Integrated System of Energy Storage - Structure - Intelligent Self - Repair for Offshore Wind Turbine Monopile Foundation

Abstract

The present invention is an integrated energy storage - structure - intelligent self - repair system for an offshore wind power monopile foundation, which includes a monopile foundation and a concrete disk - shaped expansion structure; an inverter cabin, a honeycomb battery cabin and a counterweight cabin are arranged inside the monopile foundation; a plurality of regions are evenly divided in the circumferential direction, and seawater batteries, fiber Bragg grating strain sensors, piezoelectric actuators, shape memory alloy actuators and broadband acoustic emission sensors are installed in each region; the truss of the honeycomb battery cabin in each region is connected to the inner wall of the monopile foundation through prestressed steel strands and anchors, and resistance wires are arranged on the surface of the shape memory alloy actuator; bacillus microcapsules are incorporated into the concrete; impedance probes, self - priming pumps and automatic control valves are installed on the seawater battery; an AI architecture for outputting three - dimensional fault probabilities is integrated in the inverter cabin; corresponding - dimension fault self - repair in the corresponding region is triggered according to the probability threshold. The closed - loop management of perception - prediction - repair for the whole life cycle of the offshore wind turbine is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of offshore wind power, and particularly relates to an integrated system of energy storage - structure - intelligent self - repair for offshore wind power monopile foundations. This system integrates energy storage, structural reinforcement, AI fault prediction, and intelligent self - repair, and is particularly suitable for the high - reliability and intelligent operation and maintenance requirements of deep - sea and far - sea wind farms. Background Art

[0002] The volatility and randomness of wind power generation cause significant fluctuations in power supply at different times. Especially in offshore wind farms far from onshore power grids, the problems of power transmission and storage are particularly prominent. Existing energy storage technologies (such as pumped - storage energy storage, compressed - air energy storage, etc.) have problems such as large space occupation, complex installation, and high maintenance costs in the marine environment, and are difficult to meet the actual needs of offshore wind farms. As a low - cost, high - efficiency, and sustainable energy storage technology, seawater batteries have a wide range of cheap raw material sources, can achieve low battery prices, and are suitable for large - scale applications.

[0003] Monopile foundations have significant problems of insufficient structural reliability under extreme sea conditions. Especially when facing strong winds, waves, and tidal changes, their stability is easily threatened. The health monitoring of offshore wind turbines faces many challenges. Traditional monitoring methods are not only costly but also difficult to cope with the complex and variable marine environment. In deep - sea and far - sea areas, if AI - driven fault prediction technology is used for health monitoring, it can effectively improve the monitoring efficiency and reduce costs.

[0004] In response to the technical pain points of the electric energy storage system in offshore wind farms, the present invention proposes an innovative solution for the coordination of basic structure - battery function, establishes a coupling structure between the honeycomb battery compartment and the monopile foundation, effectively combines the honeycomb battery compartment and the monopile foundation structure, enables these two parts to cooperate with each other physically and functionally, support each other, and work together, reduces the separate layout between the energy storage system and the basic structure in traditional wind farms, reduces the vibration transmission between the wind turbine unit and the energy storage system, improves the operation efficiency and safety of the overall system, realizes integrated design and dynamic response optimization; and realizes the health monitoring and self - repair of the internal and external structures of offshore wind turbines through AI - driven fault monitoring. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the technical problem to be solved by the present invention is: to provide an integrated system of energy storage - structure - intelligent self - repair for offshore wind power monopile foundations, improve the foundation stability through the coupling of the honeycomb battery compartment and the monopile foundation, break through the structural - functional separation design limitation between the energy storage system and the wind turbine foundation, perform zoning control on the structure, use the AI architecture to achieve fault prediction, and drive the execution end to perform self - repair, so as to realize the closed - loop management of "perception - prediction - repair" for the entire life cycle of offshore wind turbines.

[0006] The technical solution adopted by the present invention to solve the above technical problems is as follows:

[0007] An integrated energy storage - structure - intelligent self - repair system for an offshore wind power monopile foundation, comprising a monopile foundation and a concrete disk - shaped expansion structure cast at the junction of the monopile foundation and the seabed mud surface; an inverter cabin and a honeycomb battery cabin are arranged inside the monopile foundation; the honeycomb battery cabin and the concrete disk - shaped expansion structure are at the same height;

[0008] The monopile foundation is evenly divided into multiple regions in the circumferential direction. Seawater batteries are installed in the honeycomb battery cabins in each region. Meanwhile, fiber Bragg grating strain sensors and piezoelectric actuators are installed in each region. The piezoelectric actuators are located in the gap between the honeycomb battery cabin and the inner wall of the monopile foundation; the fiber Bragg grating strain sensors are located at the connection interface between the concrete disk - shaped expansion structure and the monopile foundation for monitoring fiber strain;

[0009] The truss of the honeycomb battery cabin in each region is connected to the inner wall of the monopile foundation through prestressed steel strands and anchors. Shape memory alloy actuators are installed on the steel strands, resistance wires are arranged on the surface of the shape memory alloy actuators, and broadband acoustic emission sensors are installed at the anchors for acquiring acoustic emission signals;

[0010] Bacillus microcapsules are incorporated into the concrete of the concrete disk - shaped expansion structure;

[0011] An impedance probe for monitoring the battery internal resistance change rate, a self - priming pump for controlling the cyclic inflow and outflow of the electrolyte, and an automatic control valve are installed on the seawater battery;

[0012] An AI architecture for multi - modal intelligent diagnosis is integrated in the inverter cabin; the input of the AI architecture is the fiber strain, acoustic emission signal, and battery internal resistance change rate of each region, and the output is a three - dimensional fault probability P composed of the crack level probability, prestress loss level probability, and electrolyte deterioration level probability;

[0013] A probability threshold is set. When a certain dimension of the three - dimensional fault probability P of a certain region output by the AI architecture is greater than the probability threshold, a fault self - repair instruction corresponding to the corresponding dimension of the corresponding region is triggered;

[0014] The fault self - repair instructions include a crack self - repair instruction to cause the directional breakage of the Bacillus microcapsules in the region through the piezoelectric actuator, a prestress self - repair instruction to make the shape memory alloy actuator perform tensioning at high temperature through the resistance wire, and an electrolyte deterioration self - repair instruction to change the electrolyte of the seawater battery through the self - priming pump and the automatic control valve.

