Collaborative optimization method and device for offshore wind power environment and power coupling system
By establishing a marine environment-electricity coupling model and a multi-objective optimization algorithm, the interaction between the marine environment and the power system in offshore wind power systems was solved, realizing the system's collaborative optimization and autonomous decision-making, and improving power generation efficiency, stability, and economy.
Patent Information
- Application Number
- CN202511711390.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-20
- Publication Date
- 2026-02-27
AI Technical Summary
Existing technologies cannot fully consider the interaction and coupling relationship between the marine environment and the power system in offshore wind power and power coupling systems, resulting in low power generation efficiency, system instability and significant environmental impact in complex marine environments.
A marine environment-electricity coupling model was established. By collecting data on the marine environment and the power system, a multi-objective function was constructed and solved using a non-dominated sorting genetic algorithm II. Combined with model predictive control, the system's collaborative optimization was achieved.
It improves the power generation efficiency of offshore wind power systems, ensures system stability, reduces environmental impact, optimizes economic benefits, and enhances adaptive control capabilities.
Smart Images
Figure CN121584760A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The application belongs to the technical field of wind turbine control, and particularly relates to a collaborative optimization method and device for an offshore wind power environment and power coupling system. BACKGROUND
[0002] With the increasing demand for clean energy worldwide, offshore wind power, as an important form of renewable energy, has developed rapidly. However, the offshore wind power environment and power coupling system face many challenges. The complex and variable marine environment, such as strong winds, waves, and currents, can affect the operating efficiency and safety of wind turbine generators; at the same time, the stability and power quality of the power system are also affected by the marine environment. In addition, how to ensure the efficiency of power generation while reducing the impact on the marine environment and achieving sustainable development of the system is a problem that needs to be solved in the current offshore wind power field.
[0003] Traditional control methods use control strategies based on fixed parameters, such as PID (Proportional-Integral-Derivative) control, to control the pitch angle of wind turbine generators and the torque of generators. This method can ensure the stable operation of the system to some extent, but it is not suitable for complex and variable marine environments and cannot achieve collaborative optimization of the system.
[0004] Single-objective optimization methods mainly optimize a single target such as power generation efficiency or economy, ignoring the mutual relationship between other targets. For example, simply pursuing power generation efficiency may increase the impact on the marine environment or increase the maintenance cost of equipment.
[0005] Most existing technologies consider the power system and marine environment of offshore wind power separately, without fully considering the interaction and coupling relationship between the two, making it difficult to achieve optimal operation of the entire system. SUMMARY
[0006] The purpose of the present application is to provide a collaborative optimization method and device for an offshore wind power environment and power coupling system to solve the technical problem of not fully considering the interaction and coupling relationship between the power system and marine environment of offshore wind power in the prior art.
[0007] To achieve the above-mentioned purpose, the present application adopts the following technical solutions: A collaborative optimization method for an offshore wind power environment and power coupling system, comprising the following steps: Collecting marine environment data, wind turbine generator operating state data, and power system data; Considering the influence of marine environmental factors on wind turbine generators and power systems, based on the collected marine environment data, wind turbine generator operating state data, and power system data, a marine environment-power coupling model is established; A multi-objective function including a power generation efficiency target, a system stability target, an environmental impact target and an economic target is constructed, and a set of Pareto optimal solutions is obtained by solving the multi-objective function based on the ocean environment-power coupling model, considering the physical limitations and operation requirements of the wind turbine generator set; In each control cycle, a future state change of the system is predicted based on the ocean environment-power coupling model according to the current system state and predicted future environmental data, a finite time domain optimization problem is solved, an optimal solution is selected from the set of Pareto optimal solutions, and the optimal control input is obtained by correcting the optimal solution according to the deviation between the current system state and the future state of the system; The ocean environment-power coupling model includes a wind turbine generator set and ocean environment coupling model and a power transmission system and ocean environment coupling model; The system state includes wind turbine generator set operating state and power system state.
[0008] Further, the ocean environment data at least includes wind speed, wind direction, wave height and current speed; The wind turbine generator set operating state data at least includes power generation power, pitch angle and generator torque; The power system data at least includes voltage, current and power factor data of the power transmission system.
[0009] Further, the wind turbine generator set and ocean environment coupling model includes: A wave-tower dynamics model is established considering the influence of wave on the structural dynamics of the wind turbine generator set; A current-foundation drag force model is established considering the influence of current on the foundation of the wind turbine generator set; A wind turbine generator set power generation performance model is established considering the influence of ocean environment on the power generation performance of the wind turbine generator set.
