Wind Turbine Control Optimization Method and System Based on Maximum Flow Analysis

Abstract

A control optimization method and system for wind turbines based on maximum flow analysis. The method includes: obtaining the real-time parameters of the wind turbines and the real-time data measured by sensors, performing topological sorting on the real-time parameters and real-time data to determine the upstream and downstream relationships among the wind turbines, and dividing the wind turbines into upstream wind turbines and downstream wind turbines based on the upstream and downstream relationships. Calculating the thrust coefficient of the wind turbines according to the angle of attack, pitch angle, wind speed vector value, blade radius, and blade rotation speed of the wind turbines. Invoking the wake model to calculate the wake wind speed deficit of the wind turbines based on the diameter of the wake of the first wind turbine at the second wind turbine, the axial induction factor and wind speed of the first wind turbine, and the wake overlap degree between the first wind turbine and the second wind turbine. Invoking the particle swarm algorithm and combining it with the maximum flow algorithm to determine the loss function of the wind turbines based on the wake wind speed deficit, so as to calculate the ideal wake wind speed deficit of the wind turbines through the loss function and optimize the control of the wind turbines.

Description

Technical Field

[0001] The present invention belongs to the technical field of wind turbine power generation control, and more specifically, relates to a method and system for optimizing the control of wind turbines based on maximum flow analysis. Background Art

[0002] With the continuous increase in the demand for renewable energy, wind power generation has gradually become an important part of the energy structure. In wind power generation, in order to improve the overall power generation and the stability of grid connection, it is usually necessary to deploy multiple wind turbines in parallel operation. Through parallel operation, the wind farm can more effectively utilize wind energy resources and at the same time provide a stable and continuous power output to the grid. However, in this multi-unit parallel configuration, the interaction between the wind turbines becomes complex, especially in the case of drastic changes in wind speed and direction, and a single wind turbine control algorithm cannot cope with the dynamic interaction between the wind turbine groups.

[0003] The current wind turbine control systems mostly adopt the proportional-integral-derivative (PID) control algorithm. PID control is simple and effective in the control of a single wind turbine and is suitable for the control requirements under stable working conditions. However, in the environment of multi-wind turbine parallel operation, the adaptability of PID control to external disturbances is limited, and it is difficult to accurately respond to the complex changes in wind speed and direction. At the same time, PID control lacks a response strategy for the interaction between wind turbines and cannot dynamically adjust the control parameters of each wind turbine to cope with the interaction between them. Therefore, for the case of multi-unit parallel operation, a single PID control is difficult to ensure the optimal efficiency of the overall system.

[0004] In the parallel structure of a wind farm, there will be an interaction between the wind turbines. Especially in an environment with frequent wind speed changes and turbulence, the change in the output power of a single wind turbine will affect the surrounding wind turbines through air flow disturbances, resulting in a decrease in the overall power generation efficiency. The traditional PID control lacks an adaptive adjustment function for this complex interaction, resulting in problems such as inflexible response and difficulty in achieving collaborative optimization when multiple units are operating in parallel. In addition, PID control cannot achieve personalized adjustment for each wind turbine when a large number of wind turbines are operating in parallel, and it is easy to produce phenomena such as "obsolescence" or "error accumulation" of the control strategy, thereby reducing the overall control accuracy and power generation efficiency. Therefore, it is urgent to introduce more intelligent algorithms (such as turbulence estimation, adaptive control) to optimize the multi-unit coordinated operation of wind turbines. Summary of the Invention

[0005] To solve the deficiencies in the prior art, the object of the present invention is to solve the above-mentioned defects and further propose a method and system for optimizing the control of wind turbines based on maximum flow analysis.

[0006] The present invention adopts the following technical solutions.

[0007] The first aspect of the present invention discloses a method for optimizing the control of a wind turbine based on maximum flow analysis, and the method includes:

[0008] Obtain the real-time parameters of the wind turbine and the real-time data measured by sensors. The real-time parameters at least include the rotor diameter, hub height, pitch angle, and blade rotational speed, and the real-time data at least includes temperature, wind speed, and humidity;

[0009] Perform a topological sort on the real-time parameters and real-time data to determine the upstream and downstream relationships among the wind turbines, and divide the wind turbines into upstream wind turbines and downstream wind turbines based on the upstream and downstream relationships;

[0010] Calculate the thrust coefficient of the wind turbine according to the angle of attack, pitch angle, wind speed vector value, blade radius, and blade rotational speed of the wind turbine;

[0011] Call the wake model to calculate the wake wind speed deficit of the wind turbine based on the diameter of the wake of the first wind turbine at the second wind turbine, the axial induction factor and wind speed of the first wind turbine, and the wake overlap degree between the first wind turbine and the second wind turbine;

[0012] Call the particle swarm optimization algorithm and combine it with the maximum flow algorithm to determine the loss function of the wind turbine based on the wake wind speed deficit, so as to calculate the ideal wake wind speed deficit of the wind turbine through the loss function and optimize the control of the wind turbine.

[0013] Further, the calculation formula for the thrust coefficient of the wind turbine is:

[0014]

[0015]

[0016] In the formula, C i is the thrust coefficient, i is the label of the wind turbine, a i is the angle of attack of the wind turbine, θ i is the pitch angle of the wind turbine, v i is the wind speed vector value, r is the blade radius of the wind turbine, w i is the blade rotational speed.

