Direct-drive propulsion ship navigational speed optimization control method

Abstract

The invention discloses a direct-drive propulsion ship navigational speed optimization control method, which comprises the following steps of: acquiring ship navigational speed and wind, wave and flow meteorological data in a ship channel, combining ship navigational resistance data, a preset navigational time limit and a ship direct-drive motor efficiency characteristic curve, and taking the output rotating speed of a ship direct-drive motor as an optimization variable; the particle swarm optimization algorithm is adopted for optimization calculation, the optimal rotating speed target value of the direct-drive motor is obtained, the output rotating speed of the direct-drive motor is adjusted, on the basis that the sailing time limit requirement is met, the running efficiency of the direct-drive motor is improved, and the consumption and operation cost of a ship are effectively reduced. The optimization control method is suitable for an existing direct-drive propulsion ship, it can be guaranteed that the ship sails stably under the complex sea condition, ship body shaking and speed fluctuation caused by wind wave flow impact are reduced, the motor operation efficiency can be effectively improved, and energy consumption and operation cost are reduced.

Description

Technical Field

[0001] This invention belongs to the field of direct-drive propulsion ship technology, and in particular relates to a method for optimizing the speed of direct-drive propulsion ships. Background Technology

[0002] With the increasing demands for energy conservation, emission reduction, and navigation safety in the global shipping industry, traditional ship propulsion systems based on fixed speed control are no longer able to adapt to the complex and ever-changing marine environment.

[0003] Although direct-drive electric propulsion systems have become an important direction for the development of green ships due to their advantages such as high transmission efficiency and low maintenance costs, ships still face challenges from the dynamic changes in wind, waves and current resistance during actual navigation: a single speed setting cannot take into account the resistance characteristics and motor operating efficiency under different sea conditions, resulting in energy waste or power system overload risks.

[0004] Therefore, how to combine real-time marine environmental data with ship dynamic characteristics to build an intelligent speed optimization mechanism has become the key to improving the overall performance of direct-drive propulsion ships. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method for optimizing the speed of direct-drive propulsion ships based on particle swarm optimization algorithm, aiming to overcome the limitations of traditional speed control.

[0006] The technical solution adopted by this invention to solve its technical problem is: a method for optimizing the speed control of a direct-drive propulsion ship, applicable to a direct-drive ship propulsion system, comprising the following steps:

[0007] Step 1: The direct-drive ship propulsion system acquires the ship's speed, wind, wave, and current meteorological data in the ship's channel, as well as the operating speed and sailing time limits of the direct-drive motor.

[0008] Step 2: Analyze and calculate the current navigation resistance and motor operating efficiency based on meteorological data. Combine the ship's navigation resistance data, the predetermined navigation time limit, and the efficiency characteristic curve of the ship's direct drive motor to determine whether the resistance level has changed. If the resistance level has changed, calculate the target speed of the ship's direct drive motor using an optimization model based on the evaluation function, with the output speed of the ship's direct drive motor as the optimization variable.

[0009] The evaluation function is quantified using two evaluation index parameters: motor speed fluctuation and direct-drive propulsion ship operating efficiency. The calculation function for the motor speed fluctuation evaluation index is as follows: ,in This is the original speed of the ship's direct drive motor. Given the target rotational speed of the ship's direct-drive motor, the calculation function for the ship's operating efficiency evaluation index is as follows: ,in For the output power of ship propellers, This refers to the output power of the ship's direct drive motor.

[0010] Step 3: Based on the target speed of the ship's direct drive motor calculated by the optimization model, the output command is sent to the controller of the ship's direct drive motor through coordinated control, and the output speed of the ship's direct drive motor is instantaneously coordinated and controlled.

[0011] Step 4: If the actual ship operating conditions change, causing the speed of the ship's direct drive motor to change, then execute steps 1, 2, and 3 to control the ship's speed optimization under different navigation environment conditions.

[0012] Furthermore, the steps for obtaining relevant technical parameters of the ship's direct drive motor in step 1 are as follows: by integrating the ship's navigation system, continuously acquire the ship's actual ground speed and water speed as a baseline for speed optimization; utilize the ship's onboard meteorological station, radar, and accessed maritime meteorological forecast data stream to collect real-time wind, wave, and current environment data of the ship's current channel and the route ahead, including key parameters such as wind speed and direction, wave height and direction, and current speed and direction, to quantify the comprehensive impact of wind, waves, and current on the ship's navigation resistance; directly read the current operating speed, output torque, and power parameters from the ship's direct drive motor control system.

