to operate at a certain fixed flow rate, and then adjust the operating state of the constant-speed pump to set different combined conditions. The third is to adjust the flow rates of both the constant-speed pump and the variable-frequency pump simultaneously. According to the actual requirements of the system and the performance of the pumps, the flow rates of both the constant-speed pump and the variable-frequency pump are adjusted simultaneously. For example, increase or decrease the flow rates of both at a certain proportional relationship to set multiple different operating condition combinations.

[0125] It should be noted that in each combined operating condition, in addition to setting the flow rate parameter of the pump, other relevant parameters need to be considered, such as the head, power, efficiency, etc. of the pump. At the same time, according to the characteristics of the flow field model, other boundary conditions of the system are set, such as the inlet pressure, outlet flow rate, etc., to ensure the rationality of the operating condition setting.

[0126] Specifically, on the basis of the combined simulation operating conditions, the cavitation monitoring parameters of key points are extracted. In the process of determining the effective cavitation margin when the variable-frequency pump and the constant-speed pump operate in parallel according to the cavitation monitoring parameters, the key points can be the inlet of the pump and the area near the impeller. These positions are where cavitation is likely to occur because when the fluid enters the pump and passes through the impeller, the pressure and flow velocity change greatly, which may cause the local pressure to drop below the vaporization pressure of the liquid, thus triggering cavitation. The key points can also be the narrow sections and elbows of the pipeline. At these positions, the flow velocity of the fluid will increase and the pressure will decrease, and cavitation is also likely to occur. The cavitation monitoring parameters can include pressure parameters, flow velocity parameters, and bubble volume fraction. After completing the calculation of the combined simulation operating conditions, the simulation results of the flow field model can be used to extract the change data of cavitation monitoring parameters such as pressure, flow velocity, and bubble volume fraction at the key points over time or other variables. Further, according to the working principle of the pump and the theory of fluid mechanics, the theoretical cavitation margin is calculated using relevant formulas. According to factors such as the type of pump and the shape of the impeller, corresponding correction factors are introduced to correct the theoretical calculation results to obtain a preliminary theoretical critical cavitation margin value closer to the actual situation. If it is monitored that the pressure at the key point is close to the vaporization pressure and the pressure pulsation is large, it indicates that the actual cavitation risk increases, and the theoretical critical cavitation margin value needs to be appropriately increased. When the bubble volume fraction reaches a certain proportion, or the bubble frequency increases significantly, it indicates that cavitation is already relatively serious, and the theoretical critical cavitation margin should be adjusted downward according to the actual monitoring data to ensure the pump operates within a safe range.

[0127] Specifically, on the basis of the combined simulation operating conditions, the flow rate monitoring parameters of key points are extracted. In the process of determining the efficiency-flow rate curve when the variable-frequency pump and the constant-speed pump operate in parallel according to the flow rate monitoring parameters, the key points representative of the pump operating state and the system flow rate can be determined according to the characteristics of the circulating water system and the flow field model. For example, the inlet and outlet of the pump, pipeline elbows, branch points, etc. The flow rate changes at these positions can reflect the operating characteristics of the pump and the system. After completing the calculation of the combined simulation operating conditions, the simulation results of the flow field model can be used to extract the flow rate data of each key point, including instantaneous flow rate and average flow rate, etc. For different flow rate values under each combined operating condition, the corresponding pump efficiency is calculated, and the extracted flow rate monitoring parameters are matched with the calculated efficiency data to form a series of data points, and the efficiency-flow rate curve is correspondingly plotted. Further, the position of the efficiency-flow rate inflection point is determined by the derivative method.

[0128] Specifically, as Figure 6 shown, step 103 includes the following steps:

[0129] 601. Take the critical net positive suction head as the first objective function for minimizing energy consumption, and the efficiency-flow inflection point as the second objective function for maximizing equipment life. Use a fitness function to combine the first objective function and the second objective function to obtain a multi-objective function.

