Wave energy power generation adaptive rectification system adaptive to complex sea conditions

Abstract

The application discloses a wave energy power generation adaptive rectification system suitable for complex sea conditions, and belongs to the fields of new energy power generation, ocean engineering and power electronics. The system comprises a bionic flexible module, a multi-physical field data acquisition module, a central coordination module, a communication and broadcasting module, a distributed rectification and local control module, an electromechanical fusion decision module, a bionic structure tuning module and a direct current bus module. The bionic flexible module is adopted to capture wave energy, wave prediction parameters are generated by acquiring hydrodynamic field and solid mechanics field data, global instructions are further generated, and are broadcast to the distributed rectification and control nodes. The nodes independently adjust the rectification switch parameters, and generate electromechanical fusion decision data, which is used for dynamically tuning the local rigidity of the bionic flexible module. The system realizes the cooperative adaptive control of mechanical collection and electrical rectification, can cope with complex and changeable marine environment, and improves the capture efficiency of wave energy and the stability of electric energy output.

Description

Technical Field

[0001] This invention relates to the fields of new energy power generation, marine engineering and power electronics, and in particular to an adaptive rectification system for wave energy power generation that adapts to complex sea conditions. Background Technology

[0002] Wave energy, as a clean and renewable marine energy source, is characterized by its large reserves and high energy density, making it an important direction for current new energy development. Wave energy generation (WEC) devices typically involve multiple stages, including energy capture, conversion, rectification, and output. The randomness, complexity, and intensity of waves pose significant challenges to the energy capture efficiency and power output stability of WEC devices.

[0003] Among related technologies, Chinese invention patent application CN112594120A discloses an integrated marine energy device combining an observation buoy and a wave and current power generation system. The device itself is equipped with both a wave power generation system and a tidal current power generation system, achieving comprehensive collection and utilization of marine energy across water layers. The wave power generation system includes a wave buoy and a wave energy harvesting module. This device aims to address the power supply needs of marine observation buoys and navigation lights, combining marine energy power generation technology with the power supply of marine instruments and equipment, thus accelerating the transformation of technological achievements into productivity.

[0004] Regarding the aforementioned technologies, existing WEC devices primarily rely on rigid structures or wave floats with fixed parameters for energy capture. Their natural mechanical frequencies struggle to match the variable wave frequencies under complex sea conditions in real time, resulting in low energy capture efficiency for most of the time. More importantly, the power rectification (AC / DC conversion) stage of existing systems typically employs a fixed control strategy, lacking the ability to perceive and coordinate adjustments to the mechanical motion state of the acquisition end and changes in sea conditions in real time. This disconnect between acquisition and rectification leads to severe fluctuations and poor quality of output power under complex and severe sea conditions, while also increasing the risk of system structural damage due to excessive impacts, hindering long-term, efficient, and reliable operation. Summary of the Invention

[0005] To address the aforementioned issues, this invention provides an adaptive rectification system for wave energy generation that adapts to complex sea conditions. It employs an electromechanical fusion method that integrates biomimetic flexible structure tuning and distributed rectification control, enabling real-time adjustment of the system's physical form and electrical parameters based on predicted sea conditions, thereby achieving a synergistic improvement in energy capture efficiency and operational stability.

[0006] To achieve the above objectives, this application adopts the following technical solution:

[0007] Firstly, an adaptive rectification system for wave energy generation adapted to complex sea conditions is provided, comprising: a biomimetic flexible module, a multiphysics data acquisition module, a central coordination module, a communication and broadcasting module, a distributed rectification and local control module, an electromechanical fusion decision-making module, a biomimetic structural tuning module, and a DC combiner module; wherein:

[0008] The biomimetic flexible module is used to capture wave energy. It integrates several energy-capturing regions with independently tunable local stiffness, a distributed energy conversion substructure, and an intelligent fluid cavity for tuning local stiffness.

[0009] The multiphysics data acquisition module is used to acquire data from hydrodynamic field sensors and solid mechanical field sensors from biomimetic flexible modules, and fuse these data to generate wave prediction parameters.

[0010] The central coordination module is used to receive the wave prediction parameters and generate global target working mode instructions based on the wave prediction parameters.

[0011] The communication and broadcasting module is used to broadcast the global target working mode command to several distributed rectification and control nodes;

[0012] The distributed rectification and local control module includes several distributed rectification and control nodes, which are used to adjust the switching parameters of the rectifier circuit based on the global target working mode command and its own local sensing data, and broadcast the local rectification parameters.

[0013] The electromechanical integration decision module is used to monitor the back electromotive force signal generated by the distributed energy conversion substructure and combine it with the local rectification parameters received from the neighboring distributed rectification and control nodes to generate electromechanical integration decision data. The neighboring distributed rectification and control nodes are distributed rectification and control nodes in the biomimetic flexible module where there is a direct or closest mechanical coupling or power transmission path between the distributed energy conversion substructure and another distributed energy conversion substructure.

[0014] A biomimetic structure tuning module is used to receive the electromechanical fusion decision data and control the intelligent fluid cavity based on the electromechanical fusion decision data to dynamically tune the local stiffness of the biomimetic flexible module.

[0015] The DC combiner module is used to connect the DC outputs of all the distributed rectifiers and control nodes in parallel to the DC bus to generate stable DC power.

[0016] Based on the above technical solution, in the wave energy power generation adaptive rectification system adapted to complex sea conditions provided in this application, an electromechanical fusion method integrating biomimetic flexible structure tuning and distributed rectification control is adopted. This method can adjust the physical form and electrical parameters of the system in real time according to the predicted sea conditions, thereby achieving a synergistic improvement in energy capture efficiency and operational stability.

[0017] In conjunction with the first aspect above, in one possible implementation, the multiphysics data acquisition module includes: a hydrodynamic field data unit, a solid mechanics field data processing unit, and a multi-field data fusion processing unit;

[0018] The hydrodynamic field data unit is used to acquire wave height, period, and direction data through the hydrodynamic field sensor.

[0019] The solid mechanical field data processing unit is used to acquire the structural strain data of the biomimetic flexible module through the solid mechanical field sensor, and calculate the deformation rate data based on the structural strain data.

[0020] The multi-field data fusion processing unit is used to fuse the wave height, period, and direction data with the structural strain and deformation velocity data, and obtain wave prediction parameters through model predictive control algorithms.

[0021] In conjunction with the first aspect above, in one possible implementation, the central coordination module includes: a prediction parameter parsing unit, a target decision-making unit, and a global instruction generation unit;

[0022] A prediction parameter parsing unit is used to receive and parse the wave prediction parameters;

[0023] The target decision unit is used to determine the working objectives based on the wave prediction parameters, wherein the working objectives include prioritizing output stability and prioritizing power capture;

[0024] The global instruction generation unit is used to generate a global target working mode instruction containing weight coefficients according to the working target, and output it to the communication and broadcasting module.

[0025] In conjunction with the first aspect above, in one possible implementation, the communication and broadcasting module includes: an instruction encoding unit, a message sending unit, and an instruction receiving and decoding unit;

[0026] An instruction encoding unit is used to encode the global target working mode instruction into a broadcast message;

[0027] The message sending unit is used to send broadcast messages through a communication network;

[0028] The instruction receiving and decoding unit is configured within the plurality of distributed rectification and control nodes, and is used to receive the broadcast message and decode it to obtain the global target working mode instruction.

[0029] In conjunction with the first aspect above, in one possible implementation, the distributed rectification and local control module includes: a local sensing data acquisition unit, a local control strategy execution unit, and a local rectification parameter broadcasting unit;

[0030] The local sensing data acquisition unit is used to acquire local motion acceleration data, local temperature data, and local output current and voltage data through local motion sensors, temperature sensors, and current and voltage sampling circuits, and fuse these data to form local sensing data.

[0031] The local control strategy execution unit is used to autonomously adjust the switching frequency and duty cycle of the rectifier circuit based on the received global target working mode command and the local sensing data.

[0032] The local rectification parameter broadcasting unit is used to broadcast the local rectification parameters determined by the switching frequency and duty cycle.

