Offshore floating fan liquid tank fluid motion control method and system

Abstract

The invention provides an offshore floating type fan liquid tank fluid motion control method and system, and relates to the technical field of ocean energy high-end equipment development and manufacturing, and the method comprises the steps that a valve and a pump body are arranged in a liquid tank of an offshore floating type fan; the state of fluid in the liquid tank is monitored in real time, and sensor collection data are obtained; the data collected by the sensor comprises liquid level data, pressure data, temperature data, flow data, inclination angle data, vibration data and corrosion data; performing preprocessing, feature extraction and data analysis on the data acquired by the sensor, predicting the motion state of the fan, and obtaining a prediction result of the motion state of the fan; and the valve and the pump body are adjusted according to the prediction result of the motion state of the fan, and automatic control over the offshore floating type fan liquid tank is achieved. A plurality of sensors are arranged in the liquid tank of the floating fan, and a wireless data transmission technology is adopted, so that the state of fluid in the liquid tank and the movement condition of the fan are monitored in real time and accurately analyzed.

Description

Technical Field

[0001] This application relates to the technical field of the development and manufacturing of high-end marine energy equipment, and particularly relates to a method and system for controlling the fluid motion of a liquid tank of an offshore floating wind turbine. Background Art

[0002] With the rapid development of offshore wind power technology, floating wind turbines have become an important development direction for future offshore wind power due to their suitability for deep-water areas. However, floating wind turbines face challenges from external loads such as wind waves and ocean currents in complex and variable marine environments, and are prone to problems such as tilting, swaying, and vibration, which affect their stability and power generation efficiency. At present, traditional floating wind turbine motion control technologies mostly rely on structural design optimization or passive damping devices, and it is difficult to achieve real-time and precise motion adjustment, resulting in a decline in the performance of the wind turbine under severe sea conditions, and even possibly causing structural fatigue damage and shortening the service life.

[0003] In the prior art, the motion control of floating wind turbines mainly relies on fixed damping devices or simple liquid tank adjustment systems, lacking the ability to monitor the fluid state in the liquid tank in real time and dynamically adjust it. In addition, most existing systems use wired data transmission methods, with high installation and maintenance costs, and limited data processing capabilities, making it difficult to achieve precise motion prediction and rapid response. These technical defects lead to insufficient motion control accuracy and limited shock absorption effect of floating wind turbines when dealing with complex marine environments, unable to fully exert their power generation potential, and at the same time increasing the operation and maintenance difficulty and cost. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and system for controlling the fluid motion of a liquid tank of an offshore floating wind turbine in order to solve the problem that the motion control of floating wind turbines mainly relies on fixed damping devices or simple liquid tank adjustment systems and lacks the ability to monitor the fluid state in the liquid tank in real time and dynamically adjust it.

[0005] The above object of this application is achieved through the following technical solutions:

[0006] S1: Set valves and pumps in the liquid tank of the offshore floating wind turbine;

[0007] S2: Monitor the state of the fluid in the liquid tank in real time to obtain the data collected by the sensors;

[0008] S3: Preprocess, extract features, and analyze the data collected by the sensors, predict the motion state of the wind turbine, and obtain the prediction result of the motion state of the wind turbine;

[0009] S4: Adjust the valves and pumps according to the prediction result of the motion state of the wind turbine to achieve automatic control of the liquid tank of the offshore floating wind turbine.

[0010] Optionally, step S1 includes:

[0011] The valve includes: an inlet valve, an outlet valve, and a branch pipe valve;

[0012] The inlet valve is installed at the inlet position of the liquid tank and is used to control the entry of the fluid and regulate the flow rate;

[0013] The outlet valve is installed on the pipe where the fluid is discharged from the liquid tank and is used to control the discharge of the fluid and regulate the flow rate;

[0014] The branch pipe valve is arranged on the branch pipe;

[0015] The pump body is installed at the bottom or side of the liquid tank and is used to pump the liquid.

[0016] Optionally, step S2 includes:

[0017] The data collected by the sensor includes: liquid level data, pressure data, temperature data, flow rate data, inclination data, vibration data, and corrosion data.

[0018] Optionally, step S3 includes:

[0019] Transmitting the data collected by the sensor through a wireless network;

[0020] The steps for preprocessing the data collected by the sensor include: removing high-frequency noise, removing low-frequency drift, and unifying the data dimension;

[0021] The steps for feature extraction of the data collected by the sensor include: extracting vibration spectrum features, extracting the trend of inclination angle change, and extracting the trend of inclination angle change;

[0022] The steps for data analysis of the data collected by the sensor include:

[0023] Processing the data collected by the sensor through Kalman filtering or Bayesian network;

[0024] Based on the processed data collected by the sensor, combined with the LSTM neural network, predicting the movement trend of the fan and determining the prediction result of the fan movement state.

[0025] Optionally, step S4 includes:

[0026] Calculating the required damping force and restoring moment according to the prediction result of the fan movement state;

[0027] Determining the target value of the liquid level height in each area of the liquid tank according to the required damping force and restoring moment;

[0028] Based on the PID control method, combined with the target value of the liquid level height, the valves and pumps in each area of the liquid tank are adjusted to automatically adjust the flow rate and pressure of the liquid in the liquid tank, realizing the automatic control of the liquid tank of the offshore floating wind turbine.

[0029] An offshore floating wind turbine liquid tank fluid motion control system, the system includes:

[0030] An acquisition module, a data processing module, and a control module;

[0031] A liquid tank sensor is set in the acquisition module; the liquid tank sensor includes: a liquid level sensor, a pressure sensor, a temperature sensor, a flow sensor, an inclination sensor, a vibration sensor, and a corrosion sensor;

[0032] The control module includes: valves and pumps;

[0033] The acquisition module, the data processing module, and the control module are sequentially connected;

[0034] Through the liquid tank sensor in the acquisition module, the state of the fluid in the liquid tank is monitored in real time to obtain the sensor acquisition data;

[0035] The acquisition module is also used to transmit the sensor acquisition data to the data processing module;

[0036] The data processing module is used to preprocess, extract features, and analyze the sensor acquisition data, predict the motion state of the wind turbine, and obtain the prediction result of the wind turbine motion state;

[0037] The control module is used to adjust the valves and pumps according to the prediction result of the wind turbine motion state, realizing the automatic control of the liquid tank of the offshore floating wind turbine.

