Wave height feature recognition-based floating wind turbine protection control method and system

Abstract

The application discloses a floating wind turbine protection control method and system based on wave height feature recognition, and the method comprises the following steps: acquiring basic motion sensing information of the floating wind turbine; extracting a wave height characteristic value from the acquired basic motion sensing information; acquiring environmental information of the floating wind turbine, and dynamically calculating a protection threshold corresponding to the wave height characteristic value according to the wave height characteristic value and the environmental information; comparing the wave height characteristic value with the corresponding protection threshold in real time, and judging a protection decision of the floating wind turbine according to a comparison result; and inputting the protection decision into a main control system of the floating wind turbine, and controlling corresponding protection actions to be executed by the main control system; the application does not need an additional wave measuring device, directly uses floating wind turbine sensing information to realize wave height feature recognition, designs a floating wind turbine operation protection strategy based on the wave height characteristic value, and improves self-adaptive capability and safe operation level of the floating wind turbine in a complex environment.

Description

Technical Field

[0001] This invention relates to the technical field of wind turbine control, and in particular to a floating wind turbine protection and control method and system based on wave height feature recognition. Background Technology

[0002] Floating wind turbines differ from traditional stationary offshore wind turbines. Their foundations float on the sea surface via mooring systems, relying on the stability of the floating body itself and the constraints of the mooring system to maintain their position and attitude. In the marine environment, floating wind turbines are subjected to the complex coupling effects of wind loads, wave loads, and ocean current loads, resulting in significant motion responses in all six degrees of freedom. Among these, wave loads have a decisive impact on the motion response and structural safety of floating wind turbines. The interaction between wave loads and the floating wind turbine structure is one of the important causes of power fluctuations, structural fatigue, and exceeding motion response limits. With the continuous increase in single-unit capacity, the ongoing evolution of floating structure forms, and cost reduction designs, the impact of wave loads on the tower base load and safe operation of floating wind turbines has become more prominent. Wave loads under extreme sea conditions have become a core factor that must be carefully considered in the structural design and operational control of floating wind turbines.

[0003] Therefore, accurately acquiring real-time wave information of the sea area where the floating wind turbine is located is of great significance for implementing active load control, optimizing power generation, ensuring structural safety, and guiding operation and maintenance scheduling during the operation of floating wind turbines. However, current wave monitoring methods for floating wind turbines have significant technical limitations. Commonly used wave monitoring methods include wave buoy observation, shore-based wave measuring radar, shipborne wave measuring radar, and airborne wave measuring radar. Gravity-based wave buoy observation requires the deployment of wide-range buoys in the operating sea area, which is not only costly and requires extensive maintenance, but may also affect the safety of ship navigation, making large-scale deployment and continuous all-weather monitoring difficult. Shore-based wave measuring radar has a limited detection range, which cannot meet the monitoring needs of deep-sea floating wind farms far from the coast. In recent years, some researchers have attempted to install wave measuring radar directly on the floating wind turbine platform for wave measurement, but wave measuring radar is susceptible to interference from sea fog splash caused by wave direction, resulting in false wave peak measurements. Furthermore, the movement of the floating platform itself affects the stability of the measurement reference, making it difficult to accurately extract wave parameters. Furthermore, while some progress has been made in lidar-based wave measurement and reconstruction systems, this technology still has high requirements for weather conditions and the stability of the installation platform, and is far from practical engineering applications. Overall, existing wave monitoring technologies struggle to simultaneously meet multiple requirements such as ease of installation, cost-effectiveness, real-time performance, and accuracy. In particular, there is a lack of a low-cost, highly reliable solution that can directly utilize existing sensor information from the floating wind turbine itself for wave feature identification without requiring additional monitoring equipment.