[0010] Further, the wave-tower dynamics model formula is:
[0011] In the formula, M is the mass matrix of the tower, C is the damping matrix, K is the stiffness matrix, is the acceleration of the tower, is the velocity of the tower, is the displacement vector of the tower, is the force of the wind acting on the tower, is the force vector of the wave acting on the tower, including vertical and horizontal components; The formula of the current-foundation drag force model is:
[0012] wherein, is the drag force of the sea current on the foundation of the wind turbine generator, is the density of seawater, is the drag coefficient of the foundation, is the projected area of the foundation in the direction of the sea current, is the sea current velocity; the formula of the power generation performance model of the wind turbine generator is:
[0013] wherein, P is the theoretical power generation of the wind turbine generator, is the air density, A is the swept area of the wind wheel, is the wind energy utilization coefficient, and the tip speed ratio and the pitch angle are related, is the wind speed.
[0014] Further, the coupling model of the power transmission system and the marine environment comprises: a sea current-cable drag force model is established by considering the influence of the sea current on the submarine cable; a sea current-cable vibration model is established by considering the influence of the sea current on the vibration of the cable; an electrical performance model of the power transmission system is established by considering the influence of the marine environment on the electrical performance of the power transmission system.
[0015] Further, the formula of the sea current-cable drag force model is:
[0016] wherein, is the drag force of the sea current on the cable, is the density of seawater, is the drag coefficient of the cable, is the projected area of the cable in the direction of the sea current, is the sea current velocity; the formula of the sea current-cable vibration model is:
[0017] wherein, is, is the bending stiffness of the cable, is the density of the cable, is the projected area of the cable in the direction of the sea current, y is the transverse displacement of the cable, x is the axial displacement of the cable, and t is time, is; the formula of the electrical performance model of the power transmission system is:
[0018]
[0019] In the formula, For insulation resistance, It is the insulation resistance in a dry, salt spray-free environment, where H is the relative humidity and S is the salt spray concentration. and It is an empirical coefficient. Where T is the cable resistance and T is the actual temperature. For reference temperature, Reference temperature The cable resistance below, It is the temperature coefficient of resistance of the cable material.
[0020] Furthermore, the multi-objective function is solved using a non-dominated sorting genetic algorithm II, with the following steps: Population initialization: Randomly generate a set of initial solutions, each solution represented as a vector containing the control parameters of the wind turbine generator and the control parameters of the power transmission system; Fitness evaluation: Calculate multiple objective function values for each solution; Non-dominated sorting: Sort the solutions in the population in a non-dominated manner, and divide the solutions into different levels; Crowding Calculation: Calculate the crowding degree of the solution within each level; Selection, crossover, and mutation operations: New populations are generated through selection, crossover, and mutation operations; Repeat the above steps until the termination condition is met.
[0021] Secondly, the present invention provides a collaborative optimization system for offshore wind power environment and power coupling system, comprising a data acquisition module, a construction module, a solution module, and an output module, wherein: Data Acquisition Module: Used to collect marine environmental data, wind turbine operating status data, and power system data; Building module: Used to consider the impact of marine environmental factors on wind turbine generators and power systems, and to establish a marine environment-power coupling model based on collected marine environmental data, wind turbine generator operating status data and power system data; The solution module is used to construct a multi-objective function that includes power generation efficiency, system stability, environmental impact, and economic objectives. Considering the physical limitations and operational requirements of wind turbine generators, the module solves the multi-objective function based on the marine environment-electricity coupling model to obtain a set of Pareto optimal solutions. The output module is configured to, in each control cycle, predict system future state changes based on the marine environment-power coupling model according to current system state and predicted future environmental data, solve a finite time domain optimization problem, select an optimal solution from a Pareto optimal solution set, correct the optimal solution according to the deviation of the current system state and the system future state to obtain an optimal control input; The marine environment-power coupling model comprises a coupling model of the wind turbine generator set and the marine environment and a coupling model of the power transmission system and the marine environment. The system state comprises a wind turbine generator set operating state and a power system state.
[0022] In a third aspect, a terminal device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.
[0023] In a fourth aspect, a computer readable storage medium is provided, which stores a computer program, wherein the computer program is executable by a processor to implement the steps of the above method.