[0038] An electronic device includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions. The user interface and the network interface are used to communicate with other devices. The processor is used to execute the instructions stored in the memory, so that the electronic device executes a method for controlling the fluid motion of the liquid tank of an offshore floating wind turbine.

[0039] A computer-readable storage medium stores instructions, and when the instructions are executed, a method for controlling the fluid motion of the liquid tank of an offshore floating wind turbine is executed.

[0040] The beneficial effects brought by the technical solution provided in this application are:

[0041] The present invention sets a variety of sensors in the liquid tank of the floating wind turbine to monitor the fluid state and the movement of the wind turbine in real time, wirelessly transmits the data to the central control system for precise analysis, predicts the movement state of the wind turbine, and then dynamically adjusts the distribution of the fluid in the liquid tank, thereby generating necessary damping forces or restoring moments to effectively counter external loads, significantly reducing the tilt, sway, and vibration of the wind turbine. This system has functions of real-time monitoring, precise control, and intelligent regulation, can improve the stability, safety, and operation efficiency of the wind turbine, extend its service life, reduce operation and maintenance costs, and adapt to complex and changeable marine environments, providing strong technical support for the reliable operation and efficient power generation of offshore floating wind turbines. Brief Description of the Drawings

[0042] The following will further illustrate the present application in conjunction with the drawings and embodiments. In the drawings:

[0043] Figure 1 is the step diagram in the embodiment of the present application;

[0044] Figure 2 is the structural diagram of the LSTM computing unit for predicting the movement of the offshore floating wind turbine in the embodiment of the present invention;

[0045] Figure 3 is the technical route diagram of the PID control method for the offshore floating wind turbine in the embodiment of the present invention;

[0046] Figure 4 is the flow chart for verifying, testing, and optimizing the control system of the offshore floating wind turbine in the embodiment of the present invention;

[0047] Figure 5 is the structural schematic diagram of the electronic device in the embodiment of the present application. Detailed Description of the Embodiments

[0048] In order to have a clearer understanding of the technical features, objectives, and effects of the present application, the specific embodiments of the present application will now be described in detail with reference to the drawings.

[0049] The embodiment of the present application provides a method for controlling the movement of the fluid in the liquid tank of an offshore floating wind turbine.

[0050] Please refer to Figure 1 , Figure 1 which is the step diagram of a method for controlling the movement of the fluid in the liquid tank of an offshore floating wind turbine in the embodiment of the present application, and includes:

[0051] S1: Set valves and pumps in the liquid tank of the offshore floating wind turbine;

[0052] S2: Monitor the state of the fluid in the liquid tank in real time to obtain the data collected by the sensors;

[0053] S3: Preprocess the data collected by the sensors, extract features, and perform data analysis to predict the motion state of the wind turbine, and obtain the prediction result of the wind turbine motion state;

[0054] S4: According to the prediction result of the wind turbine motion state, adjust the valve and the pump body to achieve the automatic control of the liquid tank of the offshore floating wind turbine.

[0055] Step S1 includes:

[0056] The valve includes: an inlet valve, an outlet valve, and a branch pipe valve;

[0057] The inlet valve is installed at the inlet position of the liquid tank to control the entry of the fluid and regulate the flow rate;

[0058] As an embodiment, the valve is installed at key positions such as the inlet, outlet, and branch pipes of the fluid system to effectively control and regulate the fluid. The inlet valve is set on the pipe where the fluid enters the liquid tank, near the inlet position of the liquid tank, to control the entry of the fluid and regulate the flow rate.

[0059] The outlet valve is installed on the pipe where the fluid is discharged from the liquid tank to control the discharge of the fluid and regulate the flow rate;

[0060] The branch pipe valve is set on the branch pipe;

[0061] As an embodiment, for the branch pipes in the liquid tank, corresponding valves should be set to control the flow direction and flow rate of the fluid. These valves should be arranged at the starting end or key positions of the branch pipes to ensure that the flow of the fluid in the branch pipes can be effectively controlled. Valves of a type with good operating performance and durability are selected.

[0062] The pump body is installed at the bottom or side of the liquid tank to pump the liquid.

[0063] As an embodiment, the installation height of the pump should consider the suction pressure of the liquid to ensure the normal operation of the pump. The inlet and outlet pipes of the pump should be arranged as straight and short as possible to reduce pipeline resistance and fluid turbulence. At the same time, it should be ensured that the connections of the pipes are well sealed to prevent liquid leakage. The pump should be installed on a stable bracket or foundation to ensure that the pump will not shake or vibrate during operation. At the same time, the influence of the weight of the pump and the vibration during operation on the bracket or foundation should be considered. A suitable centrifugal pump is selected according to factors such as the nature of the fluid, the flow rate requirement, and the conveying distance. The pump body has high efficiency, low noise, and long life performance.

[0064] As an embodiment, the layout of valves and pumps should be coordinated with other system equipment (such as pipelines, instruments, etc.) to ensure the smoothness and efficiency of the entire system. In the limited space of the liquid tank, the valves and pumps should be reasonably laid out to make full use of space resources and avoid waste and congestion. Considering that the system may need to be expanded or upgraded in the future, the layout of valves and pumps should have a certain degree of flexibility and scalability.

[0065] Step S2 includes:

[0066] The data collected by the sensor includes: liquid level data, pressure data, temperature data, flow rate data, inclination data, vibration data, and corrosion data.

