Frequency conversion control simulation analysis system of ship central cooling water system

Abstract

The invention relates to the technical field of cooling system control, in particular to a ship central cooling water system frequency conversion control simulation analysis system, which comprises a dynamic load prediction module for predicting cooling load requirements of ship main engine and auxiliary engine equipment in different time periods in the future in real time; the load grading control module is used for dividing loads into a plurality of grades, including light load grades, medium load grades and heavy load grades, and generating multi-layer frequency conversion control strategies for cooling water requirements under different load grades; the amplitude control module optimizes the variation amplitude of the rotating speed of the water pump; the load peak value regulation and control module is used for carrying out short-time overclocking control on the water pump when the load fluctuates violently within a short time; and the load distribution simulation module is used for simulating behaviors and responses of the cooling system under different load conditions by performing simulation analysis on distribution of loads of all cooling loops in the ship cooling water system. According to the invention, it is ensured that the equipment obtains sufficient cooling support under a high-load condition, and the load adaptive capacity and the operation efficiency of the cooling system are effectively improved.

Description

Technical Field

[0001] The invention relates to the technical field of cooling system control, and in particular to a frequency conversion control simulation analysis system for a central cooling water system of a ship. Background Art

[0002] The ship's central cooling water system is crucial to maintaining the normal operation of key ship equipment (such as main engines and auxiliary engines). Especially during long-term navigation, the cooling water system must work stably and reliably to avoid equipment overheating, reduced efficiency, and even failure. At present, many ship cooling systems use a fixed-frequency control mode. Even if the equipment load changes, the speed of the water pump and the cooling water flow rate remain basically unchanged. Although this control method can meet basic cooling needs, it has the following main problems when dealing with complex navigation conditions:

[0003] Excessive energy consumption: Fixed frequency control cannot flexibly adjust the pump speed according to changes in equipment load. Under light load or low demand conditions, it still maintains a high water flow, resulting in a large amount of energy waste. As the shipping industry's requirements for energy conservation and consumption reduction increase, the high energy consumption of traditional fixed frequency systems has become an important bottleneck.

[0004] Insufficient system response speed: Under complex marine conditions, equipment load may fluctuate violently. For example, under high load or sudden load conditions, the fixed frequency control cannot respond quickly, resulting in a delayed cooling effect, which may cause equipment overheating and affect equipment life and navigation safety.

[0005] Unbalanced load distribution of cooling circuits: In large ships, the cooling system is usually divided into multiple cooling circuits serving different equipment and systems. Summary of the invention

[0006] The invention provides a frequency conversion control simulation analysis system for a central cooling water system of a ship.

[0007] A frequency conversion control simulation analysis system for a ship central cooling water system, comprising:

[0008] Dynamic load prediction module: Based on the comprehensive data of the ship's navigation path, equipment operating conditions and ocean conditions, it predicts the cooling load requirements of the ship's main engine and auxiliary equipment at different times in the future in real time;

[0009] Load classification control module: According to the output results of the dynamic load prediction module, the load is divided into multiple levels, including light load, medium load and heavy load levels, and a multi-layer variable frequency control strategy is generated for the cooling water demand under different load levels. Based on the multi-layer variable frequency control strategy, the speed of the water pump and the cooling water flow rate are dynamically adjusted to ensure that the cooling system can be adaptively adjusted under load fluctuations;

[0010] Amplitude control module: Using nonlinear optimization algorithm, taking into account the frequency of system inertia and load changes, the amplitude of the pump speed change is optimized to avoid system instability caused by frequent speed regulation;

[0011] Load peak control module: When the load fluctuates violently in a short period of time, the water pump is overclocked for a short period of time to cope with the load peak and avoid overheating of the equipment. After the load peak, the water pump is automatically restored to normal operation to ensure the long-term stability and energy consumption balance of the cooling system;

[0012] Load distribution simulation module: By simulating and analyzing the distribution of loads of each cooling circuit in the ship's cooling water system, the behavior and response of the cooling system under different load conditions are simulated to verify the variable frequency control strategy.

[0013] Optionally, the dynamic load prediction module specifically includes:

[0014] Navigation path data acquisition submodule: predict future equipment operating load based on the ship's speed, scheduled route, and ocean environment:

[0015] Navigation path parameters: Get the navigation speed v based on the ship navigation system ship (t), navigation distance D next , and the estimated voyage time T route ;

[0016] Path impact formula: Estimate the equipment load at each time period by sailing speed and the planned route length: Among them, P travel (t) is the basic power consumption requirement of the equipment during navigation;

[0017] Equipment operation condition monitoring submodule: predicts equipment heating and cooling requirements based on equipment power output and speed changes:

[0018] Equipment status monitoring: Sensors are used to collect real-time equipment conditions, including power output P engine (t), speed R engine (t);

[0019] Cooling demand calculation: The heat generated by the equipment is related to the power output and cooling efficiency, and the cooling demand is proportional to the power output, calculated as: L engine (t) = P engine (t)·η cooling (t), where P engine (t) is the real-time power, η cooling (t) is the cooling efficiency, which depends on the current state of the cooling system;

