Intelligent control type floating fan system oriented to extreme sea conditions

Abstract

The invention discloses an intelligent control type floating fan system for extreme sea conditions. The system is composed of a sensing layer, a risk identification layer, a control decision layer, an adaptive learning module and a structure response layer. The sensing layer obtains wind speed, wave height, attitude inclination angle and anchoring tension, and calculates disturbance indexes; the risk identification layer generates a risk index by using a Sigmoid function; the control decision-making layer outputs connecting rod compression, air bag release and thruster thrust instructions according to the control decision-making layer; and the adaptive learning updates the weight according to the action deviation. The structure response layer comprises a carbon fiber adjustable connecting rod, a multi-cavity air bag provided with an air storage tank and a high-speed valve, a multi-direction vector propeller and a modularized interlocking floating body. According to the system, rapid risk identification and linkage control are realized, and the steady state and risk avoiding capability of the platform are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of offshore wind power, in particular to an intelligent control type floating wind turbine system for extreme sea conditions. BACKGROUND

[0002] With the continuous development of offshore wind power industry, its application range is gradually extending from the near-shore shallow water area to the deep sea complex sea area. Due to the limitation of water depth and geological conditions, the traditional fixed wind turbine platform is difficult to be applied in the deep sea environment, so the floating wind turbine gradually becomes an important development direction.

[0003] The existing floating platform structure forms include semi-submersible, tension leg and cylinder platforms, etc. Although these platforms have certain adaptability, they generally have the following shortcomings: first, the overall structure is relatively rigid, and the anti-disturbance ability is insufficient when encountering storms, extreme swell and other sea conditions; second, the response to sudden extreme conditions is lagging, and it is difficult to take effective measures in time. This can easily lead to platform attitude instability, abnormal anchoring cable stress and even breakage, and further cause serious safety hazards such as drifting, platform damage or wind turbine falling off.

[0004] At present, the research on platform safety mainly focuses on the optimization of structural configuration, such as adjusting the connecting rod or changing the platform moment of inertia to improve the response performance. However, this kind of method is mostly passive, lacking active identification and intelligent response ability to extreme environment. In actual operation, the platform still needs to rely on manual intervention or fixed threshold control mode, and cannot realize real-time, closed-loop dynamic control. Especially in key links such as autonomous disconnection for danger avoidance, platform rapid compression adjustment and emergency navigation, the existing technology has not formed a complete systematic solution.

[0005] Therefore, how to construct a kind of active perception of environmental state, identification of risk level, and based on the identification result linkage driving structure execution unit, so as to realize the self-regulation and danger avoidance of platform, becomes the technical problem that needs to be solved for floating wind turbine in deep sea complex environment. SUMMARY

[0006] To solve the above technical problems, the present application provides an intelligent control type floating wind turbine system for extreme sea conditions, comprising a perception layer, a risk identification layer, a control decision layer, a self-adaptive learning module and a structural response layer. The perception layer is used to obtain wind speed, wave height, attitude inclination angle and anchoring tension signal through multiple sensors, combine to form disturbance input vector, and obtain disturbance index through weighted sum of weight factor; The risk identification layer is used to input the disturbance index into the continuous function of Sigmoid type, and output the risk level index; a control decision layer configured to generate execution instructions including a connecting rod compression amount instruction, an airbag release intensity instruction, and a thruster thrust ratio instruction according to the risk level index; an adaptive learning module configured to adjust weight factors in the perturbation input vector based on an action deviation evaluation function; a structure response layer including a flexible adjustable connecting rod, a multi-cavity high-expansion composite membrane airbag and its matching gas tank and high-speed valve, an electric multi-directional vector underwater thruster, and a modular topological interlocking float unit; The flexible adjustable connecting rod is made of carbon fiber composite material and is designed with a ball head structure and a limiting groove to realize axial adjustment and multi-axis angle deflection. The multi-cavity high-expansion composite membrane airbag and its matching gas tank and high-speed valve are used to release buoyancy within seconds. The electric multi-directional vector underwater thruster is used to assist in attitude control and autonomous navigation of the platform. The modular topological interlocking float unit is used to realize splicing expansion and dynamic deformation.