[0024] Compared with the prior art, the present application has the following beneficial technical effects: The offshore wind power environment and power coupling system collaborative optimization method fully considers the influence of the marine environment factor on the power system and the feedback of the power system operation on the marine environment, solves a Pareto optimal solution set considering power generation efficiency, system stability, environmental friendliness and economic efficiency, and adopts a combination strategy of multi-objective optimization and model predictive control, thereby realizing collaborative optimization and autonomous decision-making of the offshore wind power system under complex marine environment, improving power generation efficiency, guaranteeing system operation stability, reducing the influence on the environment and optimizing economic efficiency, and significantly enhancing the comprehensive performance and adaptive control ability of the offshore wind power system. BRIEF DESCRIPTION OF DRAWINGS
[0025] Figure 1 The offshore wind power environment and power coupling system collaborative optimization method flowchart in the embodiments of the present application. DETAILED DESCRIPTION
[0026] 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 part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in 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.
[0027] In the description of the present application, it should be understood that the terms "include" and "contain" indicate the presence of described features, whole, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, whole, steps, operations, elements, components and / or sets thereof.
[0028] It should be understood that although the terms first, second, third, etc. may be employed in the embodiments of the present application to describe preset ranges, etc., these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from each other. For example, the first preset range can also be referred to as the second preset range, and similarly, the second preset range can also be referred to as the first preset range, without departing from the scope of the embodiments of the present application.
[0029] Depending on the context, the word "if" as used herein can be interpreted to mean "when" or "while" or "in response to determining" or "in response to detecting". Similarly, depending on the context, the phrase "if it is determined" or "if (a stated condition or event) is detected" can be interpreted to mean "when it is determined" or "in response to determining" or "when (a stated condition or event) is detected" or "in response to detecting (a stated condition or event)".
[0030] Term explanation: Offshore wind power environment and power coupling system: refers to a complex system formed by the interaction of wind turbines, power transmission systems, marine environment (such as sea waves, ocean currents, weather conditions, etc.) of offshore wind farms, where power generation is affected by marine environmental factors, while the operation of the power system may also have certain feedback on the marine environment.
[0031] Synergistic optimization: comprehensive consideration and coordination of multiple subsystems or elements in the offshore wind power environment and power coupling system to achieve optimal goals in power generation efficiency, stability, economy and environmental friendliness, etc.
[0032] Automatic control: using control theory and technology to enable the offshore wind power environment and power coupling system to automatically adjust operating parameters according to system state and external environmental changes without human intervention or with only a small amount of human supervision.
[0033] Model predictive control (MPC): an advanced control strategy based on model, which solves an optimization problem in each control period to determine the optimal control input at the current time by predicting the future dynamic behavior of the system.
[0034] Multi-objective optimization algorithm: an algorithm for handling optimization problems with multiple conflicting objectives, such as the non-dominated sorting genetic algorithm II (NSGA-II), which can find a set of Pareto optimal solutions, making it impossible to further optimize a certain objective without compromising the performance of other objectives.
[0035] Pareto optimal solution: In a multi-objective optimization problem, a set of solutions is called a Pareto optimal solution if there is no other solution that can outperform any solution in the set on all objectives.
[0036] The invention will be described in further detail below with reference to the accompanying drawings: As shown in Figure 1 A collaborative optimization method for offshore wind power environment and power coupling system, comprising the following steps: Step 1: Collect marine environment data, wind turbine operating state data and power system data; Real-time collection of wind speed, wind direction, wave height and current speed data using meteorological stations, wave monitors, current meters and other equipment installed on the sea; Wind turbine operating state data at least includes power generation, pitch angle and generator torque; Power system data includes voltage and price data of the power transmission system.
[0037] Filter, denoise and normalize the collected data to improve data quality and usability.
[0038] Step 2: Consider the influence of marine environment factors on wind turbine and power system, and establish marine environment-power coupling model based on collected marine environment data, wind turbine operating state data and power system data; Consider the influence of marine environment factors (such as waves and currents) on wind turbines and power transmission systems, and establish a marine environment-power coupling model.
[0039] The marine environment-power coupling model includes a wind turbine-marine environment coupling model and a power transmission system-marine environment coupling model, which specifically includes: 1) Wind turbine-marine environment coupling model (1) Influence of waves on wind turbine structural dynamics The periodic impact of waves can cause wind turbines to vibrate, affecting their structural stability and fatigue life. This effect can be described by a multi-body dynamics model. Assume that the tower of the wind turbine is an elastic body, and consider the vertical and horizontal forces of the waves.
