Main and auxiliary power conversion control method and system for main and auxiliary power hybrid ship

Abstract

The invention relates to the technical field of ship power, in particular to a main and auxiliary power conversion control method and system for a main and auxiliary power hybrid ship, and the method comprises the following steps: predicting the power demand of the ship in future time based on multi-source information in the ship navigation process; based on the predicted power demand, establishing a power dynamic optimization model, and adjusting an optimal power conversion scheme in real time; the dynamic optimization model can continuously optimize a control strategy based on historical operation data; multi-source information in the ship navigation process is collected, including navigation states, meteorological sea conditions and real-time performance parameters of a main engine and an auxiliary engine, and a deep learning algorithm or a machine learning algorithm (especially a long short-term memory (LSTM) network) is adopted to accurately predict the power demand. Therefore, the operation efficiency of the power system can be improved, and unnecessary energy consumption can be reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of ship power, and in particular to a main-auxiliary power conversion control method and system for a main-auxiliary power hybrid ship. Background Art

[0002] As the main means of water transportation, the energy efficiency and environmental performance of ships are increasingly valued. Especially in the context of global climate change and increased awareness of environmental protection, improving the energy efficiency of ships and reducing emissions have become important development directions for the shipping industry. As a new type of ship, the main-auxiliary hybrid ship achieves power optimization under different navigation conditions by combining the main engine and auxiliary power source, thereby improving energy efficiency and environmental performance.

[0003] The power conversion control of main-auxiliary hybrid ships is usually based on fixed control strategies or simple rules, which are difficult to adapt to complex and changing navigation conditions. For example, traditional control methods may only adjust the output of the power system according to the speed or load of the ship, while ignoring the impact of external factors such as weather and sea conditions on power demand. In addition, existing control systems often lack real-time and intelligence, and cannot accurately predict and adapt to changes in the power demand of ships under different conditions.

[0004] The problems existing in the prior art are summarized as follows:

[0005] Existing technologies are usually unable to accurately predict the power requirements of ships under different sailing conditions, resulting in inaccurate control of the power system and the inability to fully utilize energy efficiency and environmental protection performance.

[0006] Traditional control methods lack intelligence and adaptability, and are unable to dynamically adjust control strategies based on real-time data to adapt to complex and changing navigation conditions.

[0007] The fault detection and emergency response mechanisms in existing technologies are usually not timely enough to provide effective early warning and intervention before potential faults occur, increasing the risk of ship operation. Summary of the invention

[0008] In view of the technical problems existing in the prior art, the present invention provides a main-auxiliary power conversion control method and system for a main-auxiliary power hybrid ship to solve the problem that the existing control strategies or simple rules are difficult to adapt to complex and changeable navigation conditions.

[0009] The technical solution of the present invention to solve the above technical problems is as follows: A main-auxiliary power conversion control method for a main-auxiliary power hybrid ship comprises the following steps:

[0010] Based on multi-source information during the ship's voyage, predict the ship's power demand in the future;

[0011] Based on the predicted power demand, a power dynamic optimization model is established to adjust the optimal power conversion plan in real time;

[0012] The power dynamic optimization model can continuously optimize the control strategy based on historical operation data;

[0013] Real-time monitoring of the operating status of the main and auxiliary power, and use of big data analysis technology to identify and analyze abnormal signals to detect potential faults;

[0014] When the detected abnormal signal indicates a potential fault, the preset emergency response plan is automatically triggered;

[0015] The power conversion status, power demand prediction results and optimized power conversion plan are transmitted to the terminal display device, allowing remote monitoring and control.

[0016] Based on the above technical solution, the present invention can also be improved as follows.

[0017] Furthermore, the multi-source information includes ship navigation status information, weather and sea condition information, and real-time performance parameters of main and auxiliary engines.

[0018] Furthermore, deep learning algorithms or machine learning algorithms are used to combine and process ship navigation status information, meteorological and sea condition information, and real-time performance parameters of main and auxiliary engines from multiple data sources to predict the power demand of the ship within a predetermined time period in the future.

[0019] Furthermore, the deep learning algorithm or machine algorithm is a long short-term memory network (LSTM), and the prediction step includes:

[0020] Collect multi-source information during the ship's voyage;

[0021] Clean the collected multi-source information data to remove outliers and missing values;

[0022] Normalize the data to improve the convergence speed and prediction accuracy of the algorithm;

[0023] By using statistical analysis and data mining techniques, characteristic variables related to ship power demand are extracted from the preprocessed data;

[0024] Constructing a long short-term memory network (LSTM) model, wherein the model consists of an input layer, a hidden layer, and an output layer;

[0025] The input layer receives the preprocessed feature variables as input.

[0026] The hidden layer contains multiple LSTM units to capture long-term dependencies in time series data.

[0027] The output layer outputs the predicted ship power demand value;

[0028] The LSTM model is trained using historical navigation data, and the model parameters are adjusted through the back-propagation algorithm to minimize the prediction error.

