A wind farm unit collaborative optimization method, system, device and storage medium

Abstract

The present invention provides a method for collaborative optimization of wind farm units, including: collecting power generation performance parameters of multiple wind turbines, constructing control constraint relationship functions and full power characteristic models between multiple wind turbines; collecting load data of multiple wind turbines, and calculating the fatigue damage degree of each wind turbine according to the load data; combining the fatigue damage degree, control constraint relationship function and full power characteristic model into a wind farm multi-objective optimization model, and optimizing the wind farm multi-objective optimization model according to power grid dispatch using a particle swarm algorithm to obtain the optimal power generation of each wind turbine, and enable the corresponding wind turbine to operate at the optimal power generation. The present invention solves the problem that the prior art only focuses on the operating status and performance optimization of a single wind turbine, while ignoring the synergy between units.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power generation, and in particular to a method, system, device and storage medium for collaborative optimization of wind farm units. Background Art

[0002] With the rapid development of wind power technology and the continuous increase in wind power installed capacity, wind farms play an increasingly important role in energy supply. However, wind farms face the contradiction between unit fatigue and power generation during operation. On the one hand, in order to increase power generation, wind farms need to use wind energy as much as possible for power generation; on the other hand, long-term operation and complex wind conditions will cause fatigue damage to unit components (especially blades), which will affect the reliability and life of the unit. Therefore, in order to balance power generation and fatigue damage, the existing technology usually builds a corresponding fatigue loss model for a single wind turbine, and regulates the operating state of the single wind turbine according to the model. However, wind farms usually consist of multiple units, which will affect and restrict each other during operation. Traditional single-machine control methods often only focus on the operating state and performance optimization of a single wind turbine, while ignoring the synergy between units, resulting in the pursuit of high power generation while causing the wind turbine to produce fatigue damage faster than expected. Summary of the invention

[0003] In view of the deficiencies in the prior art, the present invention provides a method for collaborative optimization of wind farm units, which solves the problem that the prior art only focuses on the operating status and performance optimization of a single wind turbine, while ignoring the synergy between units, resulting in the wind turbine units generating fatigue damage faster than expected while pursuing high power generation.

[0004] According to an embodiment of the present invention, a method for collaborative optimization of wind farm units includes:

[0005] Collect the power generation performance parameters of multiple wind turbines, and build the control constraint relationship function and full power characteristic model between multiple wind turbines;

[0006] Collect load data of multiple wind turbines, and calculate the fatigue damage degree of each wind turbine according to the load data;

[0007] The fatigue damage degree, control constraint relationship function and full power characteristic model are combined into a multi-objective optimization model of a wind farm. According to the grid dispatch, the particle swarm algorithm is used to optimize the multi-objective optimization model of the wind farm to obtain the optimal power generation of each wind turbine and make the corresponding wind turbine operate at the optimal power generation.

[0008] Preferably, the method for obtaining the fatigue damage degree includes:

[0009] Use the correlation analysis method to filter out the effective load signal from the load data, and then use the time domain method to convert the load signal into the time domain to obtain the load spectrum;

[0010] The load spectrum is discretized using the rainflow counting method;

[0011] According to the discretized load spectrum, the SN curve is established, and the load cycle damage degree of each cycle in the rain flow counting method is calculated according to the SN curve. Then, all load cycle damage degrees are accumulated to obtain the fatigue damage degree.

[0012] Preferably, the calculation formula of the fatigue damage degree is as follows:

[0013]

[0014] where F i (k) is the fatigue coefficient of wind turbine i at time k, p i (t) is the output power of wind turbine i, P e is the rated power of wind turbine i, T ser is the design life of wind turbine i, D dis is the wind farm eddy current intensity equivalent coefficient, M rep To maintain the compensation coefficient, is the effective turbulence intensity of the wind turbine at time t.

[0015] Preferably, the wind farm multi-objective optimization model is as follows:

[0016] max f(x)=w1f1(x)-w2f2(x)-w3f3(x)

[0017] st min ≤γ i (k+h|h)≤γ max

[0018] C Tmin ≤C Ti (k+h|h)≤C Tmax

[0019] Among them, f1(x) is the full power characteristic model, f2(x) is the control constraint relationship function, f3(x) is the fatigue damage degree, T is the unit service time, C is the i power coefficient of the wind turbine, and γ is the blade rotation angle of the wind turbine.

[0020] Preferably, the method of using the particle swarm algorithm to optimize the wind farm multi-objective optimization model to obtain the optimal power generation of each wind turbine is as follows:

[0021] S1: Initialize a particle swarm, wherein the solution of each particle in the particle swarm corresponds to the optimal power of each wind turbine generator set;

[0022] S2: Randomly select a particle and use the optimal power of the particle as the optimal power of all particles in the current particle swarm;

[0023] S3: Update the parameters of all particles, then calculate the target vector of each particle and screen all particles with non-inferior solutions in the current particle swarm and add them to the elite set;

[0024] S4: Calculate the fitness of each particle in the particle swarm using the fitness function formula, randomly select a particle from the elite set according to a probability method proportional to the fitness value, and use the particle as the optimal power of all particles in the current particle swarm;

[0025] S5: Repeat steps S3-S4 until the number of iterations reaches the maximum number, and take the solution of the particles in the elite set at this time as the optimal power generation of the corresponding wind turbine. Then, clear the particles in the elite set, and repeat steps S2-S5 with the remaining particles until there are no more particles in the particle group.