[0067] As an embodiment, a liquid tank sensor is provided in the liquid tank to monitor the state of the fluid in the liquid tank (such as liquid level, flow rate, pressure distribution, etc.) and the movement of the floating wind turbine (such as tilt angle, swing amplitude, vibration frequency, etc.) in real time.

[0068] As an example, the liquid level sensor of the offshore floating wind turbine liquid tank adopts ultrasonic technology, calculates the liquid level height by emitting ultrasonic waves and receiving the liquid surface reflection signal, and realizes non-contact and high-precision liquid level monitoring. It monitors the height of the liquid (ballast water) in the liquid tank in real time, ensures that the liquid level is within the safe range, prevents overflow or too low liquid level from affecting the stability of the wind turbine, and provides data support for system control and fault diagnosis at the same time. The liquid pressure sensor of the offshore floating wind turbine liquid tank adopts piezoresistive technology, converts the liquid pressure in the liquid tank into an electrical signal through the piezoresistive effect, realizes high-precision and fast-response pressure monitoring, ensures that the liquid tank pressure is within the safe range, and thus maintains the stable operation of the wind turbine and prevents system failures. The temperature sensor of the offshore floating wind turbine liquid tank adopts thermocouple or resistance temperature detector (RTD) technology, accurately monitors the liquid temperature in the liquid tank by measuring the change in resistance or voltage, ensures that the temperature is within the safe range, and prevents the liquid performance or equipment operation from being affected by too high or too low temperature. It monitors the temperature of the liquid (ballast water) in the liquid tank in real time and provides key data for system control, fault diagnosis and performance optimization. The flow sensor of the offshore floating wind turbine liquid tank adopts electromagnetic flowmeter technology, calculates the flow rate by measuring the induced electromotive force generated when the liquid flows through the magnetic field, has the characteristics of high precision, no moving parts and corrosion resistance, and is suitable for monitoring the inflow and outflow of the liquid (ballast water) in the liquid tank. Its main function is to monitor the liquid flow state in real time, ensure that the flow rate is within the set range, and provide data support for system control, fault diagnosis and performance optimization. The inclination sensor of the offshore floating wind turbine liquid tank usually adopts MEMS (microelectromechanical system) technology, measures the inclination angle and attitude change of the liquid tank through the built-in accelerometer and gyroscope, has the characteristics of high precision, fast response and anti-vibration interference, and is suitable for real-time monitoring of the balance state of the liquid tank. Its main function is to ensure that the floating wind turbine remains stable under the action of wind and waves, prevent structural damage or system failure caused by excessive inclination, and provide key data for attitude adjustment and fault diagnosis at the same time. The vibration sensor of the offshore floating wind turbine liquid tank adopts piezoelectric technology, converts mechanical vibration into an electrical signal through piezoelectric materials, and monitors the vibration frequency and amplitude of the liquid tank and its supporting structure in real time, with the characteristics of high sensitivity, wide frequency response and strong anti-interference ability. Its main function is to detect abnormal vibrations of the liquid tank caused by wind and waves, mechanical operation or structural looseness, prevent equipment damage or system failures, and provide data support for vibration analysis and maintenance decision-making at the same time. The corrosion sensor of the offshore floating wind turbine liquid tank usually adopts electrochemical impedance spectroscopy (EIS) technology, monitors the corrosion rate and degree by measuring the change in the electrochemical impedance between the metal surface and the liquid, and has the characteristics of high sensitivity, real-time monitoring and quantitative analysis. Its main function is to evaluate the corrosion state of the inner wall of the liquid tank or key metal structures in real time, prevent material failure or leakage caused by corrosion, and provide data support for maintenance decision-making and life prediction at the same time. A variety of sensors in the liquid tank form an integrated monitoring network through data complementarity and collaborative analysis to evaluate the fluid state and the movement of the floating wind turbine in real time.

[0069] As an example, the liquid level sensor monitors the liquid height in the liquid tank. Combining with the data of the pressure sensor (hydrostatic pressure of the liquid), the accuracy of the liquid level can be cross-verified. At the same time, the flow sensor monitors the liquid inflow and outflow, and analyzes whether the change of the liquid level is normal (such as leakage or abnormal consumption). The temperature sensor detects the change of the liquid temperature. If the temperature rises abnormally, it may be related to the mechanical friction monitored by the vibration sensor or the abnormal flow rate shown by the flow sensor, indicating overheating of the equipment or fluid circulation failure. The inclination sensor monitors the overall inclination angle of the floating wind turbine in real time. If the inclination exceeds the threshold, the data of the liquid level sensor and the pressure sensor are linked to judge whether the attitude is abnormal due to the imbalance of the liquid tank loading (such as uneven distribution of ballast water). The vibration sensor detects the vibration frequency and amplitude of the liquid tank and the support structure. If the vibration is abnormal, combining with the data of the corrosion sensor, it can be analyzed whether the stiffness decreases due to structural corrosion, or the data of the inclination sensor indicates whether resonance is caused by wind and wave impact. The corrosion sensor evaluates the corrosion degree of the inner wall of the liquid tank. If the corrosion rate accelerates, it may affect the sealing performance of the liquid tank. It is necessary to combine the data of the pressure sensor and the liquid level sensor to judge whether there is a leakage risk. The inclination sensor provides attitude data, and the vibration sensor monitors the dynamic response of the structure. Combining with the ballast water distribution information of the liquid level sensor, the liquid tank loading is adjusted in real time to offset the inclination or sway caused by wind and waves. If the vibration amplitude increases abnormally, it may trigger the flow sensor to control the ballast pump to accelerate the leveling speed, and at the same time the pressure sensor ensures that the pipeline pressure is within the safe range during the leveling process. The temperature sensor and the flow sensor cooperate to monitor the circulation efficiency of the coolant or lubricating oil. If the insufficient flow leads to an increase in temperature, the system automatically adjusts the pump speed or alarms. The corrosion sensor tracks the state of the inner wall of the liquid tank for a long time, combines with the data of the pressure sensor to predict the trend of the structural strength attenuation, and plans the maintenance cycle in advance. The multi-dimensional data of the sensors are cross-verified to reduce the false alarm rate. The real-time dynamic leveling and fault warning improve the survivability and power generation efficiency of the wind turbine.