[0004] In vibration monitoring, accelerometers have been widely used in structural health monitoring and load analysis of offshore wind turbine towers. By deploying accelerometers at key locations on the tower, the vibration response of the wind turbine tower under the combined effects of environmental excitation loads such as wind and waves can be captured, providing important data for structural condition assessment and fault diagnosis. However, existing technologies mostly use acceleration data for structural damage detection and modal parameter identification of the tower itself, without fully exploring its intrinsic correlation with external environmental excitations. Specifically, the heave motion of the floating body caused by waves directly acts on the tower base, resulting in a clear physical correlation between the vertical acceleration response at the tower base and the wave rise. In the frequency domain, the dominant wave frequency has a strong correspondence with the response components near the natural frequency of the floating body's heave motion; in the time domain, there is a significant correlation between the fluctuation amplitude of the vertical acceleration at the tower base and the effective wave height.

[0005] In summary, there is an urgent need to develop a method and device that can overcome the limitations of existing monitoring technologies, eliminate the need for additional dedicated wave measurement equipment, and directly utilize the sensing information of the floating wind turbine itself to identify wave height characteristics, so as to improve the adaptability and safe operation level of floating wind turbines in complex deep-sea environments. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the prior art and propose a floating wind turbine protection and control method and system based on wave height feature recognition. It does not require additional dedicated wave measurement equipment and directly uses the sensing information of the floating wind turbine itself to realize wave height feature recognition. Based on the wave height feature value, a floating wind turbine operation protection strategy is designed to improve the adaptability and safe operation level of the floating wind turbine in the complex environment of deep sea.

[0007] The objective of this invention is achieved through the following technical solution: a floating wind turbine protection and control method based on wave height feature identification, comprising the following steps:

[0008] S1. Obtain basic motion sensing information of the floating wind turbine;

[0009] S2. Extract wave height feature values ​​from the basic motion sensing information obtained in step S1;

[0010] S3. Obtain environmental information of the floating wind turbine, and dynamically calculate the protection threshold corresponding to the wave height characteristic value based on the wave height characteristic value and the environmental information.

[0011] S4. Compare the wave height characteristic value with the corresponding protection threshold in real time, and determine the protection decision to be made by the floating wind turbine based on the comparison result;

[0012] S5. Input the protection decision into the main control system of the floating wind turbine, and the main control system controls the execution of the corresponding protection actions.

[0013] Furthermore, step S1 includes:

[0014] At least one accelerometer is installed at the bottom of the tower of the floating wind turbine and at the transition section between the tower and the floating structure. The accelerometer measures the real-time acceleration signal of the floating wind turbine in the heave direction. The accelerometer is a low-frequency response sensor with an effective measurement frequency band of 0.05 Hz to 0.5 Hz to highlight the wave-dominated motion information.

[0015] Furthermore, step S2 includes:

[0016] The acceleration signal is bandpass filtered to retain the effective components within the main frequency band of the wave and filter out wind-induced high-frequency disturbances and sensor background noise. Then, the root mean square or standard deviation of the filtered acceleration signal is calculated within a preset time window and used as the wave height characteristic value.

[0017] Furthermore, step S3 includes:

[0018] The main control system of the floating wind turbine acquires real-time wind speed signals through the wind speed measurement equipment preset on the floating wind turbine, and dynamically calculates the protection threshold corresponding to the wave height characteristic value based on the real-time wind speed signal. According to the preset safety level of the floating wind turbine, the calculated protection thresholds are designed in a graded manner, and the protection thresholds after graded design include primary thresholds and secondary thresholds.

[0019] Furthermore, step S4 includes:

[0020] The wave height characteristic value is compared with the corresponding protection threshold in real time. If the wave height characteristic value is less than the first-level threshold, it is judged as a low wave level, and the floating wind turbine continues to operate normally. If the wave height characteristic value is within the range of [first-level threshold, second-level threshold], it is judged as a medium wave level, and the power limiting coefficient D and speed limiting coefficient E are output to the main control system of the floating wind turbine. If the wave height characteristic value is not less than the second-level threshold, it is judged as a high wave level, and the shutdown command G is output to the main control system of the floating wind turbine.