[0029] Input the multi-source information collected in real time into the trained LSTM model to make real-time predictions of power demand;

[0030] According to the prediction results, the operating status of the main and auxiliary power systems is adjusted to meet the power requirements of the ship. Furthermore, the LSTM unit state update formula includes:

[0031] f t =σ(W f ·[h t-1 , x t ]+b f )

[0032] i t =σ(W i ·[h t-1 , x t ]+b i )

[0033]

[0034] o t =σ(W o ·[h t-1 , x t ]+b o )

[0035] h t =o t *tanh(C t );

[0036] Among them, f t 、i t , o t They are the activation function outputs of the forget gate, input gate, and output gate respectively;

[0037] C t , They are the current moment and candidate unit states respectively;

[0038] h t is the hidden layer output at the current moment;

[0039] W f , W i , W c , W o are the weight matrices of the forget gate, input gate, candidate unit state, and output gate respectively;

[0040] b f 、b i 、b c 、b o are the corresponding bias terms respectively;

[0041] σ is the Sigmoid activation function; tanh is the hyperbolic tangent activation function;

[0042] x t is the input feature vector at the current moment;

[0043] h t-1 is the hidden layer output of the previous moment;

[0044] C t-1 is the unit state at the previous moment;

[0045] W and b are the weight and bias parameters learned during model training.

[0046] Furthermore, the power dynamic optimization model adopts a hybrid model combining a multi-objective optimization algorithm and a machine learning prediction model, and the steps of real-time adjustment and determination of the optimal power conversion solution include:

[0047] Collect real-time data during the ship's voyage;

[0048] Preprocess the collected data;

[0049] Define optimization objective functions, including minimizing energy costs, minimizing total emissions, and maximizing system stability;

[0050] A non-dominated sorting genetic algorithm (NSGA-II) multi-objective optimization algorithm is used to construct a multi-objective optimization model; the multi-objective optimization model uses power combination as a decision variable, and energy consumption cost, total emissions and system stability as optimization objectives;

[0051] Using historical navigation data, we build a machine learning prediction model to predict energy consumption, emissions, and system stability under different power combinations.

[0052] Input real-time data into the multi-objective optimization model to generate a series of feasible power combination solutions;

[0053] The machine learning prediction model is used to predict the energy consumption, emissions and system stability of the generated solutions.

[0054] Based on the prediction results, the Pareto frontier analysis method is used to select the power conversion solution with the lowest energy consumption, the least emissions and the best system stability;

[0055] Collect new navigation data in real time and use it to update machine learning prediction models and multi-objective optimization models;

[0056] Further, the multi-objective optimization objective function is:

[0057] minF(x)={f1(x), f2(x), f3(x)};

[0058] Among them, x is the decision variable of the power combination scheme; f1(x) is the energy cost function, which represents the energy cost under the power combination scheme; f2(x) is the total emission function, which represents the total emission under the power combination scheme; f3(x) is the system stability function, which represents the system stability under the power combination scheme;

[0059] The machine learning prediction model is a neural network, and the neural network formula is:

[0060] y=f(W,b,x);

[0061] Among them, y is the prediction result;

[0062] f is the neural network model;

[0063] W and b are the weight and bias of the neural network respectively;

[0064] x is the input feature.

[0065] Furthermore, triggering the emergency response plan includes at least one of switching to a backup power source and adjusting the navigation plan.

[0066] Furthermore, it also includes a user-friendly human-computer interaction interface for displaying the power conversion status, the predicted results of power demand and the optimized power conversion plan, and allows the user to remotely monitor and control through the interface.

[0067] Furthermore, the human-computer interaction interface also includes a fault early warning prompt function, which alerts the user visually and / or auditorily when a potential fault is detected.

[0068] The present invention also provides a main-auxiliary power conversion control system for a main-auxiliary power hybrid ship, comprising:

[0069] The power demand prediction module is used to predict the power demand of the ship in the future based on multi-source information during the ship's navigation process;

[0070] A power dynamic optimization module is used to establish a power dynamic optimization model based on the predicted power demand, and to adjust and determine the best power conversion plan in real time; the power dynamic optimization model can continuously optimize the control strategy based on historical operation data;

[0071] The status monitoring and fault detection module is used to monitor the operating status of the main and auxiliary power in real time, and use big data analysis technology to identify and analyze abnormal signals to detect potential faults;

[0072] The emergency response module automatically triggers the preset emergency response plan when the detected abnormal signal indicates a potential fault;

[0073] Human-machine interface module, used to display power conversion status, power demand forecast results and optimized power conversion plan, and allow remote monitoring and control

[0074] Furthermore, the multi-source information includes ship navigation status information, weather and sea condition information, and real-time performance parameters of main and auxiliary engines;

[0075] The power demand prediction module uses a deep learning algorithm or a machine learning algorithm to combine and process the multi-source information from multiple data sources to predict the power demand of the ship within a predetermined time period in the future;

[0076] The power demand prediction module adopts the long short-term memory network (LSTM) algorithm to predict the power demand, including a data collection and preprocessing unit, a feature extraction unit, an LSTM model construction and training unit, and a real-time prediction and adjustment unit;

[0077] The power dynamic optimization module adopts a hybrid model that combines a multi-objective optimization algorithm with a machine learning prediction model to adjust and determine the optimal power conversion plan in real time;.

[0078] The power dynamic optimization module includes a data collection and preprocessing unit, a multi-objective optimization model building unit, a machine learning prediction model building unit, an optimization scheme generation and selection unit, and a model update and optimization unit.

