Intelligent vibration control system and control method for offshore wind turbine

Abstract

The invention discloses an intelligent vibration control system and control method for an offshore wind driven generator, and belongs to the technical field of vibration reduction control of wind driven generators, and the control system comprises a multi-field coupling modeling module, a control module, a self-adaptive control algorithm module and a digital twinborn integrated platform module. According to the method, a multi-field coupling modeling module is constructed, an adaptive tuned mass damper (ATMD) and a fuzzy PID-LQR hybrid controller are deployed, response prediction and parameter optimization are performed in combination with a physical information neural network (PINN), structural state visualization, remote control and strategy optimization are realized through a digital twinborn platform, and the method is suitable for large-scale popularization and application. The method achieves the sensing, modeling, control and feedback closed loop of the vibration of the offshore wind power structure, has the advantages of being high in modeling precision, fast in control response, high in adaptive capacity and the like, and is suitable for the anti-vibration operation requirements of an offshore wind turbine and a complex structure.

Description

Technical Field

[0001] The present invention belongs to the technical field of wind turbine vibration reduction control, and in particular relates to an intelligent vibration control system and a control method for an offshore wind turbine. Background Art

[0002] With the rapid development of deep-sea wind power, offshore wind turbines (hereinafter referred to as wind turbines) face a more complex coupled environment of wind, waves, and earthquakes. Vibration problems are becoming increasingly serious, seriously affecting the service life and operational stability of the structures. The gradient of wind speed in the vertical direction, the irregular impact of waves, and the strong and complex vibrations caused by earthquakes often cause the structures to produce multimodal, high-frequency, and nonlinear dynamic responses. Long-period excitation can also induce resonance, further amplifying the forces on the tower and foundation, bringing potential risks of structural fatigue and damage.

[0003] Existing wind turbine control technologies often rely on fixed-parameter or single-mode controllers, making them difficult to handle the uncertainties and rapid changes in loads in dynamic ocean environments. This poses a particular risk of control failure under extreme operating conditions, such as typhoons, strong waves, and major earthquakes. Furthermore, traditional structural response prediction methods are computationally expensive and lack adaptability, and efficient modeling methods for rapid regulation and control feedback have yet to be developed.

[0004] In addition, the large-scale deployment of wind farms places higher demands on intelligent operation and maintenance. However, current control systems generally lack the capabilities of remote diagnosis, strategy self-adjustment, and real-time mapping of structural status, making it difficult to meet the operation and maintenance trends of smart wind farms: "active perception - intelligent response - remote optimization."

[0005] Therefore, there is an urgent need to develop an offshore wind turbine vibration control system that integrates multi-physics modeling, adaptive control and intelligent integration platform to achieve the perception, prediction and active regulation of structural responses under complex load conditions, and improve the operational resilience and service life of offshore wind power equipment. Summary of the Invention

[0006] The present invention discloses an intelligent vibration control system and control method for offshore wind turbines. The system and method integrate multi-source excitation modeling, adaptive control algorithm and digital twin system, and have functions such as rapid prediction of structural response, self-adjustment of control device, remote monitoring and optimization, which significantly improve the vibration control capability and operational resilience of offshore wind turbines in complex environments.

[0007] To achieve the above object, the technical solution of the present invention is:

[0008] An intelligent vibration control system for offshore wind turbines, comprising: a multi-field coupling modeling module, a control module, an adaptive control algorithm module, and a digital twin integration platform module;

[0009] The multi-field coupling modeling module is used to construct a multi-field coupling dynamic model of offshore wind turbines under the coupled effects of wind, waves, and earthquakes to evaluate the impact of environmental load changes on structural vibration and provide input for the control module;

[0010] The control module is used to achieve multi-objective coordinated control of offshore wind turbine vibration reduction under extreme typhoon, resonance and major earthquake conditions;

[0011] The adaptive control algorithm module adaptively updates the control parameters based on real-time monitoring feedback data to ensure the stability and responsiveness of the system under different sea conditions;

[0012] The digital twin integration platform module establishes a data interaction channel between the physical wind turbine and the digital twin model through real-time data mapping and dynamic simulation.