[0017] Further, the expression for calculating the wake wind speed deficit is:

[0018]

[0019] In the formula, ΔV ij is the wake wind speed deficit of the first wind turbine to the second wind turbine, D ij represents the diameter of the wake of the first wind turbine i at the second wind turbine j, α i is the axial induction factor of the first wind turbine i, is the wake overlap of the first and second wind turbines, V i is the wind speed at the first wind turbine i.

[0020] Furthermore, the diameter expression of the wake of the first wind turbine at the second wind turbine is:

[0021] D ij = 2r + 2kx ij cos(δ ij );

[0022] In the formula, k is the wake expansion coefficient, x ij is the distance between the first wind turbine i and the second wind turbine j, and δ ij represents the deflection angle of the first wind turbine i and the second wind turbine j relative to the wind speed;

[0023] The expression for the wake overlap of the first and second wind turbines is:

[0024]

[0025] y ij = x ij sin(δ ij );

[0026] In the formula, y ij represents the lateral offset distance;

[0027] The expression for the deflection angle of the first and second wind turbines relative to the wind speed is:

[0028] δ ij = θ ij - τ,

[0029]

[0030] In the formula, τ is the wind direction angle, and θ ij is the relative azimuth angle between the wind turbines. (x i , y i ) and (x j , y j ) represent the coordinates of wind turbine i and wind turbine j respectively.

[0031] Furthermore, calling the particle swarm optimization algorithm and combining it with the maximum flow algorithm to determine the loss function of the wind turbine based on the wake wind speed deficit, so as to calculate the ideal wake wind speed deficit of the wind turbine through the loss function and optimize the control of the wind turbine, including:

[0032] Obtaining the optimization objective function of the wind turbine based on the particle swarm optimization algorithm. The expression of the optimization objective function is:

[0033]

[0034] In the formula, ΔV′ ij is the ideal wake wind speed deficit of the wind turbine, and n is the number of wind turbines.

[0035] Furthermore, the upstream wind turbines include upstream non-superior wind turbines and upstream superior wind turbines according to the upstream and downstream relationship, and the downstream wind turbines include downstream non-inferior wind turbines and downstream inferior wind turbines according to the upstream and downstream relationship;

[0036] The method of calling the particle swarm optimization algorithm and combining with the maximum flow algorithm to determine the loss function of the wind turbine based on the wake wind speed deficit, so as to calculate the ideal wake wind speed deficit of the wind turbine through the loss function and optimize the control of the wind turbine further includes:

[0037] Introduce a source point and a sink point, connect the upstream non-superior wind turbine to the source point, connect the downstream non-inferior wind turbine to the sink point, and define the capacity of the edge between the source point and the upstream non-superior wind turbine as the upper limit value of the wake wind speed deficit; and

[0038] When any intermediate node corresponding to the upstream superior wind turbine or the downstream inferior wind turbine overflows, determine the adjacent node corresponding to the intermediate node, and modify the wake wind speed deficit between the intermediate node and its adjacent node;

[0039] Among them, the adjacent node corresponding to the intermediate node is the inferior wind turbine of any corresponding intermediate node, and the modification expression of the wake wind speed deficit is:

[0040] v′(i, j) = min(i.e + v(i, j), c(i, j));

[0041] In the formula, c(i, j) represents the capacity of the edge from node i to node j, v(i, j) is the wake wind speed deficit of the edge from node i to node j before modification, and i.e is the overflow wake wind speed deficit of node i.

[0042] Furthermore, the method of calling the particle swarm optimization algorithm and combining with the maximum flow algorithm to determine the loss function of the wind turbine based on the wake wind speed deficit, so as to calculate the ideal wake wind speed deficit of the wind turbine through the loss function and optimize the control of the wind turbine further includes:

[0043] Judge whether there is a shortest path from any source point to any sink point according to the single-source shortest path algorithm, and when there is no such shortest path, modify the wake wind speed deficit between the intermediate node and its adjacent node between the source point and the sink point; and

[0044] When there is such a shortest path, output the ideal wake wind speed deficit, and the expression of the ideal wake wind speed deficit is:

[0045] ΔV′ ij = v′(i, j);

[0046] Wherein, ΔV′ ij is the ideal wake wind speed deficit, and v′(i, j) is the wake wind speed deficit obtained by the last modification when the shortest path exists.

[0047] The second aspect of the present invention discloses a wind turbine control optimization system based on maximum flow analysis. The system includes:

[0048] A data acquisition module, configured to acquire real-time parameters of the wind turbine and real-time data measured by sensors. The real-time parameters at least include rotor diameter, hub height, pitch angle, and blade rotation speed. The real-time data at least includes temperature, wind speed, and humidity;

[0049] A data processing module, configured to perform topological sorting on the real-time parameters and real-time data to determine the upstream and downstream relationships between wind turbines, and classify the wind turbines into upstream wind turbines and downstream wind turbines based on the upstream and downstream relationships;

[0050] A first calculation module, configured to calculate the thrust coefficient of the wind turbine according to the angle of attack, pitch angle, wind speed vector value, blade radius, and blade rotation speed of the wind turbine;

[0051] A second calculation module, configured to call a wake model to calculate the wake wind speed deficit of the wind turbine based on the diameter of the wake of the first wind turbine at the second wind turbine, the axial induction factor and wind speed of the first wind turbine, and the wake overlap degree between the first wind turbine and the second wind turbine;

[0052] A control optimization module, configured to call a particle swarm algorithm and combine it with a maximum flow algorithm to determine the loss function of the wind turbine based on the wake wind speed deficit, so as to calculate the ideal wake wind speed deficit of the wind turbine through the loss function and perform control optimization on the wind turbine.