[0130] 602. Use a genetic algorithm to map the set control parameters in the circulating water into chromosome individuals with variable constraint ranges. Based on the multi-objective function, iteratively adjust the variable constraint ranges corresponding to the chromosome individuals to generate a set of constraint conditions for the set control parameters.

[0131] In a circulating water system, there is usually a certain relationship between the critical net positive suction head and energy consumption. Generally speaking, to avoid cavitation, the system will maintain a certain net positive suction head, but too high a net positive suction head may mean higher energy consumption. Correspondingly, operating near the efficiency-flow inflection point, the working state of the pump is relatively stable, and the wear of the equipment is small, which is beneficial to extending the equipment life.

[0132] It can be understood that the role of the fitness function is to combine the two objective functions according to a certain weight to balance the two objectives of minimizing energy consumption and maximizing equipment life. Let ω1 and ω2 be the weights of the first objective function and the second objective function respectively, and ω1 + ω2 = 1, 0 ≤ ω1, ω2 ≤ 1. The multi-objective function F can be defined as: F = ω1·f1 + ω2·f2. Where f1 is the first objective function and f2 is the second objective function. The values of ω1 and ω2 can be adjusted according to actual needs and importance. If more attention is paid to minimizing energy consumption, the value of ω1 can be appropriately increased; if more attention is paid to equipment life, the value of ω2 is increased.

[0133] Among them, the set control parameters in the circulating water include the frequency range f of the variable-frequency pump min ≤f≤f max , the start-stop threshold Q of the constant-speed pump start ,Q stop and the flow distribution balance coefficient β of multiple pumps in parallel.

[0134] Define Q var as the flow rate of the variable-frequency pump, Q fix as the flow rate of the constant-speed pump. The above flow distribution balance coefficient β can be calculated by the following formula:

[0135]

[0136] In this embodiment, the set control parameters in the circulating water system are encoded as chromosome individuals. Suppose there are n control parameters in total, and each parameter has its corresponding variable constraint range. The real number encoding method can be adopted. Each chromosome consists of n genes, and each gene corresponds to the value of a control parameter.

[0137] Specifically, in the process of iteratively adjusting the variable constraint range corresponding to the chromosome individuals to generate a set of constraint conditions for the set control parameters, a certain number (set as N) of chromosome individuals are randomly generated to form the initial population P0. The gene values of each individual are randomly generated within their corresponding variable constraint ranges to ensure the diversity of the initial population. For each chromosome individual in the population, it is decoded into the actual control parameter value, and then substituted into the multi-objective function to calculate the function value. The parameters in the multi-objective function are parameters related to the critical cavitation margin and the efficiency flow inflection point obtained by means of flow field simulation, pump performance calculation, etc. according to the current control parameter values. Since the multi-objective function needs to consider both energy consumption minimization and equipment life maximization simultaneously, the design of the fitness function should comprehensively reflect the achievement degrees of these two objectives. The fitness can be determined according to the proximity of the multi-objective function value to the ideal value. Then, genetic operations are performed on the chromosome individuals in the population, including selection, crossover, and mutation. In each generation of the population, the individual with the highest fitness is recorded. Statistical analysis is performed on the values of each control parameter in the optimal individuals of multiple generations. According to this statistical information, the variable constraint range of each control parameter can be adjusted. For example, if the value of a certain control parameter always concentrates in a certain part of its original constraint range in the optimal individuals of multiple generations, the constraint range of this part can be appropriately narrowed to improve the search efficiency; on the contrary, if the value of a certain control parameter is relatively evenly distributed within the original constraint range, the constraint range can be considered to be appropriately expanded. According to the analysis results, the variable constraint range of each control parameter is updated and used in subsequent encoding, crossover, and mutation operations. When the iteration termination condition is met, the individuals in the final population are analyzed, and the control parameter values corresponding to the optimal individual and their adjusted variable constraint ranges are sorted out to form a set of constraint conditions for the set control parameters. This set of constraint conditions reflects the reasonable value range and constraint relationship of the set control parameters of the circulating water system in the multi-objective optimization process considering both energy consumption minimization and equipment life maximization.