[0070] Step S3 includes:

[0071] Transmit the data collected by the sensor through the wireless network;

[0072] As an example, sensors are used for data collection. Sensors such as liquid level, pressure, temperature, flow, inclination, vibration, and corrosion sensors collect the fluid state in the liquid tank and the motion data of the wind turbine in real time. The sensor is built-in with a signal conditioning circuit to preliminarily filter and amplify the original signal to ensure the data quality.

[0073] As an example, the wireless network is used to transmit data. Low Power Wide Area Network (LPWAN) technology (such as LoRa, NB-IoT) or Wireless Local Area Network (Wi-Fi / Bluetooth) is used to transmit data. The sensor data is encapsulated as a standardized data packet (such as JSON format) through the wireless module, including information such as timestamp, sensor ID, measurement value, etc.

[0074] As an embodiment, the central control system receives sensor data. The central control system receives sensor data packets through a wireless gateway. The gateway performs preliminary verification on the data packets (such as CRC verification), and discards invalid or duplicate data. The received data is stored in the central database for subsequent processing and analysis.

[0075] The steps of preprocessing and feature extraction of sensor data include: removing high-frequency noise (such as vibration noise caused by sea wave impact) and low-frequency drift (such as sensor zero drift). Unifying the data of different sensors into the same dimension for subsequent analysis. Extracting key features from the original data, extracting vibration spectrum features (such as main frequency, amplitude), extracting the change trend of tilt angle, extracting the liquid level fluctuation frequency, etc.

[0076] The steps of preprocessing the sensor-collected data include: removing high-frequency noise, removing low-frequency drift, and unifying the data dimension;

[0077] Unifying the data of different sensors into the same dimension for subsequent analysis. Extracting key features from the original data, extracting vibration spectrum features (such as main frequency, amplitude), extracting the change trend of tilt angle, extracting the liquid level fluctuation frequency, etc.

[0078] The steps of feature extraction of the sensor-collected data include: extracting vibration spectrum features, extracting the change trend of tilt angle, and extracting the change trend of tilt angle;

[0079] As an embodiment, key features are extracted from the original data, vibration spectrum features (such as main frequency, amplitude) are extracted, the change trend of tilt angle is extracted, the liquid level fluctuation frequency is extracted, etc.

[0080] The steps of data analysis of the sensor-collected data include:

[0081] Processing the sensor-collected data through Kalman filtering or Bayesian network;

[0082] Based on the processed sensor-collected data, combined with the LSTM neural network, predicting the movement trend of the fan and determining the prediction result of the fan movement state.

[0083] As an example, algorithms such as Kalman filtering and Bayesian networks are used to fuse multi-sensor data, eliminate the errors of a single sensor, and improve data reliability. For example, by combining the data of liquid level, pressure, and flow sensors, it is possible to determine whether there is a leak. A normal state model is established based on historical data to detect abnormalities in real time (such as a sudden drop in liquid level or increased vibration). Machine learning algorithms such as Isolation Forest and Support Vector Machine are used to identify abnormal patterns. Time series analysis (LSTM neural network) is used to predict the movement trend of the fan (such as the future tilt angle and vibration amplitude). The prediction accuracy is optimized by combining environmental data (such as wind speed and wave height).

[0084] As an example, by integrating a liquid level sensor, a pressure sensor, a temperature sensor, a flow sensor, an inclination sensor, a vibration sensor, and a corrosion sensor, the operating state information of the fan can be comprehensively obtained. The liquid level sensor monitors the change in liquid level, the pressure sensor captures the internal pressure fluctuation of the fan, the temperature sensor records the temperature change, the flow sensor measures the liquid flow, the inclination sensor detects the change in the fan angle, the vibration sensor analyzes the mechanical vibration characteristics, and the corrosion sensor evaluates the equipment corrosion status. These data are preprocessed and feature-extracted to provide input for the neural network model.

[0085] By constructing models such as multi-layer perceptron, recurrent neural network, or convolutional neural network, and training the sensor data, the hidden patterns and time series features in the data can be mined. During the model training process, historical data is used as the supervision signal to optimize the network parameters and improve the prediction accuracy. At the same time, through cross-validation and hyperparameter tuning, the generalization ability and stability of the model are ensured.

[0086] Through model prediction, the model outputs the prediction of the fan operating state in the future for a period of time, providing a basis for fault warning, performance optimization, and decision support. In addition, visualization technology can be combined to display the prediction results in an intuitive way, facilitating the understanding and analysis by the operators. In practical applications, it is necessary to continuously monitor the model performance and update the model regularly to adapt to the changes in the fan operating state, ensuring the accuracy and reliability of the prediction results.

[0087] The algorithm process of the LSTM neural network is as follows ( Figure 2 ):

[0088] Input: Information on the previous state of the liquid tank. The LSTM has three thresholds, namely the input gate (i t ), the forget gate (f t ), and the output gate (o t ), and the subscript t represents the time. In addition, c t is used to represent the cell state of the LSTM at time t. Information on the previous state of the liquid tank: h t -1 (the hidden state at the previous time) and Ct -1 (cell state at the previous moment). Input data at the current moment: x t .

[0089] Step 1: Receive input information. Calculate the value i of the input gate t , and the input gate is used to control the influence of the current data input on the value of the memory cell. The calculation of all gates is affected by the current input data, the output value of the LSTM cell at the previous moment, and also by the value of the memory cell at the previous moment; the input gate is used to control the influence of the current input data on the cell state. The calculation formula is:

[0090] i t = σ(W i · [h t -1, x t + b i )

[0091] Where: Wi is the weight matrix of the input gate. bi is the bias term of the input gate. σ is the Sigmoid activation function, which maps the value to the range [0, 1]. [ht-1, xt] represents the concatenation of the hidden state at the previous moment and the current input data.