[0053] The third aspect of the present invention discloses a terminal, including a processor and a storage medium; characterized in that:

[0054] The storage medium is used to store instructions;

[0055] The processor is configured to operate according to the instructions to execute the steps of the method described in the first aspect.

[0056] The fourth aspect of the present invention discloses a computer-readable storage medium, on which a computer program is stored. The program is characterized in that when it is executed by a processor, it implements the steps of the method described in the first aspect.

[0057] The beneficial effects of the present invention are that, compared with the prior art, the present invention has the following advantages:

[0058] By obtaining the real-time parameters of the wind turbines and the real-time data measured by sensors, performing a topological sort on the real-time parameters and real-time data to determine the upstream and downstream relationships between the wind turbine units, and classifying the wind turbine units into upstream wind turbines and downstream wind turbines based on these upstream and downstream relationships. Subsequently, calculate the thrust coefficient of the wind turbine units, and call the wake model to calculate the wake wind speed deficit of the wind turbine units. Then call the particle swarm optimization algorithm and combine it with the maximum flow algorithm to determine the loss function of the wind turbine units based on the wake wind speed deficit, so as to calculate the ideal wake wind speed deficit of the wind turbines through the loss function and optimize the control of the wind turbine units. This method optimizes the control of the entire wind turbine unit by minimizing the wake wind speed deficit between the wind turbines, thereby reducing the situation where the downstream wind turbines receive a reduced wind speed in the wake of the upstream wind turbines and reducing the turbulence of the downstream wind turbines, and thus improving the operating efficiency of the entire wind turbine unit. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 is a schematic flow chart of the wind turbine unit control optimization method based on maximum flow analysis provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0060] The following further describes the present application with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and cannot be used to limit the protection scope of the present application.

[0061] As Figure 1 shown, in one embodiment, a wind turbine unit control optimization method based on maximum flow analysis includes the following steps:

[0062] Step S110, obtain the real-time parameters of the wind turbines and the real-time data measured by sensors. The real-time parameters at least include the rotor diameter, hub height, pitch angle, and blade rotation speed, and the real-time data at least include temperature, wind speed, and humidity.

[0063] Step S120, perform a topological sort on the real-time parameters and real-time data to determine the upstream and downstream relationships between the wind turbine units, and classify the wind turbine units into upstream wind turbines and downstream wind turbines based on the upstream and downstream relationships.

[0064] Step S130, calculate the thrust coefficient of the wind turbine units according to the angle of attack of the wind turbines, pitch angle, wind speed vector value, blade radius of the wind turbines, and blade rotation speed.

[0065] In some embodiments, for the wind turbine unit control optimization method based on maximum flow analysis provided by the present invention, the calculation formula for the thrust coefficient of the wind turbine units is:

[0066]

[0067]

[0068] Wherein, C i is the thrust coefficient, i is the label of the wind turbine, a i is the angle of attack of the wind turbine, θ i is the pitch angle of the wind turbine, v i is the wind speed vector value, r is the blade radius of the wind turbine, w i is the blade rotational speed.

[0069] Step S140, call the wake model to calculate the wake wind speed deficit of the wind turbine based on the diameter of the wake of the first wind turbine at the second wind turbine, the axial induction factor and the wind speed of the first wind turbine, and the wake overlap degree between the first wind turbine and the second wind turbine.

[0070] In some embodiments, for the wind turbine control optimization method based on maximum flow analysis provided by the present invention, the expression for calculating the wake wind speed deficit is:

[0071]

[0072] Wherein, ΔV ij is the wake wind speed deficit of the first wind turbine on the second wind turbine, D ij represents the diameter of the wake of the first wind turbine i at the second wind turbine j, α i is the axial induction factor of the first wind turbine i, is the wake overlap degree between the first wind turbine and the second wind turbine, V i is the wind speed at the first wind turbine i.

[0073] In some embodiments, for the wind turbine control optimization method based on maximum flow analysis provided by the present invention, the expression for the diameter of the wake of the first wind turbine at the second wind turbine is:

[0074] D ij = 2r + 2kx ij cos(δ ij ).

[0075] Wherein, k is the wake expansion coefficient, x ij is the distance between the first wind turbine i and the second wind turbine j, δ ij represents the offset angle between the first wind turbine i and the second wind turbine j relative to the wind speed.

[0076] The expression for the wake overlap degree between the first wind turbine and the second wind turbine is:

[0077]

[0078] y ij = x ij sin(δ ij ).

[0079] Wherein, y ijIndicates the horizontal offset distance.

[0080] The expression for the offset angle of the first fan and the second fan relative to the wind speed is:

[0081] δ ij = θ ij - τ,

[0082]

[0083] In the formula, τ is the wind direction angle, and θ ij is the relative azimuth angle between the fans, (x i , y i ) and (x j , y j ) represent the coordinates of fan i and fan j respectively.

[0084] Step S150, call the particle swarm algorithm and combine it with the maximum flow algorithm to determine the loss function of the wind turbine based on the wake wind speed deficit, so as to calculate the ideal wake wind speed deficit of the fan through the loss function and optimize the control of the wind turbine.