[0013] Preferably, the multi-field coupling modeling module uses the Kaimal wind speed spectrum to describe the wind shear characteristics, adopts the Pierson-Moskowitz wave spectrum to represent the random wave energy distribution, and adopts the improved Kanai-Tajimi seismic motion spectrum to represent the earthquake characteristics. It comprehensively considers the spatiotemporal evolution characteristics and correlation of the three-field excitation of wind-wave-earthquake, comprehensively analyzes the probability distribution characteristics of the environmental load through the Copula function, and outputs the multi-modal dynamic response simulation data of the wind turbine structure based on the stochastic differential equation.

[0014] Preferably, the control module includes: integrating an adaptive tuned mass damper at the tower top or key stress-bearing position of the wind turbine, the adaptive tuned mass damper is equipped with a vibration energy harvesting module and has self-power supply capability, and based on Lyapunov stability theory, a fuzzy PID-LQR hybrid controller is designed to optimize the control of the adaptive tuned mass damper, and a multi-objective genetic algorithm is used to optimize the control parameters to achieve multi-objective coordinated control under extreme typhoon, resonance, and major earthquake conditions.

[0015] Preferably, the adaptive control algorithm module includes: introducing a physical information deep neural network, integrating traditional control theory and deep learning methods, and realizing real-time prediction and analysis of complex wind-wave-earthquake excitations and structural dynamic responses under the constraints of physical laws.

[0016] Preferably, the digital twin integrated platform module is used to build a real-time virtual mapping system for structure-controller-environment interaction, integrating the sensor network and edge computing method arranged on the wind turbine, and establishing a data interaction channel between the physical wind turbine and the digital twin model through real-time data mapping and dynamic simulation, thereby realizing real-time monitoring of structural response, simulation optimization of control strategy, remote diagnosis and active control decision-making.

[0017] A control method for an intelligent vibration control system of an offshore wind turbine includes the following steps: (1) collecting environmental excitation and structural response data based on a sensor network; (2) simulating structural response through a multi-field coupling modeling module; (3) predicting structural state and optimizing control parameters using an adaptive control algorithm module; (4) performing active control through control instructions by a control module; and (5) completing remote optimization and control feedback closed loop through a digital twin integration platform module.

[0018] The beneficial effects of the intelligent vibration control system and control method of an offshore wind turbine of the present invention are as follows:

[0019] 1. Multi-physics field joint modeling: Accurately describe the dynamic characteristics of complex marine environmental loads and structural responses, and improve vibration prediction accuracy.

[0020] 2. Adaptive active control: The use of self-powered active control devices and advanced control algorithms significantly improves the adaptability and responsiveness of the control strategy.

[0021] 3. Intelligent operation and maintenance management: Digital twin technology is combined with edge computing to achieve real-time status monitoring, remote diagnosis and dynamic optimization of control strategies.

[0022] 4. Strong generalization ability: It is applicable to different working conditions and different types of offshore wind turbine structures, and has broad prospects for engineering promotion. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 : The present invention has a working principle flow chart from input to output;

[0024] Figure 2 : Flowchart of wind-wave-seismic joint modeling, including load spectrum selection, computational fluid dynamics (CFD) modeling and joint distribution simulation structure.

[0025] Figure 3 : Control strategy structure diagram, showing the coupling structure of ATMD and LQR fuzzy control and the simulation control effect results.

[0026] Figure 4 : PINN-assisted adaptive control framework diagram, showing the data flow and parameter adjustment logic.

[0027] Figure 5 : The integrated structure diagram of digital twin and control platform, illustrating the simulation model-sensor data-remote control closed loop. DETAILED DESCRIPTION

[0028] The following description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

[0029] The following embodiments may be understood as individually expressing a part of a local structure or method of the present invention, or may be understood as a combination of the embodiments to explain the connotation of a larger structure or method of the present invention.