[0092] Step 2: Forget state information. The first step in LSTM is to decide which information passed from the previous cell needs to be discarded from the cell state, which is determined by a sigmoid output layer called the "forget gate". Then, a value in the range [0, 1] is output for each number in the cell state, that is, using the input of the current layer and the output of the previous layer as inputs, and the cell state output ft at time t - 1; the forget gate is used to decide which information to discard from the cell state Ct-1 at the previous moment. The calculation formula is as follows:

[0093] f t = σ(W f · [h t-1 , x t + b f )

[0094] Where: Wf is the weight matrix of the forget gate. bf is the bias term of the forget gate.

[0095] Step 3: Update the status information. Determine which new information needs to be stored in the cell state. This layer can be divided into two parts. First, a sigmoid layer determines which values need to be updated, and this layer is called the "input gate". Then, a tanh layer creates a vector containing new information to be added, which is the information that can be added to the new cell state. Next, combine the information from these two parts to update the cell state. First, update the state of the old cell, and then input new information into the new cell state, that is, update the information in the cell state. The update of the cell state is divided into two parts:

[0096] 1. Generate candidate information

[0097] Use the tanh function to generate a new candidate value vector C 1 t , representing the new information that may be added to the cell state. The calculation formula is as follows:

[0098] C 1 t = tanh(W C · [h t-1 , x t + b C )

[0099] Where: W C is the weight matrix of the candidate information. b C is the bias term of the candidate information.

[0100] 2. Update the cell state:

[0101] Combine the forget gate f t , the input gate i t and the candidate information C 1 t to update the cell state:

[0102] C t = f t · C t-1 + i t · C 1 t

[0103] Where: f t · C t-1 represents the information of the previous cell state that the forget gate decides to retain. i t · C 1 t represents the new information that the input gate decides to add.

[0104] Step 4: Output new information. Use the sigmoid layer to determine the relevant information to output the cell state. This layer is called the "output gate", and then use tanh to process the cell state. The product of these two parts of information is the information to be output. The output gate is used to determine which information in the current cell state C t needs to be output.

[0105] 1. Calculate the output gate o t :

[0106] o t = σ(W o · [h t-1 , x t + b o )

[0107] where: W o is the weight matrix of the output gate. b o is the bias term of the output gate.

[0108] 2. Generate the output h t :

[0109] Use the tanh function to process the cell state c t , and then multiply it by the output gate o t to obtain the hidden state h at the current moment t :

[0110] h t = o t · tanh(C t )

[0111] where: tanh(C t ) scales the value of the cell state to the range [-1, 1]. o t controls the amount of information output.

[0112] Output: Information on the next state of the liquid tank. (Hidden state at the current moment)(Cell state at the current moment).

[0113] As an example, through the coordinated action of the input gate, forget gate, and output gate, the LSTM can effectively update the state information of the liquid tank, while retaining long-term dependencies and filtering out irrelevant information. The final output h t is the information on the next state of the liquid tank, identifying the movement mode of the fan and potential unstable states.

[0114] Step S4 includes:

[0115] According to the prediction result of the fan movement state, calculate the required damping force and restoring moment;

[0116] Determine the target values of the liquid level heights in each area of the liquid tank according to the required damping force and restoring moment;

[0117] Based on the PID control method and combined with the target values of the liquid level heights, adjust the valves and pumps in each area of the liquid tank to automatically adjust the flow rate and pressure of the liquid in the liquid tank, and achieve the automatic control of the liquid tank of the offshore floating wind turbine.

[0118] As an embodiment, as Figure 3 shown, the damping force can be generated by the flow damping of the fluid in the liquid tank, and the restoring moment can be achieved by adjusting the position of the center of gravity of the fluid in the liquid tank. By precisely adjusting the liquid flow, the liquid tank can quickly respond to the motion changes of the wind turbine and effectively suppress unnecessary vibrations and offsets, thereby reducing the motion response of the wind turbine.

[0119] As an embodiment, based on the prediction results of the wind turbine motion state, execute dynamic adjustment instructions through mechanisms such as pumps and valves to control the distribution of the fluid in the liquid tank (such as by changing the liquid level height, fluid flow direction, etc.) to generate the necessary damping force or restoring moment, counteract the external load, reduce the wind turbine motion response, and achieve the adjustment of the wind turbine motion.

[0120] As an embodiment, verify, test, and optimize the liquid tank control system of the offshore floating wind turbine to ensure the reliability, stability, and performance of the system, and evaluate its performance indicators such as shock absorption effect, response speed, and motion control accuracy. The specific steps are as Figure 4 shown:

[0121] S501 Simulation and modeling verification of the liquid tank control system of the offshore floating wind turbine.

[0122] First, it is necessary to establish the dynamic models of the floating wind turbine and the liquid tank, including the motion model of the wind turbine platform, the motion model of the fluid in the liquid tank, and the interference model of the external environment (wind, wave, current). The platform motion model is usually based on rigid body dynamics and considers the motion equations of six degrees of freedom (surge, sway, heave, roll, pitch, and yaw); the liquid tank fluid model uses hydrodynamic methods such as potential flow theory or computational fluid dynamics (CFD) to simulate the flow and sloshing behavior of the liquid in the tank; the external environment model simulates the actual sea conditions through wave spectra (such as JONSWAP spectrum) and wind spectra. Integrate these models into a simulation platform (such as MATLAB / Simulink, OrcaFlex, or ANSYS) to build a complete system simulation environment.