[0085] In some embodiments, the wind turbine control optimization method based on maximum flow analysis provided by the present invention calls the particle swarm algorithm and combines it with the maximum flow algorithm to determine the loss function of the wind turbine based on the wake wind speed deficit, so as to calculate the ideal wake wind speed deficit of the fan through the loss function and optimize the control of the wind turbine. Specifically, it includes the following steps:

[0086] Step S151, obtain the optimization objective function of the wind turbine based on the particle swarm algorithm. The expression of the optimization objective function is:

[0087]

[0088] In the formula, ΔV′ ij is the ideal wake wind speed deficit of the fan, and n is the number of fans.

[0089] In some embodiments, the wind turbine control optimization method based on maximum flow analysis provided by the present invention calls the particle swarm algorithm and combines it with the maximum flow algorithm to determine the loss function of the wind turbine based on the wake wind speed deficit, so as to calculate the ideal wake wind speed deficit of the fan through the loss function and optimize the control of the wind turbine. Specifically, it further includes the following steps:

[0090] Step S152, introduce a source point and a sink point, connect the upstream fan without a superior to the source point, and connect the downstream fan without a subordinate to the sink point. Define the capacity of the edge between the source point and the upstream fan without a superior as the upper limit value of the wake wind speed deficit.

[0091] Step S153, when any intermediate node corresponding to an upstream higher-level fan or a downstream lower-level fan overflows, determine the adjacent nodes corresponding to the intermediate node, and modify the wake wind speed deficit between the intermediate node and its adjacent nodes.

[0092] Among them, the adjacent node corresponding to the intermediate node is the lower-level fan of any intermediate node corresponding thereto, and the modification expression of the wake wind speed deficit is:

[0093] v′(i, j) = min(i.e + v(i, j), c(i, j)).

[0094] In the formula, c(i, j) represents the capacity of the edge from node i to node j, v(i, j) is the wake wind speed deficit of the edge from node i to node j before modification, and i.e is the overflow wake wind speed deficit of node i.

[0095] In some embodiments, for the wind turbine control optimization method based on maximum flow analysis provided by the present invention, call the particle swarm algorithm and combine it with the maximum flow algorithm to determine the loss function of the wind turbine based on the wake wind speed deficit, so as to calculate the ideal wake wind speed deficit of the fan through the loss function and optimize the control of the wind turbine. Specifically, it further includes the following steps:

[0096] Step S154, according to the single-source shortest path algorithm, determine whether there is a shortest path from any source point to any sink point, and when there is no shortest path, modify the wake wind speed deficit between the intermediate node and its adjacent nodes between the source point and the sink point.

[0097] Step S155, when there is a shortest path, output the ideal wake wind speed deficit, and the ideal wake wind speed deficit expression is:

[0098] ΔV′ ij = v′(i, j).

[0099] In the formula, ΔV′ ij is the ideal wake wind speed deficit, and v′(i, j) is the wake wind speed deficit obtained by the last modification when there is a shortest path.

[0100] The above wind turbine control optimization method based on maximum flow analysis obtains the real-time parameters of the wind turbines and the real-time data measured by sensors, performs topological sorting on the real-time parameters and real-time data to determine the upstream and downstream relationships among the wind turbines, and divides the wind turbines into upstream wind turbines and downstream wind turbines based on this upstream and downstream relationship. Subsequently, the thrust coefficient of the wind turbines is calculated, and the wake wind speed deficit of the wind turbines is calculated by invoking the wake model. Then, the particle swarm algorithm is invoked and combined with the maximum flow algorithm to determine the loss function of the wind turbines based on the wake wind speed deficit, so as to calculate the ideal wake wind speed deficit of the wind turbines through the loss function and optimize the control of the wind turbines. This method optimizes the control of the entire wind turbine by minimizing the wake wind speed deficit between the wind turbines, thereby reducing the reduction in the wind speed received by the downstream wind turbines in the wake of the upstream wind turbines and reducing the turbulence of the downstream wind turbines, and thus improving the operating efficiency of the entire wind turbine.

[0101] In a specific embodiment, for the wind turbine control optimization method based on maximum flow analysis provided by the present invention, the purpose of multiple wind turbines operating in parallel is to improve the overall power generation capacity and stability of the wind farm. The power output of a single wind turbine is limited and it is difficult to meet the grid power demand under low wind speed conditions. However, the wind turbines operating in parallel can capture more wind energy in different regions, enhance the power generation of the wind farm and the ability to resist wind speed fluctuations, thereby improving the continuity and reliability of power supply. In addition, the parallel structure also allows the wind turbines to be connected to the grid through power transformers to achieve unified management and optimal scheduling of the overall output power of the wind farm.

[0102] In a wind farm, the parallel structure of wind turbines usually includes multiple groups of wind turbines arranged in a grouped manner. The wind turbines in each group are connected through an internal control unit to form an independent control area for implementing an optimized strategy of zone control. The wind turbines in different groups are usually arranged at intervals and maintain a certain distance from each other to reduce the influence of airflow interference on adjacent wind turbines. In addition, each group of wind turbines is connected through a local area network, and the output power of each group is collected at a central transformer substation and then unifiedly connected to the grid. In this distributed grouped structure, the wind turbines in each group can cooperate with each other to ensure that the wind farm has high operating efficiency and anti-interference ability at both the overall and local levels.