[0033] In conjunction with the first aspect above, in one possible implementation, the local sensing data acquisition unit includes:

[0034] The multi-dimensional sensor subunit includes a local motion sensor, a temperature sensor, and current and voltage sampling circuits;

[0035] The data acquisition and fusion subunit is used to acquire local motion acceleration data, local temperature data, and local output current and voltage data through the multi-dimensional sensors, and fuse the data to form local sensing data.

[0036] In conjunction with the first aspect above, in one possible implementation, the electromechanical fusion decision module includes: a back electromotive force feature extraction unit, a neighboring distributed rectifier and control node parameter receiving unit, and a fusion decision generation unit;

[0037] The back electromotive force feature extraction unit is used to monitor the back electromotive force signal generated by the distributed energy conversion substructure and extract the back electromotive force features of the back electromotive force signal.

[0038] The neighboring distributed rectification and control node parameter receiving unit is used to receive local rectification parameters broadcast from the neighboring distributed rectification and control node;

[0039] The fusion decision generation unit is used to fuse the back electromotive force characteristics, the received local rectification parameters, and the data from the local motion sensor to generate electromechanical fusion decision data.

[0040] In conjunction with the first aspect above, in one possible implementation, the biomimetic structure tuning module includes: a matching state judgment unit, a tuning instruction generation unit, and an intelligent fluid cavity control unit;

[0041] The matching state judgment unit is used to determine the matching state between the local stiffness of the current bionic flexible module and the wave excitation based on the electromechanical fusion decision data.

[0042] A tuning instruction generation unit is used to generate a tuning instruction based on the mismatch state when the matching state determination unit determines that there is a mismatch.

[0043] The intelligent fluid cavity control unit is used to control the fluid viscosity and internal pressure of the intelligent fluid cavity according to the tuning command, and dynamically tune the local stiffness of the biomimetic flexible module.

[0044] In conjunction with the first aspect above, in one possible implementation, the DC combiner module includes: a DC bus and parallel connection unit, a filtering unit, and a stable output unit;

[0045] The DC bus and parallel connection unit are used to connect the DC outputs of all the distributed rectifiers and control nodes to the DC bus in parallel.

[0046] A filtering unit is installed on the DC bus to filter the collected electrical energy.

[0047] A stable output unit is used to output stable DC power after the filtering process.

[0048] Secondly, an adaptive rectification method for wave energy power generation that adapts to complex sea conditions is provided, including:

[0049] It captures wave energy and integrates several energy-capturing regions with independently tunable local stiffness, a distributed energy conversion substructure, and an intelligent fluid cavity for tuning local stiffness.

[0050] Data from hydrodynamic field sensors and solid mechanical field sensors are acquired and fused to generate wave prediction parameters;

[0051] Receive the wave prediction parameters and generate a global target working mode command based on the wave prediction parameters;

[0052] The global target operating mode command is broadcast to several distributed rectification and control nodes;

[0053] Based on the global target operating mode command and its own local sensing data, the switching parameters of the rectifier circuit are adjusted, and the local rectifier parameters are broadcast.

[0054] The back electromotive force signal generated by the distributed energy conversion substructure is monitored, and combined with the local rectification parameters received from the neighboring distributed rectification and control nodes, electromechanical fusion decision data is generated. The neighboring distributed rectification and control nodes are distributed rectification and control nodes that have a direct or closest mechanical coupling or power transmission path between the distributed energy conversion substructure and another distributed energy conversion substructure.

[0055] Compared with the prior art, the present invention has the following advantages:

[0056] This invention predicts sea conditions by constructing a multi-physics data acquisition module and combines it with an electromechanical fusion decision-making and biomimetic structural tuning module, enabling the power generation system to actively adjust its local stiffness to match real-time wave excitation characteristics. This proactive adaptation capability in physical form allows the system to find a better mechanical resonance state in waves of different periods, broadening the wave frequency range for efficient energy capture and thus improving the overall energy capture efficiency under complex and variable sea conditions.

[0057] This invention, through the global goal decision-making of the central coordination module and the local perception of distributed nodes, can switch the system's operating objective from pursuing maximum power to prioritizing operational stability when extreme sea conditions are predicted. Combined with the biomimetic structural tuning module's ability to proactively increase structural stiffness, it effectively avoids structural damage caused by excessive motion response under harsh sea conditions, while ensuring relatively stable power output, thus enhancing the overall power generation system's survivability and long-term operational reliability in harsh marine environments.

[0058] This invention employs a distributed rectification and control architecture, where each node can autonomously optimize the power conversion process based on global commands and local real-time data, and achieve local coordination through information interaction with neighboring nodes. This control strategy, combining distributed and centralized approaches, not only improves the system's response speed and control accuracy to local wave changes, but also effectively smooths out the inherent volatility of wave energy by uniformly filtering and stabilizing all distributed power sources through a DC combiner module, ultimately outputting higher-quality, stable DC power. Attached Figure Description

[0059] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0060] Figure 1A flowchart illustrating an adaptive rectification method for wave energy generation adapted to complex sea conditions, provided in an embodiment of this application;

[0061] Figure 2 A structural architecture diagram of an adaptive rectification system for wave energy generation adapted to complex sea conditions is provided in an embodiment of this application.

[0062] Figure 3 This is a contour plot of the local objective function provided in an embodiment of this application;

[0063] Figure 4 This is a timing curve diagram of biomimetic tuning control provided in the embodiments of this application. Detailed Implementation

[0064] The wave energy generation adaptive rectification system provided in this application embodiment, which adapts to complex sea conditions, can be applied to, for example... Figure 1 In the adaptive rectification method for wave energy generation adapted to complex sea conditions shown, such as Figure 1 As shown, the method includes:

[0065] It captures wave energy and integrates several independently tunable local stiffness-capturing regions, a distributed energy conversion substructure, and an intelligent fluid cavity for tuning local stiffness.

[0066] Data from hydrodynamic field sensors and solid mechanical field sensors are acquired and fused to generate wave prediction parameters;

[0067] Receive the wave prediction parameters and generate a global target working mode command based on the wave prediction parameters;

[0068] The global target operating mode command is broadcast to several distributed rectification and control nodes;

[0069] Based on the global target operating mode command and its own local sensing data, the switching parameters of the rectifier circuit are adjusted, and the local rectifier parameters are broadcast.

[0070] The back electromotive force signal generated by the distributed energy conversion substructure is monitored, and combined with the local rectification parameters received from the neighboring distributed rectification and control nodes, electromechanical fusion decision data is generated. The neighboring distributed rectification and control nodes are distributed rectification and control nodes that have a direct or closest mechanical coupling or power transmission path between the distributed energy conversion substructure and another distributed energy conversion substructure.

[0071] like Figure 2As shown in the figure, this application provides an adaptive rectification system for wave energy generation that adapts to complex sea conditions, including: a biomimetic flexible module, a multiphysics data acquisition module, a central coordination module, a communication and broadcasting module, a distributed rectification and local control module, an electromechanical fusion decision module, a biomimetic structure tuning module, and a DC combiner module; wherein:

[0072] The biomimetic flexible module is used to capture wave energy. It integrates several energy-capturing regions with independently tunable local stiffness, a distributed energy conversion substructure, and an intelligent fluid cavity for tuning local stiffness.

[0073] The multiphysics data acquisition module is used to acquire data from hydrodynamic field sensors and solid mechanical field sensors from biomimetic flexible modules, and fuse these data to generate wave prediction parameters.

[0074] The central coordination module is used to receive the wave prediction parameters and generate global target working mode instructions based on the wave prediction parameters.

[0075] The communication and broadcasting module is used to broadcast the global target working mode command to several distributed rectification and control nodes;

[0076] The distributed rectification and local control module includes several distributed rectification and control nodes, which are used to adjust the switching parameters of the rectifier circuit based on the global target working mode command and its own local sensing data, and broadcast the local rectification parameters.

[0077] The electromechanical integration decision module is used to monitor the back electromotive force signal generated by the distributed energy conversion substructure and combine it with the local rectification parameters received from the neighboring distributed rectification and control nodes to generate electromechanical integration decision data. The neighboring distributed rectification and control nodes are distributed rectification and control nodes in the biomimetic flexible module where there is a direct or closest mechanical coupling or power transmission path between the distributed energy conversion substructure and another distributed energy conversion substructure.