[0030] Example 1

[0031] An intelligent vibration control system for offshore wind turbines, such as Figure 1-5 As shown, it includes: multi-field coupling modeling module, control module, adaptive control algorithm module, and digital twin integration platform module;

[0032] The multi-field coupling modeling module is used to construct a multi-field coupling dynamic model of offshore wind turbines under the coupled effects of wind, waves, and earthquakes, breaking through the limitations of traditional single load assumptions to evaluate the impact of environmental load changes on structural vibration and provide input for the control module;

[0033] The control module is used to achieve multi-objective coordinated control of offshore wind turbine vibration reduction under extreme typhoon, resonance and major earthquake conditions;

[0034] The adaptive control algorithm module adaptively updates the control parameters based on real-time monitoring feedback data to ensure the stability and responsiveness of the system under different sea conditions;

[0035] The digital twin integration platform module establishes a data interaction channel between the physical wind turbine and the digital twin model through real-time data mapping and dynamic simulation.

[0036] Example 2

[0037] Based on Example 1, this embodiment discloses: Figure 2 As shown, the multi-field coupling modeling module uses the Kaimal wind speed spectrum to describe the wind shear characteristics, adopts the Pierson-Moskowitz wave spectrum to represent the random wave energy distribution, and adopts the improved Kanai-Tajimi seismic motion spectrum to represent the earthquake characteristics. It comprehensively considers the spatiotemporal evolution characteristics and correlation of the three-field excitation of wind-wave-earthquake, comprehensively analyzes the probability distribution characteristics of the environmental load through the Copula function, and outputs the multi-modal dynamic response simulation data of the wind turbine structure based on the stochastic differential equation.

[0038] Example 3

[0039] Based on Example 1, this embodiment discloses: Figure 3As shown, the control module includes: an adaptive tuned mass damper (ATMD) integrated at the top of the wind turbine tower or key load-bearing location (the area with the greatest change in shear and bending moment on the tower) for active or semi-active control strategies suitable for offshore wind turbines. The ATMD is equipped with a vibration energy harvesting module and has self-powered capability. It uses the self-powered principle to convert structural vibration energy into control energy, improving the independence and sustainability of the system. Based on Lyapunov stability theory, a fuzzy PID-LQR hybrid controller is designed, and the control parameters are optimized using a multi-objective genetic algorithm (NSGA-II) to optimize the control of the ATMD, achieving multi-objective coordinated control under extreme typhoon, resonance, and major earthquake conditions. The main power supply source of the ATMD control system of the present invention is the electricity generated by the offshore wind turbine itself to ensure the stable operation of the controller and actuator. On this basis, an energy harvesting module is integrated inside the ATMD device to collect mechanical energy from structural vibration (such as through piezoelectric elements). After processing by the energy management circuit, it provides backup power to the controller when the main power supply fails, thereby maintaining basic monitoring and emergency vibration reduction functions in a low-power state.

[0040] Example 4

[0041] Based on Example 1, this embodiment discloses: Figure 4 As shown, the adaptive control algorithm module includes: To enhance the control system's adaptability to uncertain offshore environments, a physics-informed neural network (PINN) is introduced. This integrates traditional control theory with deep learning methods to achieve real-time prediction and analysis of complex wind-wave-seismic excitations and structural dynamic responses under the constraints of physical laws. The adaptive control algorithm module constructs a PINN model whose inputs are wind speed, waves, earthquakes, structural acceleration, and sensor data. The output is a predicted value for the structure's future response and the optimal controller parameters. This model is trained and updated online to enhance the controller's adaptability to vibration responses in complex marine environments.

[0042] Example 5

[0043] Based on Example 1, this embodiment discloses: Figure 5As shown, the digital twin integrated platform module is used to build a real-time virtual mapping system for structure-controller-environment interaction. It integrates a sensor network deployed on the wind turbine with edge computing methods. Through real-time data mapping and dynamic simulation, a data exchange channel is established between the physical wind turbine and the digital twin model, enabling real-time monitoring of structural response, control strategy simulation optimization, remote diagnosis, and proactive control decision-making. The sensor network consists of three layers of sensors deployed on the wind turbine's tower, tower, and foundation. These sensors include accelerometers, strain gauges, displacement gauges, and environmental wind, wave, and seismic sensors. The sensor network integrates a LoRa wireless network with edge computing nodes, which perform real-time fusion and preprocessing of vibration and environmental data. The integration and functional deployment of the digital twin integrated platform module builds a digital twin platform consisting of a virtual wind turbine modeling module, a structural state mapping module, and a control strategy simulation module. The platform receives sensor network data in real time, drives the virtual model, and simulates structural response under different control strategies, assisting in remote decision-making and optimization.