[0007] Preferably, the control decision layer generates the execution instructions to satisfy the following timing constraints: the risk judgment delay is not more than 1.2 seconds, the connecting rod compression and airbag release are completed within 3 to 5 seconds, and the thruster response delay is not more than 800 milliseconds.

[0008] Preferably, the risk level index is expressed as: ; wherein, wherein is a set threshold value, is the steepness of the control function, represents the perturbation index.

[0009] Preferably, the adaptive learning module is expressed as: ; wherein, is an action deviation evaluation function; is a learning rate; represents the weight factor before updating; represents the weight factor after updating.

[0010] Preferably, the action deviation evaluation function is based on attitude deviation or energy consumption.

[0011] The beneficial effects of the present application at least include: 1) The accuracy and real-time performance of risk identification are improved: the present application fuses information such as wind speed, wave height, attitude and anchoring tension through multi-dimensional sensors, realizes smooth determination of risk level by using a continuous function of Sigmoid type, avoids the hysteresis and misjudgment caused by the traditional hard threshold method, and enables the platform to identify risks in time and trigger responses before extreme sea conditions arrive.

[0012] 2) The linkage control response speed is fast and the action is complete: the present application establishes a multi-stage linkage mechanism driven by a risk index, which can complete the compression of the connecting rod, the release of the air bag and the action of the propeller within a few seconds, the overall linkage response time is not more than 6 seconds, forming a continuous action flow, which effectively improves the attitude stability and risk avoidance ability of the platform in extreme disturbance environment.

[0013] 3) Self-adaptive learning improves long-term adaptability of the system: by introducing a self-adaptive learning mechanism, the control system can continuously adjust the disturbance weight and control parameters according to the historical disturbance and action deviation, avoiding response delay or excessive action caused by parameter fixation, thereby reducing energy consumption while ensuring safety, and improving the flexibility and energy efficiency of the platform operation.

[0014] 4) Deep coupling of structure and control strategy: the platform structure adopts carbon fiber composite connecting rod, limit groove-ball head connection method, multi-cavity high-speed release air bag and vector propeller, which are matched with the control algorithm, ensuring lightweight, high strength and fast response capability, and realizing the complete risk avoidance path of active compression, anchor release and navigation transfer.

[0015] 5) Wide application range and strong expansibility: the modular topology interlocking floating body design makes the platform have good expansibility and on-site assembly convenience, which is suitable for offshore light-load wind power platforms and has the potential to expand to deep sea complex environment, providing a technical path for the intelligent upgrading of subsequent floating wind turbines. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 is the component structure schematic diagram of the system in the first angle of the present application; Figure 2 is the component structure schematic diagram of the system in the second angle of the present application; Figure 3 is the component structure schematic diagram of the system in the third angle of the present application; Figure 4 is the structure schematic diagram of the triangular arrangement of the support column of the floating wind turbine platform of the present application; Figure 5 is the limit groove and ball head connection structure schematic diagram of the flexible adjustable connecting rod of the present application; Figure 6 is the linkage response flowchart of the floating wind turbine platform under extreme sea conditions of the present application; Figure 7The function module structure diagram of the intelligent control system of the floating wind turbine platform of the present application; Figure 8 The schematic diagram of the mapping relationship between the mathematical control model and the platform response structure of the present application. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the present application.

[0018] To overcome the problems of strong structural rigidity, response lag and weak intelligent control capability of the existing floating wind turbine, the present application proposes an intelligent control type floating wind turbine system for extreme sea conditions. The system integrates multi-source sensing perception, environmental state judgment, autonomous control decision and structural execution response functions, aiming to realize rapid identification and multi-level control linkage of complex sea conditions, so as to improve the operation stability and survival ability of the wind turbine platform in extreme environment.