[0026] Preferably, if the number of particles in the elite set exceeds the maximum capacity, particles twice the number in excess are selected, and 50% of them are randomly eliminated.

[0027] Preferably, when selecting, selection is made from low to high fitness.

[0028] On the other hand, according to an embodiment of the present invention, a wind farm unit collaborative optimization system is also provided. The system uses the above-mentioned wind farm unit collaborative optimization method, including:

[0029] A collection module, which is used to collect power generation performance parameters and load data of the wind turbine;

[0030] An analysis module, wherein the analysis module is used to construct a wind farm multi-objective optimization model according to power generation performance parameters and load data, and optimize the wind farm multi-objective optimization model to obtain the optimal power generation of each wind turbine;

[0031] A control module is used to control the corresponding wind turbine group to operate at the optimal power generation power according to the optimal power generation power of each wind turbine group.

[0032] On the other hand, according to an embodiment of the present invention, there is further provided a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the above-mentioned method for collaborative optimization of wind farm units.

[0033] On the other hand, according to an embodiment of the present invention, there is further provided a computer storage medium storing a computer program, and when the computer program is executed by a processor, the processor executes the above-mentioned method for collaborative optimization of wind farm units.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] Based on the control constraint relationship function and full power characteristic model between multiple wind turbines as power constraints, the synergy between multiple wind turbines is considered, and then according to the fatigue loss degree of each wind turbine, a multi-objective optimization model of the wind farm is constructed, and fatigue loss constraints are added to the wind turbines. Then, the optimal power generation power of each generator set is obtained according to the multi-objective optimization model of the wind farm. In this way, attention is paid to the operating status of multiple wind turbines at the same time, and the performance of multiple wind turbines is optimized at the same time. A reasonable control strategy is formulated to achieve the coordinated optimization of fatigue and power generation of wind farm units, so that the wind turbines can generate high power while maximizing fatigue losses, thereby promoting the long-term stability and economic optimal operation of the wind farm. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is the optimization process of the embodiment of the present invention. DETAILED DESCRIPTION

[0037] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0038] like Figure 1 As shown, the embodiment of the present invention proposes a wind farm unit collaborative optimization method, including:

[0039] Collect the power generation performance parameters of multiple wind turbines, and build the control constraint relationship function and full power characteristic model between multiple wind turbines;

[0040] The full power characteristic model of all wind turbines is as follows:

[0041]

[0042] Where N is the number of wind turbines in the wind farm, p i (k+h|k) represents the power that wind turbine i can capture, p i (k) is the predicted value based on the current moment at the k+1 moment in the future. The calculation formula is as follows:

[0043]

[0044] Among them, V in and V e are the cut-in wind speed and rated wind speed of the wind turbine, P eis the rated power of the wind turbine, T is the service life of the wind turbine, C is the i power coefficient of the wind turbine, γ is the blade rotation angle of the wind turbine, v i (t) is the power of wind turbine i at time t, D is the rotation radius of the wind turbine blades, and ρ is the air density.

[0045] In order to balance the thrust load of the wind turbine, the objective function of minimizing the thrust load of the wind turbine within the prediction step M can be expressed as

[0046]

[0047] Collect load data of multiple wind turbines, and calculate the fatigue damage degree of each wind turbine according to the load data;

[0048] After collecting the load data of the wind turbine, the load data is cleaned and normalized, and then the previous correlation analysis is used to combine the unit's operating status such as power generation, off-grid, power limitation, pitch change, rotation speed, wind speed and other information to screen out the effective load signal, and then the time domain method and frequency domain method are used to analyze the effective load signal. The time domain method directly records the change of the load over time, and the frequency domain method converts the load signal to the frequency domain for analysis through methods such as Fourier transform. The time domain method is used in the present invention to obtain the load spectrum of the wind turbine.

[0049] The rain flow counting method is used to simplify the continuous load time history into a series of discrete load cycles. Then, the SN curve is established based on the discrete load spectrum. SN describes the number of cycles required for fatigue failure of the material at different stress levels. It is the basis for fatigue damage calculation. Combined with the "Miner linear cumulative damage theory", the damage degree is obtained by the ratio of the actual number of cycles at the stress level to the corresponding number of rain flow counting cycles on the SN curve. The damage calculation is performed on the load cycle, and the damage degrees of all cycles are accumulated to obtain the total damage degree.