[0044] Example 6

[0045] Based on Examples 1-5, this embodiment discloses:

[0046] A control method for an intelligent vibration control system of an offshore wind turbine, such as Figure 1-5 As shown in the figure, the following steps are included: (1) collecting environmental excitation and structural response data based on the sensor network; (2) simulating structural response through the multi-field coupling modeling module; (3) predicting the structural state and optimizing the control parameters using the adaptive control algorithm module; (4) the control module performs active control through control instructions; (5) completing the remote optimization and control feedback loop through the digital twin integration platform module.

[0047] The simulation verification and working condition evaluation of the present invention are based on a multi-field coupling modeling module. 10,000 sets of structural vibration simulations under different wind-wave and earthquake combinations are carried out to evaluate the vibration suppression effect, energy consumption and stability of the control module under different working conditions (normal operation, strong winds, and long-period seismic motions) and verify the robustness of the system.

[0048] The closed-loop operation process and application demonstration of this invention deploys a complete system on a prototype wind turbine tower, implementing a closed-loop process of data acquisition, predictive analysis, control optimization, and execution feedback. The system collects real-time data through sensors, dynamically adjusts the control strategy after edge processing and PINN analysis, and implements vibration reduction control in the control module. The digital twin platform displays wind turbine status in real time and records historical control data, providing support for system maintenance and parameter adjustment.

[0049] Working principle of the present invention:

[0050] This invention is based on the five-in-one intelligent control closed-loop principle of "perception-modeling-prediction-control-feedback". Through the collaboration of multiple modules, it can achieve real-time monitoring, prediction and active control of offshore wind turbine structural vibration in complex marine environments. The system workflow is as follows:

[0051] First, the sensor network is deployed in the key parts of the wind turbine structure (the area where the shear force and bending moment of the tower change the most) to collect structural responses (such as acceleration, displacement, strain) and environmental stimulus information (such as wind speed, waves, and earthquakes) in real time. The data is transmitted to the digital twin integration platform module via wireless communication.

[0052] During the modeling phase, a wind-wave-seismic excitation model was constructed by combining Kaimal wind speed spectrum, PM wave spectrum, and improved Kanai-Tajimi seismic spectrum simulations. This was combined with structural finite element analysis to generate nonlinear coupled dynamic response simulation data. Furthermore, an intelligent prediction model combining PINN and DNN was employed to achieve online prediction of structural response trends and assist in determining the optimal control strategy.

[0053] The control decision module utilizes a fuzzy PID-LQR hybrid controller based on Lyapunov stability theory, dynamically adjusting control parameters based on simulation feedback and real-time prediction results. The control device is a self-powered ATMD, which effectively reduces the dynamic response of the tower structure through control force output.

[0054] Ultimately, all monitoring data and control behaviors form a virtual mapping of structure, control, and environment in the digital twin integrated platform module. The platform is used for control strategy simulation verification, remote status visualization, and intelligent diagnosis to achieve vibration suppression and operation optimization throughout the life cycle of the wind turbine.

[0055] Application prospects of the present invention:

[0056] This invention is aimed at meeting the demand for reliable operation of offshore wind turbines, has good technological advancement and broad engineering promotion potential, and its application prospects are mainly reflected in the following aspects:

[0057] 1. Applicable to the vibration-resistant design of deep-sea wind farm structures: As wind farms move into deeper waters, wind turbine structural vibration becomes increasingly prominent. This invention can provide customized intelligent control solutions for high-tower, large-capacity wind turbines, significantly improving the structural safety and service life.

[0058] 2. Can be integrated into the construction of smart wind farms: This invention is highly compatible with digital twin platforms and edge computing architectures, supports intelligent monitoring, remote control, and adaptive optimization of wind farms, and is the key technical foundation for realizing a smart wind power operation and maintenance system.