[0021] Furthermore, step S5 includes:

[0022] The protection decisions are input into the main control system of the floating wind turbine, which then controls the execution of corresponding protection actions. Upon receiving a power limiting factor D, the main control system limits the output power to D times the rated power by adjusting the pitch angle or generator torque; upon receiving a speed limiting factor E, the main control system limits the maximum rotor speed to E times the rated speed; upon receiving a shutdown command G, the main control system executes feathering and braking operations according to a preset safe shutdown procedure, ensuring the safe shutdown of the floating wind turbine.

[0023] A floating wind turbine protection and control system based on wave height feature recognition is used to implement the aforementioned floating wind turbine protection and control method based on wave height feature recognition, including:

[0024] The floating body motion measurement unit is used to measure the real-time acceleration signal of the floating body in the heave direction;

[0025] The data processing unit is used to extract wave height feature values ​​from basic motion sensing information; at the same time, it dynamically calculates the protection threshold corresponding to the wave height feature values ​​based on the wave height feature values ​​and the environmental information of the floating wind turbine.

[0026] The decision execution unit is used to compare the wave height characteristic value with the corresponding protection threshold in real time, determine the protection decision to be taken by the floating wind turbine based on the comparison result, and output the protection decision to the main control system.

[0027] The main control system is used to control the floating wind turbine to make protection decisions and to provide the data processing unit with environmental information about the floating wind turbine.

[0028] Furthermore, the output of the floating body motion measurement unit is communicatively connected to the input of the data processing unit; the output of the main control system is communicatively connected to the input of the data processing unit; the output of the data processing unit is communicatively connected to the input of the decision execution unit; and the output of the decision execution unit is communicatively connected to the input of the main control system.

[0029] A non-transitory computer-readable medium storing instructions that, when executed by a processor, perform the steps of the floating wind turbine protection control method based on wave height feature identification described above.

[0030] A computing device includes a processor and a memory for storing processor-executable programs. When the processor executes the program stored in the memory, it implements the above-described floating wind turbine protection and control method based on wave height feature recognition.

[0031] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0032] 1. No additional dedicated wave measurement equipment is required, significantly reducing system costs and maintenance burden:

[0033] This invention directly utilizes accelerometers already deployed or easily deployed at the base or transition section of floating wind turbine towers to extract wave height characteristics through signal processing, eliminating the need for deploying wave buoys or installing specialized equipment such as wave measuring radar or lidar. It avoids engineering challenges such as buoy maintenance, radar calibration, and sea fog interference, significantly reducing the environmental perception costs and operational complexity of deep-sea wind farms, demonstrating good economic viability and engineering feasibility.

[0034] 2. The signal source is pure, and feature extraction is accurate and reliable:

[0035] Traditional nacelle accelerometers are susceptible to interference from multiple sources, including flexible tower bending, blade rotation, and transmission chain vibration, resulting in mixed signals. This invention mounts the sensor at the tower base or transition section, a location dominated by the rigid motion of the floating body. The measured vertical acceleration directly reflects the floating body's heave response under wave excitation, effectively filtering out high-frequency structural vibrations and transmission system noise. Furthermore, bandpass filtering and sliding window standard deviation or root mean square statistical processing are employed to extract wave height feature values ​​that exhibit a strong linear positive correlation with the effective wave height, demonstrating robustness and avoiding misjudgments caused by instantaneous impacts or measurement noise.

[0036] 3. Achieving refined operation control through adaptive protection thresholds based on wind speed gain scheduling:

[0037] This invention fully considers the physical coupling relationship between wind speed and wave height. Instead of setting a fixed constant for the protection threshold, it obtains real-time wind speed signals through the floating wind turbine's main control communication and dynamically calculates a threshold adapted to the current wind speed using a gain scheduling strategy. Compared to fixed threshold methods, this design avoids being overly conservative at low wind speeds, thus preventing power generation losses, and also prevents the threshold from being too high at high wind speeds, resulting in insufficient protection. This achieves an optimized balance between safety and power generation efficiency.