[0040] In the vertical direction, the vertical force at the bottom of the tower caused by the waves
[0041] where, is the sea water density, g is the gravity acceleration, is the projected area of the tower in vertical direction, calculated based on the tower geometry, is the wave height, is the force coefficient in vertical direction, obtained from standard specifications or actual engineering calculation, which is related to the wave frequency, tower geometry, etc.
[0042] In horizontal direction, the horizontal force on the tower bottom caused by the wave can be expressed as:
[0043] where, is the horizontal velocity of the wave, is the force coefficient in horizontal direction, obtained from actual engineering calculation.
[0044] After considering these forces, the wave-tower dynamics model is the dynamics equation of the wind turbine tower, expressed as a second-order ordinary differential equation:
[0045] where, M is the mass matrix of the tower, C is the damping matrix, K is the stiffness matrix, is the acceleration of the tower, is the velocity of the tower, is the displacement vector of the tower, is the force of the wind acting on the tower, is the force vector of the wave acting on the tower, including vertical and horizontal components, among the above parameters, in addition to the variable, other data parameters are system constants, which have parameter reference values in the tower design document or construction document.
[0046] (2) Influence of sea current on wind turbine foundation Sea current can generate drag force on the foundation of wind turbine, affecting the stability of the foundation. The drag force of sea current on the foundation can be expressed by the Morison equation:
[0047] where, is the drag force of sea current on the wind turbine foundation, is the sea water density, is the drag force coefficient, determined according to the shape and surface condition of the foundation component, usually 0.6-1.2 for single pile foundation, is the projected area of the foundation in the direction of the current, calculated from the geometry of the foundation, is the current velocity.
[0048] Considering the drag force of the current, the stability analysis of the foundation is carried out by calculating the sliding resistance and overturning resistance of the foundation. The sliding resistance can be expressed as:
[0049] where, is the friction coefficient between the foundation and the seabed, and N is the vertical reaction force of the foundation. The overturning resistance needs to consider the weight of the foundation, the weight of the superstructure, and the point and arm of the drag force of the current, etc.:
[0050] is the arm of the drag force, which refers to the vertical distance from the point of action of the drag force of the current (usually taken as the midpoint of the foundation in the water depth direction) to the bottom of the seabed foundation. It is a key design parameter for offshore wind turbine foundations.
[0051] (3) Influence of marine environment on power generation performance of wind turbine generator set Wind turbine generator set model: Establish a dynamic model of the wind turbine generator set to describe its power generation characteristics under different wind speeds, wind directions and pitch angles. The commonly used model is:
[0052] where P is the theoretical power generation of the wind turbine generator set, is the air density, A is the swept area of the wind wheel, is the wind energy utilization coefficient, which is related to the tip speed ratio and the pitch angle . The tip speed ratio , is the angular velocity of the wind wheel, R is the radius of the wind wheel, is the wind speed. The generator torque T g is proportional to the angular velocity of the wind wheel, and the fixed wind turbine type has its fixed wind energy utilization coefficient curve. The tip speed ratio and the pitch angle are the variables to be solved.
[0053] 2) Coupling model of power transmission system and marine environment (1) Influence of current on submarine cable The current will generate drag force and vibration on the submarine cable, affecting the service life and power transmission safety of the cable. The drag force of the current on the cable can also be represented by the Morison equation:
[0054] In the formula, The drag force of the ocean current on the cable is determined by referring to tables or curves based on the shape and surface conditions of the cable components. It is the density of seawater. It is the drag coefficient of the cable. It is the projected area of the cable in the direction of the ocean current, calculated based on the cable's geometry. It's the speed of the ocean current.
[0055] The cable vibration caused by ocean currents can be described by a vibration equation. Assuming the cable is an elastic beam, its vibration equation can be expressed as:
[0056] In the formula, for, It refers to the bending stiffness of the cable. It is the density of the cable. y is the projected area of the cable in the direction of the ocean current, x is the lateral displacement of the cable, t is the axial displacement of the cable, and t is time. The partial differential operator is used. Bending stiffness and density are inherent properties of the submarine cable, determined during the design, construction, and material selection phases. The projected area is calculated based on the cable's geometric dimensions, while the lateral axial displacement is to be solved. (2) The impact of marine environment on the electrical performance of power transmission system Factors such as humidity and salt spray in the marine environment can affect the insulation performance of power transmission systems. Insulation resistance. The value decreases with increasing humidity and salt spray concentration. Based on experimental data, the following empirical formula is established:
[0057] in, For insulation resistance, It is the insulation resistance in a dry, salt spray-free environment, where H is the relative humidity and S is the salt spray concentration. and It is an empirical coefficient.