[0078] A biomimetic structure tuning module is used to receive the electromechanical fusion decision data and control the intelligent fluid cavity based on the electromechanical fusion decision data to dynamically tune the local stiffness of the biomimetic flexible module.

[0079] The DC combiner module is used to connect the DC outputs of all the distributed rectifiers and control nodes in parallel to the DC bus to generate stable DC power.

[0080] It should be noted that the multiphysics data acquisition module senses and fuses the external wave environment and the structural response of the module to generate predictive wave parameters. Based on this prediction, the central coordination module formulates macroscopic-level global target operating mode commands, balancing power pursuit with stability assurance. These commands are transmitted to several distributed rectifier and local control nodes distributed throughout the biomimetic flexible module via the communication and broadcasting module. Each node, combining the global commands with its own local sensing data, autonomously adjusts the rectifier circuit parameters to optimize local power conversion and shares its state with neighboring nodes. A deeper electromechanical fusion decision module listens to the back electromotive force signal of the energy conversion substructure and, combined with the electrical parameters of neighboring nodes, deeply evaluates the matching degree between mechanical motion and power conversion, generating electromechanical fusion decision data. Based on this decision data, the biomimetic structure tuning module controls the intelligent fluid cavity to dynamically tune the local stiffness of the biomimetic flexible module, achieving physical adaptation. The DC power generated by all distributed nodes is finally collected, filtered, and stably output via the DC combiner module.

[0081] In one possible implementation of the embodiments of this application, combined with Figure 2 The aforementioned multiphysics data acquisition module includes: a hydrodynamic field data unit, a solid mechanics field data processing unit, and a multi-field data fusion processing unit, which are described in detail below:

[0082] The hydrodynamic field data unit is used to acquire wave height, period, and direction data through the hydrodynamic field sensor.

[0083] In some implementations, the hydrodynamic field data unit acquires real-time wave information about the environment through hydrodynamic field sensors deployed near or integrated onto the biomimetic flexible module, such as an acoustic Doppler current profiler (ADCP), a wave rider buoy, or an underwater pressure sensor array. The collected data primarily includes wave height data reflecting wave energy intensity, periodic data reflecting wave energy transfer frequency, and directional data reflecting the main energy source direction. This data constitutes a direct description of the external environmental excitation.

[0084] For example, the hydrodynamic field data unit employs an underwater pressure sensor array and an acoustic Doppler current profiler (ADCP) integrated onto a biomimetic flexible module base or anchoring structure. The sensor array acquires static pressure and dynamic pressure changes at different depths in real time at a frequency of 10 Hz, calculating wave height and period from pressure fluctuations. Simultaneously, the ADCP measures the water velocity vector at a frequency of 5 Hz, determining wave direction through spectral analysis of the velocity vector. This unit synchronizes these three data points in time to form a timestamped external excitation parameter vector, which is then input into the multi-field data fusion processing unit.

[0085] The solid mechanical field data processing unit is used to acquire the structural strain data of the biomimetic flexible module through the solid mechanical field sensor, and calculate the deformation rate data based on the structural strain data.

[0086] In some implementations, the solid mechanics field data processing unit utilizes solid mechanics field sensors, such as fiber Bragg grating sensors or strain gauge arrays, deployed in key stress-bearing regions of the biomimetic flexible module to continuously monitor the real-time response of the energy-harvesting structure under wave action. The sensors directly measure structural strain data, a time-series signal describing the degree of minute deformation of the structure. To obtain information that better reflects the dynamic characteristics of the structure, the processing unit needs to process the acquired structural strain data. By performing time derivative operations on the time-series structural strain data, the strain rate of the structure is obtained. Then, combined with the known geometric model of the biomimetic flexible module, the deformation rate data of a specific energy-harvesting region is calculated. This step transforms the static deformation information of the structure into dynamic response information.

[0087] For example, the solid mechanics field data processing unit utilizes a fiber Bragg grating (FBG) sensor array embedded in the key bending region of the biomimetic flexible module to acquire structural strain data at a high frequency of 20Hz. The unit first applies Kalman filtering to reduce noise in the raw strain signal, then executes a digital differential algorithm to perform first-order time derivative calculation on the filtered strain time series to obtain the strain rate signal. This strain rate signal is combined with pre-stored structural geometric models and material parameters to calculate and derive the deformation velocity data for each energy-harvesting region. This set of data is integrated into a structural response parameter vector, reflecting the dynamic transient response of the energy-harvesting module under wave action.

[0088] The multi-field data fusion processing unit is used to fuse the wave height, period, and direction data with the structural strain and deformation velocity data, and obtain wave prediction parameters through model predictive control algorithms.

[0089] In some implementations, the multi-field data fusion processing unit receives wave height, period, and direction data from the hydrodynamic field data unit, and structural strain and deformation rate data from the solid mechanics field data processing unit. The core of this unit is a Model Predictive Control (MPC) algorithm. This algorithm incorporates a dynamic system model describing the interaction between waves and the flexible structure, treating external hydrodynamic data as input and internal solid mechanics data as state feedback. By fusing these two types of data, the algorithm can more accurately calibrate and correct its internal model, thereby improving prediction accuracy. This fusion processing is represented by the following functional relation:

[0090] ;

[0091] in, This represents the final output wave prediction parameters, which are predictions of wave characteristics over a short future time window. This represents the computational process of the model predictive control algorithm. This represents a vector of external environmental parameters collected and integrated by hydrodynamic field sensors, with specific values ​​obtained through the hydrodynamic field data unit. This represents the structural response parameter vector acquired and processed by solid mechanical field sensors, with specific values ​​derived from the solid mechanical field data processing unit. The model predictive control algorithm continuously optimizes and solves the problem to generate wave prediction parameters that will perform optimally over a future period, and then outputs these parameters to the central coordination module.

[0092] For example, a multi-field data fusion processing unit in a certain The MPC algorithm is executed continuously. Assume the current model predicts the effective wave height. Depends on the currently observed external environment parameter vector and structural response parameter vector Its simplified prediction relation is: , where the coefficient and These weights are adaptively determined by the MPC algorithm during the rolling optimization process based on historical data and constraints. At any given time, assume the sensor observation data is as follows: hydrodynamic field input wave period : Seconds. Solid mechanical field input to structural strain rate. : / second. Substitute into the prediction formula for calculation: Therefore, the future predicted by the multi-field data fusion processing unit... The effective height of the second wave is Meters. This prediction parameter This serves as the basis for generating global target working mode instructions, which are then output to the central coordination module.

[0093] In one possible implementation, combining Figure 2 The aforementioned central coordination module includes: a prediction parameter parsing unit, a target decision-making unit, and a global instruction generation unit, which are described in detail below:

[0094] A prediction parameter parsing unit is used to receive and parse the wave prediction parameters;

[0095] In some implementations, the prediction parameter analysis unit receives wave prediction parameters from the multiphysics data acquisition module. This unit first analyzes these parameters, extracting key features such as predicted wave height, principal period, spectral width, and wave direction. Based on preset thresholds and rules, it classifies the sea state over a future period into stable power generation conditions, high-efficiency power generation conditions, or extreme survival conditions. For example, when the predicted wave height and period are stable within a range favorable for energy conversion, it is determined to be a high-efficiency power generation condition; when the predicted wave height exceeds the equipment safety threshold or the wave period changes drastically, it is determined to be an extreme survival condition.

[0096] For example, the prediction parameter parsing unit receives wave prediction parameters from the multiphysics data acquisition module, such as predicted wave height. Meters, predicting the main cycle Seconds, and predicted wave direction stability ,scope This unit has the following built-in classification rules: If Miqie If the time is less than 1 second, it is considered a high-efficiency power generation condition; if rice or If the time exceeds a certain threshold, it is considered an extreme survival condition; otherwise, it is considered a stable condition. In this example, because... Mihe The average per second falls within the high-efficiency range, therefore the prediction parameter analysis unit determines that the current sea state is a high-efficiency power generation condition.