[0123] Test the performance of the control system in a simulation environment. By inputting different sea conditions (such as calm, moderate wind and waves, extreme wind and waves), observe the system's response, including the sway amplitude of the platform, the fluid motion state in the liquid tank, and the execution effect of the control algorithm. Adopt an algorithm based on model predictive control (LSTM), combine real-time sensor data, dynamically adjust the flow path and flow rate of the fluid in the liquid tank, and verify the stability and adaptability of the system under different working conditions. By comparing the simulation results with the design objectives, optimize the control algorithm parameters (such as prediction horizon, control horizon, weight matrix, etc.), and identify potential problems (such as response delay, insufficient control accuracy, etc.). Finally, generate a detailed simulation report, including indicators such as system response time, stability analysis, and control accuracy, providing a theoretical basis for subsequent laboratory tests and sea tests.

[0124] S502 Laboratory prototype test of the liquid tank control system for offshore floating wind turbines.

[0125] First, it is necessary to establish the dynamic models of the floating wind turbine and the liquid tank, including the motion model of the wind turbine platform, the motion model of the fluid in the liquid tank, and the interference model of the external environment (wind, waves, current). The platform motion model is usually based on rigid body dynamics, considering the motion equations of six degrees of freedom (surge, sway, heave, roll, pitch, and yaw); the liquid tank fluid model uses hydrodynamic methods, such as potential flow theory or computational fluid dynamics (CFD), to simulate the flow and sloshing behavior of the liquid in the tank; the external environment model simulates the actual sea conditions through wave spectra (such as the JONSWAP spectrum) and wind spectra. Integrate these models into a simulation platform (such as MATLAB / Simulink, OrcaFlex or ANSYS) to construct a complete system simulation environment.

[0126] Test the performance of the control system in a simulation environment. By inputting different sea conditions (such as calm, moderate wind and waves, extreme wind and waves), observe the system's response, including the sway amplitude of the platform, the fluid motion state in the liquid tank, and the execution effect of the control algorithm. Adopt an algorithm based on model predictive control (LSTM), combine real-time sensor data, dynamically adjust the flow path and flow rate of the fluid in the liquid tank, and verify the stability and adaptability of the system under different working conditions. By comparing the simulation results with the design objectives, optimize the control algorithm parameters (such as prediction horizon, control horizon, weight matrix, etc.), and identify potential problems (such as response delay, insufficient control accuracy, etc.). Finally, generate a detailed simulation report, including indicators such as system response time, stability analysis, and control accuracy, providing a theoretical basis for subsequent laboratory tests and sea tests.

[0127] S503 Hardware integration and debugging of the liquid tank control system for offshore floating wind turbines.

[0128] First, the designed hardware components need to be installed on the floating wind turbine platform, including sensors (such as acceleration sensors, tilt sensors, liquid level sensors), control hardware (such as adjustable valves, hydraulic cylinders or electric pumps), and the core equipment of the control system (such as PLC, industrial computer). The sensors should be arranged to cover the key positions of the platform to ensure accurate monitoring of the platform's sway and the movement state of the fluid in the liquid tank; the installation of valves and active damping devices needs to ensure that they can quickly and accurately adjust the flow path and flow rate of the liquid. After the hardware installation is completed, conduct a preliminary power-on test to check whether the connections of all devices are normal, ensure that the sensor data can be accurately transmitted to the control system, and at the same time verify whether the actions of the valves and damping devices are consistent with the control instructions.

[0129] Conduct hardware-software joint debugging to ensure the coordination and stability of the entire system. By simulating different sea conditions (such as calm, moderate wind and waves, extreme wind and waves), run the control algorithm and observe the system's response. During the debugging process, focus on the real-time performance of sensor data, the execution efficiency of the control algorithm, and the action accuracy of valves and damping devices. If problems such as response delay, control error or hardware failure are found, adjust the hardware configuration or optimize the control algorithm parameters in a timely manner. In addition, the safety of the system under extreme conditions needs to be tested, such as simulating sensor or valve failures to verify whether the system can switch to the backup plan or safely shut down. Finally, generate a detailed debugging report, including hardware performance evaluation, system response data, and problem solutions, laying the foundation for subsequent offshore tests and actual operations.

[0130] S504 Offshore Prototype Test of the Liquid Tank Control System for Floating Wind Turbines

[0131] First, select a suitable offshore test site, usually choose an area with representative sea conditions (such as a sea area with a high frequency of moderate wind and waves), and deploy the floating wind turbine prototype. Before deployment, ensure that all hardware devices (such as sensors, valves, active damping devices, and control systems) have completed laboratory tests and debugging and have good waterproof and anti-corrosion performance. During the installation process, accurately calibrate the sensor positions to ensure that they can accurately monitor the platform movement and the fluid state in the liquid tank; at the same time, check the sealing performance and action reliability of the valves and damping devices to prevent seawater intrusion from causing failures. After deployment, conduct a preliminary operation test to verify the startup and operation status of the system in the real ocean environment and ensure that all components are working properly.

[0132] Conduct long-term offshore tests to collect the operation data of the system under different sea conditions. By real-time monitoring the sway amplitude of the platform, the fluid motion state in the liquid tank, the power generation efficiency of the wind turbine, and the response of the control system, evaluate the improvement effect of the liquid tank control system on the platform stability. During the test process, various sea conditions (such as calm, moderate wind and wave, extreme wind and wave) need to be covered to comprehensively verify the adaptability and robustness of the system. At the same time, record the working state of the hardware equipment, identify possible faults (such as sensor data drift, valve jamming, etc.), and formulate corresponding maintenance and improvement plans. After the test, conduct a detailed analysis of the collected data, generate an offshore test report, including system performance evaluation, problem analysis and optimization suggestions, providing a practical basis for subsequent large-scale promotion and application.

[0133] Data Analysis and Performance Optimization of the Liquid Tank Control System for S505 Offshore Floating Wind Turbines.

