Self-adaptive energy recovery system, device and method

Abstract

The embodiment of the invention provides a self-adaptive energy recovery system, device and method, and the system comprises an environment sensing module which is used for collecting external environment parameters and device operation state data; the data analysis module is connected with the environment sensing module and used for processing the collected data and obtaining sea ice height information, threat level and position information of the energy recovery device through analysis; the control execution module is connected with the data analysis module and used for generating a control instruction according to the sea ice height information, the threat level and the position information of the energy recovery device; the energy recovery device is connected with the control execution module and is used for executing the control instruction and carrying out position adjustment and / or energy recovery operation; and the energy storage module is connected with the energy recovery device and is used for storing the recovered electric energy. External information can be sensed in real time, self-adaptive adjustment can be made, damage to a wind power airport is reduced, and secondary utilization of sea ice mechanical energy can be achieved.

Description

Technical Field

[0001] The present disclosure relates to the field of offshore wind power technology, and in particular to an adaptive energy recovery system, device, and method. Background Art

[0002] As global demand for clean energy continues to rise, offshore wind power, with its advantages such as abundant resources and lack of land occupation, has become a key development direction in the renewable energy sector. However, in high-latitude waters, wind turbines in offshore wind farms have long been subjected to the severe test of the marine environment, with the threat of sea ice being particularly prominent. In winter, sea ice moves under the influence of tides and wind, and the collision and compression between sea ice and the foundation structure of wind turbines can directly cause damage such as dents and cracks on the surface of the structure. At the same time, the repeated action of sea ice accelerates the fatigue process of the structure and shortens the service life of the wind turbine. In addition, the high-salt and humid environment in the ocean is prone to structural corrosion, and the collision of sea ice destroys the protective coating on the surface of the structure, further exacerbating the degree of corrosion.

[0003] Existing sea ice protection measures, such as installing ice cones and deflectors, are mostly passive. While these measures can mitigate ice impact to a certain extent, they lack the ability to sense and adapt to real-time changes in sea ice, making them difficult to cope with complex and changing sea ice conditions. Summary of the Invention

[0004] The embodiments of the present disclosure provide an adaptive energy recovery system, device, and method to solve the related problems existing in existing technical solutions.

[0005] Based on the above problems, in a first aspect, an adaptive energy recovery system is provided, comprising:

[0006] Environmental perception module, used to collect external environmental parameters and device operating status data;

[0007] a data analysis module connected to the environmental perception module, configured to process the collected data and obtain sea ice height information, threat level, and location information of the energy recovery device through analysis;

[0008] a control execution module connected to the data analysis module, and configured to generate a control instruction based on the sea ice height information, the threat level, and the position information of the energy recovery device;

[0009] The energy recovery device is connected to the control execution module and is used to execute the control instruction to perform position adjustment and / or energy recovery operations;

[0010] The energy storage module is connected to the energy recovery device and is used to store the recovered electric energy.

[0011] In combination with the first aspect, in a possible implementation, the environmental perception module includes at least one of the following: a lidar, a camera, an acceleration sensor, and a vibration sensor, for collecting the distance and image of the sea ice, and the acceleration and vibration data of the energy recovery device.

[0012] In conjunction with the first aspect, in one possible implementation, the data analysis module uses a multi-source data fusion algorithm to perform spatiotemporal registration and feature fusion on data collected by multiple sensors, and outputs sea ice height information, threat level, and location information of the energy recovery device;

[0013] Wherein, the multi-source data fusion algorithm is used to:

[0014] Perform spatiotemporal registration based on filtering algorithms, unifying different sensor data into a global coordinate system by establishing a time synchronization model and a spatial transformation matrix;

[0015] Use machine learning algorithms to extract multimodal data features and construct feature vectors;

[0016] The sea ice threat level is calculated through a classification model, and the sea ice height information and the position information of the energy recovery device are estimated in real time using a state estimation algorithm.

[0017] In combination with the first aspect, in a possible implementation, the control execution module includes: a fuzzy PID controller;

[0018] The fuzzy PID controller is used to calculate and output control instructions based on the sea ice height information, threat level and position information of the energy recovery device output by the data analysis module to control the position adjustment and / or energy recovery operation of the energy recovery device;

[0019] The fuzzy PID controller is further used to set the membership function of the input and output variables, dividing the input parameters into multiple fuzzy sets;

[0020] By designing fuzzy control rules, the control parameters of the PID controller are dynamically adjusted according to the fuzzy reasoning results of the input variables;

[0021] An incremental control algorithm is used to calculate the control quantity and generate control instructions.

[0022] In combination with the first aspect, in a possible implementation, the energy storage module includes: a lithium-ion battery and a supercapacitor,

[0023] The energy storage module is used to achieve energy management of lithium-ion batteries and supercapacitors through a bidirectional DC / DC converter, giving priority to charging the supercapacitor, and then charging the lithium-ion battery when the supercapacitor is fully charged.

[0024] In combination with the first aspect, in a possible implementation, the energy recovery device includes: an SMA energy recovery module, an electromagnetic induction module, and a position adjustment module;

[0025] The SMA energy recovery module comprises a shape memory alloy element for absorbing external impact mechanical energy and converting it into mechanical deformation energy;

[0026] The electromagnetic induction module is mechanically connected to the SMA energy recovery module and is used to convert the mechanical deformation energy into electrical energy;

[0027] The position adjustment module is installed on the tower of the wind turbine generator set and adopts an electromechanical drive mechanism to receive control instructions and drive the SMA energy recovery module and the electromagnetic induction module to adjust their positions according to the control instructions.