[0020] Ocean conditions on cooling demand analysis submodule: Ocean conditions, such as sea water temperature and current speed, directly affect the cooling effect of cooling water. These conditions need to be considered in load forecasting:

[0021] Sea water temperature T sea (t) affects the heat exchange efficiency and is expressed as: Among them, T set is the set cooling target temperature. The larger the temperature difference, the better the cooling effect; the smaller the temperature difference, the greater the cooling demand;

[0022] Current speed v current (t): The speed of the ocean current affects the ship's sailing resistance, thus affecting the equipment load: F drag (t) = k·v current (t)·v ship (t), where k is the drag coefficient, combined with the current speed and ship speed, to estimate the effect of ocean conditions on the load;

[0023] Comprehensive cooling load prediction model submodule: Combines the three parts to comprehensively calculate the cooling load demand:

[0024] L total (t) = αL path (t)+βL engine (t)+γL sea (t), where L total (t) is the total cooling load demand, α, β, and γ are the weights of different factors. The weights are dynamically adjusted after training with historical data. A neural network is used to continuously optimize the three weights based on historical data, so that the model can more accurately predict future load demands. A multi-layer perceptron neural network is used to optimize the weights.

[0025] Optionally, the load classification control module specifically includes:

[0026] Load classification submodule: Based on the future cooling load demand L provided by the dynamic load prediction module total (t), the load value is divided into multiple levels, including light load L light , medium load L medium and heavy load L heavy The specific classification criteria are based on historical load data, equipment operating characteristics and cooling system design parameters. threshold1 and L threshold2 ,definition:

[0027] Light load: L total (t)≤L threshold1 ;

[0028] Medium load: L threshold1 <Ltotal (t)≤L threshold2 ;

[0029] Heavy load: L total (t)>L threshold2 ;

[0030] Multi-layer variable frequency control strategy generation submodule: Generates corresponding variable frequency control strategies for each load level according to the load level:

[0031] For light loads L light , reduce the pump speed and cooling water flow to save energy;

[0032] For medium load L medium , maintain the water pump running at a medium speed to ensure the balance between cooling demand and energy efficiency;

[0033] For heavy loads L heavy , increase the speed of the water pump and the cooling water flow rate to ensure that the equipment is fully cooled under high load;

[0034] Through the multi-layer control strategy, the speed S of the water pump is dynamically adjusted pump (t) and cooling water flow F cooling (t), specifically expressed as:

[0035] S pump (t) = f(L total (t), level);

[0036] F cooling (t) = g(L total (t), level);

[0037] Among them, f and g are adjustment functions based on load level, which are used to generate water pump speed and cooling water flow control strategies under different load levels.

[0038] Optionally, the amplitude control module specifically includes:

[0039] System inertia analysis submodule: According to the inertia characteristics of the water pump system, the inertia response model of the water pump is established. Considering that the change rate of the water pump speed is limited by physical inertia, the maximum allowable change rate ΔS is set. max , expressed as: ΔS pump (t)≤ΔS max , where ΔS pump (t) is the speed change of the water pump at time t, ΔS max Depends on the physical limitations of the pump and the system dynamics;

[0040] Load change frequency calculation submodule: through real-time monitoring of load L total(t) is the rate of change, and the load change frequency ΔL is calculated total (t), and combined with historical data, the trend and fluctuation range of load changes are evaluated. The load change frequency is expressed as: in, represents the load change rate, t is the time variable;

[0041] Nonlinear optimization model building submodule: Incorporate load change frequency and system inertia into the nonlinear optimization model, with the goal of minimizing the transient fluctuation of the pump speed;

[0042] Optimize the output submodule: According to the optimization results, dynamically adjust the change range of the water pump speed to ensure that when the load changes drastically, the water pump responds quickly but does not exceed the physical limit; when the load changes gently, the water pump speed changes smoothly to avoid frequent adjustments causing system instability.

[0043] Optionally, the inertial response model is expressed as: Among them, I represents the moment of inertia of the water pump, α(t) represents the angular acceleration of the water pump, T(t) is the torque applied at time t, ω(t) is the angular velocity of the water pump at time t, which is proportional to the speed of the water pump and represents the rate of change of the water pump speed, and dω(t) represents the instantaneous change of the angular velocity of the water pump at time t.

[0044] Optionally, the variation range of the water pump speed is expressed as: Among them, ΔS pump (t) is the change in speed at time t, Δt is the time interval, and I is the moment of inertia of the pump.