[0138] In an actual application scenario, as an implementation manner of the genetic algorithm, the population size is 50 - 100; the crossover probability is 0.7 - 0.9; the mutation probability is 0.01 - 0.05; the maximum number of iterations is 200 - 500.

[0139] Specifically, as Figure 7 shown, step 104 includes the following steps:

[0140] 701. Taking the ambient temperature, wind speed, and unit load as input variables, and the frequency set value of the variable-frequency pump, the start-stop state of the constant-speed pump, and the valve opening as output variables, a parameter prediction model of the circulating water system is pre-constructed.

[0141] 702. Based on a pre-defined weighted evaluation function, iteratively solve the target control parameters output by the parameter prediction model in the future time period.

[0142] 703. If the target control parameters output by the parameter prediction model in the future time period are not within the numerical range of the condition constraint set, adaptively adjust the weight coefficients of the weighted evaluation function to correspondingly adjust the mapping relationship between the input variables and output variables in the parameter prediction model.

[0143] 704. If the target control parameters output by the parameter prediction model in the future time period are within the numerical range of the condition constraint set, use the target control parameters that meet the constraint condition set to perform variable-frequency control on the circulating water system.

[0144] In this embodiment, the weighted evaluation function has weight coefficients that dynamically regulate the mapping relationship between the input variables and output variables in the parameter prediction model. The specific weighted evaluation function can be expressed by the following formula:

[0145]

[0146] where ω1, ω2, and ω3 are weight coefficients, P 能耗 is the total energy consumption of the pump group, T 寿命 is the predicted value of the equipment fatigue life, and σ 振动 is the standard deviation of vibration. The adjustment rules for the weight coefficients of the specific weighted evaluation function need to follow the following points:

[0147] (1) When the ambient temperature T > 35°C, ω3 (system stability weight) is increased to 0.5 - 0.6;

[0148] (2) When the unit load L < 50% of the rated load, ω1 (energy consumption weight) is reduced to 0.3, and ω2 (life weight) is increased to 0.5;

[0149] (3) The sum of the weight coefficients is always 1, that is, ω1 + ω2 + ω3 = 1.

[0150] It can be understood that the predicted target control parameters will be brought into the weighted evaluation function to calculate the current weighted evaluation function value. If the predicted target control parameters are not within the numerical range of the condition constraint set, the weight coefficient of the weighted evaluation function will be adjusted according to the above adjustment rules until the predicted target control parameters are within the numerical range of the condition constraint set or the preset number of iterations is reached. That is to say, when the target control parameters output by the parameter prediction model in the future time period are within the numerical range of the condition constraint set, these target control parameters that meet the constraint conditions are used to perform variable frequency control on the circulating water system. Specifically, the operating frequency of the variable frequency pump is adjusted according to the set value of the variable frequency pump frequency, the start or stop of the constant speed pump is controlled according to the start-stop state of the constant speed pump, and the opening of the valve is adjusted according to the set value of the valve opening to achieve the optimal operation of the circulating water system.

[0151] Further, as Figure 1-7 a specific implementation of the method, an embodiment of the present application provides a variable frequency control device for a circulating water system, as Figure 8 shown. The device includes: a modeling unit 81, an extraction unit 82, a generation unit 83, and a control unit 84.

[0152] The modeling unit 81 is used to perform a flow field modeling based on multi-source characteristic parameters collected in real time by the circulating water system, so as to simulate the fluid flow state in the circulating water system through the flow field model;

[0153] The extraction unit 82 is used to extract the critical cavitation margin and the efficiency flow inflection point when the variable frequency pump and the constant speed pump operate in parallel according to the simulation result of the flow field model;

[0154] The generation unit 83 is used to globally optimize the set control parameters in the circulating water system with the critical cavitation margin as the first objective function for minimizing energy consumption and the efficiency flow inflection point as the second objective function for maximizing the equipment life, and generate a constraint condition set for the set control parameters;

[0155] The control unit 84 is used to pre-construct a parameter prediction model of the circulating water system and use the model predictive control method to control the parameter prediction model to output target control parameters that meet the constraint condition set in the future time period.