[0028] In a second aspect, an adaptive energy recovery device is provided, comprising:

[0029] SMA energy recovery module, which contains shape memory alloy elements to absorb external impact mechanical energy and convert it into mechanical deformation energy;

[0030] an electromagnetic induction module, mechanically connected to the SMA energy recovery module, for converting the mechanical deformation energy into electrical energy;

[0031] The position adjustment module is installed on the tower of the wind turbine generator set and is used to receive control instructions and adjust the spatial position of the SMA energy recovery module according to the control instructions.

[0032] In combination with the second aspect, in a possible embodiment, the shape memory alloy element of the SMA energy recovery module adopts a variable diameter spiral structure or a corrugated structure, and a protective coating is provided on the surface. The protective coating is prepared by a chemical deposition process and has wear-resistant and corrosion-resistant properties.

[0033] In combination with the second aspect, in a possible implementation, the electromagnetic induction module includes a multi-layer hollow coil and a magnetic material, and the multi-layer hollow coil is coupled with the mechanical deformation region of the SMA energy recovery module to convert mechanical deformation energy into induced electrical energy.

[0034] In a third aspect, an adaptive energy recovery method is provided, comprising:

[0035] Real-time collection of external environment data and device operation status data;

[0036] The collected data is pre-processed, feature extracted, and multi-source fused to output sea ice height information, threat level, and energy recovery device location information, generating corresponding control instructions;

[0037] Adjust the SMA energy recovery module to the target position according to the control instructions;

[0038] When an external impact acts on the SMA energy recovery module, the shape memory alloy element absorbs mechanical energy and converts it into mechanical deformation energy, which is then converted into electrical energy through the electromagnetic induction module.

[0039] The electrical energy is rectified and filtered and then stored in the energy storage module.

[0040] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of an adaptive energy recovery method as described in the third aspect or in combination with any possible implementation of the third aspect are executed.

[0041] The beneficial effects of the embodiments of the present disclosure include:

[0042] The embodiments of the present disclosure provide an adaptive energy recovery system, device and method, which are mainly used in offshore wind farms in high-latitude waters. To address the problem that the foundation structure of wind turbines is subjected to long-term collision and compression of sea ice and the existing protection measures lack real-time response capabilities, sea ice energy recovery and structural protection are achieved through the collaborative work of multiple modules.

[0043] The system includes an environmental perception module that collects real-time data on external environmental parameters and device operating status, including key information such as sea ice movement speed, temperature, and pressure. This provides basic data support for the system's subsequent analysis and decision-making, ensuring the timeliness and accuracy of the data. The data analysis module performs in-depth processing on the collected data, accurately obtaining information on sea ice height, threat level, and the location of the energy recovery device. This converts the raw data into effective information for decision-making, providing a basis for system control. Based on the output of the data analysis module, the control execution module generates targeted control instructions, enabling flexible and rapid response to changes in sea ice threat levels and ensuring the accuracy of the energy recovery device's execution. The energy recovery device executes the control instructions, adjusting its position or performing energy recovery operations according to actual needs, converting the mechanical energy of sea ice movement into electrical energy, achieving energy recovery. The energy storage module stores the recovered electrical energy to provide additional energy for the wind farm's own operations.

[0044] This adaptive energy recovery system can not only sense the threat of sea ice in real time and make adaptive adjustments, effectively reducing the damage caused by sea ice to the structure of wind turbines, but also convert the mechanical energy contained in sea ice into electrical energy, realizing the secondary utilization of energy, and improving the safety and energy utilization efficiency of offshore wind farms. It has significant practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1A schematic structural diagram of an adaptive energy recovery system provided in an embodiment of the present disclosure;

[0046] Figure 2 A schematic structural diagram of an adaptive energy recovery device provided in an embodiment of the present disclosure;

[0047] Figure 3 A flowchart of an adaptive energy recovery method provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0048] The present disclosure provides an adaptive energy recovery system, device, and method. Preferred embodiments of the present disclosure are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are intended only to illustrate and explain the present disclosure and are not intended to limit the present disclosure. Furthermore, the embodiments and features within the embodiments of the present disclosure may be combined with one another unless there is a conflict.

[0049] The present disclosure provides an adaptive energy recovery system. Figure 1 As shown, including:

[0050] Environmental sensing module 101, used to collect external environmental parameters and device operating status data;

[0051] The data analysis module 102 is connected to the environmental perception module and is used to process the collected data and obtain sea ice height information, threat level and location information of the energy recovery device 104 through analysis;

[0052] The control execution module 103 is connected to the data analysis module and is used to generate control instructions based on the sea ice height information, threat level and location information of the energy recovery device 104;

[0053] The energy recovery device 104 is connected to the control execution module 103 and is used to execute control instructions to perform position adjustment and / or energy recovery operations;

[0054] The energy storage module 105 is connected to the energy recovery device 104 and is used to store the recovered electric energy.

[0055] As offshore wind power development expands into higher-latitude waters, the interaction between wind turbine foundations and sea ice has become a key research topic. While traditional research has focused on mitigating the threat of sea ice, re-examining this phenomenon from an energy conversion perspective reveals that the continuous movement of sea ice driven by wind and tidal currents, if effectively captured and utilized, can provide dual benefits to offshore wind power systems.

[0056] Through research on the dynamic characteristics of sea ice and the structural response of wind turbines, the feasibility of converting sea ice's mechanical energy into electrical energy has been clarified. This conversion not only serves as a supplementary energy source for wind farms, but also, through dynamic adjustments to the system during the energy recovery process, allows the structure to actively change its stress state when exposed to sea ice. Based on this idea, a proposal was made to construct an adaptive energy recovery system, utilizing a modular design to implement environmental perception, data analysis, and control execution. This system, while recovering sea ice energy, adjusts wind turbine protection strategies in real time, transforming the previously destructive effects of sea ice into a beneficial process that combines protection and energy supply, providing a new path for the safe and efficient operation of offshore wind power in icy environments.