[0097] The target decision unit is used to determine the working objectives based on the wave prediction parameters, wherein the working objectives include prioritizing output stability and prioritizing power capture;

[0098] In some implementations, the target decision unit determines the primary objective for the current stage based on the classification results of the prediction parameter analysis unit. If the sea state is determined to be a high-efficiency power generation condition, the unit sets its objective to prioritize power capture, aiming to maximize the energy absorbed by the system from the waves. Conversely, if the sea state is determined to be an extreme survival condition or volatile sea state, the objective is set to prioritize ensuring stable output. In this case, the primary task is to ensure the safety of the system's structure and the quality of the output power, rather than pursuing maximum power.

[0099] For example, the target decision unit receives the judgment result from the prediction parameter analysis unit: "High-efficiency power generation condition". According to the preset strategy mapping table, when in a high-efficiency power generation condition, the opportunity of strong and stable wave energy should be seized to maximize energy capture. Therefore, the target decision unit immediately determines that the primary working objective of the current stage is: to prioritize power capture. If the judgment result is an extreme survival condition, the target decision unit will determine that the working objective is to prioritize ensuring output stability, thereby guiding the subsequent entry into the protective operation mode.

[0100] The global instruction generation unit is used to generate a global target working mode instruction containing weight coefficients according to the working target, and output it to the communication and broadcasting module.

[0101] In some implementations, a global instruction generation unit translates this objective into executable, quantized instructions. This unit constructs a global performance evaluation function to guide the behavior.

[0102] ;

[0103] in, This represents the overall performance index. It is a normalized power capture performance metric, and its value reflects the efficiency of energy capture. It is a normalized stability index, whose value integrates factors such as structural stress, temperature and output voltage fluctuations, reflecting operational stability and safety. This is a key weighting coefficient, with a value ranging from 0 to 1. The value is directly determined by the working objective set by the target decision unit. When the working objective is to prioritize power capture, A larger value, such as 0.8, is assigned to it, giving power capture a greater weight in the global optimization. This is important when the objective is to prioritize output stability. It is then assigned a smaller value, such as 0.2, to emphasize the importance of stability and security. This evaluation function The construction is based on the time-varying operational boundary conditions of wave energy power generation systems under random sea states. Physical-level weight allocation: coefficients The value of this parameter directly corresponds to the system's "operating mode." For example, when the multiphysics data acquisition module predicts that the sea state is at the edge of the equipment's fatigue limit, the system needs to actively adjust its settings according to structural mechanics safety principles. ,like At this point, the evaluation function is changed to use The system is led by a distributed rectifier node that increases electromagnetic damping and incorporates a biomimetic tuning module to increase stiffness, thereby reducing vibration and load. The system's collaborative logical mapping: This formula serves as a bridge connecting "macro-level sea state prediction" and "micro-level node control." The central coordination module calculates... In extreme value directions, broadcast messages are used to inform all distributed nodes, enabling several nodes to spontaneously coordinate and avoid the risk of overall structural damage due to excessive local energy harvesting. This ensures a synergistic improvement in energy capture efficiency and system survivability. The global instruction generation unit calculates the weight coefficients containing the current optimal weights. The global target working mode instruction is output to the communication and broadcast module so that this top-level policy can be broadcast to all distributed nodes.

[0104] For example, the global instruction generation unit uses the global performance evaluation function shown below. To generate global target working mode instructions: ,set up Value: Working objective: Prioritize power capture. Weight setting: At this time, the global instruction generation unit will set the weight coefficients. Set it to a large value, for example This emphasizes the importance of power. Assume that in a given iteration or simulation, the performance is as follows: Normalized power capture performance 0.92. Normalized stability index. : 0.75. Substitute into the evaluation function for calculation: The global instruction generation unit calculates the overall global performance index. Most importantly, the global target operating mode command generated by this unit will include this weighting coefficient. This guides all distributed rectifier and control nodes to prioritize a power balance of 85% and stability of 15% when adjusting switching parameters locally, thereby achieving the global objective.

[0105] In one possible implementation, combining Figure 2 The aforementioned communication and broadcasting module includes: an instruction encoding unit, a message sending unit, and an instruction receiving and decoding unit, which are described in detail below:

[0106] An instruction encoding unit is used to encode the global target working mode instruction into a broadcast message;

[0107] In some implementations, the instruction encoding unit receives the global target operating mode instruction, containing weighting coefficients, from the central coordination module. For reliable transmission within the communication network, this unit must convert this logical instruction into standardized physical data packets, i.e., broadcast messages. This process is not a simple information transfer but involves constructing data frames with a specific structure. The structure of this broadcast message can be expressed as: "Broadcast message = [Frame header | Data payload | Checksum]". The frame header identifies the start and type of the message, ensuring that all nodes in the network can correctly recognize it. The data payload is the core component, carrying the binary-encoded global target operating mode instruction, especially the crucial weighting coefficients. The checksum, such as a Cyclic Redundancy Check (CRC), is appended to the end of the data frame to ensure data integrity during transmission and prevent instruction errors caused by factors such as electromagnetic interference.

[0108] For example, the instruction encoding unit receives a global target operating mode instruction, which includes weighting coefficients. This unit encapsulates this instruction into an 8-byte broadcast message: first, it constructs a 2-byte frame header (0xABCD) to identify the message type; then, it... The data payload is converted to a 4-byte floating-point number format using the IEEE 754 standard. Finally, a 2-byte checksum of 0x1F3E based on the CRC-16 standard is calculated. The resulting complete broadcast message frame is 0xABCD0x4059999A0x1F3E, ensuring the standardization and reliability of the instructions before transmission.

[0109] The message sending unit is used to send broadcast messages through a communication network;

[0110] In some implementations, the message sending unit takes over the complete broadcast message generated by the instruction encoding unit. This unit modulates the digitized broadcast message into an electrical signal or electromagnetic wave suitable for transmission over the communication network via a physical layer interface, such as a CAN bus transceiver or a wireless RF module. This signal is then broadcast over the communication network, meaning that all distributed rectification and control nodes on the network can receive the same message simultaneously without point-to-point addressing, thus improving the efficiency of instruction distribution.

[0111] For example, the message sending unit transmits encoded broadcast message frames, such as 0xABCD..., via an underwater CAN bus transceiver integrated into the biomimetic flexible module backbone. This unit converts the digital signal into a differential voltage signal to... The signal is broadcast at a rate along a communication network constructed with watertight cables. Because it uses a CAN bus, the transmitting unit does not need to specify a target address; all distributed rectifier and control nodes connected to the bus can simultaneously receive this globally transmitted signal. The instructions enable low-latency, high-reliability cluster instruction distribution.

[0112] The instruction receiving and decoding unit is configured within the plurality of distributed rectification and control nodes, and is used to receive the broadcast message and decode it to obtain the global target working mode instruction.

[0113] In some implementations, each distributed rectification and control node is equipped with an instruction receiving and decoding unit. This unit's communication interface continuously monitors signals on the communication network. Once a broadcast message frame header conforming to the protocol is detected, it begins receiving complete data frames. After reception, the unit first performs integrity verification on the received broadcast message using a checksum. Only if the verification passes, confirming that the data has not been corrupted during transmission, will the decoding operation continue. The decoding process is the reverse of the encoding process, parsing the data payload from binary format back to the original global target working-mode instruction, and extracting the core weight coefficients. This successfully decoded instruction is then submitted to the local control policy execution unit within the node as the highest-level basis for adjusting its own behavior.

[0114] For example, within each distributed rectification and control node, the instruction receiving and decoding unit continuously monitors the data flow on the CAN bus. Upon receiving a complete broadcast message frame, it immediately performs an integrity check, recalculating the CRC-16 checksum using the received data payload and comparing it with the received checksum 0x1F3E. Assuming the check passes, the unit confirms the data is valid and then performs a reverse decoding operation on the 4-byte data payload 0x4059999A, successfully parsing and restoring the weighting coefficients. This decoded The value is then submitted to the local control policy execution unit for local power and stability trade-off adjustments.

[0115] In one possible implementation, combining Figure 2 The aforementioned distributed rectification and local control module includes: a local sensing data acquisition unit, a local control strategy execution unit, and a local rectification parameter broadcasting unit, which are described in detail below:

[0116] The local sensing data acquisition unit is used to acquire local motion acceleration data, local temperature data, and local output current and voltage data through local motion sensors, temperature sensors, and current and voltage sampling circuits, and fuse these data to form local sensing data.