[0079] The beneficial effects of the present invention are:

[0080] 1. By collecting multi-source information during the ship's voyage, including navigation status, weather and sea conditions, and real-time performance parameters of main and auxiliary engines, deep learning algorithms or machine learning algorithms (especially long short-term memory networks LSTM) are used to accurately predict power demand. This helps improve the operating efficiency of the power system and reduce unnecessary energy consumption.

[0081] 2. Based on the predicted power demand, a dynamic power optimization model is established, and a hybrid model combining a multi-objective optimization algorithm (such as NSGA-II) and a machine learning prediction model (such as SVM, RF, NN) is used to adjust and determine the optimal power conversion solution in real time. This hybrid model can comprehensively consider multiple objectives such as energy consumption cost, total emissions, and system stability to achieve global optimization of the power system.

[0082] 3. Real-time monitoring of the operating status of the main and auxiliary power sources, using big data analysis technology to identify and analyze abnormal signals to detect potential faults. Once an abnormality is detected, the system automatically triggers the preset emergency response plan, such as switching to the backup power source or adjusting the navigation plan, to ensure the safe operation of the ship. At the same time, through the fault warning prompt function of the human-computer interaction interface, users are promptly reminded of potential faults to enhance the safety and reliability of the system.

[0083] 4. By accurately predicting power demand and optimizing power conversion solutions in real time, the system can reasonably allocate power resources according to the actual needs of the ship, avoiding unnecessary energy consumption and emissions. This helps reduce the operating costs of the ship, while reducing the impact on the environment and achieving green navigation.

[0084] 5. Provide intuitive information display and remote control functions. Users can view the power conversion status, power demand forecast results and optimized power conversion plan through the human-computer interaction interface, and perform necessary remote monitoring and control. This design improves the intelligence level of the system and user experience, and enhances the interactivity and convenience between users and the system.

[0085] 6. Provide intuitive information display and remote control functions. Users can view the power conversion status, power demand forecast results and optimized power conversion plan through the human-computer interaction interface, and perform necessary remote monitoring and control. This design improves the intelligence level of the system and user experience, and enhances the interactivity and convenience between users and the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] Figure 1 It is a schematic diagram of the arrangement process of the present invention;

[0087] Figure 2 A flow chart showing the power demand within a predetermined time period of the present invention;

[0088] Figure 3 It is a schematic diagram of the principle of the power dynamic optimization model of the present invention. DETAILED DESCRIPTION

[0089] The principles and features of the present invention are described below in conjunction with the accompanying drawings. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0090] like Figure 1-3 The present invention relates to a main-auxiliary power conversion control method for a main-auxiliary power hybrid ship, comprising the following steps:

[0091] Based on multi-source information during the ship's voyage, predict the ship's power demand in the future;

[0092] Based on the predicted power demand, a power dynamic optimization model is established to adjust the optimal power conversion plan in real time;

[0093] The power dynamic optimization model can continuously optimize the control strategy based on historical operation data;

[0094] Real-time monitoring of the operating status of the main and auxiliary power, and use of big data analysis technology to identify and analyze abnormal signals to detect potential faults;

[0095] When the detected abnormal signal indicates a potential fault, the preset emergency response plan is automatically triggered;

[0096] The power conversion status, power demand prediction results and optimized power conversion plan are transmitted to the terminal display device, allowing remote monitoring and control.

[0097] This method predicts future power demand based on multi-source information during the ship's voyage, adjusts the optimal power conversion plan in real time by establishing a dynamic power optimization model, and continuously optimizes the control strategy based on historical data. At the same time, it monitors the operating status of the main and auxiliary power in real time, uses big data analysis technology to detect potential faults, and automatically triggers emergency response plans. All power conversion status, prediction results, and optimization plans are transmitted to the terminal display to support remote monitoring and control.

[0098] As an implementation method, the multi-source information includes ship navigation status information (specifically, including but not limited to the ship's speed, heading, and load condition), meteorological and sea condition information (specifically, including but not limited to wind speed, wave height, and water flow speed), and real-time performance parameters of main and auxiliary engines.

[0099] As a supplement, deep learning algorithms or machine learning algorithms are used to combine and process the ship's navigation status information, meteorological and sea conditions information, and real-time performance parameters of the main and auxiliary engines from multiple data sources to predict the ship's power demand in a predetermined time period in the future.

[0100] Specifically, the deep learning algorithm or machine algorithm is a long short-term memory network (LSTM), and the prediction step includes:

[0101] Collect multi-source information during the ship's voyage (specifically, including but not limited to the ship's speed, heading, load conditions, wind speed, wave height, water flow speed, and real-time performance parameters of main and auxiliary engines, etc.);

[0102] Clean the collected multi-source information data to remove outliers and missing values; ensure the accuracy and completeness of the data;

[0103] Normalize the data to improve the convergence speed and prediction accuracy of the algorithm;

[0104] Utilize statistical analysis and data mining techniques to extract characteristic variables related to ship power requirements from preprocessed data; (Specifically, these characteristic variables include ship acceleration, deceleration, turning rate, wind and wave resistance coefficient, etc.)

[0105] Constructing a long short-term memory network (LSTM) model, wherein the model consists of an input layer, a hidden layer, and an output layer;

[0106] The input layer receives the preprocessed feature variables as input.

[0107] The hidden layer contains multiple LSTM units to capture long-term dependencies in time series data.