[0057] In an embodiment of the present disclosure, a structural composition and functional description of each part of an adaptive energy recovery system are provided. The system is intended to recycle and utilize sea ice energy in offshore wind farms and reduce damage to wind turbines caused by sea ice through adaptive adjustment.

[0058] The environment perception module 101 can adopt a distributed or centralized layout by deploying various data collection devices to collect external environment and device operation data in real time, providing basic information support for system operation.

[0059] After receiving data from the environmental perception module 101, the data analysis module 102 uses machine learning algorithms or pre-set rule models to perform in-depth processing on the perceived data, extracting key information such as sea ice height, threat level, and device location, and transforming the raw data into decision-making information. For example, by analyzing sea ice pressure and movement speed data, sea ice height information is calculated; based on parameters such as sea ice height and movement speed, combined with pre-set thresholds, the threat level of sea ice to wind turbines is assessed; and the real-time location information of the energy recovery device 104 is simultaneously determined, transforming the raw data into effective information for decision-making.

[0060] Control execution module 103 generates corresponding control instructions based on the sea ice height information, threat level, and energy recovery device 104 position information obtained by data analysis module 102. For example, if the threat level is higher than the threshold (the probability of collision between sea ice and wind turbines is high) and the sea ice height information indicates that the sea ice position is rising, control execution module 103 will generate a corresponding control instruction to raise the energy recovery device 104.

[0061] Energy recovery device 104 receives instructions from control execution module 103 and performs position adjustment and / or energy recovery operations. For example, upon receiving a position adjustment instruction, it mechanically changes its height. During energy recovery, it utilizes the material properties of SMA (Shape Memory Alloy) to convert mechanical energy generated by sea ice collision into electrical energy.

[0062] The energy storage module 105 is responsible for storing the electric energy converted by the energy recovery device 104 and storing it for subsequent use by the wind farm.

[0063] The adaptive energy recovery system works through the collaborative work of multiple modules, combining environmental perception with energy recovery and structural protection to achieve system adaptive adjustment. Therefore, the system can not only recover sea ice energy to power wind farms, but also reduce sea ice damage to wind turbines through active protection, thereby improving the safety and energy utilization efficiency of offshore wind farms.

[0064] In another embodiment provided by the present disclosure, the above-mentioned environmental perception module 101 includes at least one of the following: a lidar, a camera, an acceleration sensor and a vibration sensor, which is used to collect the distance and image of the sea ice, and the acceleration and vibration data of the energy recovery device 104.

[0065] In the embodiment of the present disclosure, the specific structure and functions of the environment perception module 101 include:

[0066] By emitting laser beams and receiving reflected signals, the lidar can accurately measure the distance between sea ice and the energy recovery device 104, providing data for determining the proximity of sea ice. It can use single-line or multi-line scanning modes. Multi-line lidar can more comprehensively obtain sea ice contour information in complex sea ice environments.

[0067] By capturing images of sea ice, cameras can provide intuitive visual information such as its shape and density. In terms of implementation, multiple cameras at different angles can be deployed to form a visual monitoring network, enabling all-round observation of sea ice.

[0068] The acceleration sensor is used to detect changes in the acceleration of the energy recovery device 104 under the influence of sea ice. By analyzing the acceleration data, the impact force of the sea ice collision can be determined. Sensors based on different principles, such as piezoelectric and piezoresistive, can be flexibly selected based on actual needs.

[0069] The vibration sensor monitors the vibration of the energy recovery device 104 in real time, converts the vibration signal into an electrical signal output, and assists in determining the operating status of the device and the degree of influence of sea ice.

[0070] These sensors collectively form the environmental perception module, collecting data from different dimensions and complementing each other. The lidar provides distance information, the camera provides image information, and the accelerometer and vibration sensor provide information on device forces and operating status. This provides a rich and accurate data foundation for the data analysis module, enabling the system to more accurately analyze sea ice height, threat level, and the location of the energy recovery device 104.

[0071] The environmental perception module 101 enhances the system's ability to perceive the external environment and its own status through the collaborative work of multiple types of data acquisition devices, ensuring the system's responsiveness to sea ice threats and energy recovery efficiency.

[0072] In another embodiment provided by the present disclosure, the data analysis module 102 uses a multi-source data fusion algorithm to perform spatiotemporal registration and feature fusion on data collected by multiple sensors, and outputs sea ice height information, threat level, and location information of the energy recovery device 104;

[0073] The multi-source data fusion algorithm is used to:

[0074] Perform spatiotemporal registration based on filtering algorithms, unifying different sensor data into a global coordinate system by establishing a time synchronization model and a spatial transformation matrix;

[0075] Use machine learning algorithms to extract multimodal data features and construct feature vectors;

[0076] The sea ice threat level is calculated through a classification model, and the state estimation algorithm is used to estimate the sea ice height information and the position information of the energy recovery device 104 in real time.

[0077] In the embodiment of the present disclosure, the functions of the data analysis module 102 and the core algorithms that may be used in its implementation are as follows:

[0078] During the spatiotemporal registration process, the extended Kalman filter algorithm is used to establish a time synchronization model. By performing weighted averaging on the sensor timestamps, the time deviation caused by different sampling frequencies is eliminated. The homogeneous coordinate transformation matrix is ​​used to construct a spatial transformation matrix, and the polar coordinate data of the lidar and the image coordinate data of the camera are uniformly converted to the global Cartesian coordinate system to ensure the consistency of data from different sensors in the spatiotemporal dimensions.