[0045] Optionally, the load peak control module specifically includes:

[0046] Peak load detection submodule: real-time monitoring of the load change rate ΔL in the cooling system total (t), when it is detected that the load rises sharply in a short period of time and exceeds the set peak threshold L peak When the system enters the peak load state, it is judged that the system has entered the peak load state and triggers the short-term overclocking control;

[0047] Short-term overclocking control submodule: When the load exceeds the peak threshold, increase the pump speed S pump (t), and allow the water pump to run at an overclocked state higher than the normal working frequency for a short period of time to ensure sufficient cooling water supply to cope with the high-load operation of the equipment. The overclocked speed is determined by the following formula: S pump (t) = S normal (t)+ΔS peak , where S normal (t) is the normal speed of the water pump, ΔS peak It is the increase in overclocking speed for peak load;

[0048] Overclocking time control submodule: set the maximum overclocking time M max , ensuring that the pump is only overclocked for short periods of time during peak loads, and preventing long-term overclocking that may cause equipment damage or overheating: peak ≤M max , where t peak The time the water pump is in overclocking state, ensuring that the overclocking duration does not exceed the safety range of the equipment;

[0049] Automatic recovery submodule: When the load peak subsides and the load drops below the peak threshold L total (t)≤L peak When the pump speed is automatically reduced, it returns to normal working state.

[0050] Optionally, the nonlinear optimization model is expressed as:

[0051] Among them, S optimal (t) is the optimal speed predicted according to the load demand at time t, S pump (t) is the actual pump speed at time t. By minimizing the difference between the two, the speed change is optimized. pump (t)-S optimal (t)| represents the difference between the actual speed of the pump and the optimal speed, T represents the optimization time interval, It represents the integration of the time interval 0 to T, which means optimizing the cumulative effect of the pump speed difference during this time period, and dt represents a small increment of the time variable.

[0052] Optionally, the load distribution simulation module specifically includes:

[0053] Cooling circuit load modeling submodule: According to the actual structure of the ship cooling water system, the system is divided into multiple cooling circuits, and a load model L is established for each circuit. circuit (t), based on the equipment type, working status and cooling demand of each cooling circuit, form the cooling circuit load distribution corresponding to the actual operation:

[0054] Multi-scenario load distribution simulation submodule: By simulating the cooling circuit load under different navigation conditions and equipment operation states, the behavior of the cooling system under different load conditions is simulated, load distribution scenarios are generated, and the response of each cooling circuit under different load conditions is analyzed. The simulation scenarios include light load, medium load and heavy load conditions;

[0055] Frequency conversion control strategy verification submodule: Apply the frequency conversion control strategy in the simulation environment, observe the speed, flow and pressure response of the water pump under various load conditions, verify the effectiveness of the frequency conversion control strategy, verify the response speed and stability when the load fluctuates, and optimize the control strategy by analyzing the simulation results: S optimal (t) = f(L circuit (t), load level), where S optimal (t) is the optimal pump speed obtained by simulation, L circuit (t) is the load distribution of each cooling circuit.

[0056] Beneficial effects of the present invention:

[0057] The present invention uses a dynamic load prediction module to obtain comprehensive data on the ship's navigation path, equipment operating conditions and ocean conditions in real time, and accurately predicts the cooling load demand. When the load fluctuates violently for a short period of time, the load peak control module controls the water pump speed through short-term overclocking to ensure that the system can quickly respond to load changes and avoid equipment overheating, and then automatically recover to a normal state. This real-time prediction and short-term overclocking strategy significantly improves the response speed of the cooling system, ensures that the equipment obtains sufficient cooling support under high load conditions, and effectively improves the load adaptability and operating efficiency of the cooling system.

[0058] In the present invention, the amplitude control module utilizes a nonlinear optimization algorithm, takes into account the inertia of the water pump system and the frequency of load changes, optimizes the variation amplitude of the water pump speed, and avoids frequent speed regulation causing system instability. This innovative design enables the water pump to save energy when the load is stable, and to smoothly adjust the speed when the load fluctuates, thereby ensuring the stability of the system. At the same time, the load classification control module generates a multi-layer variable frequency control strategy according to different load levels (light load, medium load, heavy load), so that the cooling water flow and the water pump speed can be flexibly adjusted, maximizing energy efficiency while meeting cooling needs and reducing energy consumption losses.

[0059] The present invention simulates the load distribution of each cooling circuit under different load conditions through a load distribution simulation module, analyzes the dynamic behavior and response of the system, and verifies the effectiveness of the variable frequency control strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0061] Figure 1 A schematic diagram of a system module according to an embodiment of the present invention;

[0062] Figure 2 A schematic diagram of a load classification control module according to an embodiment of the present invention;

[0063] Figure 3 Schematic diagram of an amplitude control module according to an embodiment of the present invention. DETAILED DESCRIPTION

[0064] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0065] It should be noted that the references to "one embodiment", "an embodiment", "an exemplary embodiment", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0066] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0067] like Figure 1-Figure 3 As shown, a frequency conversion control simulation analysis system for a ship central cooling water system comprises:

[0068] Dynamic load prediction module: Based on the comprehensive data of the ship's navigation path, equipment operating conditions and ocean conditions, it predicts the cooling load requirements of the ship's main engine and auxiliary equipment at different times in the future in real time;