[0103] Therefore, the optimal control of a single wind turbine can be determined based on the particle swarm algorithm. However, the optimization of a single wind turbine does not mean the optimization of the entire wind turbine. Therefore, the wind turbine control optimization method based on maximum flow analysis provided by the present invention reduces the wake effect of the downstream wind turbines by optimizing the wake wind speed loss of the wind turbines, thereby realizing the optimized control of the wind turbines.

[0104] Among them, the wake effect refers to that when the wind passes through the wind turbine, it will decelerate and become more turbulent, forming a "wake" behind the wind turbine, which means that the downstream wind turbine is located in the wake of the upstream wind turbine, receiving a reduced wind speed and increased turbulence, thereby affecting the efficiency and structural load of the entire wind turbine unit.

[0105] In this embodiment, the wind turbine control optimization method based on maximum flow analysis provided by the present invention includes steps 1 to 5:

[0106] Step 1: Based on sensors, obtain the real-time data of the wind turbine.

[0107] Specifically, the real-time data mainly includes the parameters of the wind turbine (for example: rotor diameter D i , hub height H i , pitch angle θ i , blade rotational speed w i , etc.), as well as the data measured by the sensors in real time, such as temperature data, wind speed data, and humidity data, etc.

[0108] Step 2: Based on topological sorting, determine the upstream and downstream relationships among wind turbine units according to the real-time data.

[0109] Specifically, a wind turbine unit is composed of multiple wind turbines, which can be divided into upstream wind turbines and downstream wind turbines according to the upstream and downstream relationships. The upstream wind turbines and downstream wind turbines can be further subdivided into upstream wind turbines without upper-level wind turbines, upstream wind turbines with upper-level wind turbines, downstream wind turbines without lower-level wind turbines, and downstream wind turbines with lower-level wind turbines.

[0110] Step 3: Calculate the thrust coefficient C i of the wind turbine, and the calculation formula is:

[0111]

[0112]

[0113] In the formula, i is the label of the wind turbine, a i is the angle of attack of the wind turbine, θ i is the pitch angle of the wind turbine, v i is the wind speed vector value, r is the blade radius of the wind turbine, w i is the blade rotational speed.

[0114] Step 4: Apply the wake model to calculate the wake wind speed deficit. The wake wind speed deficit ΔV ij of wind turbine i on wind turbine j is calculated by the following formula:

[0115]

[0116] In the formula, D ij represents the diameter of the wake of wind turbine i at wind turbine j, α iis the axial induction factor of wind turbine i. is the wake overlap of two wind turbines, V i is the wind speed at wind turbine i.

[0117] The diameter D of the wake of wind turbine i at wind turbine j ij The calculation formula is:

[0118] D ij = 2r + 2kx ij cos(δ ij ).

[0119] In the formula, k is the wake expansion coefficient, x ij is the distance between wind turbine i and wind turbine j, δ ij represents the deflection angle of wind turbine i and wind turbine j relative to the wind speed.

[0120] The wake overlap The calculation formula is:

[0121]

[0122] y ij = x ij sin(δ ij ).

[0123] In the formula, y ij represents the lateral offset distance.

[0124] The deflection angle δ of wind turbine i and wind turbine j relative to the wind speed ij The calculation formula is:

[0125] δ ij = θ ij - τ,

[0126]

[0127] In the formula, τ is the wind direction angle, θ ij is the relative azimuth angle between wind turbines, (x i , y i ) and (x j , y j ) represent the coordinates of wind turbine i and wind turbine j respectively.

[0128] Step 5: Determine the loss function through the particle swarm algorithm and based on the maximum flow algorithm, so as to optimize the wake wind speed deficit.

[0129] Through the particle swarm algorithm, the following formula is used as the objective for optimization.

[0130]

[0131] In the formula, ΔV′ij is the ideal wake wind speed deficit of the wind turbine.

[0132] It should be noted that since the wake wind speed deficit cannot be completely eliminated, therefore, ΔV′ can be calculated based on the maximum flow algorithm. ij value.

[0133] Define nodes: Each node represents a wind turbine. Edge set: The wake wind speed deficit, and define the capacity of the edge as the upper limit L of the wake wind speed deficit. Since the actual expectation is that the smaller the wake wind speed deficit, the better, therefore, when using the maximum flow algorithm, the interval of the wake wind speed deficit (i.e., [0, L]) needs to be inverted.

[0134] Specifically, step 5 includes steps 51 to 54:

[0135] Step 51: Introduce a source point and a sink point, connect the upper-level wind turbines without upper-levels upstream to the source point, and connect the lower-level wind turbines without lower-levels downstream to the sink point. Define the capacity of the edge between the source point and the upper-level wind turbines without upper-levels upstream as the upper limit value of the wake wind speed deficit.

[0136] Among them, introduce a virtual new source point and a new sink point, so that the source point connects all the upper-level wind turbines without upper-levels, and connects all the lower-level wind turbines without lower-levels to the sink point. After introducing the source point and the sink point, it can effectively avoid the problems of the lower-level wind turbines without lower-level nodes and the upper-level wind turbines without upper-level nodes.

[0137] Step 52: When any intermediate node corresponding to an upper-level wind turbine with an upper-level or a lower-level wind turbine with a lower-level overflows, then determine the adjacent node corresponding to the intermediate node, and modify the wake wind speed deficit between the intermediate node and its adjacent node.

[0138] Among them, the adjacent node corresponding to the intermediate node is the lower-level wind turbine of any corresponding intermediate node, and the modification expression of the wake wind speed deficit is:

[0139] v′(i, j) = min(i.e + c(i, j), c(i, j)).