[0079] During feature fusion, a convolutional neural network (CNN) is used to process the sea ice images captured by the camera, and visual features such as the sea ice edge and texture are extracted through multiple convolutional layers and pooling layers. At the same time, a long short-term memory network (LSTM) is used to analyze the time series data of the acceleration sensor and vibration sensor to capture the dynamic mechanical characteristics during the sea ice collision process. Finally, the two types of features are connected in series to form a multi-dimensional feature vector.

[0080] The threat level calculation utilizes a support vector machine (SVM) classification model. A classifier is trained based on historical sea ice data and structural damage. After inputting a multidimensional feature vector into the model, the model outputs corresponding results based on preset low, medium, and high threat level criteria. Sea ice height information and the location of the energy recovery device 104 are estimated using a particle filter algorithm. This algorithm simulates the distribution of sea ice and the device using a large number of particles, and updates particle weights based on real-time sensor data, achieving high-precision dynamic estimation.

[0081] This data analysis module uses specific algorithms to construct a multi-source data fusion system, effectively integrating the complementary information of heterogeneous sensor data and avoiding the one-sidedness of single data. Its innovation lies in significantly improving data processing accuracy through precise spatiotemporal calibration and deep feature extraction; and providing a reliable basis for system decision-making through modeled threat assessment and state estimation. Ultimately, it achieves accurate identification of sea ice threats and real-time understanding of device status, ensuring the efficient and adaptive operation of the energy recovery system. The above algorithm can also be implemented as other algorithms with similar functions, such as unscented Kalman filtering for spatiotemporal alignment and Transformer model for feature extraction, which are not restricted here.

[0082] In another embodiment provided by the present disclosure, the control execution module 103 includes: a fuzzy PID controller;

[0083] a fuzzy PID controller, configured to calculate and output control instructions based on the sea ice height information, threat level, and position information of the energy recovery device 104 output by the data analysis module 102 , to control the position adjustment and / or energy recovery operation of the energy recovery device 104 ;

[0084] The fuzzy PID controller is also used to set the membership function of the input and output variables, dividing the input parameters into multiple fuzzy sets;

[0085] By designing fuzzy control rules, the control parameters of the PID controller are dynamically adjusted according to the fuzzy reasoning results of the input variables;

[0086] An incremental control algorithm is used to calculate the control quantity and generate control instructions.

[0087] In the adaptive energy recovery system, the sea ice height information, threat level and location information of the energy recovery device 104 output by the data analysis module 102 need to be further converted into actual control actions through the control execution module.

[0088] In the embodiment of the present disclosure, the control execution module 103 uses a fuzzy PID controller as a core control unit to achieve precise control of the energy recovery device 104.

[0089] The fuzzy PID controller first applies membership functions to the input and output variables, classifying input parameters such as sea ice height and threat level into multiple fuzzy sets, such as "low," "medium," and "high." For example, sea ice heights of 0-1 meter are classified as "low," 1-3 meters as "medium," and above 3 meters as "high." In this way, precise numerical values ​​are converted into fuzzy language variables that facilitate logical judgment.

[0090] Subsequently, fuzzy control rules are designed to establish a relationship between the input variables and the PID control parameters (proportional coefficient P, integral coefficient I, and differential coefficient D). For example, when the sea ice threat level is "high" and the energy recovery device 104 is positioned outside the optimal recovery area, the fuzzy control rule can be set to increase the proportional coefficient P to speed up the response and decrease the integral coefficient I to prevent system overshoot. Based on the fuzzy inference results of the input variables, the control parameters of the PID controller are dynamically adjusted to adapt the controller to different operating conditions.

[0091] Finally, an incremental control algorithm is used to calculate the control variable. This algorithm generates control instructions based on the difference between the control parameters at the current moment and the previous moment, such as controlling the displacement of the energy recovery device 104 or the adjustment amplitude of the energy recovery power. Compared with the position-based PID algorithm, the incremental algorithm has a smaller computational load and faster response speed, and can better cope with the dynamic changes in the sea ice environment.

[0092] The fuzzy PID controller combines the nonlinear processing capabilities of fuzzy logic with the precise adjustment characteristics of PID control. It can not only handle the uncertainty in the sea ice environment, but also achieve precise control of the energy recovery device 104. By optimizing the PID parameters through fuzzy reasoning, the shortcomings of the traditional PID controller, such as fixed parameters and poor adaptability, are overcome. In actual applications, the controller can quickly generate and output control instructions based on the sea ice state and device position, driving the energy recovery device 104 to adjust its position or perform energy recovery operations, significantly improving the system's adaptability and control accuracy in complex sea ice environments, and ensuring the efficient and stable operation of the energy recovery system.

[0093] In another embodiment provided by the present disclosure, the energy storage module 105 includes: a lithium-ion battery and a supercapacitor,

[0094] The energy storage module 105 is used to manage the energy of the lithium-ion battery and the supercapacitor through a bidirectional DC / DC converter, and to charge the supercapacitor first, and then charge the lithium-ion battery when the supercapacitor is fully charged.

[0095] In the adaptive energy recovery system, after the energy recovery device 104 converts the sea ice mechanical energy into electrical energy, a reliable storage unit is required to achieve effective utilization of the energy.

[0096] In the embodiment of the present disclosure, the energy storage module 105 uses a combination of lithium-ion batteries and supercapacitors, and is collaboratively managed through a bidirectional DC / DC converter to construct an efficient electric energy storage system.