[0015] Furthermore, the honeycomb battery compartment includes a truss and a number of seawater batteries evenly arranged along the circumferential direction within the truss, and a set of four-wire AC impedance meter probes are installed at the anode and cathode ends of each seawater battery; the trusses are connected by flanges;

[0016] Each area uses a group of steel strands, and a shape memory alloy driver is installed in the middle of each group of steel strands through a connector. The connector adopts a bidirectional threaded sleeve, one end of the bidirectional threaded sleeve is anchored to the shape memory alloy driver through a thread, and the other end is anchored to the steel strand through a thread.

[0017] Furthermore, anchors are provided at both ends of the steel strand, and a broadband acoustic emission sensor is installed at each anchor. The broadband acoustic emission sensors at all anchors in each area are used together to capture the acoustic emission signals of the steel strand slippage in real time.

[0018] Furthermore, the concrete disc-shaped expansion structure includes a disc surface, a number of ribs arranged on the disc surface, and concrete poured outside the disc surface. Bacillus microcapsules are mixed into the concrete poured for the concrete disc-shaped expansion structure, and a slow-release nutrient is pre-embedded.

[0019] The action range of the sound waves generated by each piezoelectric actuator can cause the Bacillus microcapsules in the corresponding area of the concrete disk-shaped expansion structure to be directionally damaged.

[0020] Furthermore, the converter cabin also integrates a converter, an MPC controller, an SOC equalizer and a programmable logic controller;

[0021] The MPC controller collects grid frequency, grid voltage, grid load, wind turbine output power, and battery state of charge (SOC) in real time, and predicts the grid status over a period of time in the future. Based on the grid demand in the future period, the MPC controller generates power allocation instructions and charge and discharge scheduling instructions, and transmits these instructions to the converter and seawater battery through the programmable logic controller to stabilize the grid output power.

[0022] Furthermore, all seawater batteries are electrically connected to the SOC equalizer. During the stage of charging the seawater batteries by the offshore wind turbine, the SOC equalizer actively discharges the seawater batteries with SOC>55% to the power grid or other seawater batteries other than itself; during the stage of discharging the seawater batteries, the offshore wind turbine boosts the voltage to compensate for the seawater batteries with SOC<45%.

[0023] Furthermore, the threshold judger in the programmable logic controller is used to monitor whether the three-dimensional fault probability output by the AI architecture exceeds the probability threshold value, and the logic arbitration module in the programmable logic controller is used to independently judge the three types of faults; the programmable logic controller outputs a fault self-repair instruction.

[0024] Furthermore, the AI architecture adopts a CNN-BiLSTM model based on the attention mechanism, a multi-modal fusion architecture based on Transformer, a spatial topology architecture driven by a graph neural network, a multi-task learning and dynamic gating architecture, a self-supervised contrastive learning pre-training architecture, a lightweight edge computing architecture, etc.

[0025] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0026] In the present invention, the honeycomb battery compartment is coupled with the monopile foundation. The honeycomb battery compartment adopts a truss structure and is embedded with seawater batteries. The truss is connected to the inner wall of the monopile foundation through prestressed steel strands to form a collaborative bearing system, optimizing the dynamic response of the monopile foundation. The AI architecture integrates acoustic emission signals, the change rate of battery internal resistance, and fiber optic strain data to achieve the joint prediction of the prestress loss level, electrolyte deterioration level, and crack level. When the prediction result exceeds the set threshold, the self-repair mechanism is triggered. Through the integrated structural design, the electric energy storage, the foundation of the offshore wind turbine, and the intelligent diagnosis are closely combined to form an efficient, stable, and intelligent comprehensive solution, which not only improves the reliability and flexibility of the overall system, but also reduces the maintenance cost and operation risk, providing a solid technical guarantee for the sustainable development of offshore wind farms.

[0027] The present invention uses an AI technology framework for multi-modal intelligent diagnosis, integrating three-dimensional data of fiber optic strain, acoustic emission signals, and the change rate of battery internal resistance, and outputs the diagnosis results of the crack level, prestress loss level, and electrolyte deterioration level, that is, outputs the three-dimensional fault probability. It automatically determines whether to trigger the corresponding actuator for self-repair according to the probability of fault prediction. When the probability of fault prediction exceeds the set probability threshold, the self-repair of the corresponding fault is triggered. This method can minimize the system downtime, ensure the high availability of the system, and reduce the fault risk.

[0028] In the present invention, through the prestress coupling of the honeycomb battery compartment and the monopile foundation, and in combination with the concrete disk-shaped expansion structure, compared with the traditional monopile foundation, the anti-overturning ability of the foundation is significantly enhanced, and the dynamic response characteristics of the system are optimized.