[0156] The variable-frequency control device for the circulating water system provided by the embodiment of the present invention, compared with the prior art that uses PID regulation, segmented control, constant temperature difference / constant pressure difference control, multi-pump cooperative control, etc. to perform variable-frequency control on the circulating water system, in this application, a flow field model is established based on multi-source characteristic parameters collected in real time by the circulating water system to simulate the fluid flow state in the circulating water system through the flow field model. According to the simulation results of the flow field model, the critical cavitation margin and the efficiency flow inflection point when the variable-frequency pump and the constant-speed pump operate in parallel are extracted; using the critical cavitation margin as the first objective function to minimize energy consumption and the efficiency flow inflection point as the second objective function to maximize the equipment life, the set control parameters in the circulating water system are globally optimized to generate a constraint condition set for the set control parameters; a parameter prediction model of the circulating water system is pre-constructed, and the model predictive control method is used to control the parameter prediction model to output target control parameters that meet the constraint condition set in the future time period. The entire process performs flow field modeling through the circulating water system, which can incorporate multi-source characteristic parameters, improve the adaptability of the variable-frequency control strategy to the multi-variable coupling system, and combine the critical cavitation margin and the efficiency flow inflection point when the fixed-frequency pump and the constant-speed pump operate in parallel to increase the dynamic flow balance mechanism of the coordinated operation of the equipment, enabling the parameter prediction model of the circulating water system to output control parameters with stronger adaptability to external disturbances.

[0157] In a specific application scenario, the modeling unit is specifically used for:

[0158] Performing flow field modeling using a preset physical model according to the flow characteristics of each component in the circulating water system to establish a control equation describing fluid flow through the preset physical model;

[0159] Applying the multi-source characteristic parameters collected in real time by the circulating water system to the control equation to dynamically update the boundary conditions of the control equation;

[0160] Using the finite volume method to solve and iterate the control equation with dynamically updated boundary conditions to simulate the fluid flow state in the circulating water system through the output parameters of the control equation.

[0161] In a specific application scenario, the device further includes:

[0162] The mesh generation unit is used to, after performing flow field modeling using a preset physical model according to the flow characteristics of each component in the circulating water system to establish a control equation describing fluid flow through the preset physical model, select a suitable mesh type to perform mesh generation on the preset physical model according to the complexity of each component in the circulating water system to obtain a model mesh. Unstructured meshes are used for the complex components in the circulating water system, and structured meshes are used for the non-complex components.

[0163] A local encryption unit, which is used to locally encrypt the key areas in the mesh model through local refinement of the mesh, where the key areas are the areas with complex fluidity in the circulating water system.

[0164] In a specific application scenario, the device further includes:

[0165] A marking unit, which is used to mark the damage-sensitive areas according to the simulation results of the flow field model after performing flow field modeling based on the multi-source characteristic parameters collected in real time from the circulating water system to simulate the fluid flow state in the circulating water system through the flow field model, where the damage-sensitive areas include the areas in the circulating water system that are prone to local damage;

[0166] A correction unit, which is used to collect pressure loss data for the damage-sensitive areas to correct the boundary conditions of the flow field model through the collected pressure loss data.

[0167] In a specific application scenario, the extraction unit is specifically further used for:

[0168] Setting the combined simulation working conditions when the constant-speed pump and the variable-frequency pump operate in parallel according to the simulation results of the flow field model;

[0169] Extracting the cavitation monitoring parameters of key points based on the combined simulation working conditions, and determining the critical cavitation margin when the variable-frequency pump and the constant-speed pump operate in parallel according to the cavitation monitoring parameters; and

[0170] Extracting the flow monitoring parameters of key points based on the combined simulation working conditions, and determining the efficiency flow inflection point when the variable-frequency pump and the constant-speed pump operate in parallel according to the flow monitoring parameters.

