Wind turbine generator control optimization method and system based on maximum flow analysis

Abstract

The invention discloses a wind turbine generator control optimization method and system based on maximum flow analysis, and the method comprises the steps: obtaining the real-time parameters of a fan and the real-time data measured by a sensor, carrying out the topological sorting of the real-time parameters and the real-time data, so as to determine the upstream and downstream relation between wind turbine generators, and the wind turbine generator is divided into an upstream fan and a downstream fan based on the upstream and downstream relationship. And the thrust coefficient of the wind turbine generator is calculated according to the attack angle, the pitch angle and the wind speed vector value of the fan and the blade radius and the blade rotating speed of the fan. And calling a wake flow model to calculate the wake flow wind speed loss of the wind turbine generator based on the diameter of the wake flow of the first fan at the second fan, the axial induction factor and the wind speed of the first fan and the wake flow overlapping degree of the first fan and the second fan. And calling a particle swarm algorithm and combining a maximum flow algorithm to determine a loss function of the wind turbine generator based on the wake flow wind speed loss so as to calculate the ideal wake flow wind speed loss of the fan through the loss function, and performing control optimization on the wind turbine generator.

Description

Technical Field

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

[0002] With the increasing 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 access, it is usually necessary to deploy multiple wind turbines in parallel. Through parallel operation, wind farms can more effectively utilize wind energy resources while providing stable and continuous power output to the grid. However, in this multi-unit parallel configuration, the mutual influence between wind turbines becomes complicated, especially when the wind speed and wind direction change dramatically. A single wind turbine control algorithm cannot cope with the dynamic interaction between wind turbines.

[0003] Current wind turbine control systems mostly use proportional-integral-differential (PID) control algorithms. PID control is simple and effective in controlling a single wind turbine and is suitable for control requirements under stable conditions. However, in an environment where multiple wind turbines are operating in parallel, PID control has limited adaptability to external disturbances and is difficult to accurately respond to complex changes in wind speed and direction. At the same time, PID control lacks a response strategy for the mutual influence between wind turbines and cannot dynamically adjust the control parameters of each wind turbine to cope with the mutual influence between them. Therefore, for the situation where multiple units are operating in parallel, a single PID control is difficult to guarantee the optimal efficiency of the overall system.

[0004] In the parallel structure of a wind farm, wind turbines will interact with each other, especially in an environment with frequent wind speed changes and turbulence. The output power change of a single wind turbine will affect the surrounding wind turbines through airflow disturbances, resulting in a decrease in overall power generation efficiency. Traditional PID control lacks adaptive adjustment functions for this complex interaction, resulting in inflexible response and difficulty in achieving collaborative optimization when multiple units are operated in parallel. In addition, PID control cannot achieve personalized adjustment for each wind turbine when large-scale wind turbines are operated in parallel, which easily leads to "outdated" or "accumulated errors" in 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 and adaptive control) to optimize the coordinated operation of multiple wind turbines. Summary of the invention

[0005] In order to solve the deficiencies in the prior art, the purpose of the present invention is to solve the above-mentioned defects and further propose a wind turbine control optimization method and system based on maximum flow analysis.

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

[0007] A first aspect of the present invention discloses a wind turbine control optimization method based on maximum flow analysis, the method comprising:

[0008] 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;

[0009] 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;

[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 speed of the wind turbine;

[0011] 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;

[0012] 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.

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

[0014]

[0015]

[0016] 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.

[0017] Furthermore, the expression for calculating the wake wind speed loss is:

[0018]

[0019] Where ΔV 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.

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

[0021] D ij =2r+2kx ij cos(δ ij );

[0022] 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;

[0023] The expression of the wake overlap between the first wind turbine and the second wind turbine is:

[0024]

[0025] y ij =x ij sin(δ ij );

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

[0027] The expression of the offset angle between the first fan and the second fan relative to the wind speed is:

[0028] δ ij =θ ij -τ,

[0029]

[0030] 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.

[0031] Further, 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, including:

[0032] 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:

[0033]

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

[0035] Further, 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;

[0036] 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:

[0037] 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

[0038] 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;

[0039] 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:

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

[0041] 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.