[0013] Furthermore, in step 2, the time constraint is comprehensively considered and transformed into boundary conditions in the control algorithm, providing input for the next decision-making step; the optimization model uses a particle swarm optimization algorithm for optimization calculation.

[0014] In step 2, the optimization model is optimized using a particle swarm optimization algorithm.

[0015] (1) Algorithm parameter settings: Assume the number of particles in the population is N The j-th dimension position of the i-th particle can be represented as X i,j The positions of all particles are arranged as X=[X 1 ,X 2 ,...,X N ] T Each position parameter corresponds to the target speed of the ship's direct drive motor during the speed optimization process, and each position represents a feasible solution;

[0016] (2) Initialize the particle swarm: Perform a global search. During the search, the initial position of the particle numbered i is... X i,j = Lj +U(0,1)·(U j -L j ) ,in L j and U j These are the lower and upper bounds of the j-th dimension, respectively. U(0,1) It is a uniformly distributed random number within the range [0,1], which is related to the parameter inertia weight ω∈[0.5,1.0] and the individual learning coefficient in the optimization algorithm. c 1 Group learning coefficient c 2 Convergence factor The maximum number of iterations M is initialized.

[0017] (3) Determine the number of iterations t Is it less than the maximum number of iterations? M Otherwise, output the optimal target speed for the direct drive motor.

[0018] (4) If so, the optimization model is invoked to calculate the fitness value of each particle: When the optimization model is running, the results of different combinations of control parameters are evaluated through the established evaluation function, the fitness value of each particle is calculated and compared, and the position of the particle with the optimal fitness value is defined as... The average position of the squirrel is The particle's current position is ;

[0019] (5) Select random individuals , Crossover operation generates a new population: the particle position update formula is as follows The particle velocity update formula is: , As an inertial weight of the influence of the previous step velocity on the current position, the best position among the new position and its intersection position is compared with the old position during the selection process to participate in the next generation of the population;

[0020] (6) Determine if the fitness value is better; otherwise, repeat step (5) to find the best individual. , Crossover operations are used to generate a new population until the globally optimal result is output. The optimization process ends and the corresponding optimal direct drive motor target speed is obtained; if so, the optimal fitness value is replaced and updated, and step (3) is repeated. The iteration number t is judged again to see if it is less than the maximum iteration number M.

[0021] Furthermore, in step 3, the ship's direct drive motor speed output control is as follows: the target speed determined in step 2 is used as a control command, which is output to the ship's direct drive motor controller in real time through the ship's industrial network. After receiving the command, the controller dynamically adjusts the electromagnetic torque by adjusting the voltage frequency and amplitude output to the motor, thereby realizing the adjustment of the ship's direct drive motor output speed.

[0022] Furthermore, in step 4, the speed of the ship's direct-drive motor is continuously optimized and adjusted: the direct-drive ship propulsion system continuously monitors the actual speed of the ship's direct-drive motor; if the actual speed of the motor deviates from the optimal target value under the current operating conditions due to external meteorological disturbances or human intervention, the direct-drive ship propulsion system re-collects the latest navigation status and waterway meteorological data, runs the optimization algorithm again based on the new data input, calculates the optimal target speed adapted to the new operating conditions, and quickly issues new control commands to adjust the motor output.

[0023] The beneficial effects of this invention are:

[0024] This invention collects real-time meteorological data on wind, waves, and currents in the waterway, along with ship operating parameters, and dynamically calculates and outputs the optimal target speed value for the direct-drive motor. This significantly improves motor operating efficiency while meeting navigation time limits and safety requirements. The method effectively suppresses load disturbances caused by sudden changes in sea state, reduces peak loads on the power system under extreme conditions, and minimizes fuel consumption due to unreasonable speeds, achieving a dual optimization of ship operating economy and carbon emission indicators.