[0117] In some implementations, the local sensing data acquisition unit integrates a series of local sensors, including local motion sensors for measuring the local motion state of the energy harvesting area, temperature sensors for monitoring the operating temperature of key electronic components and energy conversion substructures, and current and voltage sampling circuits for accurately measuring the output power characteristics of the node. Through these sensors, the unit acquires in real time local motion acceleration data reflecting the node's dynamic response, local temperature data reflecting its operational health, and local output current and voltage data reflecting its energy output performance. All this data is fused in real time to form comprehensive local sensing data, providing detailed and immediate on-site information for subsequent control decisions.

[0118] For example, the local sensing data acquisition unit in Data is continuously acquired at a frequency of 50Hz. A triaxial accelerometer integrated within the energy harvesting area obtains local motion acceleration data, with an instantaneous peak value reaching 3.2g; a thermistor obtains local temperature data for the IGBT bridge arm. The current transformer and voltage sampling circuit obtained a local output current of 15A and a voltage of 400V from the rectifier circuit. This unit then normalized, synchronized, and filtered these four sets of raw data to form a multidimensional vector. This serves as the local perception data input for the subsequent local control policy execution unit.

[0119] The local control strategy execution unit is used to autonomously adjust the switching frequency and duty cycle of the rectifier circuit based on the received global target working mode command and the local sensing data.

[0120] In some implementations, the local control policy execution unit begins decision-making. This unit simultaneously receives two inputs: a global target operating mode command from the communication and broadcasting module, and the newly generated local sensing data. The control logic of this unit aims to optimize a localized objective function, the specific form of which is directly modulated by the weighting coefficients in the global command. Its optimization objective is expressed as:

[0121] ;

[0122] in, ;

[0123] In the formula, This represents the optimal rectifier circuit switching frequency and duty cycle that the unit ultimately needs to decide. This indicates that solving for the objective function The parameter combination that reaches the maximum value. These are weighting coefficients parsed from the global target working mode instructions. It is the instantaneous output power of the current node calculated from local output current and voltage data, representing the gain from power capture. It is a normalized local stress and risk index that integrates local motion acceleration data and local temperature data, representing the cost or risk of local operation. This is the upper limit allowed for this index. When global instructions prioritize power capture, If the value is large, the unit will adjust the switching parameters to maximize it. Even if this introduces some stress risks. Conversely, when global instructions prioritize ensuring output stability, When the value is smaller, the decision-making process in this unit will focus more on reducing it. This ensures the safe and stable operation of the nodes, even if it comes at the cost of some power output. For example... Figure 3 As shown, this illustrates the trade-off between local power gains and local stress risks. The contour line inclination direction reflects the... Under the weighting, the controller will prioritize the lower right region, which is a combination of high power gain and low stress risk, reflecting the quantitative guidance role of global instructions on local decision-making.

[0124] For example, suppose the parameters and conditions for this node in this decision are: global objective weight. Received global command settings Maximum permissible risk : Set to 1.0. This unit evaluates the local performance for two sets of candidate switching parameters (Case A and Case B):

[0125]

[0126] Substitute into the objective function Calculation: Case A: Power priority, high risk : Case B: Lower power, lower risk : .because The local control policy execution unit, according to In principle, select the switching parameters corresponding to Case A. As the optimal decision, it has been proven that in power-first... Under the target, even though Case A has a higher risk of 0.70, its higher power gain of 0.95 makes the overall performance index... To maximize returns and make decisions with high returns and controllable risks.

[0127] The local rectification parameter broadcasting unit is used to broadcast the local rectification parameters determined by the switching frequency and duty cycle.

[0128] In some implementations, once the local control policy execution unit determines the optimal switching frequency and duty cycle, the local rectification parameter broadcasting unit combines these two parameters into local rectification parameters and broadcasts them through the local communication interface. This broadcasting behavior is not a report to a higher level, but rather an announcement to other physically nearby distributed rectification and control nodes, providing a reference for their neighbors' decision-making.

[0129] For example, the local rectification parameter broadcasting unit receives the optimal switching parameters determined by the local control strategy execution unit: a switching frequency of 50.0 kHz and a duty cycle of 0.62. This unit encapsulates these two parameters into a local rectification parameter data packet. Specifically, the frequency value of 50.0 kHz is converted to a 16-bit integer 50000, and the duty cycle of 0.62 is converted to a 16-bit fixed-point number. Subsequently, the unit constructs a short local communication data frame, including a frame header, the node's unique ID, the packaged frequency and duty cycle data, and an 8-bit checksum. This complete data frame is broadcast through the node's internal local communication interface, such as a dedicated RS-485 link, enabling all neighboring distributed rectification and control nodes within the same energy-harvesting area to obtain the latest operating status of this node in real time, which is then used for local collaborative decision-making in the electromechanical integration decision module.

[0130] In one possible implementation, the aforementioned local sensing data acquisition unit includes: a multi-dimensional sensor subunit and a data acquisition and fusion subunit, which are described in detail below:

[0131] Multi-dimensional sensor subunit, including local motion sensor, temperature sensor and current and voltage sampling circuit;

[0132] In some implementations, at the hardware level, a multi-dimensional sensor subunit forms the physical foundation. This subunit integrates a specific combination of sensors on each distributed rectification and control node. Local motion sensors, typically MEMS inertial measurement units, are deployed on the energy-harvesting region of a biomimetic flexible module associated with the node. They capture the dynamic response of this region in real time due to wave action, directly outputting local motion acceleration data. Temperature sensors, such as thermistors or integrated temperature sensor chips, are tightly mounted on the power switching devices in the rectifier circuit or on the housing of the distributed energy conversion substructure to monitor the system's thermal load and output local temperature data. Current and voltage sampling circuits, typically composed of high-precision shunt resistors or Hall effect sensors combined with signal conditioning and analog-to-digital conversion circuits, are connected in series and parallel at the node's DC output to accurately measure the instantaneous state of the node's power delivery to the DC bus, outputting local output current and voltage data.

[0133] For example, the multi-dimensional sensor subunit employs a triaxial MEMS accelerometer as a local motion sensor, deployed on a flexible housing near the distributed energy conversion substructure, capturing vibrations and accelerations at a 500Hz sampling rate. Simultaneously, a PT1000 thermistor, configured on the SiC power switch heatsink, serves as a temperature sensor, accurately monitoring the temperature of the core electronic components. Power output measurement consists of a Hall-effect current sensor connected in series at the DC output terminal to measure current, and a resistor divider network working in conjunction with a high-precision ADC chip to measure voltage. The hardware of this subunit is connected to the microcontroller via a high-speed SPI bus, serving as the source of all locally sensed data.

[0134] The data acquisition and fusion subunit is used to acquire local motion acceleration data, local temperature data, and local output current and voltage data through the multi-dimensional sensors, and fuse the data to form local sensing data.

[0135] In some implementations, at the data processing level, the data acquisition and fusion subunit is responsible for integrating the raw data from different physical dimensions into a unified, structured dataset. This subunit, through its internal microcontroller, periodically acquires the latest readings from various sensors in the multi-dimensional sensor subunit. The acquired local motion acceleration data, local temperature data, and local output current and voltage data are not simply listed, but are fused into a multi-dimensional state vector, i.e., local sensed data. This fusion process is represented as:

[0136] ;

[0137] in, The final generated local perception data is a vector containing complete information about the current state of the node. This represents local motion acceleration data collected by local motion sensors. This represents local temperature data collected by a temperature sensor. This represents the local output current data acquired by the current sampling circuit. This represents the local output voltage data acquired by the voltage sampling circuit. This structured local sensing data vector is transmitted in real time to the local control strategy execution unit as the core input for its control algorithm calculations.