[0029] The present invention uses seawater batteries for electric energy storage, which can play a role in suppressing the power fluctuation of the power grid. Compared with traditional battery technologies, seawater batteries have rich resource sources, low costs, and less environmental pollution. By combining seawater batteries with offshore wind turbines, it can effectively smooth the power fluctuations caused by environmental factors and ensure the stability and reliability of power supply.

[0030] The present invention uses an MPC (Model Predictive Control) controller to monitor the grid frequency, grid voltage, grid load, wind turbine output power, and battery state of charge (SOC) in real time, predict future power demands, and dynamically adjust the output of the seawater battery to suppress power fluctuations. Meanwhile, through a battery state of charge (SOC) equalizer, the capacity decay of the seawater battery is delayed to ensure the long-term stability and efficient operation of the system.

[0031] In summary, the system of the present invention is particularly suitable for the specific requirements of offshore wind farms, demonstrating significant technical advantages and innovative achievements. This system not only effectively improves the stability of the offshore wind farm infrastructure but also solves the problem of health monitoring of offshore wind farms, reflecting excellent technical foresight and broad application potential. Brief Description of the Drawings

[0032] Figure 1 It is a schematic diagram of the internal structure of a monopile foundation and the relative position arrangement with a concrete disk-shaped expansion structure in an embodiment of the present invention.

[0033] Figure 2 It is a schematic diagram of the structure of an AI architecture - a CNN-BiLSTM model based on an attention mechanism in an embodiment of the present invention.

[0034] Figure 3 It is a schematic diagram of the arrangement of a shape memory alloy actuator and a steel strand in an embodiment of the present invention.

[0035] In the figure, 1 is a monopile foundation, 2 is a converter cabin, 3 is a concrete disk-shaped expansion structure, 4 is a honeycomb battery cabin, 5 is a counterweight cabin; 6 is a resistance wire, 7 is a shape memory alloy actuator, 8 is a steel strand, and 9 is a bidirectional threaded sleeve. Detailed Embodiment

[0036] The present invention will be further explained below in conjunction with embodiments and the accompanying drawings, but the protection scope of this application is not limited thereby.

[0037] The integrated energy storage - structure - intelligent self - repair system for the monopile foundation of offshore wind power realizes multi - element coupling by hierarchically arranging batteries, control components, and counterweights inside the monopile foundation. The interior of the monopile foundation is divided into a counterweight cabin, a honeycomb battery cabin, and an inverter cabin. The lower - layer counterweight cabin is filled with concrete to enhance the anti - overturning ability. The middle - layer honeycomb battery cabin is embedded with seawater batteries. The upper - layer inverter cabin integrates an inverter, an MPC controller, a programmable logic controller, etc. to complete grid power compensation. The SOC equalizer is used to regulate the charge - discharge balance of the seawater batteries. The AI architecture processes acoustic emission signals, the change rate of battery internal resistance, and fiber strain data in real - time, predicts concrete cracks, strand relaxation, and electrolyte deterioration faults, triggers the rupture of Bacillus microcapsules to release Bacillus repair agents, the tensioning of shape - memory alloy actuators for strands, and the electrolyte circulation replacement mechanism, realizes the self - repair of structural damage and the performance of seawater batteries, and ensures the long - term stable operation of the system in complex marine environments.

[0038] The seawater batteries of the present invention can be, but are not limited to, rechargeable chloride ion batteries, membrane - free rechargeable seawater batteries, sodium - metal seawater batteries, water - based zinc batteries, etc. For the multi - modal intelligent diagnosis task (joint prediction of crack level, prestress loss level, and electrolyte deterioration level) in the AI architecture of the present invention, a CNN - BiLSTM model based on the attention mechanism, a multi - modal fusion architecture based on Transformer, a spatial topology architecture driven by graph neural network (GNN), a multi - task learning (MTL) and dynamic gating architecture, a self - supervised contrastive learning pre - training architecture, a lightweight edge - computing architecture, etc. can be adopted.

[0039] Example 1:

[0040] The integrated energy storage - structure - intelligent self - repair system for the monopile foundation of offshore wind power of the present invention includes a monopile foundation 1 and a concrete disk - shaped expansion structure 3 poured at the junction of the monopile foundation and the seabed mud surface; an inverter cabin, a honeycomb battery cabin, and a counterweight cabin are arranged from top to bottom inside the monopile foundation;

[0041] The honeycomb battery cabin includes a truss and a number of seawater batteries evenly arranged along the circumferential direction inside the truss. The circumference of the monopile foundation is divided into multiple regions according to the number of seawater batteries. A set of four - wire AC impedance probe is installed at the anode and cathode ends of each seawater battery to monitor the change rate of battery internal resistance; a piezoelectric actuator is installed in the gap between the honeycomb battery cabin of each region and the inner wall of the monopile foundation;

[0042] The concrete disc-shaped extension structure includes a disc surface, a number of ribs arranged on the disc surface, and concrete poured outside the disc surface. The areas enclosed by adjacent ribs correspond in number and position to the areas divided according to the number of seawater batteries. In each area, a fiber Bragg grating strain sensor is arranged at the interface between the concrete disc-shaped extension structure and the monopile foundation. Bacillus microcapsules are incorporated into the concrete poured into the concrete disc-shaped extension structure 3, and a slow-release nutrient is pre-embedded.

[0043] The trusses are connected by flanges, and the trusses are connected to the inner wall of the single pile foundation by prestressed steel strands and anchors. A group of steel strands is used in each area, and a shape memory alloy driver is installed in the middle of each group of steel strands through a connector. A resistance wire 6 is provided on the surface of the shape memory alloy driver. In this embodiment, a nickel-chromium alloy resistance wire is used. Anchors are provided at both ends of the steel strands, and a broadband acoustic emission sensor is installed at each anchor. The broadband acoustic emission sensors at all anchors in each area are used together to capture the acoustic emission signals of steel strand slippage in real time.