[0171] In a specific application scenario, the generation unit is specifically further used for:

[0172] Taking the critical cavitation margin as the first objective function for minimizing energy consumption and the efficiency flow inflection point as the second objective function for maximizing the equipment life, and combining the first objective function and the second objective function using a fitness function to obtain a multi-objective function;

[0173] Using a genetic algorithm to map the set control parameters in the circulating water into chromosome individuals with variable constraint ranges, and iteratively adjusting the variable constraint ranges corresponding to the chromosome individuals based on the multi-objective function to generate a set of constraint conditions for the set control parameters.

[0174] In a specific application scenario, the control unit is specifically used for:

[0175] Taking the ambient temperature, wind speed, and unit load as input variables, and the frequency set value of the variable-frequency pump, the start-stop state of the constant-speed pump, and the valve opening as output variables, a parameter prediction model of the circulating water system is pre-constructed;

[0176] Based on a pre-defined weighted evaluation function, iteratively solve the target control parameters output by the parameter prediction model in a future time period, and the weighted evaluation function has a weight coefficient for dynamically regulating the mapping relationship between the input variables and the output variables in the parameter prediction model;

[0177] If the target control parameters output by the parameter prediction model in a future time period are not within the numerical range of the condition constraint set, adaptively adjust the weight coefficient of the weighted evaluation function to correspondingly adjust the mapping relationship between the input variables and the output variables in the parameter prediction model;

[0178] If the target control parameters output by the parameter prediction model in a future time period are within the numerical range of the condition constraint set, use the target control parameters that meet the constraint set to perform variable-frequency control on the circulating water system.

[0179] It should be noted that for other corresponding descriptions of each functional unit involved in the variable-frequency control device of a circulating water system provided in this embodiment, reference can be made to Figure 1 - Figure 7 the corresponding description in, and details will not be elaborated here.

[0180] Based on the method as described above in Figure 1 - Figure 7 Accordingly, an embodiment of the present application further provides a storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the variable-frequency control method of the circulating water system as described above in Figure 1 - Figure 5 shown.

[0181] Based on such an understanding, the technical solution of the present application can be embodied in the form of a software product, and this software product can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in various implementation scenarios of the present application.

[0182] Based on the method as described above in Figure 1 - Figure 7 shown, and Figure 8 the virtual device embodiment shown, in order to achieve the above object, an embodiment of the present application further provides an entity device for variable-frequency control of a circulating water system, which can specifically be a computer, a smart phone, a tablet computer, a smart watch, a server, or a network device, etc. This entity device includes a storage medium and a processor; the storage medium is used to store a computer program; the processor is used to execute the computer program to implement the method as described above in Figure 1 - Figure 7Frequency conversion control method for the shown circulating water system.

[0183] Optionally, the entity device may further include a user interface, a network interface, a camera, a radio frequency (RF) circuit, sensors, an audio circuit, a WI-FI module, and so on. The user interface may include a display, an input unit such as a keyboard, etc. Optionally, the user interface may further include a USB interface, a card reader interface, etc. The network interface may optionally include a standard wired interface, a wireless interface (such as a WI-FI interface), etc.

[0184] In an exemplary embodiment, referring to Figure 9 , the above entity device includes a communication bus, a processor, a memory, and a communication interface, and may further include an input / output interface and a display device. Among them, each functional unit can complete mutual communication through the bus. The memory stores a computer program, and the processor is used to execute the program stored on the memory to execute the frequency conversion control method for the circulating water system in the above embodiment.

[0185] Those skilled in the art can understand that the structure of the entity device for the frequency conversion control of the circulating water system provided in this embodiment does not limit the entity device, and it may include more or fewer components, or combine some components, or have different component arrangements.