[0058] Characterizing insulation resistance under different humidity and salinity conditions in different sea areas compared with standard insulation resistance The relationship. This refers to the inherent characteristics of the insulated cable, which can generally be obtained from the cable manufacturer or by referring to the values specified in national standards. H is relative humidity, which can be obtained from temperature and humidity sensors in key spaces or locations such as offshore wind farm substations or inside wind turbine towers, or from the wind turbine's SCADA system. S is salinity concentration, which can be found by referring to salt spray concentration distribution data for coastal areas published by meteorological departments. and is an empirical coefficient, which is estimated by laboratory simulation or engineering experience, considering the comprehensive characteristics of specific insulation material formula, process and local environment.
[0059] Meanwhile, the temperature change in the marine environment will affect the resistance of the cable, and the cable resistance and temperature T can be expressed as:
[0060] wherein, is the cable resistance, T is the actual temperature, is the reference temperature, is the cable resistance at the reference temperature , and is the resistance temperature coefficient of the cable material.
[0061] The coupling relationship of each part is integrated to obtain the overall model of the marine environment-power coupling system. The model can be represented by a set of differential equations and algebraic equations, which are solved by numerical methods such as finite element method, Runge-Kutta method, etc.
[0062] Step three, construct a multi-objective function including power generation efficiency target, system stability target, environmental impact target and economic target, consider the physical limitations and operation requirements of wind turbine generator, based on the marine environment-power coupling model, solve the multi-objective function to obtain a set of Pareto optimal solutions; Objective function: define multiple optimization objectives, including maximizing power generation efficiency, maximizing system stability, minimizing impact on marine environment, and maximizing economic efficiency.
[0063] Power generation efficiency target: wherein, is the actual power generation power, is the theoretical maximum power generation power; System stability target: wherein, is the voltage fluctuation, is the rated voltage; Environmental impact target: Quantify by evaluating the impact of marine environment on wind turbine generator:
[0064] Economic target: wherein R is the power generation income (R=P actuaL *η price , η price is the electricity market price), C is the operating cost, is the maximum possible revenue (R = P max *η price ); Constraints: consider the physical limitations and operational requirements of the system, such as the range of pitch angle , the range of generator torque T g,min <= T g <= T g,max , the power transmission limit of the transmission system.
[0065] Non-dominated Sorting Genetic Algorithm II (NSGA-II) is used to solve multi-objective optimization problem, and a set of Pareto optimal solutions is obtained. The specific steps are as follows: Initialization of population: a set of initial solutions is randomly generated, each solution is represented as a vector, containing the control parameters of wind turbine group (such as pitch angle, generator torque) and the control parameters of transmission system (such as the switching of reactive power compensation device); Fitness evaluation: calculate the multiple objective function values corresponding to each solution; Non-dominated sorting: sort the solutions in the population, and divide them into different levels, the lower the level, the better the solution; Crowding degree calculation: calculate the crowding degree of solutions in each level, which is used to maintain the diversity of the population; Selection, crossover and mutation operation: generate new population through selection, crossover and mutation operation; Repeat the above steps: until the termination condition is met (such as reaching the maximum number of iterations).
[0066] Step four, in each control period, according to the current system state and the predicted future environmental data, based on the marine environment-power coupling model to predict the future state change of the system, solve the finite time domain optimization problem, select an optimal solution from the Pareto optimal solution set, according to the deviation between the current system state and the future state of the system, correct the optimal solution to obtain the optimal control input; In the future environmental data, select an optimal solution from the Pareto optimal solution set as the current control input. Use the system model to predict the state change of the system in the future. In each control period, solve a finite time domain optimization problem to determine the optimal control input at the current time. According to the deviation between the actual measured system state and the predicted state, correct the control input; Send the optimized control parameters (such as pitch angle, generator torque, switching of reactive power compensation device) to the controllers of wind turbine group and transmission system to realize automatic control of the system.