[0097] Lithium-ion batteries have high energy density and are suitable for long-term, large-capacity energy storage, meeting the electricity needs of continuous operation of wind farm equipment. For example, during periods of low sea ice activity and low energy recovery, the energy stored in lithium-ion batteries can power wind turbine monitoring systems, communication equipment, and other equipment. Supercapacitors have the advantages of high power density and fast charge and discharge speeds, and can quickly absorb the instantaneous electrical energy generated by the energy recovery device 104, avoiding system overloads caused by sudden energy changes. For example, when large-scale sea ice hits the energy recovery device 104, the supercapacitor can complete charging in a short period of time, efficiently capturing peak energy.

[0098] As the core management component, the bidirectional DC / DC converter intelligently controls the charging and discharging of the two energy storage media by regulating voltage and current. It prioritizes the transfer of recovered energy to the supercapacitor, leveraging the supercapacitor's fast response to promptly store sudden bursts of energy. When the supercapacitor reaches full charge, the converter automatically switches the charging path, transferring the remaining energy to the lithium-ion battery for long-term storage. This hierarchical charging strategy ensures rapid energy capture while maximizing energy utilization.

[0099] The design of the energy storage module combines the complementary advantages of lithium-ion batteries and supercapacitors, achieving intelligent energy management through a bidirectional DC / DC converter. This provides a reliable storage solution for the unstable energy output and high instantaneous power required during sea ice energy recovery. In practical applications, the module can efficiently store the electrical energy generated by the energy recovery device 104, avoiding energy waste. It also provides a stable auxiliary power source for the wind farm, enhancing the offshore wind farm's energy self-sufficiency and operational reliability, and effectively supporting the sustained and stable operation of the adaptive energy recovery system.

[0100] In another embodiment provided by the present disclosure, the energy recovery device 104 includes: an SMA energy recovery module, an electromagnetic induction module, and a position adjustment module;

[0101] SMA energy recovery module, which contains shape memory alloy elements to absorb external impact mechanical energy and convert it into mechanical deformation energy;

[0102] The electromagnetic induction module is mechanically connected to the SMA energy recovery module and is used to convert mechanical deformation energy into electrical energy;

[0103] The position adjustment module is installed on the tower of the wind turbine generator set and adopts an electromechanical drive mechanism to receive control instructions and drive the SMA energy recovery module and the electromagnetic induction module to adjust the position according to the control instructions.

[0104] In the disclosed embodiment, the energy recovery device 104 converts the mechanical energy of sea ice into electrical energy through the coordinated work of the SMA energy recovery module, the electromagnetic induction module and the position adjustment module, and adjusts its own position according to the actual working conditions to achieve effective protection of the wind turbine.

[0105] The SMA energy recovery module, with shape memory alloy elements at its core, leverages its superelasticity and shape memory effects to produce reversible mechanical deformation when subjected to external impacts, such as sea ice collisions, converting mechanical energy into mechanical deformation energy. For example, when struck by sea ice, the shape memory alloy elements bend or compress, storing energy while buffering the impact, reducing direct damage to the wind turbine's foundation structure.

[0106] The electromagnetic induction module is mechanically connected to the SMA energy recovery module. Mechanical deformation of the SMA energy recovery module drives movement of the electromagnetic induction module's components. Through the principle of electromagnetic induction, this mechanical deformation energy is converted into electrical energy. Specifically, a linear generator structure can be employed. The expansion and contraction of the SMA element drives the coil to cut through the magnetic flux lines in the magnetic field, generating an induced current and achieving energy conversion.

[0107] The position adjustment module is mounted on the wind turbine tower and utilizes an electromechanical drive mechanism, such as a servo motor coupled with a screw-nut pair or a rack-and-pinion mechanism. Upon receiving instructions from the control execution module, the position adjustment module precisely drives the SMA energy recovery module and electromagnetic induction module to move horizontally or vertically. For example, when sea ice poses a high threat level, the energy recovery device 104 is adjusted to a position that is more conducive to withstanding impact and recovering energy, thereby improving energy recovery efficiency and protective effectiveness.

[0108] The design of the energy recovery device 104 combines the properties of SMA materials with electromagnetic induction technology to achieve the conversion of mechanical energy into electrical energy, and enhances system adaptability through an adjustable position adjustment module. This device combines the dual functions of energy recovery and structural protection, achieving functional decoupling through a modular design. In practical applications, the device can not only effectively recover the energy contained in sea ice, but also reduce the threat of sea ice to wind turbines by actively adjusting its position, significantly improving the operational safety and energy efficiency of offshore wind farms in icy environments.

[0109] Based on the same disclosed concept, the embodiments of the present disclosure also provide an adaptive energy recovery device and method. Since the principles of the problems solved by these devices and methods are similar to those of the aforementioned adaptive energy recovery system, the implementation of the device and method can refer to the implementation of the aforementioned system, and the repeated parts will not be repeated.

[0110] With the above Figure 1 Corresponding to the system shown, the embodiment of the present disclosure also provides an adaptive energy recovery device, such as Figure 2 As shown, including:

[0111] The SMA energy recovery module 201 comprises a shape memory alloy element for absorbing external impact mechanical energy and converting it into mechanical deformation energy;

[0112] The electromagnetic induction module 202 is mechanically connected to the SMA energy recovery module 201 and is used to convert mechanical deformation energy into electrical energy;

[0113] The position adjustment module 203 is installed on the tower of the wind turbine generator system and is used to receive control instructions and adjust the spatial position of the SMA energy recovery module 201 according to the control instructions.