[0140] In the formula, c(i, j) represents the capacity of the edge from node i to node j, v(i, j) is the wake wind speed deficit of the edge from node i to node j before modification, and i.e is the overflow wake wind speed deficit of node i.

[0141] Step 53: Judge whether there is a shortest path from any source point to any sink point according to the single-source shortest path algorithm, and when there is no shortest path, modify the wake wind speed deficit between the intermediate node and its adjacent node between the source point and the sink point.

[0142] Step 54: When there is a shortest path, output the ideal wake wind speed deficit, and the ideal wake wind speed deficit expression is:

[0143] ΔV′ ij = v′(i, j).

[0144] Where, ΔV′ ij is the ideal wake wind speed deficit, and v′(i, j) is the wake wind speed deficit obtained by the last modification when there is a shortest path.

[0145] It should be noted that steps 51 to 54 are continuously iteratively executed to output the minimum wake wind speed deficit, that is, the ideal wake wind speed deficit, through continuous iteration.

[0146] Next, the wind turbine control optimization system based on maximum flow analysis provided by the present invention will be described. The wind turbine control optimization system based on maximum flow analysis described below can be mutually referred to the wind turbine control optimization method based on maximum flow analysis described above.

[0147] In one embodiment, a wind turbine control optimization system based on maximum flow analysis includes a data acquisition module, a data processing module, a first calculation module, a second calculation module, and a control optimization module.

[0148] The data acquisition module is used to acquire the real-time parameters of the wind turbine and the real-time data measured by the sensors. The real-time parameters at least include the rotor diameter, hub height, pitch angle, and blade speed. The real-time data at least includes temperature, wind speed, and humidity.

[0149] The data processing module is used to perform topological sorting on the real-time parameters and real-time data to determine the upstream and downstream relationships between wind turbines, and classify the wind turbines into upstream wind turbines and downstream wind turbines based on the upstream and downstream relationships.

[0150] The first calculation module is used to calculate the thrust coefficient of the wind turbine according to the angle of attack of the wind turbine, pitch angle, wind speed vector value, blade radius of the wind turbine, and blade speed.

[0151] The second calculation module is used to call the wake model to calculate the wake wind speed deficit of the wind turbine based on the diameter of the wake of the first wind turbine at the second wind turbine, the axial induction factor and wind speed of the first wind turbine, and the wake overlap degree between the first wind turbine and the second wind turbine.

[0152] The control optimization module is used to call the particle swarm algorithm and combine it with the maximum flow algorithm to determine the loss function of the wind turbine based on the wake wind speed deficit, so as to calculate the ideal wake wind speed deficit of the wind turbine through the loss function and perform control optimization on the wind turbine.

[0153] In this embodiment, for the wind turbine control optimization system based on maximum flow analysis provided by the present invention, the calculation formula for the thrust coefficient of the wind turbine is:

[0154]

[0155]

[0156] Wherein, C i is the thrust coefficient, i is the label of the fan, a i is the angle of attack of the fan, θ i is the pitch angle of the fan, v i is the wind speed vector value, r is the blade radius of the fan, w i is the blade rotational speed.

[0157] In this embodiment, for the wind turbine control optimization system based on maximum flow analysis provided by the present invention, the expression for calculating the wake wind speed deficit is:

[0158]

[0159] Wherein, ΔV ij is the wake wind speed deficit of the first fan to the second fan, D ij represents the diameter of the wake of the first fan i at the second fan j, α i is the axial induction factor of the first fan i, is the wake overlap degree of the first fan and the second fan, V i is the wind speed at the first fan i.

[0160] In this embodiment, for the wind turbine control optimization system based on maximum flow analysis provided by the present invention, the expression for the diameter of the wake of the first fan at the second fan is:

[0161] D ij = 2r + 2kx ij cos(δ ij ).

[0162] Wherein, k is the wake expansion coefficient, x ij is the distance between the first fan i and the second fan j, δ ij represents the offset angle of the first fan i and the second fan j relative to the wind speed.

[0163] The expression for the wake overlap degree of the first fan and the second fan is:

[0164]

[0165] y ij = x ij sin(δ ij ).

[0166] Wherein, y ij represents the lateral offset distance.

[0167] The expression for the offset angle of the first fan and the second fan relative to the wind speed is:

[0168] δ ij = θ ij - τ,

[0169]

[0170] where τ is the wind direction angle, and θ ij is the relative azimuth angle between the fans, and (x i , y i ) and (x j , y j ) represent the coordinates of fan i and fan j respectively.

[0171] In this embodiment, for the wind turbine control optimization system based on maximum flow analysis provided by the present invention, the control optimization module is specifically used for:

[0172] Obtain the optimization objective function of the wind turbine based on the particle swarm algorithm, and the expression of the optimization objective function is:

[0173]

[0174] where ΔV′ ij is the ideal wake wind speed deficit of the fan, and n is the number of fans.

[0175] In this embodiment, for the wind turbine control optimization system based on maximum flow analysis provided by the present invention, the upstream fans include upstream non-superior fans and upstream superior fans according to the upstream and downstream relationships, and the downstream fans include downstream non-inferior fans and downstream inferior fans according to the upstream and downstream relationships. The control optimization module is specifically further used for:

[0176] Introduce a source point and a sink point, connect the upstream non-superior fans to the source point, connect the downstream non-inferior fans to the sink point, and define the capacity of the edge between the source point and the upstream non-superior fans as the upper limit value of the wake wind speed deficit.