[0067] In another embodiment of the present application, a system for collaborative optimization of offshore wind power environment and power coupling system is provided, comprising a collection module, a construction module, a solution module and an output module, wherein: The collection module is configured to collect marine environment data, wind turbine generator set operating state data and power system data; The construction module is configured to consider the influence of marine environment factors on the wind turbine generator set and the power system, and establish a marine environment-power coupling model based on the collected marine environment data, wind turbine generator set operating state data and power system data; The solution module is configured to construct a multi-objective function including a power generation efficiency objective, a system stability objective, an environmental impact objective and an economic objective, consider the physical limitations and operating requirements of the wind turbine generator set, and solve the multi-objective function based on the marine environment-power coupling model to obtain a set of Pareto optimal solutions; The output module is configured to, in each control period, predict the future state change of the system based on the marine environment-power coupling model according to the current system state and the predicted future environment data, solve a finite time domain optimization problem, select an optimal solution from the set of Pareto optimal solutions, and correct the optimal solution according to the deviation between the current system state and the future state of the system to obtain an optimal control input; The marine environment-power coupling model includes a coupling model of the wind turbine generator set and the marine environment and a coupling model of the power transmission system and the marine environment; The system state includes the wind turbine generator set operating state and the power system state.
[0068] Those skilled in the art will understand that the embodiments of the present application can be provided as a method, a system or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD ROM, optical storage, etc.) containing computer-usable program code.
[0069] The present application is described with reference to flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device that implements the flowcharts and / or block diagrams. Figure 1one or more processes and / or blocks Figure 1 an apparatus for performing the functions specified in the flowchart or multiple flows and / or blocks.
[0070] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the flowchart or multiple flows and / or blocks. Figure 1 one or more processes and / or blocks Figure 1 an apparatus for performing the functions specified in the flowchart or multiple flows and / or blocks.
[0071] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the flowchart or multiple flows and / or blocks. Figure 1 one or more processes and / or blocks Figure 1 an apparatus for performing the functions specified in the flowchart or multiple flows and / or blocks.
[0072] Finally, it should be noted that the above examples are merely used to illustrate the technical solutions of the present application and not to limit the protection scope thereof. Although the present application has been described in detail with reference to the above examples, those skilled in the art should understand that the present application can still make various changes, modifications or equivalent replacements to the specific embodiments of the present application after reading the present application. However, these changes, modifications or equivalent replacements are all within the protection scope of the claims of the present application.
Claims
1. A method for the coordinated optimization of offshore wind power environment and power coupling system, characterized in that, Includes the following steps: Collect marine environmental data, wind turbine operating status data, and power system data; Considering the impact of marine environmental factors on wind turbine generators and power systems, a marine environment-power coupling model is established based on collected marine environmental data, wind turbine generator operating status data, and power system data. A multi-objective function is constructed, including power generation efficiency, system stability, environmental impact, and economic objectives. Considering the physical limitations and operational requirements of wind turbine generators, the multi-objective function is solved based on the marine environment-electricity coupling model to obtain a set of Pareto optimal solutions. Within each control cycle, based on the current system state and predicted future environmental data, the future state changes of the system are predicted using the marine environment-electricity coupling model. A finite-time domain optimization problem is solved, and an optimal solution is selected from the Pareto optimal solution set. The optimal solution is then corrected based on the deviation between the current system state and the future system state to obtain the optimal control input. The marine environment-electricity coupling model includes a coupling model of wind turbine generators and the marine environment, and a coupling model of power transmission systems and the marine environment. The system status includes the operating status of the wind turbine generator set and the status of the power system.
2. The method for synergistic optimization of offshore wind power environment and power coupling system according to claim 1, characterized in that, The marine environmental data includes at least wind speed, wave height, and ocean current speed; The wind turbine generator set operating status data includes at least power generation, pitch angle, and generator torque; The power system data includes voltage and electricity price data for the transmission system.
3. The method for collaborative optimization of offshore wind power environment and power coupling system according to claim 1, characterized in that, The coupling model between the wind turbine generator and the marine environment includes: A wave-tower dynamics model was established to consider the impact of ocean waves on the structural dynamics of wind turbine generators. Considering the impact of ocean currents on wind turbine foundations, an ocean current-foundation drag force model is established. A wind turbine power generation performance model was established, taking into account the impact of the marine environment on the power generation performance of the wind turbine.