[0138] For example, the data acquisition and fusion subunit operates periodically at a frequency of 50Hz, responsible for converting the raw readings from the multi-dimensional sensor subunit into structured vectors. At a certain sampling time... This subunit obtains the following data from the hardware interface: local motion acceleration. The filtered, normalized RMS value is 0.75. Local temperature. The instantaneous temperature of the power switching device is Local output current Instantaneous DC current is 25.5A. Local output voltage. The instantaneous DC voltage value is 410.0V. This subunit encapsulates these synchronously acquired data according to a preset order and format to generate a local sensing data vector. The vector As a snapshot of the current state of node j, it is immediately transmitted to the local control policy execution unit. This process ensures that subsequent control decisions are based on a time-synchronized and structured multi-physical quantity input, improving the accuracy and real-time performance of local adaptive adjustments.

[0139] In one possible implementation, combining Figure 2 The aforementioned electromechanical fusion decision module includes: a back EMF feature extraction unit, a neighboring distributed rectifier and control node parameter receiving unit, and a fusion decision generation unit, which are described in detail below:

[0140] The back electromotive force feature extraction unit is used to monitor the back electromotive force signal generated by the distributed energy conversion substructure and extract the back electromotive force features of the back electromotive force signal.

[0141] In some implementations, the back EMF feature extraction unit is directly connected to the output of the distributed energy conversion substructure, monitoring the raw AC voltage signal generated by the generator before the rectifier circuit. This signal is the back EMF signal, and its waveform and amplitude directly reflect the real-time speed and position of the generator rotor. This unit does not directly use the raw signal, but rather extracts a series of key back EMF features through signal processing algorithms such as Fast Fourier Transform (FFT), zero-crossing detection, or Phase-Locked Loop (PLL). These features include the voltage peak reflecting the amplitude of the motion, the signal frequency reflecting the motion period, and the phase information reflecting the motion timing. This back EMF feature data accurately depicts the mechanical dynamics of the energy conversion process.

[0142] For example, the back EMF feature extraction unit samples the raw AC voltage signal of the distributed energy conversion substructure at a high frequency of 100kHz using a high-speed ADC. The unit then runs a phase-locked loop (PLL) based algorithm to accurately track the instantaneous frequency and phase of the back EMF signal. At any given moment, the features extracted by the algorithm include: a peak back EMF of 550V, an instantaneous frequency of 0.15Hz, and a phase angle relative to a reference clock. These feature data are packaged into a back electromotive force eigenvector. It accurately depicts the mechanical dynamic response of the generator under the current wave excitation.

[0143] The neighboring distributed rectification and control node parameter receiving unit is used to receive local rectification parameters broadcast from the neighboring distributed rectification and control node;

[0144] In some implementations, the neighboring distributed rectifier and control node parameter receiving unit continuously listens for broadcasts from neighboring nodes through its communication interface. By definition, a neighboring distributed rectifier and control node refers to a unit that is physically connected to the local node through mechanical coupling or an electrical power transmission path. This unit receives the local rectification parameters broadcast by these neighboring nodes, namely the switching frequency and duty cycle of the rectifier circuit. These parameters directly determine the magnitude and timing of the electromagnetic damping force applied by the neighboring nodes when extracting energy from waves, constituting important external load disturbance information for the local node.

[0145] For example, the neighboring distributed rectification and control node parameter receiving unit receives local rectification parameters broadcast from three physically neighboring nodes, Node A, Node B, and Node C, via a local RS-485 network. For instance, the parameter set received by this unit includes parameters from Node A. ; Node B's parameters ; NodeC parameters This unit synthesizes these three datasets into a set of local rectification parameters, the values ​​of which reflect the average strength and variability of electromagnetic damping currently being applied by the surrounding substructures, and is used to evaluate the coupling boundary conditions for decisions made at this node.

[0146] The fusion decision generation unit is used to fuse the back electromotive force characteristics, the received local rectification parameters, and the data from the local motion sensor to generate electromechanical fusion decision data.

[0147] In some implementations, the fusion decision generation unit ultimately fuses the back EMF characteristics, local rectification parameters, and its own motion state information. This unit receives three key inputs: the back EMF characteristics from the back EMF feature extraction unit, the local rectification parameters from neighboring nodes, and data from the local motion sensor in the local sensing data acquisition unit, specifically local motion acceleration data. The core of this unit's decision-making is the fusion algorithm, whose goal is to evaluate the degree of matching between mechanical motion and the energy conversion process, specifically:

[0148] ;

[0149] in, The final generated mechatronics fusion decision data is an indicator or instruction that quantifies the current degree and direction of mismatch. The pre-defined fusion decision algorithm can be implemented based on expert rules, fuzzy logic, or machine learning models. It is a feature vector containing information such as the amplitude, frequency, and phase of the back EMF signal, provided by the back EMF feature extraction unit. It is a set that contains all the local rectification parameters received from the neighboring distributed rectification and control nodes. It is local motion acceleration data directly measured by local motion sensors, reflecting the overall structural motion of the energy-harvesting region. The algorithm compares structural acceleration... With back electromotive force characteristics The phase difference and amplitude ratio are used to determine whether there is a mismatch between mechanical resonance and electromagnetic damping. Simultaneously, it measures the load conditions of adjacent nodes. As an important boundary condition, the final electromechanical fusion decision data is generated and output to the biomimetic structure tuning module.

[0150] For example, the fusion decision generation unit employs a fuzzy logic-based fusion algorithm. Its goal is to output within a certain range. Mechatronics fusion decision data Positive values ​​indicate that local stiffness needs to be increased, while negative values ​​indicate that local stiffness needs to be decreased. At a given moment, the unit receives the following three key inputs: back electromotive force characteristics. After extraction, the generator output frequency was determined. Neighboring node parameter set The average electromagnetic damping coefficient applied by neighboring nodes was calculated. Local acceleration Based on FFT analysis, the current mechanical resonance period of the energy-harvesting region is... Fusion decision algorithm Operating logic: Mismatch calculation: Periodic mismatch A short mechanical cycle indicates high current stiffness, tending towards overdamping / overstiffness. Impact trade-offs: Fuzzy logic rules determine that although the mechanical cycle is short, the adjacent damping... Too high. Decision output: Algorithm Based on these inputs, mechatronics decision data is ultimately generated. This negative value clearly indicates that the current local stiffness is too high, and the stiffness adjustment needs to be reduced by 30% to adjust the mechanical cycle. Near optimal This achieves a coordinated matching of mechanical and electromagnetic damping.

[0151] In one possible implementation, combining Figure 2 The aforementioned biomimetic structure tuning module includes: a matching state judgment unit, a tuning command generation unit, and an intelligent fluid cavity control unit, which are described in detail below:

[0152] The matching state judgment unit is used to determine the matching state between the local stiffness of the current bionic flexible module and the wave excitation based on the electromechanical fusion decision data.

[0153] In some implementations, the matching state determination unit receives electromechanical fusion decision data from the electromechanical fusion decision module. This data quantifies the degree of matching between the current mechanical motion and the electromagnetic load. The core task of this unit is to interpret this data and compare it with a preset ideal matching state model. If the electromechanical fusion decision data shows a deviation between the motion frequency and wave excitation frequency of the current energy-harvesting region, or a disproportion between the motion amplitude and energy conversion efficiency, the unit determines that the local stiffness of the current biomimetic flexible module is mismatched with the wave excitation. This determination is not a simple binary decision but can identify the specific type of mismatch, such as whether excessive stiffness leads to insufficient response or excessive stiffness leads to excessive motion and low power generation efficiency. Figure 4 As shown, the upper curve displays the dynamic matching state between the wave excitation cycle and the module response cycle, with an initial mismatch that gradually converges; the lower curve displays the pressure tuning command output by the electromechanical fusion decision module and executed by the intelligent fluid cavity, which dynamically adjusts according to the degree of cycle mismatch, achieving adaptive tuning.

[0154] For example, the matching state determination unit receives electromechanical fusion decision data. ,For example The unit compares this value with a preset matching tolerance range. Comparison. Because Less than The unit immediately determined that the local stiffness of the current biomimetic flexible module was severely mismatched with the wave excitation, and the mismatch type was "excessive stiffness leading to mechanical underdamping." This judgment result was clearly marked, along with the original... The values ​​are passed together to the tuning instruction generation unit.

[0155] A tuning instruction generation unit is used to generate a tuning instruction based on the mismatch state when the matching state determination unit determines that there is a mismatch.