[0114] An adaptive energy recovery device is provided to match the system. It realizes the conversion of mechanical energy into electrical energy through the coordinated operation of three functional modules, and dynamically adjusts its own position according to the sea ice environment to ensure the stable operation of the wind turbine.

[0115] The implementation of the adaptive energy recovery device may refer to the implementation of the energy recovery device 104 described above, and will not be repeated here.

[0116] In another embodiment provided by the present disclosure, the shape memory alloy element of the SMA energy recovery module 201 adopts a variable diameter spiral structure or a corrugated structure, and a protective coating is provided on the surface. The protective coating is prepared by a chemical deposition process and has wear-resistant and corrosion-resistant properties.

[0117] In the embodiment of the present disclosure, in the adaptive energy recovery device, the SMA energy recovery module 201 is a core component, and its performance directly affects the energy recovery efficiency and device protection capability. Its structural optimization and surface treatment scheme adopts the following implementation methods:

[0118] The shape memory alloy element with a variable diameter spiral structure, through its gradually changing diameter spiral design, undergoes differential deformation at different diameters when impacted by sea ice, enabling graded absorption of mechanical energy. For example, the thinner parts undergo elastic deformation first, absorbing smaller impact forces. As the impact intensifies, the thicker parts gradually deform, ensuring that the element maintains its structural integrity under heavy loads. The corrugated structure, with its regularly undulating curved surface design, folds and stretches the corrugations during the sea ice compression process, increasing the deformation path and prolonging the energy absorption time, thereby more fully converting mechanical energy into mechanical deformation energy.

[0119] Surface protective coatings are produced using chemical deposition techniques, either through chemical vapor deposition (CVD) or electrochemical deposition. Chemical vapor deposition forms a uniform, dense ceramic coating, such as a silicon nitride coating, on the alloy surface, effectively isolating the alloy substrate from seawater. Electrochemical deposition, on the other hand, produces metallic protective layers, such as zinc-nickel alloy coatings. This utilizes the sacrificial anode principle to preferentially consume the coating material, protecting the shape memory alloy itself. Both processes impart wear and corrosion resistance to the coating, protecting it from sea ice friction and seawater erosion.

[0120] The variable-diameter spiral and corrugated structure optimizes energy absorption efficiency through component geometry, while the protective coating enhances component durability through surface modification. The former enhances adaptability to varying degrees of sea ice impact through structural design, while the latter extends component life in marine environments through surface treatment. Together, these two ensures the SMA Energy Recovery Module 201's efficient conversion of mechanical energy under the influence of sea ice and long-term stable operation.

[0121] The adaptive energy recovery device disclosed herein combines structural optimization with surface protection technology to provide a systematic solution to the dual challenges of sea ice impact and corrosion in marine environments. In practical applications, shape memory alloy elements with variable diameter spiral or corrugated structures, combined with wear-resistant and corrosion-resistant coatings, can significantly improve the energy capture efficiency and service life of the SMA energy recovery module 201, enhancing the reliability and practicality of the adaptive energy recovery device in complex sea conditions, and providing strong support for the safe operation and energy recovery of offshore wind farms.

[0122] In parallel, another possible implementation is provided as follows:

[0123] The SMA energy recovery module 201, with shape memory alloy at its core, comes in a variety of implementations. It can be woven into a mesh structure using filaments of shape memory alloy. When sea ice collides with it, the elastic deformation of the filaments absorbs the impact energy. Alternatively, it can be designed as a plate-like element. Its large contact area allows it to bend and deform during ice compression, converting mechanical energy into internal strain energy. This deformation process not only stores energy but also reduces the direct impact of sea ice on the wind turbine through the material's cushioning properties.

[0124] In another embodiment provided by the present disclosure, the electromagnetic induction module 202 includes a multi-layer hollow coil and a magnetic material. The multi-layer hollow coil is coupled with the mechanical deformation region of the SMA energy recovery module 201 to convert the mechanical deformation energy into induced electrical energy.

[0125] After the SMA energy recovery module 201 completes the conversion of sea ice mechanical energy into mechanical deformation energy, it needs to be further converted into energy form through the electromagnetic induction module 202.

[0126] In the embodiment of the present disclosure, the electromagnetic induction module 202 adopts a combination design of multi-layer hollow coils and magnetic materials, and achieves the goal of converting mechanical deformation energy into induced electrical energy through coordinated operation with the SMA energy recovery module 201.

[0127] The multi-layer hollow coil serves as the core execution unit of the electromagnetic induction module 202. It is coupled to the mechanical deformation area of ​​the SMA energy recovery module 201 to capture the kinetic energy generated by its deformation. For example, when the shape memory alloy element in the SMA energy recovery module 201 is impacted by sea ice and expands or twists, the multi-layer hollow coil can move accordingly through connections such as rigid connecting rods and flexible transmission belts. The multi-layer structure increases the number of coil turns and the effective path for cutting magnetic flux lines, thereby improving the induced electromotive force output per unit deformation. The hollow design reduces the weight of the module while avoiding magnetic circuit saturation and ensuring energy conversion efficiency. Its specific implementation method can adopt flat spiral or three-dimensional stacked winding to adapt to different spatial layouts and deformation transfer requirements.

[0128] Magnetic materials create a magnetic field environment within electromagnetic induction module 202, forming the physical basis for energy conversion along with the multilayered hollow coils. Permanent magnetic materials such as samarium-cobalt alloys can be used to provide a stable magnetic field, leveraging their high remanence. Alternatively, soft magnetic materials such as ferrites can be used to optimize the distribution of magnetic flux through the core structure, enhancing the coupling between the coil and the magnetic field. For example, in a three-dimensional laminated coil design, a toroidal soft ferrite core can be placed at the center of the coil, guiding the magnetic flux perpendicularly through the coil plane and improving the efficiency of induced power generation.