[0069] Load classification control module: According to the output results of the dynamic load prediction module, the load is divided into multiple levels, including light load, medium load and heavy load levels, and a multi-layer variable frequency control strategy is generated for the cooling water demand under different load levels. The cooling water flow and pump speed are automatically adjusted according to each load level to ensure energy saving under light load and sufficient cooling under heavy load. Based on the multi-layer variable frequency control strategy, the speed of the water pump and the cooling water flow are dynamically adjusted to ensure that the cooling system can be adaptively adjusted under load fluctuations;

[0070] Amplitude control module: Using nonlinear optimization algorithm, taking into account the frequency of system inertia and load changes, the amplitude of the pump speed change is optimized to avoid system instability caused by frequent speed regulation;

[0071] Load peak control module: When the load fluctuates violently in a short period of time, the water pump is overclocked for a short period of time to cope with the load peak and avoid overheating of the equipment. After the load peak, the water pump is automatically restored to normal operation to ensure the long-term stability and energy consumption balance of the cooling system;

[0072] Load distribution simulation module: By simulating and analyzing the distribution of loads of each cooling circuit in the ship's cooling water system, the behavior and response of the cooling system under different load conditions are simulated to verify the variable frequency control strategy.

[0073] The dynamic load prediction module specifically includes:

[0074] Navigation path data acquisition submodule: The navigation path of the ship not only affects the navigation time, but also affects the load status of the equipment. The future operating load of the equipment is predicted based on the ship's speed, scheduled route, and marine environment:

[0075] Navigation path parameters: Get the navigation speed v based on the ship navigation system ship (t), navigation distance D next , and the estimated voyage time T route ;

[0076] Path impact formula: Estimate the equipment load at each time period by sailing speed and the planned route length: Among them, P travel (t) is the basic power consumption requirement of the equipment during navigation;

[0077] Equipment operation condition monitoring submodule: predicts equipment heating and cooling requirements based on equipment power output and speed changes:

[0078] Equipment status monitoring: Sensors are used to collect real-time equipment conditions, including power output P engine (t), speed R engine (t);

[0079] Cooling demand calculation: The heat generated by the equipment is related to the power output and cooling efficiency, and the cooling demand is proportional to the power output, calculated as: L engine (t) = P engine (t)·η cooling (t), where P engine (t) is the real-time power, η colling (t) is the cooling efficiency, which depends on the current state of the cooling system;

[0080] Ocean conditions on cooling demand analysis submodule: Ocean conditions, such as sea water temperature and current speed, directly affect the cooling effect of cooling water. These conditions need to be considered in load forecasting:

[0081] Sea water temperature T sea (t) affects the heat exchange efficiency and is expressed as: Among them, T set is the set cooling target temperature. The larger the temperature difference, the better the cooling effect; the smaller the temperature difference, the greater the cooling demand;

[0082] Current speed v current (t): The speed of the ocean current affects the ship's sailing resistance, thus affecting the equipment load: F drag (t) = k·v current (t)·v ship (t), where k is the drag coefficient, combined with the current speed and ship speed, to estimate the effect of ocean conditions on the load;

[0083] Comprehensive cooling load prediction model submodule: Combines the three parts to comprehensively calculate the cooling load demand:

[0084] L total (t) = αL path (t)+βL engine (t)+γL sea (t), where L total (t) is the total cooling load demand, α, β, and γ are the weights of different factors. The weights are dynamically adjusted after training with historical data. A neural network is used to continuously optimize the three weights based on historical data, so that the model can more accurately predict future load demand. A multi-layer perceptron neural network is used to optimize the weights. The architecture of the neural network is as follows:

[0085] Input layer: receives three input data: flight path load prediction L path (t), equipment operation load prediction L engine (t), ocean condition load prediction L sea (t). Each input represents a different influencing factor;

[0086] Hidden layer: One or more hidden layers are used. These hidden layers consist of a certain number of neurons to learn the complex nonlinear relationship between input and cooling load demand.

[0087] Output layer: Output cooling load prediction value L total (t), the final prediction result is obtained by optimizing the α, β, and γ weights. The input-output relationship of the neural network is expressed as:

[0088] L total (t) = NN(L path (t), L engine (t), L sea (t); θ), where NN represents the neural network model and θ is the parameter of the neural network, including the weights and biases of each layer.

[0089] In order to optimize the output of the neural network, a loss function needs to be defined to measure the error between the predicted value and the actual cooling demand:

[0090] Among them, L real (t) represents the actual cooling load demand at time t, which is the real data measured by the sensor, L total (t) represents the cooling load demand predicted by the neural network at time t.

[0091] The neural network updates weights through the back-propagation algorithm. The specific process is as follows:

[0092] 1. Forward propagation: input data L path (t),L engine (t),L sea (t) Calculate the cooling load prediction L through the neural network total (t);

[0093] The predicted value is propagated to the output layer layer by layer through the activation function, and finally L total (t).