[0038] 4. Graded protection logic enhances the safety of floating wind turbines under extreme sea conditions:

[0039] This invention establishes a multi-level protection threshold mechanism, which triggers power limiting, speed limiting, and safe shutdown responses sequentially based on the magnitude of wave height characteristic values. When encountering sudden large waves or extreme sea conditions, the system can autonomously reduce load or even shut down in an emergency, effectively suppressing excessive tower load and structural fatigue damage caused by large-scale heave of the floating body, and significantly improving the adaptive capability and structural integrity of the floating wind turbine in complex deep-sea environments.

[0040] 5. It is easy to integrate with existing wind turbine main control systems and has high promotional value:

[0041] This invention can be embedded into existing wind turbine main control platforms or accessed as an independent module. It only requires one acceleration signal to communicate with the wind speed signal, without the need to modify the hardware architecture. It has good technology inheritance and is suitable for the intelligent upgrade of various floating wind turbines.

[0042] In summary, this invention has made substantial progress in reducing costs, improving reliability, and achieving adaptive protection, and has application prospects and economic benefits. Attached Figure Description

[0043] Figure 1 This is a flowchart of a floating wind turbine protection and control method based on wave height feature identification.

[0044] Figure 2 This is a time-series comparison diagram of wave height, filtered wave height, and vertical displacement.

[0045] Figure 3 A protection threshold curve defined based on the eigenvalue calculation results of dlc1.2 and dlc1.6.

[0046] Figure 4 This is an architecture diagram of a floating wind turbine protection and control system based on wave height feature recognition. Detailed Implementation

[0047] The present invention will be further described below with reference to specific embodiments.

[0048] Example 1

[0049] See Figure 1 The diagram illustrates the floating wind turbine protection and control method based on wave height feature identification provided in this embodiment. The floating wind turbine uses a 15-20MW turbine paired with a tension leg floating platform. Integrated modeling is performed in the floating wind turbine design software for overall dynamic simulation. According to the wind turbine design standard IEC61400, the floating wind turbine design conditions dlc1.2 and dlc1.6 are defined as normal turbulence and normal wave power generation conditions, respectively, while dlc1.6 is normal turbulence and extreme wave power generation conditions. In traditional designs, the load on wind turbine components under dlc1.6 is typically significantly higher than under dlc1.2, placing higher demands on the safety margins of critical structures such as the tower, float, and drive train. Therefore, the purpose of applying this method in the design phase is primarily to reduce the load on wind turbine components under dlc1.6, aiming to reduce it to the same level as dlc1.2, thereby meeting design standard requirements without increasing structural weight and improving economy and safety.

[0050] The method includes the following steps:

[0051] S1. At least one accelerometer is installed at the bottom of the tower of the floating wind turbine and at the connection transition section between the tower and the floating structure. The accelerometer measures the real-time acceleration signal of the floating wind turbine in the heave direction. The accelerometer is a low-frequency response sensor with an effective measurement frequency band of 0.05 Hz to 0.5 Hz to highlight the wave-dominated motion information.