[0177] When any intermediate node corresponding to an upstream superior fan or a downstream inferior fan overflows, determine the adjacent node corresponding to the intermediate node, and modify the wake wind speed deficit between the intermediate node and its adjacent node.

[0178] Among them, the adjacent node corresponding to the intermediate node is the inferior fan of any intermediate node corresponding to it, and the modification expression of the wake wind speed deficit is:

[0179] v′(i, j) = min(i.e + c(i, j), c(i, j)).

[0180] Wherein, c(i, j) represents the capacity of the edge from node i to node j, v(i, j) is the wake wind speed deficit of the edge from node i to node j before modification, and i.e is the overflow wake wind speed deficit of node i.

[0181] In this embodiment, for the wind turbine control optimization system based on maximum flow analysis provided by the present invention, the control optimization module is specifically further configured to:

[0182] Judge whether there is a shortest path from any source point to any sink point according to the single-source shortest path algorithm, and when there is no shortest path, modify the wake wind speed deficit between the intermediate node and its adjacent node between the source point and the sink point.

[0183] When there is a shortest path, output the ideal wake wind speed deficit, and the expression of the ideal wake wind speed deficit is:

[0184] ΔV′ ij = v′(i, j).

[0185] Wherein, ΔV′ ij is the ideal wake wind speed deficit, and v′(i, j) is the wake wind speed deficit obtained by the last modification when there is a shortest path.

[0186] The present disclosure may be a system, a method, and / or a computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions thereon for causing a processor to implement various aspects of the present disclosure.

[0187] The computer-readable storage medium may be a tangible device that can retain and store instructions for use by an instruction execution device. The computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (non-exhaustive list) of the computer-readable storage medium include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disc (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punched card or raised structures in a groove having instructions stored thereon, and any suitable combination of the foregoing. The computer-readable storage medium used herein is not construed as an instantaneous signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagated through a waveguide or other transmission medium (e.g., an optical pulse through an optical fiber cable), or an electrical signal transmitted through a wire.

[0188] The computer-readable program instructions described herein can be downloaded to various computing / processing devices from a computer-readable storage medium or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in each computing / processing device.

[0189] The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state-setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages such as Smalltalk, C++, etc., and conventional procedural programming languages such as the "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider). In some embodiments, by using the state information of the computer-readable program instructions to customize an electronic circuit, such as a programmable logic circuit, a field-programmable gate array (FPGA), or a programmable logic array (PLA), the electronic circuit can execute the computer-readable program instructions to implement various aspects of the present disclosure.

[0190] Aspects of the present disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0191] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, when executed by the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / acts specified in one or more boxes of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, a programmable data processing apparatus, and / or other devices to function in a particular manner, so that the computer-readable medium storing the instructions comprises a manufacture including instructions for implementing various aspects of the functions / acts specified in one or more boxes of the flowchart and / or block diagram.

[0192] The computer-readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other devices, such that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other devices to produce a computer-implemented process, so that the instructions executed on the computer, other programmable data processing apparatus, or other devices implement the functions / acts specified in one or more boxes of the flowchart and / or block diagram.

[0193] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram may represent a module, a segment of a program, or a portion of an instruction, which contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions noted in the boxes may occur in a different order than noted in the figures. For example, two consecutive boxes may actually be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each box in the block diagrams and / or flowcharts, and combinations of boxes in the block diagrams and / or flowcharts, can be implemented by a special-purpose hardware-based system for performing the specified functions or acts, or by a combination of special-purpose hardware and computer instructions.

[0194] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications or equivalent substitutions can still be made to the specific embodiments of the present invention, and any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered by the protection scope of the claims of the present invention.

Claims

1. A wind turbine control optimization method based on maximum flow analysis, characterized in that: The method comprises: Acquire real-time parameters of the wind turbine and real-time data measured by sensors, wherein the real-time parameters include at least rotor diameter, hub height, pitch angle, and blade speed, and the real-time data includes at least temperature, wind speed, and humidity; Topologically sorting the real-time parameters and real-time data to determine the upstream and downstream relationship between wind turbines, and dividing the wind turbines into upstream wind turbines and downstream wind turbines based on the upstream and downstream relationship; Calculate the thrust coefficient of the wind turbine according to the angle of attack, pitch angle, wind speed vector value, blade radius and blade speed of the wind turbine; The wake model is called to calculate the wake wind speed loss of the wind turbine generator set based on the diameter of the wake of the first wind turbine at the second wind turbine, the axial induction factor and wind speed of the first wind turbine, and the overlap of the wakes of the first wind turbine and the second wind turbine; The particle swarm algorithm is called and combined with the maximum flow algorithm to determine the loss function of the wind turbine generator set based on the wake wind speed loss, so as to calculate the ideal wake wind speed loss of the wind turbine through the loss function and optimize the control of the wind turbine generator set.

2. The wind turbine control optimization method based on maximum flow analysis according to claim 1 is characterized in that: The thrust coefficient calculation formula of the wind turbine is: In the formula, C i is the thrust coefficient, i is the fan number, a i is the angle of attack of the fan, θ i is the pitch angle of the wind turbine, v i is the wind speed vector value, r is the blade radius of the fan, w i is the blade speed.