[0044] The acoustic waves generated by each piezoelectric actuator are capable of causing targeted damage to the Bacillus microcapsules within the corresponding area of the concrete disc-shaped expansion structure. Each seawater battery is controlled by an automatic valve to determine whether to replenish or replace the electrolyte; the seawater battery provides power to the system.

[0045] The AI architecture for multimodal intelligent diagnosis is integrated in the converter compartment 2;

[0046] The input of the AI architecture is the optical fiber strain, all acoustic emission signals, and battery internal resistance change rate of each area, and the output is the three-dimensional failure probability of each area P = [P c , P l , P d ],

[0047] Among them, P c is the crack grade probability, P l is the probability of prestress loss level, P d is the probability of electrolyte degradation level;

[0048] Set a probability threshold. When a dimension of the three-dimensional fault probability P of a certain area output by the AI architecture has a value greater than the probability threshold, the fault self-repair instruction of the corresponding dimension of the corresponding area is triggered.

[0049] Example 2:

[0050] The offshore wind power monopile foundation energy storage-structure-intelligent self-repair integrated system of this embodiment includes: a monopile foundation 1, a converter cabin 2, a concrete disc-shaped expansion structure 3 cast at the junction of the monopile foundation and the seabed mud surface, a honeycomb battery cabin 4, and a counterweight cabin 5 below the honeycomb battery cabin.

[0051] The single pile foundation 1 utilizes Q355 steel pipe piles with a diameter of 8-12 meters and a wall thickness of 60-80 mm. The pile top elevation is 3-5 meters above the extreme wave height. The pile body is hollow and divided into three layers. The lower layer is the ballast compartment 5, filled with concrete to provide the foundation with anti-overturning counterweight. The ballast compartment is 3-5 meters high and is 0.4-0.6 times the pile length from the pile bottom. The middle layer is the honeycomb battery compartment 4, which houses the seawater batteries and transmits structural loads. The honeycomb battery compartment is 2-4 meters high, adjacent to the ballast compartment's cushion layer and connected to the ballast compartment via bolts. The upper layer is the converter compartment 2, 2-3 meters high and 0.3 times the pile length from the honeycomb battery compartment 4.

[0052] Converter pod 2 integrates the converter, MPC controller, SOC equalizer, AI architecture for multimodal intelligent diagnosis, programmable logic controller, and communication interface. MPC stands for model predictive control, and SOC stands for state of charge.

[0053] After the monopile foundation 1 is installed, a concrete disc-shaped extension structure 3 is cast at the interface between the monopile foundation and the seabed mud surface. The concrete disc-shaped extension structure comprises a disc surface, a number of ribs arranged on the disc surface, and concrete cast outside the disc surface. The disc surface has a diameter of 20-25m, and the ribs are inclined at an angle of 15° to the disc surface. A rib with a thickness of 15cm is arranged every 60°, dividing the entire disc surface into six zones. Fiber Bragg grating strain sensors are also arranged at the interface between the concrete disc-shaped extension structure 3 and the monopile foundation 1, with six groups arranged every 60°, to monitor the concrete fiber strain in real time. Bacillus microcapsules are incorporated into the cast concrete of the concrete disc-shaped extension structure 3, and a pH-sensitive slow-release nutrient is pre-embedded for concrete self-repair.

[0054] The honeycomb battery compartment 4 includes a truss and a number of seawater batteries arrayed and installed within the truss. In this embodiment, 6 seawater batteries are provided. The truss is made of S420 high-strength steel with a wall thickness of 12 mm. The trusses are connected by flanges and coupled with the inner wall of the monopile foundation 1 through prestressed steel strands and anchors. The steel strand 8 has a diameter of 32 mm, a pre-tightening force ≥ 800 kN, and is arranged in groups of 60°, with a total of 6 groups of steel strands. At the same time, a shape memory alloy actuator 7 is installed in the middle of each group of steel strands through a connector. The connector uses a two-way threaded sleeve 9. One end of the two-way threaded sleeve is anchored to the shape memory alloy actuator 7 through threads, and the other end is anchored to the steel strand 8 through threads; the installation positions at both ends of the shape memory alloy actuator 7 are set at two 1 / 3 positions of the total length of the steel strand 8. The nickel-chromium alloy resistance wire is evenly wound on the surface of the shape memory alloy actuator 7, and the nickel-chromium alloy resistance wire (with a resistance of 30 Ω) is electrically connected to the seawater battery. After the steel strands are tensioned, the circumferential gap between the honeycomb battery compartment 4 and the monopile foundation ≤ 100 mm (this distance can be adjusted). One end of the steel strand is connected to the inner wall of the monopile foundation through an anchor, and the other end is connected to the truss through an anchor. Wideband acoustic emission sensors are arranged at all the anchors. The wideband acoustic emission sensors at all the anchors in each area are jointly used to capture the acoustic emission signals of the steel strand slip in real time, with a sampling rate of 1 MHz.

[0055] Power transmission is achieved between the converter compartment 2 and the honeycomb battery compartment 4 through a cable. 6 piezoelectric actuators are evenly arranged and installed in the gap between the honeycomb battery compartment and the inner wall of the monopile foundation. The installation height of the piezoelectric actuators is the same as the height of the concrete disk-shaped expansion structure outside the pile. The acoustic wave action range generated by each piezoelectric actuator is 60°. The piezoelectric actuator is electrically connected to the seawater battery to supply power to it. According to the acoustic wave action range generated by the piezoelectric actuator, it is evenly arranged in the circumferential direction, and can cause directional damage to the bacillus microcapsules in the corresponding area inside the concrete disk-shaped expansion structure.