[0042] Further, 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:

[0043] 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

[0044] When the shortest path exists, the ideal wake wind speed loss is output, and the ideal wake wind speed loss expression is:

[0045] ΔV′ ij =v′(i, j);

[0046] 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.

[0047] A second aspect of the present invention discloses a wind turbine control optimization system based on maximum flow analysis, the system comprising:

[0048] 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;

[0049] 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;

[0050] 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;

[0051] 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;

[0052] 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.

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

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

[0055] The processor is used 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 having a computer program stored thereon, characterized in that when the program is executed by a processor, the steps of the method described in the first aspect are implemented.

[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 turbine and the real-time data measured by the sensor, the real-time parameters and real-time data are topologically sorted to determine the upstream and downstream relationship between the wind turbines, and the wind turbines are divided into upstream wind turbines and downstream wind turbines based on the upstream and downstream relationship. Subsequently, the thrust coefficient of the wind turbine is calculated, and the wake model is called to calculate the wake wind speed loss of the wind turbine. The particle swarm algorithm is then called and combined with the maximum flow algorithm to determine the loss function of the wind turbine 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. This method optimizes the control of the entire wind turbine by minimizing the wake wind speed loss between wind turbines, thereby reducing the wind speed reduction received by the downstream wind turbine in the wake of the upstream wind turbine, reducing the turbulence of the downstream wind turbine, and thus improving the operating efficiency of the entire wind turbine. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0060] The present application is further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present application.

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

[0062] Step S110, acquiring real-time parameters of the wind turbine and real-time data measured by sensors, the real-time parameters at least including rotor diameter, hub height, pitch angle and blade speed, and the real-time data at least including temperature, wind speed and humidity.

[0063] Step S120 , topologically sorting the real-time parameters and the real-time data to determine the upstream and downstream relationship between the wind turbines, and dividing the wind turbines into upstream wind turbines and downstream wind turbines based on the upstream and downstream relationship.

[0064] Step S130, calculating 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.

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

[0066]

[0067]

[0068] 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.

[0069] Step S140, calling the wake model 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 between the wakes of the first wind turbine and the second wind turbine.

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

[0071]

[0072] Where ΔV 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.

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

[0074] D ij =2r+2kx ij cos(δ ij ).

[0075] 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 wind turbine i and the second wind turbine j relative to the wind speed.

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

[0077]

[0078] y ij =x ij sin(δ ij ).

[0079] In the formula, y ijIndicates the lateral offset distance.

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

[0081] δ ij =θ ij -τ,

[0082]

[0083] 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.

[0084] Step S150, calling the particle swarm algorithm and combining the maximum flow algorithm to determine the loss function of the wind turbine 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.

[0085] In some embodiments, the wind turbine control optimization method based on maximum flow analysis provided by the present invention calls a particle swarm algorithm and combines the maximum flow algorithm to determine the loss function of the wind turbine 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, specifically including the following steps:

[0086] Step S151, obtaining the optimization objective function of the wind turbine generator system based on the particle swarm algorithm, the expression of the optimization objective function is:

[0087]

[0088] Where ΔV′ ij is the ideal wake wind speed loss of the wind turbine, and n is the number of wind turbines.

[0089] In some embodiments, the wind turbine control optimization method based on maximum flow analysis provided by the present invention calls a particle swarm algorithm and combines the maximum flow algorithm to determine the loss function of the wind turbine 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, and specifically includes the following steps:

[0090] Step S152, introduce the source point and the sink point, connect the upstream wind turbine without upper stage to the source point, connect the downstream wind turbine without lower stage to the sink point, and define 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.

[0091] Step S153, when the intermediate node corresponding to any upper-level wind turbine upstream or lower-level wind turbine downstream overflows, the 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.

[0092] 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:

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

[0094] 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.

[0095] In some embodiments, the wind turbine control optimization method based on maximum flow analysis provided by the present invention calls a particle swarm algorithm and combines the maximum flow algorithm to determine the loss function of the wind turbine 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, and specifically includes the following steps:

[0096] Step S154, judging whether there is a shortest path from any source point to any sink point according to the single-source shortest path algorithm, and if there is no shortest path, modifying the wake wind speed loss between the intermediate node between the source point and the sink point and its adjacent point.