[0186] The storage medium may further include an operating system and a network communication module. The operating system is a program for managing the hardware and software resources of the entity device for the frequency conversion control of the above circulating water system, and supports the operation of information processing programs and other software and / or programs. The network communication module is used to implement communication between components inside the storage medium, as well as communication with other hardware and software in the information processing entity device.

[0187] Through the description of the above embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus a necessary general hardware platform, or by hardware. By applying the technical solution of this application, compared with the current existing methods, this application performs flow field modeling through the circulating water system, can incorporate multi-source characteristic parameters, improves the adaptability of the frequency conversion control strategy to the multi-variable coupling system, combines the critical cavitation margin and the efficiency flow inflection point when the fixed-frequency pump and the fixed-speed pump operate in parallel, and adds a dynamic flow balancing mechanism for the coordinated operation of the equipment, enabling the parameter prediction model of the circulating water system to output control parameters with stronger adaptability to external disturbances.

[0188] Those skilled in the art can understand that the attached drawings are only schematic diagrams of a preferred implementation scenario, and the modules or processes in the attached drawings are not necessarily essential for implementing the present application. Those skilled in the art can understand that the modules in the devices in the implementation scenario can be distributed in the devices in the implementation scenario according to the description of the implementation scenario, or can be correspondingly changed and located in one or more devices different from the present implementation scenario. The modules in the above implementation scenario can be combined into one module, or can be further split into multiple sub-modules.

[0189] The above serial numbers of the present application are only for description and do not represent the advantages or disadvantages of the implementation scenario. The above disclosure is only several specific implementation scenarios of the present application. However, the present application is not limited thereto, and any changes that can be thought of by those skilled in the art should fall within the protection scope of the present application.

Claims

1. A variable frequency control method for a circulating water system, characterized in that Including: Performing a flow field modeling based on multi-source characteristic parameters collected in real time from a circulating water system to simulate the fluid flow state in the circulating water system through the flow field model; Extracting the critical cavitation margin and the efficiency flow inflection point when a variable-frequency pump and a constant-speed pump operate in parallel according to the simulation results of the flow field model; Taking the critical cavitation margin as the first objective function for minimizing energy consumption and the efficiency flow inflection point as the second objective function for maximizing equipment life, globally optimizing the set control parameters in the circulating water system, and generating a set of constraint conditions for the set control parameters; Pre-constructing a parameter prediction model for the circulating water system, and using a model predictive control method to control the parameter prediction model to output target control parameters that meet the set of constraint conditions in a future time period.

2. The method according to claim 1, wherein The performing a flow field modeling based on multi-source characteristic parameters collected in real time from a circulating water system to simulate the fluid flow state in the circulating water system through the flow field model includes: Performing a flow field modeling using a preset physical model according to the flow characteristics of each component in the circulating water system to establish a control equation describing fluid flow through the preset physical model; Applying the multi-source characteristic parameters collected in real time from the circulating water system to the control equation to dynamically update the boundary conditions of the control equation; Using the finite volume method to solve and iterate the control equation with dynamically updated boundary conditions to simulate the fluid flow state in the circulating water system through the output parameters of the control equation.

3. The method according to claim 2, characterized in that, After the performing a flow field modeling using a preset physical model according to the flow characteristics of each component in the circulating water system to establish a control equation describing fluid flow through the preset physical model, the method further includes: Selecting a suitable grid type to perform grid division on the preset physical model according to the complexity of each component in the circulating water system to obtain a model grid, where unstructured grids are used for complex components in the circulating water system and structured grids are used for non-complex components; Locally encrypting key regions in the grid model by locally refining the grid, where the key regions are regions with complex fluidity in the circulating water system.