[0094] 2. Loss calculation: Calculate the predicted value L total (t) and the actual value L real (t), using the mean square error as the loss function.

[0095] 3. Back propagation: Use the gradient descent algorithm to optimize the parameters of the neural network (including the weights of the hidden layer and the output layer). For each layer’s weight W i , update the weights by gradient descent: Among them, η is the learning rate, which determines the step size of each weight update.

[0096] 4. Weight adjustment: The parameters in the neural network are updated through back propagation and gradient descent to make the neural network predict the input data more accurately. This includes adjusting the weights α, β, and γ in the neural network to make it more suitable for the actual needs of the cooling load.

[0097] The training process of the neural network is as follows:

[0098] Data preparation: Past navigation path data, equipment operating conditions, ocean conditions and corresponding actual cooling loads are used as training data sets. Each training sample includes L path (t), L engine (t), L sea (t) and the actual cooling demand L real (t).

[0099] Training steps:

[0100] 1. Input data L path (t), L engine (t), L sea (t) Input the neural network to generate the predicted value L total (t).

[0101] 2. Calculate the loss function and get the prediction error.

[0102] 3. Adjust the weights of the neural network through the back-propagation algorithm to minimize the loss function.

[0103] 4. Repeat the above steps until the loss function converges to a smaller value.

[0104] 5. Dynamic adjustment of weight optimization.

[0105] Through the above training process, the neural network continuously learns the impact of each input variable on the cooling load. As the training progresses, the neural network can dynamically adjust the three weights α, β, and γ to automatically adapt to changes in input data under different conditions, thereby achieving accurate prediction of future load demand.

[0106] α will be adjusted according to the changes in the navigation path. If the ship sails for a long time and the path is relatively stable, the system may reduce the weight of α. Conversely, it will increase α under complex navigation conditions.

[0107] β will be adjusted according to the operating load of the equipment. When the equipment load fluctuates greatly, β will be given a larger weight.

[0108] γ will adjust according to drastic changes in the external environment. For example, when the ocean temperature or flow rate changes greatly, the weight of γ will increase.

[0109] The load classification control module specifically includes:

[0110] Load classification submodule: Based on the future cooling load demand L provided by the dynamic load prediction module total (t), the load value is divided into multiple levels, including light load L light , medium load L medium and heavy load L heavy The specific classification criteria are based on historical load data, equipment operating characteristics and cooling system design parameters. threshold1 and L threshold2 ,definition:

[0111] Light load: L total (t)≤L threshold1 ;

[0112] Medium load: L threshold1 <L total (t)≤L threshold2 ;

[0113] Heavy load: L total (t)>L threshold2 ;

[0114] Multi-layer variable frequency control strategy generation submodule: Generates corresponding variable frequency control strategies for each load level according to the load level:

[0115] For light loads L light , reduce the pump speed and cooling water flow to save energy;

[0116] For medium load L medium , maintain the water pump running at a medium speed to ensure the balance between cooling demand and energy efficiency;

[0117] For heavy loads L heavy , increase the speed of the water pump and the cooling water flow rate to ensure that the equipment is fully cooled under high load;

[0118] Through the multi-layer control strategy, the speed S of the water pump is dynamically adjusted pump (t) and cooling water flow F cooling (t), specifically expressed as:

[0119] S pump (t) = f(L total (t), level);

[0120] F cooling (t) = g(L total (t), level);

[0121] Among them, f and g are adjustment functions based on load level, which are used to generate water pump speed and cooling water flow control strategies under different load levels.

[0122] Light load threshold L threshold1 :Light load usually corresponds to the navigation state of the ship in low power output, such as when cruising, or when the main engine and auxiliary engine are running at low power. According to the minimum power requirements of the main engine and auxiliary engine, combined with historical data, the cooling requirements of the system under low load conditions are determined. Light load can be defined as less than 30% of the total load demand;

[0123] Heavy load threshold L threshold2 Heavy load usually corresponds to the operation of the ship under high load state, such as high-speed navigation, heavy-load transportation or unfavorable marine conditions resulting in high-load operation of the equipment. Heavy load is defined as more than 70% of the equipment operating power, that is, exceeding 70% of the total power can be considered to enter a heavy load state.

[0124] The amplitude control module specifically includes:

[0125] System inertia analysis submodule: According to the inertia characteristics of the water pump system, the inertia response model of the water pump is established. Considering that the change rate of the water pump speed is limited by physical inertia, the maximum allowable change rate ΔS is set. max , expressed as: ΔS pump (t)≤ΔS max , where ΔS pump (t) is the speed change of the water pump at time t, ΔS max Depends on the physical limitations of the pump and the system dynamics;

[0126] Load change frequency calculation submodule: through real-time monitoring of load L total (t) is the rate of change, and the load change frequency ΔL is calculated total (t), and combined with historical data, the trend and fluctuation range of load changes are evaluated. The load change frequency is expressed as: in, represents the load change rate, t is the time variable;

[0127] Nonlinear optimization model building submodule: Incorporate load change frequency and system inertia into the nonlinear optimization model, with the goal of minimizing the transient fluctuation of the pump speed;

[0128] Optimize the output submodule: According to the optimization results, dynamically adjust the change range of the water pump speed to ensure that when the load changes drastically, the water pump responds quickly but does not exceed the physical limit; when the load changes gently, the water pump speed changes smoothly to avoid frequent adjustments causing system instability.