[0108] The output layer outputs the predicted ship power demand value;

[0109] The LSTM model is trained using historical navigation data, and the model parameters are adjusted through the back-propagation algorithm to minimize the prediction error.

[0110] Specifically, during the training process, methods such as cross-validation can be used to evaluate the generalization ability of the model and prevent overfitting;

[0111] Input the multi-source information collected in real time into the trained LSTM model to make real-time predictions of power demand;

[0112] According to the prediction results, the operating status of the main and auxiliary power systems is adjusted to meet the power requirements of the ship. As a supplement, the LSTM unit state update formula includes:

[0113] f t =σ(W f ·[h t-1 , x t ]+b f )

[0114] i t =σ(W i ·[h t-1 , x t ]+b i )

[0115]

[0116] o t =σ(W o ·[h t-1 , x t ]+b o )

[0117] h t =o t *tanh(C t );

[0118] Among them, f t 、i t , o t They are the activation function outputs of the forget gate, input gate, and output gate respectively;

[0119] C t , They are the current moment and candidate unit states respectively;

[0120] h t is the hidden layer output at the current moment;

[0121] W f , W i , W c , W o are the weight matrices of the forget gate, input gate, candidate unit state, and output gate respectively;

[0122] b f 、b i 、b c 、b o are the corresponding bias terms respectively;

[0123] σ is the Sigmoid activation function; tanh is the hyperbolic tangent activation function;

[0124] x t is the input feature vector at the current moment;

[0125] h t-1 is the hidden layer output of the previous moment;

[0126] C t-1 is the unit state at the previous moment;

[0127] W and b are the weight and bias parameters learned during model training.

[0128] The present invention also provides a specific embodiment to illustrate how to use a long short-term memory network (LSTM) algorithm to predict the power demand of a ship under different sailing conditions.

[0129] Embodiment 1:

[0130] This embodiment provides a method for predicting the power demand of a main-auxiliary hybrid ship based on a long short-term memory network (LSTM) algorithm. The method collects multi-source information during the ship's navigation process and uses the LSTM algorithm for training and prediction to accurately predict the power demand of the ship under different navigation conditions.

[0131] Step 1: Data collection and preprocessing

[0132] First, we collected multi-source information of a certain type of main and auxiliary power hybrid ship during its voyage over a period of time, including but not limited to the ship's speed, heading, load, wind speed, wave height, water flow speed, and real-time performance parameters of the main and auxiliary engines. The data comes from the ship's automatic monitoring system, ensuring the real-time and accuracy of the data.

[0133] Next, we cleaned the collected data, removed outliers and missing values, and ensured the integrity and reliability of the data. Then, we normalized the data and converted data of different dimensions to the same scale to improve the convergence speed and prediction accuracy of the algorithm.

[0134] Step 2: Feature extraction

[0135] Based on data preprocessing, we use statistical analysis and data mining techniques to extract characteristic variables related to ship power requirements from the preprocessed data. These characteristic variables include ship acceleration, deceleration, turning rate, wind and wave resistance coefficient, etc., which can reflect the dynamic characteristics and power requirements of ships under different sailing conditions.

[0136] Step 3: Model building and training

[0137] We constructed a long short-term memory network (LSTM) model, which consists of an input layer, a hidden layer, and an output layer. The input layer receives the preprocessed feature variables as input, the hidden layer contains multiple LSTM units to capture the long-term dependencies in the time series data, and the output layer outputs the predicted ship power demand value.

[0138] Then, we used historical navigation data to train the LSTM model. During the training process, we used methods such as cross-validation to evaluate the generalization ability of the model and prevent overfitting. The model parameters were adjusted through the back-propagation algorithm to minimize the prediction error. After multiple iterations of training, we obtained an LSTM prediction model with good performance.

[0139] Step 4: Real-time prediction and adjustment

[0140] During the ship's voyage, we collect multi-source information in real time and input it into the trained LSTM model to make real-time predictions of power demand. The prediction results can reflect the changes in the ship's power demand under different sailing conditions, providing an important basis for the ship's power system control.

[0141] According to the prediction results, we timely adjust the operating status of the main and auxiliary power systems to meet the power demand of the ship. When we predict that the power demand will increase, we increase the output power of the main engine or start the auxiliary power source; when we predict that the power demand will decrease, we reduce the output power of the main engine or shut down the auxiliary power source. In this way, we can achieve optimal control of the ship's power system, improve navigation efficiency and energy conservation and emission reduction effects.

[0142] Implementation Effect

[0143] Through the method of this embodiment, we have successfully realized the power demand prediction of main and auxiliary hybrid ships based on the long short-term memory network (LSTM) algorithm. The prediction results are accurate and reliable, and can provide strong support for the power system control of the ship. At the same time, this method has the advantages of strong adaptability and high prediction accuracy, and can be widely used in the power demand prediction of different types of ships.

[0144] As an implementation method, the power dynamic optimization model adopts a hybrid model combining a multi-objective optimization algorithm and a machine learning prediction model, and the steps of real-time adjustment and determination of the optimal power conversion solution include:

[0145] Collect real-time data during the ship's voyage (specifically, including but not limited to the ship's speed, heading, load conditions, wind and wave conditions, engine speed, fuel consumption rate, emission concentration, etc.).