[0052] From a mechanical perspective, the overall stiffness of the floating structure of a floating wind turbine is relatively high. Under wave excitation, its motion response is dominated by near-rigid overall motion, with local elastic deformation contributing negligibly to the acceleration at the tower base. Therefore, signals collected by placing accelerometers at the tower base or transition section can accurately and directly reflect the rigid motion characteristics of the floating body itself, especially the main wave excitation component—heavy motion. In contrast, traditional accelerometers installed inside the floating wind turbine nacelle, due to the nacelle's location at the top of the flexible tower, contain a large number of high-frequency, complex motion components introduced by factors such as tower bending deformation, blade rotational excitation, transmission chain vibration, and rotor imbalance. This results in a reduced signal-to-noise ratio and an inability to effectively separate the low-frequency rigid motion components directly related to wave excitation. Furthermore, considering that the wave-induced heave motion of the floating body is usually concentrated in a lower frequency band, generally far below the higher elastic mode frequency of the structure, in order to suppress the interference of non-wave-induced factors, such as instantaneous impact and high-frequency noise, on the measurement accuracy, this embodiment preferentially selects a low-frequency response sensor that can effectively measure acceleration signals in the 0.05 Hz to 0.5 Hz frequency band, so as to highlight the wave-dominated motion information.

[0053] Adding a Z-axis acceleration sensor at the base of the tower in floating wind turbine design software requires first determining the section number of the tower base or the transition section section number, and then adding the sensor at the corresponding location. In this embodiment, the tower base section number is 10, therefore the following code needs to be set in the Project information of the floating wind turbine design software to add the acceleration sensor at the base of the tower:

[0054] MSTART EXTRA;

[0055] Nuttower Accelerometers 1;

[0056] ACCMEMBER 10;

[0057] ACCFRACTION 0;

[0058] MEND;

[0059] After completing the above configuration, the vertical acceleration value at the base of the tower measured by the accelerometer can be read in real time through the application programming interface (API) provided by the floating wind turbine design software. The API function used is as follows:

[0060] GetMeasuredTurbineAccelerometerAccelerationZ(turbine_id, 0);

[0061] The Z direction in the API function represents the vertical direction; therefore, the vertical acceleration value at the base of the tower can be obtained in real time using this API function. This leads to step S2 for data processing.

[0062] S2. The acceleration signal A obtained from step S1 is bandpass filtered to retain the effective components in the main frequency band of the wave and filter out wind-induced high-frequency disturbances and sensor background noise. Then, the root mean square or standard deviation of the filtered acceleration signal is calculated within a 600s time window and used as the wave height characteristic value.

[0063] Simulations show that after appropriate filtering of the wave height, it exhibits a characteristic positively correlated with the vertical displacement height at the tower base. (See [link to simulation]). Figure 2 As shown, the physical essence of this correlation lies in the fact that wave energy is transferred from the free surface to the floating body, causing it to sway, through a dynamic transmission process similar to low-pass filtering. Furthermore, although the filtered wave height and the vertical displacement at the tower base are not perfectly linearly related, their strong positive correlation means that using the standard deviation over a statistical period better reflects the characteristics of the wave height. Considering that acceleration is easier to measure in practical applications, we consider statistically analyzing the standard deviation of vertical acceleration over a period of time. Since simulation times are mostly 600s, the statistical time is also set to 600s. Finally, considering that field accelerometers often contain high-frequency noise signals, appropriate low-pass filtering of the original acceleration signal is also necessary.

[0064] Based on the above analysis, a method was developed to calculate the characteristic value representing the wave height by filtering the acceleration with a low-pass filter and then taking the standard deviation of the filtered value within a 600s time window.

[0065] S3. The main control system of the floating wind turbine obtains real-time wind speed signals through the wind speed measurement equipment preset by the floating wind turbine, and dynamically calculates the protection threshold corresponding to the wave height characteristic value based on the real-time wind speed signal. According to the preset safety level of the floating wind turbine, the calculated protection thresholds are designed in a graded manner. The protection thresholds after the graded design include a first-level threshold and a second-level threshold, and may also include a third-level threshold. The first-level threshold corresponds to the power reduction operation trigger point, the second-level threshold corresponds to the further speed limit trigger point, and the third-level threshold corresponds to the safe shutdown trigger point.