[0129] Based on the inertial characteristics of the water pump, an inertial response model is established to show how the water pump responds to the speed change command. According to the physical characteristics of the water pump system, the angular velocity change of the water pump is expressed. The inertial response model is expressed as: Among them, I represents the moment of inertia of the water pump, α(t) represents the angular acceleration of the water pump, T(t) is the torque applied at time t, ω(t) is the angular velocity of the water pump at time t, which is proportional to the speed of the water pump and represents the rate of change of the water pump speed, dω(t) represents the instantaneous change of the angular velocity of the water pump at time t, dt is a very small increment of time, which represents the time period in which the angular velocity change occurs, and is used in calculus to represent instantaneous changes in time. This formula indicates that the rate of change of the water pump speed (that is, the rate at which the water pump accelerates or decelerates) is limited by the applied torque and moment of inertia. The speed of the water pump cannot increase or decrease rapidly indefinitely because the moment of inertia limits the reaction speed of the water pump.

[0130] The variation range of the water pump speed is expressed as: Among them, ΔS pump (t) is the change in speed at time t, Δt is the time interval, and I is the moment of inertia of the pump;

[0131] Set the maximum allowable speed change rate: According to the inertial response model, set the maximum allowable change rate of the pump speed ΔS max To ensure that the pump does not exceed physical limitations during acceleration or deceleration, the maximum permissible rate of change is set by:

[0132] Among them, T max is the maximum torque that the water pump motor can apply, and Δt is the time interval.

[0133] The load peak control module specifically includes:

[0134] Peak load detection submodule: real-time monitoring of the load change rate ΔL in the cooling system total (t), when it is detected that the load rises sharply in a short period of time and exceeds the set peak threshold L peak When the system enters the peak load state, it is judged that the system enters the peak load state and triggers the short-term overclocking control, that is, L total (t)>L peak ;

[0135] Short-term overclocking control submodule: When the load exceeds the peak threshold, increase the pump speed S pump (t), and allow the water pump to run at an overclocked state higher than the normal working frequency for a short period of time to ensure sufficient cooling water supply to cope with the high-load operation of the equipment. The overclocked speed is determined by the following formula: S pump (t) = S normal (t)+ΔS peak , where S normal (t) is the normal speed of the water pump, ΔS peak It is the increase in overclocking speed for peak load to ensure that the system cooling requirements are met;

[0136] Overclocking time control submodule: set the maximum overclocking time M max , ensuring that the pump is only overclocked for short periods of time during peak loads, and preventing long-term overclocking that may cause equipment damage or overheating: peak ≤M max , where t peak The time the water pump is in overclocking state, ensuring that the overclocking duration does not exceed the safety range of the equipment;

[0137] Automatic recovery submodule: When the load peak subsides and the load drops below the peak threshold L total (t)≤L peak When the pump speed is automatically reduced, it returns to normal working state: S pump (t) = S normal (t); By monitoring the load changes in real time, the system can dynamically adjust the pump speed to ensure timely recovery after the load peak, avoiding excessive energy consumption or long-term overload operation of the equipment.

[0138] The nonlinear optimization model is expressed as:

[0139] Among them, S optimal (t) is the optimal speed predicted according to the load demand at time t, S pump (t) is the actual pump speed at time t. By minimizing the difference between the two, the speed change is optimized. pump (t)-S optimal (t)| represents the difference between the actual speed of the pump and the optimal speed, T represents the optimization time interval, It means integrating the time interval from 0 to T, which means optimizing the cumulative effect of the pump speed difference within this time period. dt represents a small increment of the time variable, ensuring that the pump speed changes smoothly and close to the optimal value throughout the period.

[0140] The load distribution simulation module specifically includes:

[0141] Cooling circuit load modeling submodule: According to the actual structure of the ship cooling water system, the system is divided into multiple cooling circuits, and a load model L is established for each circuit. circuit (t), based on the equipment type, working status and cooling demand of each cooling circuit, form the cooling circuit load distribution corresponding to the actual operation:

[0142] Multi-scenario load distribution simulation submodule: By simulating the cooling circuit load under different navigation conditions and equipment operation states, the behavior of the cooling system under different load conditions is simulated, load distribution scenarios are generated, and the response of each cooling circuit under different load conditions is analyzed. The simulation scenarios include light load, medium load and heavy load conditions;

[0143] Frequency conversion control strategy verification submodule: Apply the frequency conversion control strategy in the simulation environment, observe the speed, flow and pressure response of the water pump under various load conditions, verify the effectiveness of the frequency conversion control strategy, verify the response speed and stability when the load fluctuates, and optimize the control strategy by analyzing the simulation results: S optimal (t) = f(L circuit (t), load level), where S optimal (t) is the optimal pump speed obtained by simulation, L circuit (t) is the load distribution of each cooling circuit.