[0146] Preprocess the collected data (specifically, including data cleaning, outlier processing, data normalization, etc. to ensure the accuracy and consistency of the data.)

[0147] Define the optimization objective function (specifically, including minimizing energy consumption costs, minimizing total emissions, and maximizing system stability);

[0148] A multi-objective optimization model is constructed by using a non-dominated sorting genetic algorithm (NSGA-II) multi-objective optimization algorithm; the multi-objective optimization model takes the power combination (specifically, the output power of the main engine, the state of the auxiliary power source, etc.) as the decision variable, and takes the energy consumption cost, the total amount of emissions and the system stability as the optimization objectives;

[0149] Using historical navigation data, we build a machine learning prediction model to predict energy consumption, emissions, and system stability under different power combinations.

[0150] Specifically, machine learning models include support vector machines (SVM), random forests (RF), neural networks (NN), etc. By training the model, it can accurately predict energy consumption, emissions and system stability in the future.

[0151] Input real-time data into the multi-objective optimization model to generate a series of feasible power combination solutions;

[0152] The machine learning prediction model is used to predict the energy consumption, emissions and system stability of the generated solutions.

[0153] Based on the prediction results, the Pareto frontier analysis method is used to select the power conversion solution with the lowest energy consumption, the least emissions and the best system stability;

[0154] Collect new navigation data in real time and use it to update machine learning prediction models and multi-objective optimization models;

[0155] Furthermore, through continuous learning and optimization, the prediction accuracy and optimization performance of the model can be improved.

[0156] As a supplement, the multi-objective optimization objective function is:

[0157] minF(x)={f1(x), f2(x), f3(x)};

[0158] Among them, x is the decision variable of the power combination scheme; f1(x) is the energy cost function, which represents the energy cost under the power combination scheme; f2(x) is the total emission function, which represents the total emission under the power combination scheme; f3(x) is the system stability function, which represents the system stability under the power combination scheme;

[0159] The machine learning prediction model is a neural network, and the neural network formula is:

[0160] y=f(W,b,x);

[0161] Among them, y is the prediction result (energy consumption, emissions, system stability, etc.);

[0162] f is the neural network model;

[0163] W and b are the weight and bias of the neural network respectively;

[0164] x is the input feature (real-time data).

[0165] The present invention is used to explain how to use a hybrid model combining a multi-objective optimization algorithm and a machine learning prediction model in actual operation to achieve optimal power conversion control of a main-auxiliary hybrid ship, and also provides the following optimization implementation example of power conversion control of a main-auxiliary hybrid ship.

[0166] Embodiment 2:

[0167] This embodiment focuses on a specific main-auxiliary hybrid ship, and aims to determine and adjust its optimal power conversion solution in real time by adopting a hybrid model that combines a multi-objective optimization algorithm with a machine learning prediction model.

[0168] 1. Data collection and preprocessing stage

[0169] Data collection: We collected a comprehensive range of real-time data on the ship during its voyage, covering key indicators such as speed, heading, load status, wind and wave conditions, engine speed, fuel consumption rate, and emission concentration. These data are all derived from the ship's automatic monitoring system, ensuring the real-time and accuracy of the data.

[0170] Data preprocessing: After collecting the raw data, we cleaned the data and removed outliers and missing data to ensure the integrity and accuracy of the data set. Then, we normalized the data to eliminate the differences between data of different dimensions, thereby improving the processing efficiency and prediction accuracy of subsequent algorithms.

[0171] 2. Multi-objective optimization model construction stage

[0172] Define optimization goals: We have identified three core optimization goals, namely, minimizing energy costs, minimizing total emissions, and maximizing system stability. These objective functions will serve as the basis for subsequent multi-objective optimization algorithms.

[0173] Constructing a multi-objective optimization model: Based on the non-dominated sorting genetic algorithm (NSGA-II), we constructed a multi-objective optimization model. The model uses the power combination (covering the output power of the main engine, the state of the auxiliary power source, etc.) as the decision variable, aiming to find the best balance between energy consumption cost, total emissions and system stability. Through genetic operations (such as selection, crossover, mutation) and non-dominated sorting mechanisms, we generated and screened a series of excellent power combination solutions.

[0174] 3. Machine Learning Prediction Model Construction Phase

[0175] Data preparation: We use historical navigation data as a training set, which is preprocessed and used to build a machine learning prediction model.

[0176] Model selection and training: We selected a neural network (NN) as the prediction model because of its excellent nonlinear fitting ability and generalization performance. Through training, we enabled the model to accurately predict energy consumption levels, emissions, and system stability under different power combinations. During the training process, we used methods such as cross-validation to evaluate the performance of the model and performed parameter tuning to improve prediction accuracy.

[0177] 4. Optimization Scheme Generation and Selection Phase

[0178] Generating power mix solutions: We input real-time data into a multi-objective optimization model to generate a series of feasible power mix solutions that achieve different trade-offs between energy costs, total emissions, and system stability.

[0179] Prediction and evaluation: Using the trained machine learning prediction model, we predicted the energy consumption, emissions, and system stability of the generated solutions. The prediction results provide us with an intuitive understanding of the performance of each solution.