[0056] The seawater battery is embedded in the honeycomb battery compartment. The electrolyte of the seawater battery is natural seawater electrolyte filtered from impurities. The specific internal structure of the seawater battery can be realized according to the existing technology. A set of impedance probe is installed at the anode and cathode ends of each seawater battery to monitor the change rate of the battery internal resistance. In this embodiment, the impedance probe is a four-wire AC impedance probe. A total of two self-priming pumps are placed on the top of all the seawater batteries for the replenishment or replacement of the electrolyte. Each seawater battery is equipped with two automatic control valves to control the entry and outflow of natural seawater respectively. The self-priming pump is electrically connected to the seawater battery to provide electrical energy for the self-priming pump. This embodiment is configured with 6 seawater batteries. No seawater battery is placed in the middle area of the honeycomb battery compartment to ensure the heat dissipation space.

[0057] The MPC controller predicts the state of the power grid in the next period based on the real-time collected grid frequency, grid voltage, grid load, fan output power, and battery SOC status (10 μs sampling period), and then combines the power grid demand in the future period to generate the optimal control strategy for the converter through rolling horizon optimization. The control strategy includes power distribution instructions and charge-discharge scheduling instructions.

[0058] Among them, the power distribution instruction refers to controlling the voltage / current waveform output by the converter by adjusting the duty cycle and switching frequency. The charge-discharge scheduling instruction means that when a sudden change in grid frequency or abnormal battery SOC is detected, the MPC controller adjusts the charging and discharging power of the battery to restore the stability of the power grid and ensure the safe operation of the battery. The above instructions are transmitted from the programmable logic controller to the converter and the seawater battery.

[0059] All seawater batteries are electrically connected to the SOC equalizer. The working processes of the SOC equalizer in the charging stage and the discharging stage are as follows:

[0060] Charging stage

[0061] When the state of charge (SOC) of a certain seawater battery exceeds 55%, the SOC equalizer will actively discharge it (the maximum discharge current is 10 A) to avoid overcharging.

[0062] Discharging stage

[0063] When the state of charge (SOC) of a certain seawater battery is lower than 45%, the SOC equalizer boosts the voltage to compensate it to prevent insufficient output power caused by over-discharge.

[0064] The input of the AI architecture is the fiber strain of each area, all acoustic emission signals, and the change rate of battery internal resistance, and the output is the three-dimensional fault probability P = [P c , P l , P d of each area.

[0065] Among them, P c is the crack level probability, P l is the prestress loss level probability, and P d is the electrolyte deterioration level probability.

[0066] Set the probability threshold. In this embodiment, the probability threshold is set to 0.8. When there is a value in a certain dimension of the three-dimensional fault probability P output by the AI architecture that is greater than the probability threshold, the corresponding fault self-repair instruction in that dimension of the corresponding area is triggered. The three-dimensional fault probability is transmitted to the programmable logic controller, which makes a judgment. If the judgment is successful, the fault self-repair instruction is output and transmitted to the execution end (piezoelectric brake, nickel-chromium alloy resistance wire, or self-priming pump and automatic control valve).

[0067] Example 3:

[0068] In this example, the AI architecture for multi-modal intelligent diagnosis is implemented using a CNN-BiLSTM deep learning model based on the attention mechanism. Considering the three aspects of "efficiency-accuracy-interpretability" for the single-pile diagnosis scenario of offshore wind turbines, the CNN-BiLSTM serial structure adapts to edge computing resources to meet real-time requirements; the attention mechanism dynamically focuses on key modalities and temporal segments to enhance fine-grained diagnosis capabilities.

[0069] The CNN-BiLSTM deep learning model based on the attention mechanism includes a CNN module, a BiLSTM layer, an attention mechanism, and a multi-task classifier connected in sequence. According to the layout angles of various sensors (each group is 60°), it can be divided into 6 regions, and the three types of signals in each region are used as a group of inputs. Among them, the CNN module uses 5-layer depthwise separable convolution to extract 128-dimensional time-frequency features, and the BiLSTM layer realizes cross-modal temporal correlation for a 500ms time window through gated recurrent units. Dynamic weight allocation is adopted to automatically adjust the weight coefficients of each modal feature according to the contribution degree of different types of sensor data to the current fault diagnosis. Through the attention mechanism, feature fusion is performed using the dynamically generated weights, and the multi-task classifier outputs the joint diagnosis results of the crack level (Level I-IV), prestress loss level (Level I-III), and electrolyte degradation level (Level I-V), that is, the three-dimensional fault probability P = [P c , P l , P d for each region. The implementation method is as follows:

[0070] 1. Feature extraction: After preprocessing (normalization), three types of signals, namely fiber optic strain, acoustic emission signal, and battery internal resistance change rate, enter the CNN module and output 128-dimensional feature vectors, which are the acoustic emission signal feature vector F AE , the battery internal resistance change rate feature vector F IR , and the fiber optic strain feature vector F FS . Then, the 128-dimensional feature vector output by the CNN module is input into the BiLSTM layer to output cross-modal temporal features H AE , H IR , H FS .

[0071] 2. Dynamic weight allocation:

[0072] First, calculate the attention score :

[0073]

[0074] is a learnable parameter matrix, ba is a learnable parameter, H i is the cross-modal temporal feature of the i-th modality, and tanh is the hyperbolic tangent function.