[0097] Step S155: when the shortest path exists, output the ideal wake wind speed loss. The ideal wake wind speed loss expression is:

[0098] ΔV′ ij =v′(i,j).

[0099] 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.

[0100] The above-mentioned wind turbine control optimization method based on maximum flow analysis obtains the real-time parameters of the wind turbine and the real-time data measured by the sensor, and topologically sorts the real-time parameters and real-time data to determine the upstream and downstream relationship between the wind turbines, and divides the wind turbines into upstream wind turbines and downstream wind turbines based on the upstream and downstream relationship. Subsequently, the thrust coefficient of the wind turbine is calculated, and the wake model is called to calculate the wake wind speed loss of the wind turbine. Then, the particle swarm algorithm is called and combined with the maximum flow algorithm to determine the loss function of the wind turbine 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 control the wind turbine. This method optimizes the control of the entire wind turbine by minimizing the wake wind speed loss between the wind turbines, thereby reducing the wind speed reduction received by the downstream wind turbine in the wake of the upstream wind turbine, reducing the turbulence of the downstream wind turbine, and thus improving the operating efficiency of the entire wind turbine.

[0101] In a specific embodiment, the wind turbine control optimization method based on maximum flow analysis provided by the present invention is that the purpose of parallel operation of multiple wind turbines 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 power demand of the power grid under low wind speed conditions. The wind turbines operating in parallel can capture more wind energy in different areas, enhance the power generation of the power 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 power grid through power transformers, realizing unified management and optimized scheduling of the overall output power of the power farm.

[0102] In a wind farm, the parallel structure of wind turbines usually includes multiple wind turbines arranged in groups. The wind turbines in each group are connected through an internal control unit to form an independent control area in order to implement the optimization strategy of partition control. Wind turbines in different groups are usually arranged at intervals and keep a certain distance from each other to reduce the impact 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 to the central transformer station, and then uniformly integrated into the power grid. In this distributed grouping 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 turbine by optimizing the wake wind speed loss of the wind turbine, thereby achieving the optimal control of the wind turbine.

[0104] Among them, the wake effect refers to the fact that when the wind passes through the wind turbine, it will slow down 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, the received wind speed is reduced, and the turbulence is increased, which in turn affects the efficiency and structural load of the entire wind turbine.

[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: Obtain real-time data of the wind turbine based on sensors.

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

[0108] Step 2: Based on topological sorting and real-time data, determine the upstream and downstream relationships between wind turbines.

[0109] Specifically, a wind turbine is composed of multiple wind turbines, which can be divided into upstream wind turbines and downstream wind turbines according to the upstream and downstream relationship. The upstream wind turbines and downstream wind turbines can be further divided into no upstream upper-level wind turbines, upstream upper-level wind turbines, no downstream lower-level wind turbines, and downstream lower-level wind turbines.

[0110] Step 3: Calculate the fan thrust coefficient C i , the calculation formula is:

[0111]

[0112]

[0113] In the formula, i is the number of the fan, 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.

[0114] Step 4: Apply the wake model to calculate the wake wind speed loss, the wake wind speed loss of wind turbine i to wind turbine j, ΔV ij , the calculation formula is:

[0115]

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

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

[0118] D ij =2r+2kx ij cos(δ ij ).

[0119] Where k is the wake expansion coefficient, x ij is the distance between fan i and fan j, δ ij It represents the offset angle of fans i and j relative to the wind speed.

[0120] Wake overlap The calculation formula is:

[0121]

[0122] y ij =x ij sin(δ ij ).

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

[0124] The deviation angle δ between fans i and j relative to the wind speed ij The calculation formula is:

[0125] δ ij =θ ij -τ,

[0126]

[0127] 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.

[0128] Step 5: Use the particle swarm algorithm and determine the loss function based on the maximum flow algorithm to optimize the wake wind speed loss.

[0129] Through the particle swarm algorithm, the following formula is optimized as the target.

[0130]

[0131] Where ΔV′ij is the ideal wake wind speed loss of the wind turbine.

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

[0133] Define nodes: Each node represents a wind turbine. Edge set: Wake wind speed loss, define the edge capacity as the upper limit of the wake wind speed loss L. Since the actual expectation is that the wake wind speed loss is as small as possible, when using the maximum flow algorithm, the interval of the wake wind speed loss (ie: [0, L]) needs to be inverted.