[0180] Select the best solution: Based on the forecast results, we used methods such as Pareto frontier analysis to select the power conversion solution with the lowest energy consumption, the lowest emissions and the best system stability from the generated solutions. This solution was determined to be the best choice under the current sailing conditions.

[0181] 5. Model update and optimization stage

[0182] As the ship continues to sail, we continuously collect new navigation data and use it to update the machine learning prediction model and multi-objective optimization model. Through continuous learning and optimization, we improve the prediction accuracy and optimization performance of the model, thereby ensuring the continued effectiveness and adaptability of the power conversion control solution.

[0183] As an implementation mode, the triggering of the emergency response plan includes at least one of switching to a backup power source and adjusting the navigation plan.

[0184] When a potential fault is detected, the system automatically takes measures such as switching to a backup power source or adjusting the navigation plan to ensure the safe operation of the ship and avoid adverse effects of the fault on navigation.

[0185] As an implementation method, it also includes a user-friendly human-computer interaction interface for displaying the power conversion status, the predicted results of power demand and the optimized power conversion plan, and allowing the user to remotely monitor and control through the interface.

[0186] The human-computer interaction interface also includes a fault early warning prompt function, which alerts the user visually and / or audibly when a potential fault is detected.

[0187] The interface can clearly display the power conversion status, the predicted results of power demand and the optimized power conversion plan, providing users with intuitive information display.

[0188] More importantly, the interface also has a fault warning function. Once the system detects a potential fault, it will immediately alert the user through visual (such as flashing indicator lights, red warning messages) and / or auditory (such as alarm sounds), thereby ensuring that the user can respond quickly and take necessary measures to ensure the safe operation of the ship. In this way, the method not only improves the intelligence level of the ship's power system, but also enhances the interactivity and safety between the user and the system.

[0189] Based on the main-auxiliary power conversion control method of the main-auxiliary power hybrid ship, the present invention also proposes a main-auxiliary power conversion control system for the main-auxiliary power hybrid ship.

[0190] The present invention relates to a main-auxiliary power conversion control system for a main-auxiliary power hybrid ship, comprising:

[0191] The power demand prediction module is used to predict the power demand of the ship in the future based on multi-source information during the ship's navigation process;

[0192] A power dynamic optimization module is used to establish a power dynamic optimization model based on the predicted power demand, and to adjust and determine the best power conversion plan in real time; the power dynamic optimization model can continuously optimize the control strategy based on historical operation data;

[0193] The status monitoring and fault detection module is used to monitor the operating status of the main and auxiliary power in real time, and use big data analysis technology to identify and analyze abnormal signals to detect potential faults;

[0194] The emergency response module automatically triggers the preset emergency response plan when the detected abnormal signal indicates a potential fault;

[0195] The human-machine interaction interface module is used to display the power conversion status, the predicted results of power demand and the optimized power conversion plan, and allows remote monitoring and control.

[0196] The system integrates multiple key modules to achieve intelligent management and optimization of the ship's power system. First, the power demand prediction module uses multi-source information during the ship's voyage to accurately predict the power demand in the future. Then, the power dynamic optimization module establishes a power dynamic optimization model based on these prediction results, and adjusts it in real time to determine the best power conversion solution. The model can also continuously optimize the control strategy based on historical operating data to ensure that the power system always operates in the optimal state.

[0197] At the same time, the status monitoring and fault detection module monitors the operating status of the main and auxiliary power in real time, and uses big data analysis technology to identify and analyze abnormal signals in order to detect potential faults in a timely manner. Once an abnormality is detected, the emergency response module will automatically trigger the preset emergency response plan, such as switching to the backup power source or adjusting the navigation plan, to ensure the safe operation of the ship.

[0198] Finally, the human-machine interaction interface module provides users with intuitive information display and remote control functions. Users can view the power conversion status, power demand forecast results, and optimized power conversion plans through this interface, and perform necessary remote monitoring and control. This design not only improves the intelligence level of the system, but also enhances the interactivity and convenience between users and the system.

[0199] As an implementation mode, the multi-source information includes ship navigation status information (specifically, including but not limited to the ship's speed, heading, and load condition), meteorological and sea condition information (specifically, including but not limited to wind speed, wave height, and water flow speed), and real-time performance parameters of main and auxiliary engines; as well as real-time performance parameters of main and auxiliary engines, so as to fully understand the current status of the ship and the external environment.

[0200] The power demand prediction module uses a deep learning algorithm or a machine learning algorithm to combine and process the multi-source information from multiple data sources to predict the power demand of the ship within a predetermined time period in the future;

[0201] The power demand prediction module adopts the long short-term memory network (LSTM) algorithm to predict the power demand, including a data collection and preprocessing unit, a feature extraction unit, an LSTM model construction and training unit, and a real-time prediction and adjustment unit;

[0202] In order to predict the power demand of the ship in the future predetermined time period, the system uses deep learning algorithms or machine learning algorithms, especially the long short-term memory network (LSTM) algorithm. The LSTM algorithm collects and preprocesses data through the data collection and preprocessing unit, then extracts key features through the feature extraction unit, then builds and trains the LSTM model, and finally realizes real-time prediction and adjustment. This process can accurately predict the future power demand of the ship and provide a basis for the optimization of the power system.