[0025] The particle swarm optimization algorithm-driven speed decision mechanism proposed in this invention can adapt to the resistance characteristics and energy efficiency requirements of different navigation segments. By globally searching for the optimal speed of the direct-drive motor, it avoids the limitations of traditional fixed-speed strategies. This technical solution can not only shorten the navigation time in complex sea conditions, but also extend the service life of equipment through refined management, providing reliable technical support for the low-carbon development of intelligent ships and effectively alleviating the contradiction between environmental pressure and operating costs faced by the shipping industry. Attached Figure Description

[0026] Figure 1 This is a flowchart of the control method of the present invention;

[0027] Figure 2 This is a schematic diagram of the particle swarm optimization algorithm of the present invention. Detailed Implementation

[0028] The following description, in conjunction with the accompanying drawings and specific embodiments, provides further details.

[0029] Reference Figure 1 As shown, the present invention discloses a method for optimizing the speed of a direct-drive propulsion ship based on wind, wave and current resistance analysis, which mainly includes the following steps.

[0030] Step 1: The direct-drive ship propulsion system acquires the ship's speed, wind, wave, and current meteorological data in the waterway, the operating speed of the direct-drive motor, and the sailing time limit.

[0031] The steps for obtaining relevant technical parameters of the ship's direct drive motor are as follows: By integrating the ship's navigation system, continuously obtain the ship's actual ground speed and water speed as a baseline for speed optimization; Utilize the ship's onboard meteorological station, radar, and accessed maritime meteorological forecast data stream to collect real-time wind, wave, and current environment data of the ship's current channel and the route ahead, including key parameters such as wind speed and direction, wave height and direction, and current speed and direction, in order to quantify the comprehensive impact of wind, waves, and current on the ship's navigation resistance; Directly read the current operating speed, output torque, and power parameters from the ship's direct drive motor control system.

[0032] Step 2: Analyze and calculate the current navigation resistance and motor operating efficiency based on meteorological data. Combine the ship's navigation resistance data, the predetermined navigation time limit, and the efficiency characteristic curve of the ship's direct drive motor to determine whether the resistance level has changed. If the resistance level has changed, calculate the target speed of the ship's direct drive motor through the optimization model.

[0033] Based on the principle of optimizing motor operating efficiency, the optimization model designs a speed optimization method for direct-drive propulsion ships. An evaluation function for the mode switching process is established according to the motor speed fluctuation and motor operating efficiency. The optimization method is used to optimize the target speed of the direct-drive motor in the speed optimization process and outputs the target speed of the direct-drive motor.

[0034] The optimal motor speed for direct-drive propulsion vessels in different navigation environments enables them to adjust their speed based on weather data while meeting navigation time limits, thereby improving motor operating efficiency and reducing energy consumption throughout the voyage.

[0035] This step comprehensively considers the sailing time limit, transforming the time constraint into boundary conditions in the control algorithm to provide input for the next decision; the evaluation function in the optimization model is quantified through two evaluation index parameters.

[0036] (1) Evaluation index of motor speed fluctuation, the calculation function is as follows: ,in This is the original speed of the motor. This is the target speed of the motor.

[0037] (2) Evaluation index of operating efficiency of direct-drive propulsion ships, the calculation function is as follows: , For the output power of ship propellers, This represents the output power of the direct drive motor.

[0038] Step 3: Based on the optimization model, the target speed of the ship's direct drive motor is calculated using the output speed of the ship's direct drive motor as the optimization variable. The output command is then sent to the controller of the ship's direct drive motor through coordinated control, and the output speed of the ship's direct drive motor is instantaneously coordinated and controlled.

[0039] The direct drive motor speed output control involves using the target speed determined in step 2 as a control command, which is then output in real time to the propulsion control inverter or control system of the direct drive motor via the ship's industrial network. After receiving the command, the controller dynamically adjusts the electromagnetic torque by regulating the voltage frequency and amplitude output to the permanent magnet synchronous motor, thereby adjusting the output speed of the direct drive motor.

[0040] Step 4: If the actual ship operating conditions change, causing the speed of the ship's direct drive motor to change, execute steps 1, 2, and 3 to control the ship's speed optimization under different navigation environment conditions.

[0041] The ship's direct drive motor speed is continuously optimized and adjusted: the direct drive ship propulsion system continuously monitors the actual speed of the ship's direct drive motor; if the actual speed of the motor deviates from the optimal target value under the current operating conditions due to external weather disturbances or human intervention, the direct drive ship propulsion system will repeat steps 1 to 3: re-collect the latest navigation status and waterway weather data (step 1), run the optimization algorithm again based on the new data input, calculate the optimal target speed adapted to the new operating conditions (step 2), and quickly issue new control commands to adjust the motor output (step 3).