[0075] For the attention score perform weight normalization to obtain the dynamic weight w i , where the subscript i takes the dynamic weights corresponding to the acoustic emission signal, the battery internal resistance change rate, and the fiber optic strain for AE, IR, and FS respectively;

[0076]

[0077] Use the dynamic weights to perform feature fusion on the cross-modal temporal features to obtain the fused feature F fusion :

[0078]

[0079] 3. Three-dimensional fault probability output

[0080] Map the fused feature F fusion to the dimensions required for each classification task through three fully connected layers,

[0081]

[0082]

[0083]

[0084] where, V c , V l , V d are mapped to 4-dimensional vectors for the crack level task, 3-dimensional vectors for the prestress loss level task, and 5-dimensional vectors for the electrolyte degradation level task respectively; W c , W l , W d are the weight matrices of the fully connected layers under different tasks; b c , b l , b d are the bias terms of the fully connected layers under different tasks.

[0085] After three Softmax classifiers respectively perform probability normalization on the mapped feature vectors V c , V l , V d output the joint diagnosis results of the crack level (Level I-IV), prestress loss level (Level I-III), and electrolyte degradation level (Level I-V), that is, output the three-dimensional fault probability P = [P c , P l , Pd .

[0086]

[0087]

[0088]

[0089] Among them, j represents the component corresponding to a certain level for different disease types; that is Figure 2 The Softmax classifier 1 is used for outputting the probability of the crack level, the Softmax classifier 2 is used for outputting the probability of the prestress loss level, and the Softmax classifier 3 is used for outputting the probability of the electrolyte deterioration level.

[0090] Example: The three-dimensional fault probability P of a certain area = [P c , P l , P d

[0091] P c = [0.1, 0.6, 0.2, 0.1] indicates that the probability of the crack level I is 10%, the probability of level II is 60%, the probability of level III is 20%, and the probability of level IV is 10%. The maximum value of the probabilities of the four levels is used as the crack fault level of this area.

[0092] = [0.3, 0.5, 0.2] indicates that the probability of the prestress loss level I is 30%, the probability of level II is 50%, and the probability of level III is 20%. The maximum value of the probabilities of the three levels is used as the prestress fault level of this area.

[0093] P d = [0.1, 0.2, 0.4, 0.2, 0.1] indicates that the probability of the electrolyte deterioration level I is 10%, the probability of level II is 20%, the probability of level III is 40%, the probability of level IV is 20%, and the probability of level V is 10%. The maximum value of the probabilities of the five levels is used as the electrolyte deterioration fault level of this area.

[0094] Set a probability threshold. In this embodiment, the probability threshold is set to 0.8. When there is a value greater than the probability threshold in the three-dimensional fault probability P of a certain area output by the AI architecture, the corresponding fault self-repair instruction for the corresponding area is triggered. In this example, there is no fault in all three dimensions. The three-dimensional fault probability is transmitted to the programmable logic controller, and the programmable logic controller makes a judgment. If the judgment is successful, the fault self-repair instruction is output, and the fault self-repair instruction is transmitted to the execution end (piezoelectric brake, nickel-chromium alloy resistance wire, or self-priming pump and automatic control valve).

[0095] If P c ​If there is a value greater than the probability threshold, it is a crack fault, and the piezoelectric actuator is controlled to apply a vibration wave to directionally break the microcapsule wall in this area to release Bacillus for self - repair of concrete; if P l If there is a value greater than the probability threshold, it is a prestress loss fault. The nickel - chromium alloy resistance wire is driven to convert current into heat energy to provide a working environment for the shape memory alloy actuator. The shape memory alloy actuator 7 performs tensioning at high temperature to restore the previous prestress; if P d If there is a value greater than the probability threshold, it is an electrolyte deterioration fault. The self - priming pump and the automatic control valve of the seawater battery in the corresponding area are driven to open for changing the electrolyte of the seawater battery. In this embodiment, the piezoelectric actuator installed on the inner wall of the pile applies a 10 kHz vibration wave to directionally break the capsule wall in this area to release Bacillus for self - repair of concrete; when the prestress loss rate is large, the shape memory alloy actuator 7 performs tensioning at high temperature to restore the previous prestress, and the nickel - chromium alloy resistance wire provides a working environment for the shape memory alloy actuator by converting current into heat energy, and is energized with a 1 A DC current each time, not exceeding 15 minutes each time; when replacing the electrolyte, one self - priming pump starts to work to pump away seawater from the bottom, and one self - priming pump starts to work to pump in seawater from the top; when supplementing the electrolyte, just turn on the self - priming pump to pump in seawater from the top. The directional transportation of natural seawater electrolyte can be realized through the existing automatic control valve technology.

[0096] The system of the present invention monitors the state and structural health of the seawater battery in real time through a variety of sensors, makes predictions and decisions based on the data sensed by the sensors, and ensures that the system operates in an optimal state. The seawater battery performs charge - discharge operations according to the instructions of the SOC equalizer, and at the same time, the converter adjusts the operation mode according to the instructions of the MPC controller to achieve efficient energy management and stable system operation.