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

[0135] Step 51: Introduce source points and sink points, connect the upstream wind turbine without upper stage to the source point, connect the downstream wind turbine without lower stage to the sink point, and define 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.

[0136] Among them, a virtual new source point and a new sink point are introduced, so that the source point connects all upstream wind turbines without upper levels, and all downstream wind turbines without lower levels are connected to the sink point. After the source point and sink point are introduced, the problem of no lower level wind turbines without lower level nodes downstream and no upper level wind turbines without upper level nodes upstream can be effectively avoided.

[0137] Step 52: When the intermediate node corresponding to any upper-level wind turbine upstream or lower-level wind turbine downstream overflows, the 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.

[0138] 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:

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

[0140] 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.

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

[0142] Step 54: When the shortest path exists, output the ideal wake wind speed loss. The ideal wake wind speed loss expression is:

[0143] ΔV′ ij =v′(i,j).

[0144] 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.

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

[0146] The wind turbine control optimization system based on maximum flow analysis provided by the present invention is described below. The wind turbine control optimization system based on maximum flow analysis described below and the wind turbine control optimization method based on maximum flow analysis described above can refer to each other.

[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 obtain the real-time parameters of the wind turbine and the real-time data measured by the sensor. The real-time parameters include at least the rotor diameter, hub height, pitch angle and blade speed, and the real-time data includes at least temperature, wind speed and humidity.

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

[0150] 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.

[0151] The 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.

[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 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.

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

[0154]

[0155]

[0156] 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.

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

[0158]

[0159] Where ΔV 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.

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

[0161] D ij =2r+2kx ij cos(δ ij ).

[0162] 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 wind turbine i and the second wind turbine j relative to the wind speed.

[0163] The expression of the wake overlap between the first wind turbine and the second wind turbine is:

[0164]

[0165] y ij =x ij sin(δ ij ).

[0166] In the formula, y ij Indicates the lateral offset distance.

[0167] The expression of the offset angle between 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, 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.

[0171] In this embodiment, 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] The optimization objective function of the wind turbine is obtained based on the particle swarm algorithm. The expression of the optimization objective function is:

[0173]

[0174] Where ΔV′ ij is the ideal wake wind speed loss of the wind turbine, and n is the number of wind turbines.

[0175] In this embodiment, the wind turbine control optimization system based on maximum flow analysis provided by the present invention includes upstream wind turbines without upper-level wind turbines and upstream wind turbines with upper-level wind turbines according to the upstream and downstream relationship, and downstream wind turbines include downstream wind turbines without lower-level wind turbines and downstream wind turbines with lower-level wind turbines according to the upstream and downstream relationship. The control optimization module is also specifically used for:

[0176] Introduce source points and sink points, connect the upstream wind turbine without upper stage to the source point, connect the downstream wind turbine without lower stage to the sink point, and define 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.

[0177] When there is an overflow of the intermediate node corresponding to any upper-level wind turbine upstream or a lower-level wind turbine downstream, the 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.

[0178] 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:

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

[0180] 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.

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

[0182] According to the single-source shortest path algorithm, it is determined whether there is a shortest path from any source point to any sink point. If there is no shortest path, the wake wind speed loss between the intermediate node between the source point and the sink point and its adjacent point is modified.

[0183] When the shortest path exists, the ideal wake wind speed loss is output. The ideal wake wind speed loss expression is:

[0184] ΔV′ ij =v′(i,j).

[0185] 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.

[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 carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0187] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A 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 of computer-readable storage media (a non-exhaustive list) 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 disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0188] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, 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 transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The 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 the computer-readable storage medium in each computing / processing device.

[0189] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related 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 "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via 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., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0190] Various aspects of the present disclosure are described herein with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram 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 device, thereby producing a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device that implements the functions / actions specified in one or more boxes in the flowchart and / or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable data processing device, and / or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0192] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operating steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0193] The flow chart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to multiple embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a part of a module, program segment or instruction, and a part of the module, program segment or instruction includes one or more executable instructions for realizing the specified logical function. In some alternative implementations, the function marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous square boxes can actually be executed substantially in parallel, and they can sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or action, or can be implemented with a combination of special 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 rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection 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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