[0066] Considering the strong physical coupling between wind speed and wave height in actual marine environments—generally, the higher the average wind speed, the greater the corresponding wave height—the protection threshold should not be set as a fixed constant but should be adaptively adjusted according to changes in environmental wind speed, i.e., a gain scheduling strategy should be adopted. In addition to the acceleration signal A, a real-time wind speed signal V also needs to be input. This real-time wind speed signal can be obtained through a nacelle-mounted anemometer, a nacelle-mounted LiDAR, or other wind speed measurement devices. The measured wind speed values ​​from these devices are transmitted to the main control system of the floating wind turbine via a communication interface. Therefore, the analog wind speed signal can be read in real-time from the main control system of the floating wind turbine via an industrial fieldbus or Ethernet communication protocol.

[0067] Specifically, the protection threshold corresponding to the wave height characteristic value can be dynamically calculated based on the real-time wind speed signal combined with a pre-calibrated wind speed-wave joint distribution model or an interpolation table fitted based on operational data. The dynamic calculation of the protection threshold under the real-time wind speed signal V based on the pre-calibrated wind speed-wave joint distribution model involves the following steps: The pre-calibrated wind speed-wave joint distribution model is completed based on the design load simulation. A common method is to simulate normal wave conditions (dlc1.2) and extreme wave conditions (dlc1.6) respectively, compare the differences in the distribution of wave height characteristic values ​​under different wind speeds, and obtain the pre-calibrated model. Furthermore, when the floating wind turbine is installed and undergoes grid connection commissioning, the unit's sensors can be used to collect the fluctuation of wave height characteristic values ​​under different wind speeds and wave conditions, compare them with the corresponding simulation data, and appropriately correct the pre-calibrated model.

[0068] In this embodiment, a protection threshold corresponding to the wave height feature value is adaptively generated based on auxiliary information such as environmental wind speed. The calculation of the protection threshold is shown in the following formula:

[0069] ;

[0070] ;

[0071] in, The threshold representing the boundary between low and medium wave levels in this embodiment, i.e., the first-level threshold, The threshold representing the dividing line between medium and high wave levels in this embodiment is called the secondary threshold, and the unit is m / s^2. This represents the average wind speed within a 600-second sliding time window. The wind speed is obtained through the standard API wind speed interface of the floating wind turbine design software.

[0072] In this embodiment, regarding and The design method is as follows:

[0073] By simulating the complete DLC1.2 and DLC1.6 operating conditions, the wave height characteristic values ​​under the two operating condition groups were obtained. Based on the distribution and the distribution situation, we can design... and The principle is: wave height characteristic value of dlc1.2 The scatter distribution should be lower than the corresponding wind speed values. Wave height eigenvalues ​​of dlc1.6 The scatter distribution should be lower than the corresponding wind speed values. And higher than and distance The distance should be far enough, while the distance It should be close enough. Distance The distance is far enough to ensure the robustness of power-limiting and speed-limiting triggering, while the distance... The distance is close enough to ensure the effectiveness of the shutdown protection. Based on this design principle, the wave height characteristic values ​​of dlc1.2 and dlc1.6 of this project were plotted. Scattered distribution can be used to design the corresponding and See Figure 3 As shown.

[0074] S4. Compare the wave height characteristic value with the corresponding protection threshold in real time. If the wave height characteristic value... Less than If the wave level is determined to be low, the floating wind turbine will continue to operate normally; if the wave height characteristic value is low... lie in[ , If the wave height is within the range of ), and it is determined to be of medium wave level, then the power limiting factor D=0.5 and the speed limiting factor E=0.8 are output to the main control system of the floating wind turbine; if the wave height characteristic value Greater than If the wave level is determined to be high, a shutdown command G is output to the main control system of the floating wind turbine. The values ​​of D and E are both in the range of (0,1).