4. The method for synergistic optimization of offshore wind power environment and power coupling system according to claim 3, characterized in that, The formula for the wave-tower dynamics model is: In the formula, M is the mass matrix of the tower, C is the damping matrix, and K is the stiffness matrix. For the acceleration of the tower, For the speed of the tower, Let be the displacement vector of the tower. It is the force of wind acting on the tower. It is the force vector of the waves acting on the tower, which includes vertical and horizontal components; The formula for the ocean current-based drag force model is: In the formula, This refers to the drag force of ocean currents on the foundation of the wind turbine generator. It is the density of seawater. It is the drag coefficient. It is the projected area of the foundation in the direction of the ocean current. It is the speed of the ocean current; The formula for the power generation performance model of the wind turbine generator set is as follows: In the formula, P is the theoretical power generation of the wind turbine. A is the air density, and A is the swept area of the wind turbine. It is the wind energy utilization coefficient, relative to the tip speed ratio. and propeller pitch angle related, It's wind speed.
5. The method for synergistic optimization of offshore wind power environment and power coupling system according to claim 1, characterized in that, The coupling model of the power transmission system and the marine environment includes: A current-cable drag force model was established, taking into account the influence of ocean currents on submarine cables. A current-cable vibration model was established to consider the influence of ocean currents on cable vibration. An electrical performance model for a power transmission system was established, taking into account the impact of the marine environment on the electrical performance of the power transmission system.
6. The method for collaborative optimization of offshore wind power environment and power coupling system according to claim 1, characterized in that, The formula for the ocean current-cable drag force model is: In the formula, The drag force of the ocean current on the cable, It is the density of seawater. It is the drag coefficient of the cable. It is the projected area of the cable in the direction of the ocean current. It is the speed of the ocean current; The formula for the ocean current-cable vibration model is: In the formula, for, It refers to the bending stiffness of the cable. It is the density of the cable. y is the projected area of the cable in the direction of the ocean current, x is the lateral displacement of the cable, t is the axial displacement of the cable, and t is time. It is a partial differential operator; The formula for the electrical performance model of the power transmission system is: In the formula, For insulation resistance, It is the insulation resistance in a dry, salt spray-free environment, where H is the relative humidity and S is the salt spray concentration. and It is an empirical coefficient. Where T is the cable resistance and T is the actual temperature. For reference temperature, Reference temperature The cable resistance below, It is the temperature coefficient of resistance of the cable material.
7. The method for synergistic optimization of offshore wind power environment and power coupling system according to claim 6, characterized in that, The multi-objective function is solved using a non-dominated sorting genetic algorithm II, with the following steps: Population initialization: Randomly generate a set of initial solutions, each solution represented as a vector containing the control parameters of the wind turbine generator and the control parameters of the power transmission system; Fitness evaluation: Calculate multiple objective function values for each solution; Non-dominated sorting: Sort the solutions in the population in a non-dominated manner, and divide the solutions into different levels; Crowding Calculation: Calculate the crowding degree of the solutions within each level; Selection, crossover, and mutation operations: New populations are generated through selection, crossover, and mutation operations; Repeat the above steps until the termination condition is met.
8. A collaborative optimization system for offshore wind power environment and power coupling system, characterized in that, It includes a data acquisition module, a data construction module, a solution module, and an output module, wherein: Data Acquisition Module: Used to collect marine environmental data, wind turbine operating status data, and power system data; Building module: Used to consider the impact of marine environmental factors on wind turbine generators and power systems, and to establish a marine environment-power coupling model based on collected marine environmental data, wind turbine generator operating status data and power system data; The solution module is used to construct a multi-objective function that includes power generation efficiency, system stability, environmental impact, and economic objectives. Considering the physical limitations and operational requirements of wind turbine generators, the module solves the multi-objective function based on the marine environment-electricity coupling model to obtain a set of Pareto optimal solutions. Output module: In each control cycle, based on the current system state and predicted future environmental data, it predicts the future state changes of the system according to the marine environment-electricity coupling model, solves the finite time domain optimization problem, selects an optimal solution from the Pareto optimal solution set, and corrects the optimal solution according to the deviation between the current system state and the future system state to obtain the optimal control input. The marine environment-electricity coupling model includes a coupling model of wind turbine generators and the marine environment, and a coupling model of power transmission systems and the marine environment. The system status includes the operating status of the wind turbine generator set and the status of the power system.
9. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements as claimed in claim 1.
7. The steps of any of the methods described.
10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements as described in claim 1.
7. The steps of any of the methods described.