3. The wind turbine control optimization method based on maximum flow analysis according to claim 2 is characterized in that: The expression for calculating the wake wind speed loss is: Where, △V ij is the tail wind speed loss of the first wind turbine to the second wind turbine, D ij represents the diameter of the wake of the first fan i at the second fan j, α i is the axial induction factor of the first fan i, is the overlap between the wakes of the first and second wind turbines, V i is the wind speed at the first wind turbine i.

4. The wind turbine control optimization method based on maximum flow analysis according to claim 3 is characterized in that: The diameter expression of the wake of the first fan at the second fan is: D ij =2r+2kx ij cos(δ ij ); Where k is the wake expansion coefficient, x ij is the distance between the first fan i and the second fan j, δ ij represents the offset angle of the first fan i and the second fan j relative to the wind speed; The expression of the wake overlap between the first wind turbine and the second wind turbine is: and ij =x ij sin(δ ij ); In the formula, y ij Indicates the lateral offset distance; The expression of the offset angle between the first fan and the second fan relative to the wind speed is: d ij =θ ij -t, Where τ is the wind direction angle, θ ij is the relative azimuth angle between fans, (x i ,y i ) and (x j ,y j ) represent the coordinates of fan i and fan j respectively.

5. The wind turbine control optimization method based on maximum flow analysis according to claim 4 is characterized in that: The calling of the particle swarm algorithm and the combination of the maximum flow algorithm to determine the loss function of the wind turbine generator set based on the wake wind speed loss, so as to calculate the ideal wake wind speed loss of the wind turbine through the loss function, and to perform control optimization on the wind turbine generator set, comprises: The optimization objective function of the wind turbine generator set is obtained based on the particle swarm algorithm. The expression of the optimization objective function is: In the formula, △V′ ij is the ideal wake wind speed loss of the wind turbine, and n is the number of wind turbines.

6. The wind turbine control optimization method based on maximum flow analysis according to claim 5 is characterized in that: The upstream fan includes no upper-level fan upstream and an upper-level fan upstream according to the upstream-downstream relationship, and the downstream fan includes no lower-level fan downstream and a lower-level fan downstream according to the upstream-downstream relationship; The calling of the particle swarm algorithm and the maximum flow algorithm to determine the loss function of the wind turbine generator set based on the wake wind speed loss, so as to calculate the ideal wake wind speed loss of the wind turbine through the loss function and perform control optimization on the wind turbine generator set, further includes: Introducing a source point and a sink point, connecting the upstream wind turbine without upper stage to the source point, connecting the downstream wind turbine without lower stage to the sink point, and defining the capacity of the edge between the source point and the upstream wind turbine without upper stage as the upper limit of the wake wind speed loss; and When any intermediate node corresponding to an upper-level wind turbine upstream or a lower-level wind turbine downstream overflows, an adjacent point corresponding to the intermediate node is determined, and the wake wind speed loss between the intermediate node and its adjacent point is modified; Among them, the adjacent point corresponding to the intermediate node is the lower-level wind turbine of any corresponding intermediate node, and the modified expression of the wake wind speed loss is: v′(i,j)=min(i.e+v(i,j),c(i,j)); Where c(i,j) represents the capacity of the edge from node i to node j, v(i,j) is the wake wind speed loss of the edge from node i to node j before modification, and ie is the overflow wake wind speed loss of node i.

7. The wind turbine control optimization method based on maximum flow analysis according to claim 6 is characterized in that: The calling of the particle swarm algorithm and the maximum flow algorithm to determine the loss function of the wind turbine generator set based on the wake wind speed loss, so as to calculate the ideal wake wind speed loss of the wind turbine through the loss function and perform control optimization on the wind turbine generator set, further includes: Determine whether there is a shortest path from any of the source points to any of the sink points according to a single-source shortest path algorithm, and if there is no shortest path, modify the wake wind speed deficit between the intermediate node between the source point and the sink point and its adjacent point; and When the shortest path exists, the ideal wake wind speed loss is output, and the ideal wake wind speed loss expression is: ΔV′ ij =v′(i,j); Where ΔV′ ij is the ideal wake wind speed loss, and v′(i, j) is the wake wind speed loss obtained by the last modification when the shortest path exists.

8. A wind turbine control optimization system based on maximum flow analysis, characterized in that: The system comprises: A data acquisition module, used to acquire real-time parameters of the wind turbine and real-time data measured by sensors, wherein the real-time parameters include at least rotor diameter, hub height, pitch angle and blade speed, and the real-time data includes at least temperature, wind speed and humidity; A data processing module, used for topologically sorting the real-time parameters and real-time data to determine the upstream and downstream relationship between wind turbines, and dividing the wind turbines into upstream wind turbines and downstream wind turbines based on the upstream and downstream relationship; The first calculation module is used to calculate the thrust coefficient of the wind turbine according to the angle of attack, pitch angle, wind speed vector value, blade radius and blade speed of the wind turbine; A second calculation module is used to call the wake model to calculate the wake wind speed loss of the wind turbine set based on the diameter of the wake of the first wind turbine at the second wind turbine, the axial induction factor and wind speed of the first wind turbine, and the overlap of the wakes of the first wind turbine and the second wind turbine; The control optimization module is used to call the particle swarm algorithm and combine the maximum flow algorithm to determine the loss function of the wind turbine group based on the wake wind speed loss, so as to calculate the ideal wake wind speed loss of the wind turbine through the loss function and perform control optimization on the wind turbine group.

9. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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