[0019] The present application first constructs a real-time perception system of environmental state by integrating wind speed, wave height, acceleration, attitude and other multi-dimensional sensors, uses a fusion modeling algorithm to dynamically identify and judge the risk level of the sea conditions in which the platform is located, and avoids misjudgment and response delay caused by a single indicator. After identifying the extreme state, the system can immediately trigger the internal response mechanism to drive the adjustment of the connecting structure and the rapid convergence of the structural form. Especially in the case of severe wave excitation of the platform or excessive tension of the anchor chain, the system can compress the length of the connecting rod through hydraulic means, simultaneously activate the internal flexible air bag of the platform to release the buoyancy, and link the propeller for attitude adjustment and navigation instruction execution, so as to realize the rapid compression of the platform structure, the active disconnection of the anchor chain and the autonomous transfer of the platform to the safe water area.

[0020] In terms of control strategy, the present application constructs an intelligent decision system with adaptive and closed-loop control capability, realizes a complete logical closed loop from disturbance identification, response decision, action execution to state feedback, can dynamically adjust the threshold and response strategy in the variable sea condition environment, and ensures the continuity and robustness of the platform operation. At the same time, in order to make the control strategy have a physical execution basis, the present application designs a floating foundation structure with flexible connection capability and topological modularization characteristics. The platform modules realize adaptive cooperation through adjustable connecting pieces and limiting mechanisms, and the support system has dynamic deformation capability while ensuring the stability of the structure, which can fully respond to the linkage demand of the control strategy.

[0021] Through the above-mentioned collaborative construction of multiple systems, the application establishes a complete intelligent chain of "recognition -> control -> response", realizes the whole-process closed-loop control mechanism of the platform from active recognition, rapid structural response to autonomous risk avoidance and recovery of extreme sea conditions, and is suitable for future deep sea wind farms, floating operation platforms and marine intelligent systems and other scenes, thereby providing an innovative solution path for enhancing the disaster resistance and operation safety of the floating wind turbine system.

[0022] Specifically, as shown in the accompanying drawings, Figures 1 to 8 the embodiment of the application provides an intelligent control type floating wind turbine system for extreme sea conditions.

[0023] The intelligent control type floating wind turbine platform system of the application is composed of a perception layer, a risk identification layer, a control decision layer and a structural response layer, and the whole is driven by a control algorithm as the core, and the structure is taken as an execution carrier, so that a closed-loop control system with real-time risk avoidance and steady-state regulation capability is formed through dynamic data fusion, risk identification, linkage response and adaptive feedback.

[0024] During the system operation, the platform obtains key disturbance information in real time through multiple types of sensors deployed, including wind speed , wave height , attitude inclination and anchoring tension . The perception layer combines various signals into a disturbance input vector: ; In order to avoid the unequal weight influence of different signals on the response strategy, the system constructs a weighted disturbance index: ; Wherein is the weight factor of each disturbance quantity, n represents the number of indexes, represents the i-th value in the disturbance input vector. The index measures the load intensity of the environment in which the platform is located, and is the direct input for subsequent risk judgment.

[0025] In the risk assessment stage, the system adopts a continuous function of Sigmoid type to dynamically normalize the disturbance index, and outputs a risk level index: ; Wherein is a set threshold, is the steepness of the control function. Compared with the traditional hard threshold judgment, the model has higher response smoothness and robustness, and can adapt to the fuzzy boundary of the transition from moderate disturbance to extreme state.

[0026] The control strategy is determined according to the risk level index , triggering a multi-stage response mechanism. The platform execution layer includes three control objects: connecting rods, flexible airbags, and propulsion systems. The corresponding action instructions are generated by the following functions: wherein, , and represent the action instructions of the connecting rods, flexible airbags, and propulsion systems, respectively; , and represent the action instruction coefficients of the connecting rods, flexible airbags, and propulsion systems, respectively.

[0027] Each action amount controls the compression degree of the connecting rods, the release intensity of the airbags, and the thrust ratio of the thrusters. The system has a clear timing management logic for these execution actions. Through simulation verification, the response determination delay of the platform to extreme disturbance events is controlled within 1.2 seconds, the structure compression and airbag release can be completed within 3-5 seconds, and the execution delay of the thruster response command does not exceed 800 milliseconds. The total time for completing a round of response is within 6 seconds.