[0042] This method acquires real-time data on wind speed, wave height, and current velocity in the waterway through an integrated monitoring system, simultaneously collecting key parameters such as the ship's current speed and the efficiency curve of the direct-drive motor, and establishing a resistance prediction model and evaluation system. Based on this, driven by a particle swarm optimization algorithm, the output speed of the direct-drive motor is used as a dynamic adjustment variable. Under the premise of meeting navigation time limits and safety, the optimal combination of speeds is globally searched to achieve synergistic optimization of motor operating efficiency and hydrodynamic performance. Therefore, how to dynamically optimize the matching relationship between direct-drive motor speed and propulsion power according to the ship's real-time navigation status and energy efficiency requirements, suppress the impact of sudden load changes caused by wind, waves, and current disturbances, and simultaneously ensure the stable operation of the power system within the high-efficiency range is the problem that this invention aims to solve.

[0043] The optimization method used in step 2 of the optimization model is the following particle swarm optimization algorithm.

[0044] (1) Assume the number of particles in the population is N , dimension D , No. i The j-th dimension position of a particle can be represented as X i,jThe positions of all particles are arranged as X=[X 1 ,X 2 ,...,X N ] T Each position parameter corresponds to the target speed of the ship's direct drive motor during the speed optimization process, and each position represents a feasible solution.

[0045] (2) Perform a global search. During the search, the initial position of the particle numbered i is... X i,j =L j +U(0,1)· (U j -L j ) ,in L j and U j These are the lower and upper bounds of the j-th dimension, respectively. U(0,1) It is a uniformly distributed random number within the range [0,1], which is related to the parameter inertia weight ω∈[0.5,1.0] and the individual learning coefficient in the optimization algorithm. c 1 Group learning coefficient c 2 Convergence factor Maximum number of iterations M Perform initialization settings.

[0046] (3) Determine the number of iterations t Is it less than the maximum number of iterations? M Otherwise, output the optimal target speed for the direct drive motor.

[0047] (4) If so, call the direct-drive ship propulsion system model to calculate the fitness value of each particle: When running the model, evaluate the results of different combinations of control parameters through the established evaluation function, calculate the fitness value of each particle and compare them, and determine the position of the particle with the optimal fitness value as defined. The average position of the squirrel is Update particle position .

[0048] (4) If so, the optimization model is invoked to calculate the fitness value of each particle: When the optimization model runs, the evaluation function is used to evaluate the results of different combinations of control parameters, calculates the fitness value of each particle and compares them, and determines the position of the particle with the optimal fitness value as defined in the model. The average position of the squirrel is The particle's current position is .

[0049] (5) Select random individuals , Crossover operations generate a new population and update particle positions. : Through formula Update particle positions using the formula Update particle velocity, To control the influence of the previous step velocity on the current inertia weight, the best position among the new position and its intersection position is compared with the old position during the selection process to participate in the next generation of the population.

[0050] (6) Determine whether the fitness value is better.

[0051] Otherwise, repeat step (5) of the optimization process and select a random individual. , Crossover operations are used to generate a new population until the globally optimal result is output. The optimization process ends, and the corresponding optimal target speed of the direct drive motor is obtained.

[0052] If so, replace and update the best fitness value, repeat step (3), and repeat the check again to see if the number of iterations t is less than the maximum number of iterations. M .

[0053] The above embodiments are merely illustrative of the principles and effects of the present invention, as well as some of the application examples. For those skilled in the art, various modifications and improvements can be made without departing from the inventive concept of the present invention, and these all fall within the protection scope of the present invention.