[0134] The data analysis and performance optimization work of the liquid tank control system for offshore floating wind turbines mainly includes data collection, processing, analysis and optimization strategy formulation. First, collect key parameters such as pressure, temperature, liquid level, and flow rate in the liquid tank in real time through sensors and monitoring systems, and clean and preprocess the data to ensure its accuracy and integrity. Subsequently, use methods such as statistical analysis and machine learning to analyze historical data and real-time data, identify abnormal patterns, efficiency bottlenecks and potential fault points in the liquid tank control system. By establishing a mathematical model or simulation platform, evaluate the system performance under different working conditions, providing a basis for optimization.

[0135] In the performance optimization stage, based on the data analysis results, formulate targeted optimization strategies. For example, adjust the liquid distribution in the liquid tank to improve the stability of the wind turbine, optimize the control algorithm to improve the response speed and energy efficiency, or improve the hardware configuration to enhance the system reliability. Through simulation verification and on-site tests, evaluate the optimization effect and continuously iterate and improve. At the same time, combine environmental factors (such as wind and wave conditions) and operation requirements to dynamically adjust control parameters to ensure that the system can operate efficiently and stably under different working conditions. Finally, form a complete optimization plan and implement it to improve the overall performance and economy of the liquid tank control system for offshore floating wind turbines.

[0136] Reliability Evaluation and Safety Verification of the Liquid Tank Control System for S506 Offshore Floating Wind Turbines.

[0137] The data analysis and performance optimization of the liquid tank control system for offshore floating wind turbines mainly include data collection, processing, analysis, and the formulation of optimization strategies. First, key parameters such as pressure, temperature, liquid level, and flow rate in the liquid tank are collected in real time through sensors and monitoring systems, and the data is cleaned and preprocessed to ensure its accuracy and integrity. Subsequently, historical data and real-time data are analyzed using statistical analysis, machine learning, and other methods to identify abnormal patterns, efficiency bottlenecks, and potential fault points in the liquid tank control system. By establishing a mathematical model or simulation platform, the system performance under different working conditions is evaluated to provide a basis for optimization.

[0138] In the performance optimization stage, based on the data analysis results, targeted optimization strategies are formulated. For example, adjusting the distribution of the liquid in the liquid tank to improve the stability of the wind turbine, optimizing the control algorithm to improve the response speed and energy efficiency, or improving the hardware configuration to enhance the system reliability. Through simulation verification and on-site testing, the optimization effect is evaluated, and continuous iterative improvement is carried out. At the same time, combined with environmental factors (such as wind and wave conditions) and operation requirements, the control parameters are dynamically adjusted to ensure that the system can operate efficiently and stably under different working conditions. Finally, a complete optimization plan is formed and implemented to improve the overall performance and economy of the liquid tank control system for offshore floating wind turbines.

[0139] Standardization and large-scale application of the liquid tank control system for S507 offshore floating wind turbines.

[0140] First, based on existing industry standards (such as the IEC 61400 series) and actual engineering experience, a standardized framework applicable to the liquid tank control system is constructed, covering all life cycle links such as design, manufacturing, installation, and operation and maintenance. By analyzing the requirements of different sea area environments and wind turbine models, unified technical specifications are formulated, including key technical requirements such as liquid tank structure design, hydraulic control logic, sensor selection, and communication protocol. At the same time, a standardized test process and certification system are established to ensure the compatibility and reliability of each component and system, laying a foundation for large-scale application.

[0141] In the large-scale application stage, through modular design and platform development, the production efficiency and cost control of the liquid tank control system are optimized. Using digital tools (such as digital twin technology) to simulate and optimize the system to ensure its adaptability in different wind turbine platforms and marine environments. Promote industrial chain collaboration, cooperate with suppliers, manufacturers, and operation and maintenance parties, and establish a standardized supply chain and operation and maintenance service system. Verify the feasibility and economy of the standardized design through demonstration projects, and gradually promote it to large-scale commercial applications. In addition, continuously collect operation data and feedback it to the standard system to promote technology iteration and standard upgrade, and finally achieve the efficient, reliable, and large-scale application of the liquid tank control system for offshore floating wind turbines.

[0142] Continuous monitoring and improvement of the liquid tank control system for S508 offshore floating wind turbines.

[0143] First, through a sensor network (such as pressure, temperature, liquid level, flow rate sensors, etc.) installed in the liquid tank control system, the operation data of the system is collected in real time, and the data is transmitted to the central monitoring platform using Internet of Things (IoT) technology. Combining big data analysis and artificial intelligence algorithms, the data is processed and analyzed to monitor the system status in real time, identify abnormal patterns and potential faults. At the same time, a performance evaluation index system is established to regularly evaluate the stability, energy efficiency and reliability of the system, generate operation reports and put forward improvement suggestions. Through Prognostics and Health Management (PHM) technology, potential faults are predicted in advance, reducing operation and maintenance costs and improving system availability.

[0144] In the improvement and optimization stage, based on the monitoring data and evaluation results, targeted improvement strategies are formulated. For example, optimizing the control algorithm to improve the system response speed and energy efficiency, improving the liquid tank structure design to enhance the anti-wave and anti-wind ability, or upgrading sensors and actuators to improve measurement accuracy and control stability. The effectiveness of the improvement plan is verified through a simulation platform and on-site tests, and it is gradually implemented. In addition, a knowledge base and a case base are established to accumulate operation experience and improvement results, providing reference for subsequent design and operation and maintenance. Through the closed-loop management of continuous monitoring and improvement, the performance and reliability of the liquid tank control system of the offshore floating wind turbine are continuously improved to ensure its long-term stable operation in complex marine environments.

[0145] Specifically, the fully tested motion control system of the offshore floating wind turbine will demonstrate its performance in aspects such as shock absorption, response speed, and motion control accuracy, bringing a revolutionary change to the offshore wind power industry and promoting the development of wind power equipment towards a more efficient, stable, and reliable direction. At the same time, the implementation of this project will also set a new technical benchmark for the offshore wind power field in China and even the world, promoting the continuous progress and development of offshore wind power technology.