[0129] The electromagnetic induction module 202 achieves efficient capture and electrical energy conversion of the mechanical deformation energy of the SMA energy recovery module 201 through the optimized configuration of multi-layer hollow coils and magnetic materials. Through the coordinated optimization of structural design and material selection, the efficiency bottleneck of traditional electromagnetic conversion devices has been broken through. In addition, modularization makes the device highly adaptable, and the coil layout and magnetic circuit structure can be flexibly adjusted according to the deformation form of the SMA element. In practical applications, the module can stably convert the mechanical energy generated by the action of sea ice into electrical energy to power the energy storage module, thus achieving additional energy acquisition for offshore wind farms. It also reduces the impact damage of sea ice on the wind turbine structure through mechanical buffering during the energy recovery process, thereby improving the overall operational efficiency of the system.

[0130] In parallel, another possible implementation is provided as follows:

[0131] Electromagnetic induction module 202 and SMA energy recovery module 201 utilize either a rigid or flexible mechanical connection. A rigid connection, through a linkage mechanism, transmits the linear deformation of the SMA element to a rotary electromagnetic induction device, for example, by rotating a permanent magnet in a stator coil to generate electrical energy. A flexible connection, however, is suitable for filamentary SMA materials. Using elastic cords or belts, the irregular deformation of the SMA element is converted into reciprocating motion by a linear generator, cutting through magnetic flux lines to convert mechanical energy into electrical energy. Both connection methods ensure efficient transmission of the SMA's deformation energy to the electromagnetic induction unit.

[0132] In addition, other implementations of the position adjustment module 203 are as follows:

[0133] Position adjustment module 203, deployed on the wind turbine tower, offers a variety of drive options. In electromechanical drive mode, a stepper motor coupled with a worm gear reducer enables high-precision angular adjustment of the SMA energy recovery module 201. A hydraulic drive system rapidly responds to control commands by telescoping hydraulic cylinders, pushing the entire module horizontally or vertically. A pneumatic drive utilizes compressed air to drive cylinders, making it suitable for rapid adjustment of the device's position in situations requiring high response speed, such as in response to sudden sea ice threats.

[0134] With the above Figure 1 Corresponding to the system shown, the embodiment of the present disclosure also provides an adaptive energy recovery method, such as Figure 3 As shown, including:

[0135] S301, real-time collection of external environment data and device operation status data;

[0136] S302, preprocessing, feature extraction and multi-source fusion of the collected data, outputting sea ice height information, threat level and location information of the energy recovery device, and generating corresponding control instructions;

[0137] S303, adjusting the SMA energy recovery module to the target position according to the control instruction;

[0138] S304, when an external impact acts on the SMA energy recovery module, the shape memory alloy element absorbs mechanical energy and converts it into mechanical deformation energy, and then the electromagnetic induction module converts the mechanical deformation energy into electrical energy;

[0139] S305: The electric energy is rectified and filtered and then stored in an energy storage module.

[0140] In the actual operation of the adaptive energy recovery system, a systematic approach is needed to coordinate the operation of each module to achieve the dual goals of sea ice energy recovery and wind turbine protection.

[0141] In the disclosed embodiment, the adaptive energy recovery method constructs a complete process from data collection, analysis and decision-making to energy conversion and storage through five ordered steps.

[0142] The S301 uses data acquisition equipment such as lidar and accelerometers deployed in its environmental perception module to collect real-time environmental and operational information, including the distance to sea ice and vibration data from the energy recovery device. For example, the lidar continuously monitors the distance between the sea ice and the device, providing basic data support for subsequent analysis.

[0143] S302 performs in-depth processing on the collected data. Preprocessing eliminates data noise and outliers; feature extraction utilizes machine learning algorithms to mine data features; and a multi-source fusion algorithm integrates data from different sensors, outputting key information such as sea ice height and threat level, and generating control instructions based on this information. The specific algorithms used can be found in the data analysis module 102 described above and will not be detailed here.

[0144] Based on control instructions, the S303 position adjustment module drives the SMA energy recovery module to the target position. A servo motor and a screw drive mechanism are used to precisely adjust the module's position to best withstand the impact of sea ice and recover energy.

[0145] In S304, when sea ice hits the SMA energy recovery module, the shape memory alloy element undergoes reversible deformation, converting mechanical energy into mechanical deformation energy. The electromagnetic induction module cuts the magnetic flux lines through the coil, further converting it into electrical energy.

[0146] S305 converts the generated AC power into DC power through a rectifier circuit, removes ripples through a filter circuit, and finally stores it in an energy storage module composed of lithium-ion batteries and supercapacitors for use in wind farms.

[0147] The method provided in the embodiments of the present disclosure achieves efficient recovery of sea ice energy and intelligent protection of wind turbines through the coordinated operation of multiple links. Combining a data-driven decision-making mechanism with the physical energy conversion process, it is both real-time and adaptive, and can dynamically adjust strategies based on the sea ice environment. In practical applications, this method effectively improves the ability of offshore wind farms to respond to sea ice threats, reducing the risk of structural damage, achieving secondary energy utilization, and enhancing the safety and economic efficiency of system operation.

[0148] An embodiment of the present disclosure provides a computer-readable storage medium having a computer program stored thereon. When executed by a processor, the computer program executes the steps of an adaptive energy recovery method provided in any embodiment of the present disclosure. The storage medium may be a volatile or non-volatile computer-readable storage medium.