[0156] In some implementations, once the matching state determination unit determines a mismatch, the tuning instruction generation unit immediately starts. This unit generates specific tuning instructions based on the type and degree of the mismatch. This process is dominated by the control law function, specifically:

[0157] ;

[0158] in, The final tuning command is typically a vector containing the target pressure value and the target viscosity control signal value. This represents a nonlinear control law function, which takes the input mechatronics fusion decision data as input. This is mapped to specific control outputs. For example, if... This indicates that the structural response lags behind the wave excitation, therefore the control law It will generate instructions aimed at reducing local stiffness, which may include commands to reduce internal pressure and commands to reduce fluid viscosity.

[0159] For example, the tuning instruction generation unit operates a nonlinear control law function. The received mechatronics decision data Convert to tuning commands Assume the unit receives This negative value indicates that the current stiffness is too high and needs to be reduced. (Nonlinear control law) The operating logic is as follows: The algorithm employs a piecewise linear / proportional-integral (PI) hybrid control: target pressure change command :and Proportional and subject to restrictions. Assuming a proportionality coefficient . The instruction is to reduce Pressure, so the actual operation is the target pressure. Target viscosity / current change command : Apply exponential penalties to larger mismatches to adjust fluid viscosity more quickly. Assuming a proportionality coefficient . The instruction is to reduce Current, so the actual operation is the target current. Ultimately, the tuning command generation unit generates the tuning command. It is a vector Clearly instructing the intelligent fluid cavity control unit to reduce... The internal pressure, and reduce The magnetorheological fluid controls the current, thereby dynamically tuning the local stiffness of the biomimetic flexible module.

[0160] The intelligent fluid cavity control unit is used to control the fluid viscosity and internal pressure of the intelligent fluid cavity according to the tuning command, and dynamically tune the local stiffness of the biomimetic flexible module.

[0161] In some implementations, the intelligent fluid cavity control unit receives the tuning command and performs physical operations. This unit is directly connected to the intelligent fluid cavity inside the biomimetic flexible module. The intelligent fluid cavity is a sealed capsule filled with an intelligent fluid, such as a magnetorheological fluid or an electrorheological fluid. Based on the portion of the tuning command concerning fluid viscosity, the control unit adjusts the current or voltage applied to the electromagnetic coils or electrode plates around the cavity, thereby changing the viscosity of the intelligent fluid and thus altering the cavity's resistance to shear deformation. Simultaneously, based on the portion of the tuning command concerning internal pressure, the unit injects or extracts fluid into the cavity by controlling a micro-pump or electrically controlled valve to precisely regulate the internal pressure, thereby altering its resistance to compressive deformation. Through the coordinated control of fluid viscosity and internal pressure, the unit can continuously and dynamically tune the local stiffness of specific regions of the biomimetic flexible module.

[0162] For example, the intelligent fluid cavity control unit receives a tuning command to reduce the pressure. Reduce current The unit immediately activates two actuators: a miniature electronically controlled pressure relief valve opens, releasing some fluid from the intelligent fluid chamber and precisely transferring the internal pressure from... descend to the target This reduces the cavity's resistance to compressive deformation. The current driver is activated, transferring a control current from the electromagnetic coil surrounding the cavity... Down to This reduces the viscosity of the internal magnetorheological fluid, thereby decreasing the cavity's resistance to shear deformation. Through these two synergistic actions, the unit successfully and dynamically reduces the local stiffness of the biomimetic flexible module, bringing it closer to a resonance state that matches wave excitation.

[0163] In one possible implementation, combining Figure 2 The aforementioned DC combiner module includes: a DC bus and parallel connection unit, a filtering unit, and a stable output unit, which are described in detail below:

[0164] The DC bus and parallel connection unit are used to connect the DC outputs of all the distributed rectifiers and control nodes to the DC bus in parallel.

[0165] In some implementations, the DC bus and parallel connection units perform the physical collection of electrical energy. The core of this unit is the DC bus, which serves as a common electrical connection point. It consists of two large-section conductors with extremely low resistance, acting as the positive and negative buses, respectively. The DC output terminals of all distributed rectifier and control nodes are uniformly connected to the positive bus for their positive terminals and to the negative bus for their negative terminals. This parallel connection ensures that all nodes operate at a common voltage level, and the total output current is the sum of the output currents of all nodes. For safety, each node's connection point is typically equipped with isolation diodes to prevent backflow, and fuses or circuit breakers for overcurrent protection, thus physically and safely collecting the dispersed electrical energy onto a main line.

[0166] For example, the DC bus and parallel connection unit use a pair of insulated copper busbars as the DC bus, with a design voltage level of 800VDC. The DC output terminal of each distributed rectification and control node is connected to a rated current of Schottky diodes are connected to the copper busbar as both isolation diodes and fast-acting fuses. This connection method ensures that all... Distributed nodes can be safely connected in parallel on the same voltage plane. At a certain moment, if there are Each node at average If the current output is [a certain value], then the total current on the DC bus will reach [a certain value]. All nodes share the same Voltage. The presence of isolation diodes effectively prevents any faulty or low-voltage node from drawing power back to other nodes.

[0167] A filtering unit is installed on the DC bus to filter the collected electrical energy.

[0168] In some implementations, the filtering unit performs preliminary purification of the collected electrical energy. Since the output of each distributed rectifier and control node contains voltage ripple and noise caused by wave randomness and rectifier circuit switching operations, the power quality directly collected on the DC bus is poor and fluctuates drastically. Therefore, this unit incorporates large-capacity energy storage elements on the DC bus, primarily parallel arrays of electrolytic capacitors. When the instantaneous voltage of the bus rises due to a peak, the capacitor array charges, absorbing excess energy; when the instantaneous voltage drops due to a trough, the capacitor array discharges, replenishing the energy gap. This process effectively reduces the peak-to-trough voltage difference, smooths low-frequency pulsations caused by individual wave cycles, and absorbs high-frequency noise generated by rectifier switching, improving the smoothness of the electrical energy on the DC bus.

[0169] For example, a set of filtering units with a total capacity of [missing information] are connected in parallel on the DC bus. An array of electrolytic capacitors. When wave randomness causes the bus voltage to... to When voltage fluctuations occur, the capacitor array can reduce the voltage ripple factor from 10% to below 1%. Simultaneously, this unit incorporates a common-mode inductor and a differential-mode capacitor connected in series between the bus and the capacitors. Type-A filters are used to absorb and suppress high-frequency switching operations caused by distributed rectification and control nodes, such as... The generated high-frequency electromagnetic interference (EMI) noise ensures the smoothness of the collected power and electromagnetic compatibility.

[0170] A stable output unit is used to output stable DC power after the filtering process.

[0171] In some implementations, the stable output unit finely regulates the filtered electrical energy to generate the final stable DC power. Despite filtering, the voltage on the DC bus still experiences significant macroscopic fluctuations under different sea conditions. To meet the stringent requirements of subsequent grid inverters or energy storage devices for a constant input voltage, this unit typically employs a high-power DC-DC converter. This converter continuously monitors its output voltage and, through closed-loop feedback control, frequently adjusts the duty cycle of its internal power switching devices. Regardless of how the input voltage from the DC bus fluctuates over a wide range, the converter ensures that its output voltage remains strictly stable at a preset target value, thus outputting stable DC power that can be directly utilized or fed into the grid.

[0172] For example, the stable output unit uses a rated power of A full-bridge DC-DC converter. This converter continuously monitors the input voltage from the DC bus, such as... It drives its internal SiC power switches via a digital proportional-integral-derivative (PID) controller. This PID controller adjusts the switch duty cycle in real time to ensure that its output voltage is strictly stabilized at the constant target voltage required by the grid inverter. Above, its voltage fluctuation rate is less than 0.1%. Through this fine adjustment, the unit ultimately delivers high-quality, stable DC power that has been filtered and regulated to the grid or energy storage system.

[0173] It should be noted that the electrical connections between the various units described above do not necessarily represent direct or indirect connections. Any indirect connection method can be applied to the embodiments of the present invention as long as it achieves the purpose of the present invention. The above descriptions are merely exemplary embodiments of the present invention and should not be construed as limiting the scope of the present invention.