[0146] A liquid tank fluid motion control system for an offshore floating wind turbine, the system comprising:

[0147] An acquisition module, a data processing module, and a control module;

[0148] A liquid tank sensor is provided in the acquisition module; the liquid tank sensor includes: a liquid level sensor, a pressure sensor, a temperature sensor, a flow rate sensor, an inclination sensor, a vibration sensor, and a corrosion sensor;

[0149] The control module includes: a valve and a pump body;

[0150] The acquisition module, the data processing module, and the control module are sequentially connected;

[0151] Through the liquid tank sensor in the acquisition module, the state of the fluid in the liquid tank is monitored in real time to obtain the sensor acquisition data;

[0152] The acquisition module is also used to transmit the data collected by the sensors to the data processing module;

[0153] The data processing module is used to preprocess, extract features, and analyze the data collected by the sensors, predict the motion state of the wind turbine, and obtain the prediction result of the motion state of the wind turbine;

[0154] The control module is used to adjust the valve and the pump body according to the prediction result of the motion state of the wind turbine, so as to realize the automatic control of the liquid tank of the offshore floating wind turbine.

[0155] This application also discloses an electronic device. Referring to Figure 5 , Figure 5 is a schematic structural diagram of an electronic device disclosed in an embodiment of this application. The electronic device 500 may include: at least one processor 501, at least one network interface 504, a user interface 503, a memory 505, and at least one communication bus 502.

[0156] Among them, the communication bus 502 is used to realize the connection and communication between these components.

[0157] Among them, the user interface 503 may include a display screen, and optionally, the user interface 503 may further include a standard wired interface and a wireless interface.

[0158] Among them, the network interface 504 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).

[0159] This application also discloses a computer-readable storage medium, which stores multiple instructions suitable for being loaded by a processor to execute the above-mentioned method for controlling the fluid motion of the liquid tank of an offshore floating wind turbine.

[0160] The above are only exemplary embodiments of the present disclosure, and the scope of the present disclosure cannot be limited thereby. That is, any equivalent changes and modifications made according to the teachings of the present disclosure still fall within the scope covered by the present disclosure.

[0161] This application aims to cover any variations, uses, or adaptive changes of the present disclosure, which follow the general principles of the present disclosure and include common general knowledge or conventional technical means in the technical field not recorded in the present disclosure. The description and the embodiments are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for controlling fluid motion in a liquid tank of an offshore floating wind turbine, characterized in that: The method comprises the following steps: S1: Install valves and pump bodies in the liquid tanks of offshore floating wind turbines; S2: Monitor the state of the fluid in the tank in real time to obtain sensor data; S3: Preprocessing, feature extraction and data analysis are performed on the sensor collected data to predict the fan motion state and obtain the fan motion state prediction result; S4: According to the prediction result of the wind turbine motion state, the valve and the pump body are adjusted to realize the automatic control of the offshore floating wind turbine tank.

2. A method for controlling fluid movement in a liquid tank of an offshore floating wind turbine according to claim 1, characterized in that: Step S1 includes: The valves include: an inlet valve, an outlet valve and a branch pipeline valve; The inlet valve is installed at the inlet of the tank to control the entry of fluid and adjust the flow rate; The outlet valve is installed on the pipe where the fluid is discharged from the tank to control the discharge of the fluid and adjust the flow rate; The branch pipeline valve is arranged on the branch pipeline; The pump body is installed at the bottom or side of the liquid tank to extract liquid.

3. A method for controlling fluid movement in a liquid tank of an offshore floating wind turbine according to claim 1, characterized in that: Step S2 includes: The sensor collects data including: liquid level data, pressure data, temperature data, flow data, inclination data, vibration data and corrosion data.

4. A method for controlling fluid movement in a liquid tank of an offshore floating wind turbine according to claim 1, characterized in that: Step S3 includes: Transmit the data collected by the sensor through the wireless network; The steps of preprocessing the sensor collected data include: removing high-frequency noise, removing low-frequency drift and unifying the data dimension; The steps of extracting features from the sensor collected data include: extracting vibration spectrum features, extracting tilt angle variation trends, and extracting tilt angle variation trends; The steps for analyzing the data collected by the sensor include: Process the sensor data through Kalman filtering or Bayesian network; The processed sensor data is collected and combined with the LSTM neural network to predict the fan movement trend and determine the fan movement state prediction result.

5. The method for controlling fluid movement in a liquid tank of an offshore floating wind turbine according to claim 1, characterized in that: Step S4 includes: Calculate the required damping force and restoring torque based on the prediction results of the fan motion state; Determine the target value of the liquid level height in each area of ​​the tank according to the required damping force and restoring moment; Based on the PID control method, combined with the target value of the liquid level height, the valves and pumps in each area of ​​the tank are adjusted to automatically adjust the flow and pressure of the liquid in the tank to achieve automated control of the offshore floating wind turbine tank.

6. An offshore floating wind turbine tank fluid motion control system, used to implement an offshore floating wind turbine tank fluid motion control method as claimed in any one of claims 1 to 5, characterized in that: The system comprises: Acquisition module, data processing module and control module; The liquid tank sensor is set in the acquisition module; the liquid tank sensor includes: liquid level sensor, pressure sensor, temperature sensor, flow sensor, tilt sensor, vibration sensor and corrosion sensor; The control module includes: a valve and a pump body; The acquisition module, data processing module and control module are connected in sequence; Through the liquid tank sensor in the acquisition module, the state of the fluid in the liquid tank is monitored in real time to obtain sensor acquisition data; The acquisition module is also used to transmit the sensor collected data to the data processing module; The data processing module is used to pre-process, extract features and analyze data collected by the sensor, predict the fan motion state, and obtain the fan motion state prediction result; The control module is used to adjust the valve and the pump body according to the prediction result of the wind turbine motion state, so as to realize the automatic control of the offshore floating wind turbine tank.

7. An electronic device, characterized in that: It includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed by a computer, the method according to any one of claims 1 to 5 is executed.

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