[0028] The control system has a built-in adaptive learning mechanism that dynamically adjusts the disturbance weight parameters according to the actual response effect during operation. The update rule is as follows: ; wherein is the action deviation evaluation function based on attitude deviation or energy consumption; is the learning rate. This mechanism ensures that control parameters can evolve with changes in external conditions and platform performance, improving the system's adaptability in long-term operation.

[0029] In terms of physical structure execution layer, the platform is composed of upper fan structure module and lower floating body module, which realizes structure compression, attitude adjustment and disturbance release through flexible adjustable connecting rods. The connecting rods are made of high modulus carbon fiber composite materials, which have the characteristics of light weight, high strength and excellent fatigue resistance. Combined with the design of ball head structure and limiting groove, it ensures that it can provide axial adjustment function while allowing a certain range of multi-axis angle deflection. Each structure module is connected in a topological interlocking form, which has high modular expansion and on-site assembly convenience. The airbag release device is arranged at the lower edge of the floating body, which adopts a multi-cavity high-expansion composite membrane structure, matches a redundant gas tank and a high-speed gas valve, and can release a high-float gas bag within a few seconds. The propulsion device adopts an electric multi-directional vector underwater thruster, which has the functions of heading stabilization control and high turning response capability, and can assist the platform in rapid off-site transfer.

[0030] The control strategy adopted by the present application does not rely on platform lifting, leg guiding or anchoring structure to adjust the platform response state, but through "risk identification-action generation-structure linkage-autonomous navigation" to build a complete active control loop. The platform does not need fixed legs or lifting guide rail system, and can actively compress the structure shape, disconnect the anchor cable connection, release the auxiliary buoyancy and start the propulsion system to avoid danger under the condition of severe sea state change.

[0031] The control logic is completely driven by the embedded algorithm system, covering the whole process of disturbance identification, control instruction calculation, response action execution and feedback correction, with high algorithm reconfigurability and environmental adaptability. Compared with the traditional design method relying on mechanical lifting device, the system shows significant advantages in structural freedom, response rate and deployment flexibility, especially suitable for light-load wind power platform or non-fixed anchoring application scenarios, providing a more intelligent and autonomous control path for subsequent deep sea wind power platform.

[0032] The present application designs around the intelligent response ability of the floating wind turbine system under extreme sea conditions. The system realizes active identification of sea condition risk, rapid regulation of structure state and dynamic evolution of risk avoidance strategy through a complete set of control algorithm logic such as multi-source perception, fuzzy reasoning, linkage control and adaptive learning, breaking the technical bottleneck of traditional floating platform relying on rigid structure and static configuration for passive disturbance resistance.

[0033] The control algorithm, as the core of the system, has four capabilities: environmental state identification, risk level determination, response chain triggering and self-learning correction. During sea condition monitoring, the system can fuse wind speed, wave height, attitude and anchor chain tension and other multi-dimensional data to accurately identify the risk level of the platform, and automatically allocate connection rod compression, air bag release, anchor chain disconnection, propulsion start and other control actions according to the risk level, forming a complete linkage response path. Compared with the existing technology relying on mechanical structure manual switching or single action strategy mode, the present system realizes continuous action flow response under algorithm driving, effectively improving the stability and self-avoiding ability of the platform in extreme disturbance.

[0034] In addition, the system also has a built-in self-learning mechanism, which can adjust the parameter weight in real time according to the historical sea condition disturbance and platform response effect, realizing the dual optimization of risk identification accuracy and response rationality. This learning mechanism enhances the adaptability of the platform to unknown disturbance conditions, avoids response delay or excessive action caused by parameter fixation, and thus improves the system energy efficiency and control flexibility while ensuring safety.