[0203] The power dynamic optimization module adopts a hybrid model that combines a multi-objective optimization algorithm with a machine learning prediction model to adjust and determine the optimal power conversion plan in real time; the multi-objective optimization algorithm is a non-dominated sorting genetic algorithm (NSGA-II), and the machine learning prediction model includes a support vector machine (SVM), a random forest (RF), a neural network (NN), etc.

[0204] The power dynamic optimization module includes a data collection and preprocessing unit, a multi-objective optimization model building unit, a machine learning prediction model building unit, an optimization scheme generation and selection unit, and a model update and optimization unit.

[0205] In terms of dynamic power optimization, the system adopts a hybrid model that combines a multi-objective optimization algorithm with a machine learning prediction model. Among them, the multi-objective optimization algorithm is a non-dominated sorting genetic algorithm (NSGA-II), which can find a balance between multiple objectives and generate a series of optimal solutions. Machine learning prediction models include support vector machines (SVM), random forests (RF), neural networks (NN), etc., which are used to predict the effects of different power conversion schemes. The dynamic power optimization module includes a data collection and preprocessing unit, a multi-objective optimization model construction unit, a machine learning prediction model construction unit, an optimization scheme generation and selection unit, and a model update and optimization unit. These units work together to adjust and determine the best power conversion scheme in real time to ensure the efficient, safe and reliable operation of the ship's power system.

[0206] As an implementation mode, the emergency response plan includes at least one of switching to a backup power source and adjusting a navigation plan; when the emergency response module detects a potential fault, the emergency response plan is automatically executed.

[0207] The human-machine interaction interface module also includes a fault warning prompt function, which alerts the user visually and / or auditorily when a potential fault is detected; the human-machine interaction interface module adopts a user-friendly design to facilitate remote monitoring and control by the user.

[0208] The system integrates multiple key modules to achieve comprehensive monitoring, prediction, optimization and emergency response of the ship's power system. When the system detects a potential fault through the status monitoring and fault detection module, the emergency response module will automatically execute the preset emergency response plan, including switching to the backup power source, adjusting the navigation plan, and at least one other measure to ensure the safe operation of the ship. At the same time, the human-computer interaction interface module will immediately remind the user of potential faults through visual (such as flashing indicator lights, red warning messages) and / or auditory (such as alarm sounds), and adopts a user-friendly design to facilitate remote monitoring and control.

[0209] As a supplement, a data communication module is also included to realize data transmission and communication between modules to ensure the overall coordination and efficient operation of the system.

[0210] The data communication module plays a vital role in the entire system. It is responsible for data transmission and communication between modules to ensure the overall coordination and efficient operation of the system. Through the data communication module, the power demand prediction module, power dynamic optimization module, status monitoring and fault detection module and emergency response module can share information in real time, work together, and jointly realize the intelligent management and optimization of the ship power system.

[0211] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A main-auxiliary power conversion control method for a main-auxiliary power hybrid ship, characterized in that: The following steps are involved: Based on multi-source information during the ship's voyage, predict the ship's power demand in the future; Based on the predicted power demand, a power dynamic optimization model is established to adjust the optimal power conversion plan in real time; The power dynamic optimization model can continuously optimize the control strategy based on historical operation data; Real-time monitoring of the operating status of the main and auxiliary power, and use of big data analysis technology to identify and analyze abnormal signals to detect potential faults; When the detected abnormal signal indicates a potential fault, the preset emergency response plan is automatically triggered; The power conversion status, power demand prediction results and optimized power conversion plan are transmitted to the terminal display device, allowing remote monitoring and control.

2. The main-auxiliary power conversion control method of a main-auxiliary power hybrid ship according to claim 1 is characterized in that: The multi-source information includes ship navigation status information, weather and sea condition information, and real-time performance parameters of main and auxiliary engines.

3. The main-auxiliary power conversion control method of a main-auxiliary power hybrid ship according to claim 2 is characterized in that: Using deep learning algorithms or machine learning algorithms, the ship's navigation status information, weather and sea condition information, and real-time performance parameters of main and auxiliary engines from multiple data sources are combined and processed to predict the ship's power demand within a predetermined time period in the future.

4. The main-auxiliary power conversion control method of a main-auxiliary power hybrid ship according to claim 3 is characterized in that: The deep learning algorithm or machine algorithm is a long short-term memory network (LSTM), and the prediction steps include: Collect multi-source information during the ship's voyage; Clean the collected multi-source information data to remove outliers and missing values; Normalize the data to improve the convergence speed and prediction accuracy of the algorithm; By using statistical analysis and data mining techniques, characteristic variables related to ship power demand are extracted from the preprocessed data; Constructing a long short-term memory network (LSTM) model, wherein the model consists of an input layer, a hidden layer, and an output layer; The input layer receives the preprocessed feature variables as input. The hidden layer contains multiple LSTM units to capture long-term dependencies in time series data. The output layer outputs the predicted ship power demand value; The LSTM model is trained using historical navigation data, and the model parameters are adjusted through the back-propagation algorithm to minimize the prediction error. Input the multi-source information collected in real time into the trained LSTM model to make real-time predictions of power demand; According to the prediction results, the operating status of the main and auxiliary power systems is adjusted to meet the power requirements of the ship.