[0059] 3. The invention can be extended to other marine or high-rise structure scenarios, such as offshore platforms, port pile foundation structures, marine photovoltaic foundations, long-span bridge towers, and other complex engineering structures with multi-source excitation characteristics. The intelligent vibration control framework proposed in this invention can also be applied after appropriate modification.

[0060] The present invention has significant economic and environmental benefits: by reducing structural fatigue damage, reducing the frequency of operation and maintenance, and extending the life of wind turbines, the present invention helps to reduce the cost of wind power per kilowatt-hour, improve the efficiency of clean energy utilization, and has good green and low-carbon demonstration value.

[0061] In summary, the present invention has comprehensive advantages such as advanced structural mechanism, intelligent control algorithm, flexible platform integration, and strong engineering adaptability. It has broad application prospects and promotion value in the new generation of high-performance, high-intelligence, and sustainable offshore wind power systems.

Claims

1. An intelligent vibration control system for offshore wind turbines, characterized by: include: Multi-field coupling modeling module, control module, adaptive control algorithm module, and digital twin integration platform module; The multi-field coupling modeling module is used to construct a multi-field coupling dynamic model of offshore wind turbines under the coupled effects of wind, waves, and earthquakes to evaluate the impact of environmental load changes on structural vibration and provide input for the control module; The control module is used to achieve multi-objective coordinated control of offshore wind turbine vibration reduction under extreme typhoon, resonance and major earthquake conditions; The adaptive control algorithm module adaptively updates the control parameters based on real-time monitoring feedback data to ensure the stability and responsiveness of the system under different sea conditions; The digital twin integration platform module establishes a data interaction channel between the physical wind turbine and the digital twin model through real-time data mapping and dynamic simulation.

2. The intelligent vibration control system for offshore wind turbines according to claim 1, characterized in that: The multi-field coupling modeling module uses the Kaimal wind speed spectrum to describe wind shear characteristics, the Pierson-Moskowitz wave spectrum to represent random wave energy distribution, and the improved Kanai-Tajimi seismic motion spectrum to represent earthquake characteristics. It comprehensively considers the spatiotemporal evolution characteristics and correlation of the wind-wave-earthquake three-field excitations, comprehensively analyzes the probability distribution characteristics of environmental loads through Copula functions, and outputs multimodal dynamic response simulation data of the wind turbine structure based on stochastic differential equations.

3. The intelligent vibration control system for offshore wind turbines according to claim 2, characterized in that: The control module includes: integrating an adaptive tuned mass damper at the tower top or key stress-bearing position of the wind turbine; the adaptive tuned mass damper is equipped with a vibration energy harvesting module and has self-power supply capability; based on Lyapunov stability theory, a fuzzy PID-LQR hybrid controller is designed to optimize the control of the adaptive tuned mass damper; and a multi-objective genetic algorithm is used to optimize the control parameters to achieve multi-objective coordinated control under extreme typhoon, resonance, and major earthquake conditions.

4. The intelligent vibration control system for offshore wind turbines according to claim 3, characterized in that: The adaptive control algorithm module includes: introducing a physical information deep neural network, integrating traditional control theory with deep learning methods, and realizing real-time prediction and analysis of complex wind-wave-earthquake excitations and structural dynamic responses under the constraints of physical laws.

5. The intelligent vibration control system for offshore wind turbines according to claim 4, characterized in that: The digital twin integration platform module is used to build a real-time virtual mapping system for structure-controller-environment interaction, integrating the sensor network and edge computing method deployed on the wind turbine. Through real-time data mapping and dynamic simulation, a data interaction channel is established between the physical wind turbine and the digital twin model, realizing real-time monitoring of structural response, simulation optimization of control strategy, remote diagnosis and active control decision-making.

6. A control method for an intelligent vibration control system of an offshore wind turbine as described in claim 5, comprising the following steps: (1) collecting environmental excitation and structural response data based on a sensor network; (2) simulating structural response through a multi-field coupling modeling module; (3) predicting structural state and optimizing control parameters using an adaptive control algorithm module; (4) performing active control through control instructions by a control module; and (5) completing remote optimization and control feedback closed loop through a digital twin integrated platform module.

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