[0075] S5. The protection decision is input into the main control system of the floating wind turbine, and the main control system controls the execution of the corresponding protection actions. When the power limiting factor D is received, the main control system limits the output power to D times the rated power by adjusting the pitch angle or generator torque; when the speed limiting factor E is received, the main control system limits the maximum speed of the wind turbine to no more than E times the rated speed; when the shutdown command G is received, the main control system executes feathering and braking operations according to the preset safe shutdown procedure to safely stop the floating wind turbine.

[0076] S6. Integrate the functions of steps S1 to S5 into the simulation controller, perform a complete simulation of the dlc1.6 design working condition, and check whether the load result is reduced to the load level of dlc1.2 obtained in step S3. If it is not satisfied, appropriately reduce the power limiting speed limiting coefficient in step S4 until the load of dlc1.6 meets the requirements. This completes the implementation process of this embodiment.

[0077] Example 2

[0078] See Figure 4 As shown, this embodiment discloses a floating wind turbine protection and control system based on wave height feature recognition, used to implement the above-mentioned floating wind turbine protection and control method based on wave height feature recognition, including:

[0079] The floating body motion measurement unit is used to measure the real-time acceleration signal of the floating body in the heave direction.

[0080] The data processing unit is used to extract wave height feature values ​​from basic motion sensing information; at the same time, it dynamically calculates the protection threshold corresponding to the wave height feature values ​​based on the wave height feature values ​​and the environmental information of the floating wind turbine.

[0081] The decision execution unit is used to compare the wave height characteristic value with the corresponding protection threshold in real time, determine the protection decision to be taken by the floating wind turbine based on the comparison result, and output the protection decision to the main control system.

[0082] The main control system is used to control the floating wind turbine to make protection decisions and to provide the data processing unit with environmental information about the floating wind turbine.

[0083] The output of the floating body motion measurement unit is communicatively connected to the input of the data processing unit; the output of the main control system is communicatively connected to the input of the data processing unit; the output of the data processing unit is communicatively connected to the input of the decision execution unit; and the output of the decision execution unit is communicatively connected to the input of the main control system.

[0084] Example 3

[0085] This embodiment discloses a non-transitory computer-readable medium storing instructions that, when executed by a processor, perform the steps of the floating wind turbine protection control method based on wave height feature recognition as described in Embodiment 1.

[0086] In this embodiment, the non-transitory computer-readable medium can be a disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), USB flash drive, portable hard drive, etc.

[0087] Example 4

[0088] This embodiment discloses a computing device, including a processor and a memory for storing processor-executable programs. When the processor executes the program stored in the memory, it implements the floating wind turbine protection and control method based on wave height feature recognition described in Embodiment 1.

[0089] The computing device described in this embodiment may be a desktop computer, laptop computer, smartphone, PDA handheld terminal, tablet computer, programmable logic controller (PLC), or other terminal device with processor function.

[0090] The above-described embodiments are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Therefore, any changes made in accordance with the shape and principle of the present invention should be covered within the protection scope of the present invention.

Claims

1. A floating wind turbine protection and control method based on wave height feature identification, characterized in that, Includes the following steps: S1. Obtain basic motion sensing information of the floating wind turbine; S2. Extract wave height feature values ​​from the basic motion sensing information obtained in step S1; S3. Obtain environmental information of the floating wind turbine, and dynamically calculate the protection threshold corresponding to the wave height characteristic value based on the wave height characteristic value and the environmental information. S4. Compare the wave height characteristic value with the corresponding protection threshold in real time, and determine the protection decision to be made by the floating wind turbine based on the comparison result; S5. Input the protection decision into the main control system of the floating wind turbine, and the main control system controls the execution of the corresponding protection actions.

2. The floating wind turbine protection and control method based on wave height feature recognition according to claim 1, characterized in that, Step S1 includes: At least one accelerometer is installed at the bottom of the tower of the floating wind turbine and at the transition section between the tower and the floating structure. The accelerometer measures the real-time acceleration signal of the floating wind turbine in the heave direction. The accelerometer is a low-frequency response sensor with an effective measurement frequency band of 0.05 Hz to 0.5 Hz to highlight the wave-dominated motion information.