5. The main-auxiliary power conversion control method of a main-auxiliary power hybrid ship according to claim 4 is characterized in that: The LSTM unit state update formula includes: f t =σ(W f ·[h t-1 ,x t ]+b f ) i t =σ(W i ·[h t-1 ,x t ]+b i ) the t =σ(W o ·[h t-1 ,x t ]+b o ) h t =o t *tanh(C t ); Among them, f t 、i t , o t They are the activation function outputs of the forget gate, input gate, and output gate respectively; C t , They are the current moment and candidate unit states respectively; h t is the hidden layer output at the current moment; W f , W i , W c , W o are the weight matrices of the forget gate, input gate, candidate unit state, and output gate respectively; b f 、b i 、b c 、b o are the corresponding bias terms respectively; σ is the Sigmoid activation function; tanh is the hyperbolic tangent activation function; x t is the input feature vector at the current moment; h t-1 is the hidden layer output of the previous moment; C t-1 is the unit state at the previous moment; W and b are the weight and bias parameters learned during model training.

6. The main-auxiliary power conversion control method of a main-auxiliary power hybrid ship according to claim 1, characterized in that: The power dynamic optimization model adopts a hybrid model combining a multi-objective optimization algorithm and a machine learning prediction model. The steps of real-time adjustment and determination of the optimal power conversion solution include: Collect real-time data during the ship's voyage; Preprocess the collected data; Define optimization objective functions, including minimizing energy costs, minimizing total emissions, and maximizing system stability; A non-dominated sorting genetic algorithm (NSGA-II) multi-objective optimization algorithm is used to construct a multi-objective optimization model; the multi-objective optimization model uses power combination as a decision variable, and energy consumption cost, total emissions and system stability as optimization objectives; Using historical navigation data, we build a machine learning prediction model to predict energy consumption, emissions, and system stability under different power combinations. Input real-time data into the multi-objective optimization model to generate a series of feasible power combination solutions; Use machine learning prediction models to predict energy consumption, emissions, and system stability for the generated solutions; Based on the prediction results, the Pareto frontier analysis method is used to select the power conversion solution with the lowest energy consumption, the least emissions and the best system stability; New navigation data is collected in real time and used to update machine learning prediction models and multi-objective optimization models.

7. The main-auxiliary power conversion control method of a main-auxiliary power hybrid ship according to claim 6 is characterized in that: Multi-objective optimization objective function: minF(x)={f1(x), f2(x), f3(x)}; Among them, x is the decision variable of the power combination scheme; f1(x) is the energy cost function, which represents the energy cost under the power combination scheme; f2(x) is the total emission function, which represents the total emission under the power combination scheme; f3(x) is the system stability function, which represents the system stability under the power combination scheme; The machine learning prediction model is a neural network, and the neural network formula is: y = f(W, b, x); Among them, y is the prediction result; f is the neural network model; W and b are the weight and bias of the neural network respectively; x is the input feature.

8. The main-auxiliary power conversion control method of a main-auxiliary power hybrid ship according to claim 1, characterized in that: The triggering of the emergency response plan includes at least one of switching to a backup power source and adjusting the navigation plan.

9. The main-auxiliary power conversion control method of a main-auxiliary power hybrid ship according to claim 1, characterized in that: It also includes a user-friendly human-computer interaction interface for displaying the power conversion status, power demand prediction results and optimized power conversion plans, and allows users to remotely monitor and control through the interface.

10. The main-auxiliary power conversion control method of a main-auxiliary power hybrid ship according to claim 9, characterized in that: The human-computer interaction interface also includes a fault early warning prompt function, which alerts the user visually and / or audibly when a potential fault is detected.

11. A main-auxiliary power conversion control system for a main-auxiliary power hybrid ship, characterized in that: include: The power demand prediction module is used to predict the power demand of the ship in the future based on multi-source information during the ship's navigation process; A power dynamic optimization module is used to establish a power dynamic optimization model based on the predicted power demand, and to adjust and determine the best power conversion plan in real time; the power dynamic optimization model can continuously optimize the control strategy based on historical operation data; The status monitoring and fault detection module is used to monitor the operating status of the main and auxiliary power in real time, and use big data analysis technology to identify and analyze abnormal signals to detect potential faults; The emergency response module automatically triggers the preset emergency response plan when the detected abnormal signal indicates a potential fault; The human-machine interaction interface module is used to display the power conversion status, the predicted results of power demand and the optimized power conversion plan, and allows remote monitoring and control.

12. The main-auxiliary power conversion control system of the main-auxiliary power hybrid ship according to claim 11, characterized in that: The multi-source information includes ship navigation status information, weather and sea condition information, and real-time performance parameters of main and auxiliary engines; The power demand prediction module uses a deep learning algorithm or a machine learning algorithm to combine and process the multi-source information from multiple data sources to predict the power demand of the ship within a predetermined time period in the future; The power demand prediction module adopts the long short-term memory network (LSTM) algorithm to predict the power demand, including a data collection and preprocessing unit, a feature extraction unit, an LSTM model construction and training unit, and a real-time prediction and adjustment unit; The power dynamic optimization module adopts a hybrid model that combines a multi-objective optimization algorithm with a machine learning prediction model to adjust and determine the optimal power conversion plan in real time;. The power dynamic optimization module includes a data collection and preprocessing unit, a multi-objective optimization model building unit, a machine learning prediction model building unit, an optimization scheme generation and selection unit, and a model update and optimization unit.

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