[0144] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0145] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A frequency conversion control simulation analysis system for a ship central cooling water system, characterized in that: include: Dynamic load prediction module: Based on the comprehensive data of the ship's navigation path, equipment operating conditions and ocean conditions, it predicts the cooling load requirements of the ship's main engine and auxiliary equipment at different times in the future in real time; Load classification control module: According to the output results of the dynamic load prediction module, the load is divided into multiple levels, including light load, medium load and heavy load levels, and a multi-layer variable frequency control strategy is generated for the cooling water demand under different load levels. Based on the multi-layer variable frequency control strategy, the speed of the water pump and the cooling water flow rate are dynamically adjusted to ensure that the cooling system can be adaptively adjusted under load fluctuations; Amplitude control module: uses a nonlinear optimization algorithm to optimize the amplitude of the pump speed change by taking into account the system inertia and the frequency of load changes; Load peak control module: When the load fluctuates violently in a short period of time, the water pump is overclocked for a short period of time to cope with the load peak and avoid overheating of the equipment. After the load peak, the water pump is automatically restored to normal operation; Load distribution simulation module: By simulating and analyzing the distribution of loads of each cooling circuit in the ship's cooling water system, the behavior and response of the cooling system under different load conditions are simulated to verify the variable frequency control strategy.

2. A frequency conversion control simulation analysis system for a ship central cooling water system according to claim 1, characterized in that: The dynamic load prediction module specifically includes: Navigation path data acquisition submodule: predict future equipment operating load based on the ship's speed, scheduled route, and ocean environment: Navigation path parameters: Get the navigation speed v based on the ship navigation system ship (t), navigation distance D next , and the estimated voyage time T route ; Path impact formula: Estimate the equipment load at each time period by sailing speed and the planned route length: Among them, P travel (t) is the basic power consumption requirement of the equipment during navigation; Equipment operation condition monitoring submodule: predicts equipment heating and cooling requirements based on equipment power output and speed changes: Equipment status monitoring: Sensors are used to collect real-time equipment conditions, including power output P engine (t), speed R engine (t); Cooling demand calculation: The heat generated by the equipment is related to the power output and cooling efficiency, and the cooling demand is proportional to the power output, calculated as: L engine (t) = P engine (t)·η cooling (t), where P engine (t) is the real-time power, η cooling (t) is the cooling efficiency, which depends on the current state of the cooling system; Submodule for analyzing the impact of ocean conditions on cooling demand: Sea water temperature T sea (t) affects the heat exchange efficiency and is expressed as: Among them, T set is the set cooling target temperature. The larger the temperature difference, the better the cooling effect; the smaller the temperature difference, the greater the cooling demand; Current speed v current (t): The speed of the ocean current affects the ship's sailing resistance, thus affecting the equipment load: F drag (t) = k·v current (t)·v ship (t), where k is the drag coefficient; Comprehensive cooling load prediction model submodule: Combines the three parts to comprehensively calculate the cooling load demand: L total (t) = αL path (t)+βL engine (t)+γL sea (t), where L total (t) is the total cooling load demand, and α, β, and γ are the weights of different factors.

3. A frequency conversion control simulation analysis system for a ship central cooling water system according to claim 1, characterized in that: The load classification control module specifically includes: Load classification submodule: Based on the future cooling load demand L provided by the dynamic load prediction module total (t), the load value is divided into multiple levels, including light load L light , medium load L medium and heavy load L heavy The specific classification criteria are based on historical load data, equipment operating characteristics and cooling system design parameters. threshold1 and L threshold2 ,definition: Light load: L total (t)≤L threshold1 ; Medium load: L threshold1 <L total (t)≤L threshold2 ; Heavy load: L total (t)>L threshold2 ; Multi-layer variable frequency control strategy generation submodule: Generates corresponding variable frequency control strategies for each load level according to the load level: For light loads L light , reduce the water pump speed and cooling water flow, saving energy consumption; For medium load L medium , maintain the water pump running at a medium speed to ensure the balance between cooling demand and energy efficiency; For heavy loads L heavy , increase the speed of the water pump and the cooling water flow rate to ensure that the equipment is fully cooled under high load; Through the multi-layer control strategy, the speed S of the water pump is dynamically adjusted pump (t) and cooling water flow F cooling (t), specifically expressed as: S pump (t) = f(L total (t), level); F cooling (t) = g(L total (t), level); Among them, f and g are adjustment functions based on load level, which are used to generate water pump speed and cooling water flow control strategies under different load levels.