3. The floating wind turbine protection and control method based on wave height feature recognition according to claim 2, characterized in that, Step S2 includes: The acceleration signal is bandpass filtered to retain the effective components within the main frequency band of the wave and filter out wind-induced high-frequency disturbances and sensor background noise. Then, the root mean square or standard deviation of the filtered acceleration signal is calculated within a preset time window and used as the wave height characteristic value.

4. The floating wind turbine protection and control method based on wave height feature recognition according to claim 3, characterized in that, Step S3 includes: The main control system of the floating wind turbine acquires real-time wind speed signals through the wind speed measurement equipment preset on the floating wind turbine, and dynamically calculates the protection threshold corresponding to the wave height characteristic value based on the real-time wind speed signal. According to the preset safety level of the floating wind turbine, the calculated protection thresholds are designed in a graded manner, and the protection thresholds after graded design include primary thresholds and secondary thresholds.

5. The floating wind turbine protection and control method based on wave height feature recognition according to claim 4, characterized in that, Step S4 includes: The wave height characteristic value is compared with the corresponding protection threshold in real time. If the wave height characteristic value is less than the first-level threshold, it is judged as a low wave level, and the floating wind turbine continues to operate normally. If the wave height characteristic value is within the range of [first-level threshold, second-level threshold], it is judged as a medium wave level, and the power limiting coefficient D and speed limiting coefficient E are output to the main control system of the floating wind turbine. If the wave height characteristic value is not less than the second-level threshold, it is judged as a high wave level, and the shutdown command G is output to the main control system of the floating wind turbine.

6. The floating wind turbine protection and control method based on wave height feature recognition according to claim 5, characterized in that, Step S5 includes: The protection decision is input into the main control system of the floating wind turbine, which then controls the execution of the corresponding protection actions. When the power limiting factor D is received, the main control system limits the output power to D times the rated power by adjusting the pitch angle or generator torque. When the speed limiting factor E is received, the main control system limits the maximum speed of the wind turbine to E times the rated speed. When the shutdown command G is received, the main control system executes feathering and braking operations according to the preset safe shutdown procedure, so that the floating wind turbine can be safely stopped.

7. A floating wind turbine protection and control system based on wave height feature recognition, characterized in that, The floating wind turbine protection and control method based on wave height feature identification as described in any one of claims 1-6 includes: The floating body motion measurement unit is used to measure the real-time acceleration signal of the floating body in the heave direction; The data processing unit is used to extract wave height feature values ​​from basic motion sensing information; at the same time, it dynamically calculates the protection threshold corresponding to the wave height feature values ​​based on the wave height feature values ​​and the environmental information of the floating wind turbine. The decision execution unit is used to compare the wave height characteristic value with the corresponding protection threshold in real time, determine the protection decision to be taken by the floating wind turbine based on the comparison result, and output the protection decision to the main control system. The main control system is used to control the floating wind turbine to make protection decisions and to provide the data processing unit with environmental information about the floating wind turbine.

8. The floating wind turbine protection and control system based on wave height feature recognition according to claim 7, characterized in that: The output of the floating body motion measurement unit is communicatively connected to the input of the data processing unit; the output of the main control system is communicatively connected to the input of the data processing unit; the output of the data processing unit is communicatively connected to the input of the decision execution unit; and the output of the decision execution unit is communicatively connected to the input of the main control system.

9. A non-transitory computer-readable medium storing instructions, characterized in that, When the instruction is executed by the processor, the steps of the floating wind turbine protection control method based on wave height feature recognition according to any one of claims 1-6 are performed.

10. A computing device, comprising a processor and a memory for storing a processor-executable program, characterized in that, When the processor executes the program stored in the memory, it implements the floating wind turbine protection and control method based on wave height feature recognition as described in any one of claims 1-6.

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