[0097] Embodiment 4:

[0098] In this embodiment, the converter cabin 2 integrates a converter, an MPC controller, an SOC equalizer, an AI architecture for multimodal intelligent diagnosis, a programmable logic controller, and a communication interface. The alternating current generated by the offshore wind turbine is converted into direct current for seawater battery storage by the converter, and the direct current of the seawater battery can be converted into alternating current for the power grid by the converter. The rated power of the converter is 2 MW. The MPC controller is connected to the power grid, the seawater battery, and the SOC equalizer through the communication interface, and at the same time realizes data interaction with the converter. It samples the power grid frequency, power grid voltage, power grid load, wind turbine output power, and battery charge state (SOC) in real time with a sampling period of 10 μs, predicts the state of the system in the next 200 μs, generates power distribution instructions and charge-discharge scheduling instructions through rolling horizon optimization, and transmits them to the converter and the seawater battery by the programmable logic controller to ensure stable power output. All seawater batteries are electrically connected to the SOC equalizer. The SOC equalizer actively discharges the seawater battery with SOC > 55% with a maximum of 10 A during the charging stage, and boosts the voltage of the seawater battery with SOC < 45% during the discharging stage.

[0099] The power grid transmits the power grid frequency, power grid voltage, and power grid load signals to the MPC controller;

[0100] The offshore wind turbine transmits the wind turbine output power to the MPC controller;

[0101] The SOC equalizer transmits the SOC signal to the MPC controller;

[0102] The MPC controller transmits the power distribution instructions and charge-discharge scheduling instructions to the programmable logic controller;

[0103] The programmable logic controller transmits the power distribution instructions to the converter;

[0104] The programmable logic controller transmits the charge-discharge scheduling instructions to the seawater battery;

[0105] The converter and the seawater battery transmit feedback signals to the MPC controller.

[0106] Embodiment 5:

[0107] The construction process of the system in this embodiment is as follows:

[0108] 1. Construction of the monopile foundation: According to the design requirements, calculate the bearing capacity and pile body size of the monopile foundation.

[0109] Q355 steel pipe piles are used, with diameters of 8-12 meters and wall thicknesses of 60-80 mm. The interior is divided into a lower ballast compartment, filled with concrete and 3-5 meters high, providing anti-overturning counterweight. The middle honeycomb battery compartment is arranged in a circular pattern with S420 steel trusses, 12 mm thick. These trusses are coupled to the monopile foundation via steel strands with adjustable clearances of ≤100 mm. The strands are equipped with shape-memory alloy actuators. The upper converter compartment houses the converter, MPC controller, AI architecture for multimodal intelligent diagnosis, programmable logic controller, and SOC equalizer. A concrete disc-shaped extension structure with a diameter of 20-25 meters is cast outside the piles. The ribs within the concrete disc have a 15° inclination and are 15 cm thick. Fiber Bragg grating strain sensors are placed at the interface between the concrete disc and the monopile foundation. Bacillus microcapsules are embedded in the concrete, along with a pH-sensitive slow-release nutrient.

[0110] 2. Battery compartment and structure coupling:

[0111] Seawater batteries are embedded within the honeycomb battery compartment trusses. Two self-priming pumps and automatic control valves are installed above the seawater batteries. Each seawater battery has an automatic control valve installed at the inlet and outlet of natural seawater. All seawater batteries share two self-priming pumps, enabling the circulation of natural seawater between the batteries. The seawater battery electrolyte uses filtered natural seawater. This embodiment utilizes six seawater batteries. Four-wire AC impedance meter probes are installed at the cathode and anode terminals of each battery to monitor the rate of change of the battery's internal resistance in real time.

[0112] 3. Implementation of grid power compensation:

[0113] The AC power generated by the wind turbine is converted to DC power by an inverter and stored in the seawater battery. After the DC power from the seawater battery is converted to AC power by the inverter, it can be used to compensate for grid power. Grid power compensation control is implemented using an MPC controller with a 10μs sampling period. It can predict the grid state within 200μs and generate the optimal control strategy. The SOC equalizer discharges the seawater battery with an SOC greater than 55% (≤10A) during the charging phase and boosts the voltage of the seawater battery with an SOC less than 45% during the discharging phase.

[0114] 4. AI-driven fault prediction:

[0115] Data acquisition is carried out through multiple sensors. A broadband acoustic emission sensor is used to capture the steel strand slip signal; the seawater battery state is monitored by the probe of a four-wire AC impedance meter to detect the change rate of the battery internal resistance; the concrete strain is detected in real time by a fiber Bragg grating strain sensor for the concrete disk-shaped structure. A CNN-BiLSTM deep learning model based on the attention mechanism is adopted to dynamically allocate the weights of acoustic emission, internal resistance, and strain. During the feature extraction process, the CNN module extracts 128-dimensional time-frequency features through 5 layers of depthwise separable convolution, and the BiLSTM layer performs cross-modal temporal correlation through a 500ms time window. The multi-task classifier is used for fault classification, and the crack level (I-IV levels), prestress loss level (I-III levels), and electrolyte deterioration level (I-V levels) are output, that is, three-dimensional fault probabilities are output.

[0116] 5. Self-repair implementation: When there is a value in a certain dimension of the three-dimensional fault probability P of a certain area in the output that is greater than the probability threshold, the actuator at the corresponding area is triggered to perform corresponding fault self-repair. The self-repair methods are as follows: The piezoelectric actuator emits a 10kHz vibration wave to directionally rupture the microcapsules and release the Bacillus repair material; the steel strand stress recovery is achieved by heating the shape memory alloy actuator to perform tension compensation; the electrolyte maintenance relies on the self-priming pump to supplement or replace the filtered seawater as needed.

[0117] It should be noted that, without conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0118] The parts not described in the present invention are applicable to the prior art.