4. The method according to claim 1, wherein After the performing a flow field modeling based on multi-source characteristic parameters collected in real time from a circulating water system to simulate the fluid flow state in the circulating water system through the flow field model, the method further includes: Marking damage-sensitive regions according to the simulation results of the flow field model, where the damage-sensitive regions include regions in the circulating water system that are prone to local damage; Collecting pressure loss data for the damage-sensitive regions to correct the boundary conditions of the flow field model through the collected pressure loss data.

5. The method according to claim 1, characterized in that, The extracting the critical cavitation margin and the efficiency flow inflection point when a variable-frequency pump and a constant-speed pump operate in parallel according to the simulation results of the flow field model includes: Setting a combined simulation condition when the constant-speed pump and the variable-frequency pump operate in parallel according to the simulation results of the flow field model; Extracting cavitation monitoring parameters of key points based on the combined simulation condition, and determining the critical cavitation margin when the variable-frequency pump and the constant-speed pump operate in parallel according to the cavitation monitoring parameters; and Extract the flow monitoring parameters of key points based on the combined simulation conditions, and determine the efficiency flow inflection point when the variable-frequency pump and the constant-speed pump operate in parallel according to the flow monitoring parameters.

6. The method according to any one of claims 1-5, characterized in that, Taking the critical cavitation margin as the first objective function to minimize energy consumption and the efficiency flow inflection point as the second objective function to maximize equipment life, globally optimize the set control parameters in the circulating water system to generate a set of constraint conditions for the set control parameters, including: Taking the critical cavitation margin as the first objective function to minimize energy consumption and the efficiency flow inflection point as the second objective function to maximize equipment life, use the fitness function to combine the first objective function and the second objective function to obtain a multi-objective function; Use the genetic algorithm to map the set control parameters in the circulating water into chromosome individuals with variable constraint ranges, and on the basis of the multi-objective function, iteratively adjust the variable constraint ranges corresponding to the chromosome individuals to generate a set of constraint conditions for the set control parameters.

7. The method according to any one of claims 1-5, characterized in that, Pre-construct a parameter prediction model for the circulating water system, and use the model predictive control method to control the parameter prediction model to output the target control parameters that meet the set of constraint conditions in the future time period, including: Taking the ambient temperature, wind speed, and unit load as input variables, and the set value of the variable-frequency pump frequency, the start-stop state of the constant-speed pump, and the valve opening as output variables, pre-construct a parameter prediction model for the circulating water system; On the basis of a pre-defined weighted evaluation function, iteratively solve the target control parameters output by the parameter prediction model in the future time period, and the weighted evaluation function has a weight coefficient for dynamically regulating the mapping relationship between the input variables and the output variables in the parameter prediction model; If the target control parameters output by the parameter prediction model in the future time period are not within the numerical range of the condition constraint set, adaptively adjust the weight coefficient of the weighted evaluation function to correspondingly adjust the mapping relationship between the input variables and the output variables in the parameter prediction model; If the target control parameters output by the parameter prediction model in the future time period are within the numerical range of the condition constraint set, use the target control parameters that meet the set of constraint conditions to perform variable-frequency control on the circulating water system.

8. A variable frequency control device for a circulating water system, characterized in that, Including: A modeling unit for performing a flow field modeling based on multi-source characteristic parameters collected in real time by the circulating water system, so as to simulate the fluid flow state in the circulating water system through the flow field model; An extraction unit for extracting the critical cavitation margin and the efficiency flow inflection point when the variable-frequency pump and the constant-speed pump operate in parallel according to the simulation results of the flow field model; A generation unit for globally optimizing the set control parameters in the circulating water system with the critical cavitation margin as the first objective function to minimize energy consumption and the efficiency flow inflection point as the second objective function to maximize equipment life, and generating a set of constraint conditions for the set control parameters; A control unit for pre-constructing a parameter prediction model for the circulating water system and using the model predictive control method to control the parameter prediction model to output the target control parameters that meet the set of constraint conditions in the future time period.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the steps of the variable frequency control method of the circulating water system according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, the steps of the variable frequency control method of the circulating water system according to any one of claims 1 to 7 are implemented.

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