4. A frequency conversion control simulation analysis system for a ship central cooling water system according to claim 3, characterized in that: The amplitude control module specifically includes: System inertia analysis submodule: According to the inertia characteristics of the water pump system, the inertia response model of the water pump is established. Considering that the change rate of the water pump speed is limited by physical inertia, the maximum allowable change rate ΔS is set. max , expressed as: ΔS pump (t)≤ΔS max , where ΔS pump (t) is the speed change of the water pump at time t, ΔS max Depends on the physical limitations of the pump and the system dynamics; Load change frequency calculation submodule: through real-time monitoring of load L total (t) is the rate of change, and the load change frequency ΔL is calculated total (t), and combined with historical data, the trend and fluctuation range of load changes are evaluated. The load change frequency is expressed as: in, represents the load change rate, t is the time variable; Nonlinear optimization model building submodule: Incorporate load change frequency and system inertia into the nonlinear optimization model, with the goal of minimizing the transient fluctuation of the pump speed; Optimize the output submodule: According to the optimization results, dynamically adjust the change range of the water pump speed to ensure that when the load changes drastically, the water pump responds quickly but does not exceed the physical limit; when the load changes gently, the water pump speed changes smoothly to avoid frequent adjustments causing system instability.

5. A frequency conversion control simulation analysis system for a ship central cooling water system according to claim 4, characterized in that: The inertial response model is expressed as: Among them, I represents the moment of inertia of the water pump, α(t) represents the angular acceleration of the water pump, T(t) is the torque applied at time t, ω(t) is the angular velocity of the water pump at time t, which is proportional to the speed of the water pump and represents the rate of change of the water pump speed, and dω(t) represents the instantaneous change of the angular velocity of the water pump at time t.

6. A frequency conversion control simulation analysis system for a ship central cooling water system according to claim 4, characterized in that: The variation range of the water pump speed is expressed as: Among them, ΔS pump (t) is the change in speed at time t, Δt is the time interval, and I is the moment of inertia of the pump.

7. A frequency conversion control simulation analysis system for a ship central cooling water system according to claim 4, characterized in that: The load peak control module specifically includes: Peak load detection submodule: real-time monitoring of the load change rate ΔL in the cooling system total (t), when it is detected that the load rises sharply in a short period of time and exceeds the set peak threshold L peak When the system enters the peak load state, it is judged that the system has entered the peak load state and triggers the short-term overclocking control; Short-term overclocking control submodule: When the load exceeds the peak threshold, increase the pump speed S pump (t), and allow the water pump to run at an overclocked state higher than the normal working frequency for a short period of time to ensure sufficient cooling water supply to cope with the high-load operation of the equipment. The overclocked speed is determined by the following formula: S pump (t) = S normal (t)+ΔS peak , where S normal (t) is the normal speed of the water pump, ΔS peak It is the increase in overclocking speed for peak load; Overclocking time control submodule: set the maximum overclocking time M max , ensuring that the pump is only overclocked for short periods of time during peak loads, and preventing long-term overclocking that may cause equipment damage or overheating: peak ≤M max , where t peak The time the water pump is in overclocking state, ensuring that the overclocking duration does not exceed the safety range of the equipment; Automatic recovery submodule: When the load peak subsides and the load drops below the peak threshold L total (t)≤L peak When the pump speed is automatically reduced, it returns to normal working state.

8. A frequency conversion control simulation analysis system for a ship central cooling water system according to claim 4, characterized in that: The nonlinear optimization model is expressed as: Among them, S optimal (t) is the optimal speed predicted according to the load demand at time t, S pump (t) is the actual pump speed at time t. By minimizing the difference between the two, the speed change is optimized. pump (t)-S optimal (t)| represents the difference between the actual speed of the pump and the optimal speed, T represents the optimization time interval, It represents the integration of the time interval 0 to T, which means optimizing the cumulative effect of the pump speed difference during this time period, and dt represents a small increment of the time variable.

9. A frequency conversion control simulation analysis system for a ship central cooling water system according to claim 1, characterized in that: The load distribution simulation module specifically includes: Cooling circuit load modeling submodule: According to the actual structure of the ship cooling water system, the system is divided into multiple cooling circuits, and a load model L is established for each circuit. circuit (t), based on the equipment type, working status and cooling demand of each cooling circuit, form the cooling circuit load distribution corresponding to the actual operation: Multi-scenario load distribution simulation submodule: By simulating the cooling circuit load under different navigation conditions and equipment operation states, the behavior of the cooling system under different load conditions is simulated, load distribution scenarios are generated, and the response of each cooling circuit under different load conditions is analyzed. The simulation scenarios include light load, medium load and heavy load conditions; Frequency conversion control strategy verification submodule: Apply the frequency conversion control strategy in the simulation environment, observe the speed, flow and pressure response of the water pump under various load conditions, verify the effectiveness of the frequency conversion control strategy, verify the response speed and stability when the load fluctuates, and optimize the control strategy by analyzing the simulation results: S optimal (t) = f(L circuit (t), load level), where S optimal (t) is the optimal pump speed obtained by simulation, L circuit (t) is the load distribution of each cooling circuit.

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