Claims

1. An integrated energy storage - structure - intelligent self - repair system for an offshore wind power monopile foundation, comprising a monopile foundation and a concrete disk - shaped expansion structure cast at the junction of the monopile foundation and the seabed mud surface; characterized in that, An inverter cabin and a honeycomb battery cabin are arranged inside a single-pile foundation; the honeycomb battery cabin and the concrete disk-shaped expansion structure are at the same height; The single-pile foundation is evenly divided into multiple regions in the circumferential direction. Seawater batteries are installed in the honeycomb battery cabins in each region. Meanwhile, fiber Bragg grating strain sensors and piezoelectric actuators are installed in each region. The piezoelectric actuators are located in the gap between the honeycomb battery cabin and the inner wall of the single-pile foundation; the fiber Bragg grating strain sensors are located at the connection interface between the concrete disk-shaped expansion structure and the single-pile foundation and are used to monitor the fiber strain; The trusses of the honeycomb battery cabins in each region are connected to the inner wall of the single-pile foundation through prestressed steel strands and anchors. Shape memory alloy actuators are installed on the steel strands. Resistance wires are arranged on the surface of the shape memory alloy actuators. Wideband acoustic emission sensors are installed at the anchors and are used to obtain acoustic emission signals; Bacillus microcapsules are incorporated into the concrete of the concrete disk-shaped expansion structure; An impedance probe for monitoring the change rate of the battery internal resistance, a self-priming pump for controlling the circulation of the electrolyte in and out, and an automatic control valve are installed on the seawater battery; An AI architecture for multi-modal intelligent diagnosis is integrated in the inverter cabin; the inputs of the AI architecture are the fiber strain, acoustic emission signal, and battery internal resistance change rate of each region, and the output is a three-dimensional fault probability P composed of the crack level probability, prestress loss level probability, and electrolyte deterioration level probability; A probability threshold is set. When a certain dimension of the three-dimensional fault probability P of a certain region output by the AI architecture is greater than the probability threshold, a fault self-repair instruction corresponding to the dimension of the corresponding region is triggered; The fault self-repair instructions include a crack self-repair instruction for causing the directional breakage of the Bacillus microcapsules in the region through the piezoelectric actuator, a prestress self-repair instruction for making the shape memory alloy actuator perform tensioning at high temperature through the resistance wire, and an electrolyte deterioration self-repair instruction for replacing the electrolyte of the seawater battery through the self-priming pump and the automatic control valve.

2. The integrated energy storage-structure-intelligent self-repair system for an offshore wind power monopile foundation according to claim 1, wherein The honeycomb battery cabin includes trusses and a certain number of seawater batteries evenly arranged in the circumferential direction inside the trusses. A set of four-wire AC impedance probes are installed at the anode and cathode ends of each seawater battery; the trusses are connected by flanges; Each region uses a set of steel strands. A shape memory alloy actuator is installed in the middle of each set of steel strands through a connector. The connector adopts a bidirectional threaded sleeve. One end of the bidirectional threaded sleeve is anchored to the shape memory alloy actuator through threads, and the other end is anchored to the steel strands through threads.

3. The integrated energy storage - structure - intelligent self - repair system for an offshore wind power monopile foundation according to claim 2, wherein Anchors are arranged at both ends of the steel strands. Wideband acoustic emission sensors are installed at each anchor. The wideband acoustic emission sensors at all the anchors in each region are jointly used to capture the acoustic emission signals of the steel strand slip in real time.

4. The integrated energy storage - structure - intelligent self - repair system for an offshore wind power monopile foundation according to claim 1, wherein The concrete disk-shaped expansion structure includes a disk surface, a certain number of rib plates arranged on the disk surface, and the concrete poured outside the disk surface. Bacillus microcapsules are incorporated into the concrete of the concrete disk-shaped expansion structure, and a slow-release nutrient agent is pre-buried; The action range of the sound waves generated by each piezoelectric actuator can cause the directional breakage of the Bacillus microcapsules in the corresponding region of the concrete disk-shaped expansion structure.

5. The integrated energy storage - structure - intelligent self - repair system for an offshore wind power monopile foundation according to claim 1, wherein The converter cabin also integrates a converter, an MPC controller, an SOC equalizer, and a programmable logic controller; The MPC controller collects the grid frequency, grid voltage, grid load, wind turbine output power, and the state of charge SOC of the battery in real time, predicts the state of the grid in a future period of time, generates a power distribution command and a charge and discharge scheduling command according to the grid demand in the future period, and transmits the power distribution command and the charge and discharge scheduling command to the converter and the seawater battery through the programmable logic controller to stabilize the grid output power.

6. The integrated energy storage-structure-intelligent self-repair system for an offshore wind power monopile foundation according to claim 5, wherein All seawater batteries are electrically connected to the SOC equalizer. During the charging stage of the seawater battery by the offshore wind turbine, the SOC equalizer actively discharges the seawater battery with SOC > 55% to the grid or other seawater batteries except itself; during the discharging stage of the seawater battery, the offshore wind turbine performs boost compensation on the seawater battery with SOC < 45%.

7. The integrated energy storage - structure - intelligent self - repair system for an offshore wind power monopile foundation according to claim 5, characterized in that, The threshold judge in the programmable logic controller is used to monitor whether there is a value exceeding the probability threshold in the three-dimensional fault probability output by the AI architecture, and the logic arbitration module in the programmable logic controller is used to independently judge three types of faults; the programmable logic controller outputs a fault self-repair instruction.

8. The integrated energy storage - structure - intelligent self - repair system for an offshore wind power monopile foundation according to claim 1, characterized in that, The AI architecture adopts a CNN-BiLSTM model based on the attention mechanism, a multi-modal fusion architecture based on Transformer, a spatial topology architecture driven by a graph neural network, a multi-task learning and dynamic gating architecture, a self-supervised contrast learning pre-training architecture, or a lightweight edge computing architecture.

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