[0149] Through the description of the above embodiments, those skilled in the art can clearly understand that the embodiments of the present disclosure can be implemented through hardware or by means of software plus a necessary general hardware platform. Based on this understanding, the technical solutions of the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments of the present disclosure.

[0150] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of a preferred embodiment, and the modules or processes in the accompanying drawings are not necessarily required for implementing the present disclosure.

[0151] Those skilled in the art will appreciate that the modules in the systems or devices of the embodiments may be distributed in the systems or devices of the embodiments as described in the embodiments, or may be located in one or more systems or devices different from the embodiments with corresponding changes. The modules in the above embodiments may be combined into one module or further divided into multiple submodules.

[0152] The serial numbers of the above-mentioned embodiments of the present disclosure are for description only and do not represent the advantages or disadvantages of the embodiments.

[0153] Obviously, those skilled in the art may make various changes and modifications to the present disclosure without departing from the spirit and scope of the present disclosure. Thus, if these modifications and variations of the present disclosure fall within the scope of the claims of the present disclosure and their equivalents, the present disclosure is intended to include these modifications and variations.

Claims

1. An adaptive energy recovery system, characterized in that: include: Environmental perception module, used to collect external environmental parameters and device operating status data; a data analysis module connected to the environmental perception module, configured to process the collected data and obtain sea ice height information, threat level, and location information of the energy recovery device through analysis; a control execution module connected to the data analysis module, and configured to generate a control instruction based on the sea ice height information, the threat level, and the position information of the energy recovery device; The energy recovery device is connected to the control execution module and is used to execute the control instruction to perform position adjustment and / or energy recovery operations; The energy storage module is connected to the energy recovery device and is used to store the recovered electric energy.

2. The system according to claim 1, wherein The environmental perception module includes at least one of the following: a laser radar, a camera, an acceleration sensor and a vibration sensor, which is used to collect the distance and image of the sea ice, and the acceleration and vibration data of the energy recovery device.

3. The system according to claim 1, wherein The data analysis module uses a multi-source data fusion algorithm to perform spatiotemporal registration and feature fusion on data collected by multiple sensors, and outputs sea ice height information, threat level, and location information of the energy recovery device; Wherein, the multi-source data fusion algorithm is used to: Perform spatiotemporal registration based on filtering algorithms, unifying different sensor data into a global coordinate system by establishing a time synchronization model and a spatial transformation matrix; Use machine learning algorithms to extract multimodal data features and construct feature vectors; The sea ice threat level is calculated through a classification model, and the sea ice height information and the position information of the energy recovery device are estimated in real time using a state estimation algorithm.

4. The system according to claim 1, wherein The control execution module includes: a fuzzy PID controller; The fuzzy PID controller is used to calculate and output control instructions based on the sea ice height information, threat level and position information of the energy recovery device output by the data analysis module to control the position adjustment and / or energy recovery operation of the energy recovery device; The fuzzy PID controller is further used to set the membership function of the input and output variables, dividing the input parameters into multiple fuzzy sets; By designing fuzzy control rules, the control parameters of the PID controller are dynamically adjusted according to the fuzzy reasoning results of the input variables; An incremental control algorithm is used to calculate the control quantity and generate control instructions.

5. The system according to claim 1, wherein: The energy storage module includes: lithium-ion batteries and supercapacitors, The energy storage module is used to achieve energy management of lithium-ion batteries and supercapacitors through a bidirectional DC / DC converter, giving priority to charging the supercapacitor, and then charging the lithium-ion battery when the supercapacitor is fully charged.

6. The system according to claim 1, wherein: The energy recovery device includes: an SMA energy recovery module, an electromagnetic induction module and a position adjustment module; The SMA energy recovery module comprises a shape memory alloy element for absorbing external impact mechanical energy and converting it into mechanical deformation energy; The electromagnetic induction module is mechanically connected to the SMA energy recovery module and is used to convert the mechanical deformation energy into electrical energy; The position adjustment module is installed on the tower of the wind turbine generator set and adopts an electromechanical drive mechanism to receive control instructions and drive the SMA energy recovery module and the electromagnetic induction module to adjust their positions according to the control instructions.

7. An adaptive energy recovery device, characterized in that: include: SMA energy recovery module, which contains shape memory alloy elements to absorb external impact mechanical energy and convert it into mechanical deformation energy; an electromagnetic induction module, mechanically connected to the SMA energy recovery module, for converting the mechanical deformation energy into electrical energy; The position adjustment module is installed on the tower of the wind turbine generator set and is used to receive control instructions and adjust the spatial position of the SMA energy recovery module according to the control instructions.

8. The device according to claim 7, wherein The shape memory alloy element of the SMA energy recovery module adopts a variable diameter spiral structure or a corrugated structure, and a protective coating is provided on the surface. The protective coating is prepared by a chemical deposition process and has wear-resistant and corrosion-resistant properties.

9. The device according to claim 7, wherein The electromagnetic induction module includes a multi-layer hollow coil and a magnetic material. The multi-layer hollow coil is coupled with the mechanical deformation region of the SMA energy recovery module to convert mechanical deformation energy into induced electrical energy.

10. An adaptive energy recovery method, characterized in that: include: Real-time collection of external environment data and device operation status data; The collected data is pre-processed, feature extracted, and multi-source fused to output sea ice height information, threat level, and energy recovery device location information, generating corresponding control instructions; Adjust the SMA energy recovery module to the target position according to the control instructions; When an external impact acts on the SMA energy recovery module, the shape memory alloy element absorbs mechanical energy and converts it into mechanical deformation energy, which is then converted into electrical energy through the electromagnetic induction module. The electrical energy is rectified and filtered and then stored in the energy storage module.

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