Claims

1. A direct drive propulsion vessel speed optimization control method, characterized by: Comprising the following steps Step 1, the direct drive ship propulsion system obtains the ship speed of the ship direct drive motor, the wind, wave, current weather data in the ship channel, and the running speed of the direct drive motor and the sailing time limit; Step 2, according to the weather data analysis and calculation of the current sailing resistance and motor operation efficiency, combined with the ship sailing resistance data, the predetermined sailing time limit and the ship direct drive motor efficiency characteristic curve, judge whether the resistance level changes: if the resistance level changes, through the optimization model based on the evaluation function, the target speed of the ship direct drive motor is calculated by taking the motor output speed as the optimization variable; The evaluation function is quantified by two evaluation index parameters of motor speed fluctuation and direct drive propulsion ship operation efficiency, a calculation function of the motor speed fluctuation evaluation index is , wherein is the original speed of the direct drive motor of the ship, is the target speed of the direct drive motor of the ship, and a calculation function of the direct drive propulsion ship operation efficiency evaluation index is , wherein is the output power of the propeller of the ship, is the output power of the direct drive motor of the ship. Step 3, according to the target speed, output instruction to the controller of the ship direct drive motor, instantaneous coordination control the output speed of the ship direct drive motor; Step 4, if the speed of the ship direct drive motor changes, repeat steps 1-3, control the ship to optimize the speed in different navigation environment conditions.

2. The direct drive propulsion vessel speed optimization control method of claim 1, wherein, The parameter acquisition step in step 1 is: through the navigation system of the ship, the actual ground speed and water speed of the ship are continuously acquired as the baseline of speed optimization; the key parameters of wind speed and direction, wave height and direction, current speed and direction of the channel and the front route of the ship are collected in real time by using the shipboard weather station, radar and accessed maritime weather forecast data stream, so as to quantify the comprehensive influence of wind, wave and current on the sailing resistance of the ship; the current running speed, output torque and power parameters of the ship direct drive motor are read from the control system of the ship direct drive motor.

3. The direct drive propulsion vessel speed optimization control method of claim 2, wherein, The optimization model in step 2 adopts particle swarm optimization algorithm for optimization calculation: (1) Set the number of particles in the population to be N , No. i The position of the j-th dimension of a particle is represented as X i,j The positions of all particles are arranged as X=[X 1 ,X 2 ,...,X N ] T Each position parameter corresponds to the target speed of the ship's direct drive motor during the speed optimization process, and each position represents a feasible solution; (2) Perform a global search. During the search process, the number is... i The initial position of the particle is X i,j =L j +U(0,1)·(U j -L j ) ,in L j and U j They are the first j The lower and upper bounds of a dimension, U(0,1) It is a uniformly distributed random number within the range [0,1], which is related to the parameter inertia weight ω∈[0.5,1.0] and the individual learning coefficient in the optimization algorithm. c 1 Group learning coefficient c 2 Convergence factor The maximum number of iterations M is initialized. (3) determining the number of iterations t whether it is less than the maximum number of iterations M ; otherwise, output the optimal direct-drive motor target speed; (4) is then called the optimization model to calculate the fitness value of each particle: the optimization model runs through the evaluation function to evaluate the results of different control parameter combinations, calculates the fitness value of each particle and compares them, and determines that the particle position with the optimal fitness value is defined as , the average position of the squirrel is , and the current position of the particle is ; (5) Selecting random individuals , , the crossover operation generates a new population: the particle position update formula is , the particle velocity update formula is , is the inertia weight of the influence of the previous step velocity on the current, and the new position and the best position in the crossover position are compared with the old position in the selection process to participate in the next generation population; (6) whether the fitness value is better; otherwise, repeat step (5) to optimize the process until the global optimal result is output The optimization process is ended, and the optimal direct drive motor target speed is obtained; otherwise, the best fitness value is replaced and updated, and step (3) is repeated.

4. The direct drive propulsion vessel speed optimization control method of claim 3, wherein, The target speed determined in step 2 is used as the control instruction, which is output to the controller of the ship direct drive motor in real time through the industrial network of the ship. After receiving the instruction, the electromagnetic torque is dynamically adjusted by adjusting the voltage frequency and amplitude, so as to realize the adjustment of the output speed of the ship direct drive motor.

5. The direct drive propulsion vessel speed optimization control method of claim 4, wherein, In step 4, if the actual speed of the motor deviates from the optimal target value under the current working condition due to external weather environment disturbance or human intervention, the direct drive ship propulsion system reacquires the latest sailing state and channel weather data, runs the optimization algorithm again based on the new data input, calculates the optimal target speed suitable for the new working condition, and quickly issues new control instructions to adjust the motor output.

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