[0174] All equivalent changes and modifications made in accordance with the teachings of this invention are still within the scope of this invention. Those skilled in the art will readily conceive of other embodiments of this invention upon considering the specification and the disclosure of practical truth. This application is intended to cover any variations, uses, or adaptations of this invention that follow the general principles of this invention and include common knowledge or conventional techniques in the art not described herein.

Claims

1. An adaptive rectification system for wave energy generation adapted to complex sea conditions, characterized in that, The system includes: The biomimetic flexible module is used to capture wave energy. It integrates several energy-capturing regions with independently tunable local stiffness, a distributed energy conversion substructure, and an intelligent fluid cavity for tuning local stiffness. The multiphysics data acquisition module is used to acquire data from hydrodynamic field sensors and solid mechanical field sensors from biomimetic flexible modules, and fuse these data to generate wave prediction parameters. The central coordination module is used to receive the wave prediction parameters and generate global target working mode instructions based on the wave prediction parameters. The communication and broadcasting module is used to broadcast the global target working mode command to several distributed rectification and control nodes; The distributed rectification and local control module includes several distributed rectification and control nodes, which are used to adjust the switching parameters of the rectifier circuit based on the global target working mode command and its own local sensing data, and broadcast the local rectification parameters. The electromechanical integration decision module is used to monitor the back electromotive force signal generated by the distributed energy conversion substructure and combine it with the local rectification parameters received from the neighboring distributed rectification and control nodes to generate electromechanical integration decision data. The neighboring distributed rectification and control nodes are distributed rectification and control nodes in the biomimetic flexible module where there is a direct or closest mechanical coupling or power transmission path between the distributed energy conversion substructure and another distributed energy conversion substructure. A biomimetic structure tuning module is used to receive the electromechanical fusion decision data and control the intelligent fluid cavity based on the electromechanical fusion decision data to dynamically tune the local stiffness of the biomimetic flexible module. The DC combiner module is used to connect the DC outputs of all the distributed rectifiers and control nodes in parallel to the DC bus to generate stable DC power.

2. The adaptive rectification system for wave energy generation adapted to complex sea conditions according to claim 1, characterized in that, The multiphysics data acquisition module includes: a hydrodynamic field data unit, a solid mechanics field data processing unit, and a multi-field data fusion processing unit; The hydrodynamic field data unit is used to acquire wave height, period, and direction data through the hydrodynamic field sensor. The solid mechanical field data processing unit is used to acquire the structural strain data of the biomimetic flexible module through the solid mechanical field sensor, and calculate the deformation rate data based on the structural strain data. The multi-field data fusion processing unit is used to fuse the wave height, period, and direction data with the structural strain and deformation velocity data, and obtain wave prediction parameters through model predictive control algorithms.

3. The adaptive rectification system for wave energy generation adapted to complex sea conditions according to claim 1, characterized in that, The central coordination module includes: a prediction parameter parsing unit, a target decision-making unit, and a global instruction generation unit; A prediction parameter parsing unit is used to receive and parse the wave prediction parameters; The target decision unit is used to determine the working objectives based on the wave prediction parameters, wherein the working objectives include prioritizing output stability and prioritizing power capture; The global instruction generation unit is used to generate a global target working mode instruction containing weight coefficients according to the working target, and output it to the communication and broadcasting module.

4. The adaptive rectification system for wave energy generation adapted to complex sea conditions according to claim 1, characterized in that, The communication and broadcasting module includes: an instruction encoding unit, a message sending unit, and an instruction receiving and decoding unit; An instruction encoding unit is used to encode the global target working mode instruction into a broadcast message; The message sending unit is used to send broadcast messages through a communication network; The instruction receiving and decoding unit is configured within the plurality of distributed rectification and control nodes, and is used to receive the broadcast message and decode it to obtain the global target working mode instruction.

5. The wave energy power generation adaptive rectification system for complex sea conditions according to claim 1, characterized in that, The distributed rectification and local control module includes: a local sensing data acquisition unit, a local control strategy execution unit, and a local rectification parameter broadcasting unit; The local sensing data acquisition unit is used to acquire local motion acceleration data, local temperature data, and local output current and voltage data through local motion sensors, temperature sensors, and current and voltage sampling circuits, and fuse these data to form local sensing data. The local control strategy execution unit is used to autonomously adjust the switching frequency and duty cycle of the rectifier circuit based on the received global target working mode command and the local sensing data. The local rectification parameter broadcasting unit is used to broadcast the local rectification parameters determined by the switching frequency and duty cycle.

6. The wave energy power generation adaptive rectification system for complex sea conditions according to claim 5, characterized in that, The local sensing data acquisition unit includes: The multi-dimensional sensor subunit includes a local motion sensor, a temperature sensor, and current and voltage sampling circuits; The data acquisition and fusion subunit is used to acquire local motion acceleration data, local temperature data, and local output current and voltage data through the multi-dimensional sensors, and fuse the data to form local sensing data.

7. The adaptive rectification system for wave energy generation adapted to complex sea conditions according to claim 6, characterized in that, The electromechanical fusion decision module includes: a back electromotive force feature extraction unit, a neighboring distributed rectifier and control node parameter receiving unit, and a fusion decision generation unit; The back electromotive force feature extraction unit is used to monitor the back electromotive force signal generated by the distributed energy conversion substructure and extract the back electromotive force features of the back electromotive force signal. The neighboring distributed rectification and control node parameter receiving unit is used to receive local rectification parameters broadcast from the neighboring distributed rectification and control node; The fusion decision generation unit is used to fuse the back electromotive force characteristics, the received local rectification parameters, and the data from the local motion sensor to generate electromechanical fusion decision data.

8. The adaptive rectification system for wave energy generation adapted to complex sea conditions according to claim 1, characterized in that, The biomimetic structure tuning module includes: a matching state judgment unit, a tuning command generation unit, and an intelligent fluid cavity control unit; The matching state judgment unit is used to determine the matching state between the local stiffness of the current bionic flexible module and the wave excitation based on the electromechanical fusion decision data. A tuning instruction generation unit is used to generate a tuning instruction based on the mismatch state when the matching state determination unit determines that there is a mismatch. The intelligent fluid cavity control unit is used to control the fluid viscosity and internal pressure of the intelligent fluid cavity according to the tuning command, and dynamically tune the local stiffness of the biomimetic flexible module.

9. The adaptive rectification system for wave energy generation adapted to complex sea conditions according to claim 1, characterized in that, The DC combiner module includes: a DC bus and parallel connection unit, a filtering unit, and a stable output unit; The DC bus and parallel connection unit are used to connect the DC outputs of all the distributed rectifiers and control nodes to the DC bus in parallel. A filtering unit is installed on the DC bus to filter the collected electrical energy. A stable output unit is used to output stable DC power after the filtering process.

10. An adaptive rectification method for wave energy power generation adapted to complex sea conditions, characterized in that, The method is used in an adaptive rectification system for wave energy generation adapted to complex sea conditions as described in any one of claims 1-9, the method comprising: It captures wave energy and integrates several energy-capturing regions with independently tunable local stiffness, a distributed energy conversion substructure, and an intelligent fluid cavity for tuning local stiffness. Data from hydrodynamic field sensors and solid mechanical field sensors are acquired and fused to generate wave prediction parameters; Receive the wave prediction parameters and generate a global target working mode command based on the wave prediction parameters; The global target operating mode command is broadcast to several distributed rectification and control nodes; Based on the global target operating mode command and its own local sensing data, the switching parameters of the rectifier circuit are adjusted, and the local rectifier parameters are broadcast. The back electromotive force signal generated by the distributed energy conversion substructure is monitored, and combined with the local rectification parameters received from the neighboring distributed rectification and control nodes, electromechanical fusion decision data is generated. The neighboring distributed rectification and control nodes are distributed rectification and control nodes that have a direct or closest mechanical coupling or power transmission path between the distributed energy conversion substructure and another distributed energy conversion substructure. The system receives the electromechanical fusion decision data and controls the intelligent fluid cavity based on the electromechanical fusion decision data, dynamically adjusting the local stiffness of the biomimetic flexible module; The DC outputs of all the distributed rectifiers and control nodes are connected in parallel to the DC bus to generate stable DC power.

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