[0035] The technical advantages of the present application are not limited to the change of the structural form, but in that a dynamic perception, intelligent judgment, autonomous decision and flexible response integrated intelligent control platform is constructed, the steady state control and emergency response ability of the platform in the complex environment at sea is improved, and the intelligent upgrading demand of the offshore light load platform is particularly suitable. On the basis of continuous optimization of structure and algorithm, the system also has the technical potential to expand to the high-risk environment of the middle and far sea, provides an important implementation path and model reference for the subsequent deep sea wind power platform. Compared with the traditional floating platform risk avoidance method taking the structure lifting as the core, the present application provides an intelligent control path with more flexibility and initiative. The platform no longer relies on heavy pile legs or fixed lifting system when the sea conditions change dramatically, but realizes the anchor compression, directional separation and track control through the adjustable structural components and propulsion system, and builds a complete dynamic closed loop between the algorithm judgment and action execution. In this way, the platform has stronger posture recovery ability, faster response speed and higher system survivability, is suitable for the light load platform application scene lacking fixed anchoring conditions at present, and lays a foundation for the floating system with higher degree of freedom and stronger autonomous ability in the future far sea environment.

[0036] The technical features of the above embodiments can be combined in any manner. To make the description concise, all possible combinations of the technical features in the above embodiments are not described, only the preferred embodiments of the present application are expressed, and the description is more specific and detailed, but it should not be understood as limiting the scope of the present application. As long as the combination of these technical features does not exist, it should be considered as the scope of the present application.

[0037] It should be noted that for those skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are within the scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. An intelligent control oriented floating wind turbine system for extreme sea conditions, characterized by: The application relates to a control system for a floating platform, comprising a perception layer, a risk identification layer, a control decision layer, an adaptive learning module and a structural response layer. The perception layer is used for acquiring wind speed, wave height, attitude inclination and anchoring tension signals through multiple sensors, combining to form a disturbance input vector, and performing weighted summation through a weight factor to obtain a disturbance index. The risk identification layer is used for inputting the disturbance index into a continuous function of Sigmoid type to output a risk level index. The control decision layer is used for generating an execution instruction according to the risk level index, wherein the execution instruction comprises a connecting rod compression amount instruction, an airbag release intensity instruction and a thruster thrust ratio instruction. The adaptive learning module is used for adjusting the weight factor in the disturbance input vector based on an action deviation evaluation function. The structural response layer comprises a flexible adjustable connecting rod, a multi-cavity high-expansion composite membrane airbag and a matching gas tank and high-speed valve, an electric multi-directional vector underwater thruster and a modular topological interlocking float unit. The flexible adjustable connecting rod is made of carbon fiber composite material and is combined with a ball head structure and a limiting groove design, and is used for realizing axial adjustment and multi-axis angle deflection. The multi-cavity high-expansion composite membrane airbag and the matching gas tank and high-speed valve are used for releasing buoyancy within several seconds. The electric multi-directional vector underwater thruster is used for assisting attitude control and autonomous navigation of the platform. The modular topological interlocking float unit is used for realizing splicing expansion and dynamic deformation.

2. A smart control oriented floating wind turbine system for extreme sea conditions according to claim 1, characterized in that: The control decision layer satisfies the following timing constraints for the generation of the execution instruction: the risk judgment delay is not more than 1.2 seconds, the connecting rod compression and airbag release are completed within 3-5 seconds, and the thruster response delay is not more than 800 milliseconds.

3. A smart control oriented floating wind turbine system for extreme sea conditions according to claim 1, characterized in that: The risk level index The expression of the risk level index is: ； wherein is a set threshold value, a steepness of the control function, denotes a perturbation indicator.

4. The intelligent control oriented floating wind turbine system for extreme sea conditions as claimed in claim 1 wherein: The expression of the adaptive learning module is as follows: ； In the formula, is an action deviation evaluation function; is a learning rate; denotes a weight factor before update; denotes a weight factor after update.

5. A smart control oriented floating wind turbine system for extreme sea conditions according to claim 4, characterized in that: The action deviation evaluation function is based on attitude deviation or energy consumption.

Images