[0050] The objective function of the total damage degree is as follows:

[0051]

[0052] In the formula, F i (k) is the fatigue coefficient of unit i at time k, and its calculation formula is as follows

[0053]

[0054] Among them, p i (t) Output power of wind turbine i, T ser is the design life of wind turbine i, D dis is the wind farm eddy current intensity equivalent coefficient, M rep To maintain the compensation coefficient, is the effective turbulence intensity of wind turbine i at time t.

[0055] Effective turbulence intensity The wake turbulence intensity is and ambient turbulence intensity Composition, its

[0056] The calculation formula is as follows:

[0057]

[0058] Where L is the ratio between the fan spacing and the fan diameter, I ref It is the reference value of environmental turbulence intensity. Since offshore wind farms are low turbulence intensity processes, a value of 0.12 can generally be used.

[0059] The fatigue damage degree, control constraint relationship function and full power characteristic model are combined into a multi-objective optimization model of a wind farm. According to the grid dispatch, the particle swarm algorithm is used to optimize the multi-objective optimization model of the wind farm to obtain the optimal power generation of each wind turbine and make the corresponding wind turbine operate at the optimal power generation.

[0060] Combining the above formulas, the following wind farm multi-objective optimization model for load safety was finally established:

[0061] max f(x)=w1f1(x)-w2f2(x)-w3f3(x)

[0062] st min ≤γ i (k+h|h)≤γ max

[0063]

[0064] Then the particle swarm optimization algorithm is used to optimize the multi-objective optimization model. The field group control multi-objective non-convex nonlinear optimization problem of the particle swarm algorithm is non-trivial and is solved by MOPSO. The specific description is as follows:

[0065] Initialize the particle swarm. Each particle in the particle swarm corresponds to each wind turbine. Each particle includes three kinds of data: particle position, particle velocity, and particle solution. The solution of each particle corresponds to the optimal power of each wind turbine. The initial value of the optimal power of each wind turbine is the rated power.

[0066] Then a particle is randomly selected and the solution of the particle is used as the solution of all particles in the current particle swarm. The positions and velocities of all particles are updated. Then the target vector of each particle is calculated and all particles with non-inferior solutions in the current particle swarm are screened.

[0067] The particle velocity and position update formulas are as follows:

[0068]

[0069] V is the velocity and X is the position.

[0070] Create an elite set and add all particles with non-inferior solutions to the elite set. Then use the following fitness function formula to calculate the fitness of each particle in the particle swarm:

[0071]

[0072] Where P is the elite set, and s(d(i,j)) is the fitness of the i-th particle and the j-th particle in the elite, as follows:

[0073]

[0074] Where d(i,j) is the distance between the i-th particle and the j-th particle, and the decision variable space distance d(i,j)=||x i -x j ||, s is the niche radius

[0075] Then, a particle is randomly selected from the elite set according to a probability method proportional to the fitness value, and the particle is used as the optimal power of all particles in the current particle swarm. The above steps are repeated for iteration until the number of iterations reaches the maximum number. The solution of the particle in the elite set at this time is used as the optimal power generation of the corresponding wind turbine.

[0076] Then the elite set is cleared and the remaining particles are iterated again until there are no more examples in the particle group. At this time, all generators can generate electricity at the optimal power. In this way, we can pay attention to the operating status of multiple wind turbines at the same time, optimize the performance of multiple wind turbines at the same time, formulate reasonable control strategies, and achieve coordinated optimization of wind farm unit fatigue and power generation. While making wind turbines generate high power, fatigue losses are minimized to the greatest extent, promoting long-term stability and economic optimal operation of wind farms.

[0077] In addition, if the number of particles in the elite set exceeds the maximum capacity, the particles in the elite set are sorted from low to high according to fitness, and particles that exceed the number by 2 times are selected in order, and 50% of them are randomly eliminated.

[0078] On the other hand, an embodiment of the present invention further provides a wind farm unit collaborative optimization system, which uses the above-mentioned wind farm unit collaborative optimization method, including:

[0079] A collection module, which is used to collect power generation performance parameters and load data of the wind turbine;

[0080] An analysis module, wherein the analysis module is used to construct a wind farm multi-objective optimization model according to power generation performance parameters and load data, and optimize the wind farm multi-objective optimization model to obtain the optimal power generation of each wind turbine;

[0081] A control module is used to control the corresponding wind turbine group to operate at the optimal power generation power according to the optimal power generation power of each wind turbine group.

[0082] On the other hand, an embodiment of the present invention further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the above-mentioned method for collaborative optimization of wind farm units.

[0083] On the other hand, an embodiment of the present invention further provides a computer storage medium storing a computer program, and when the computer program is executed by a processor, the processor executes the above-mentioned method for collaborative optimization of wind farm units.

[0084] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

Claims

1. A method for collaborative optimization of wind farm units, characterized in that: include: Collect the power generation performance parameters of multiple wind turbines, and build the control constraint relationship function and full power characteristic model between multiple wind turbines; Collect load data of multiple wind turbines, and calculate the fatigue damage degree of each wind turbine according to the load data; The fatigue damage degree, control constraint relationship function and full power characteristic model are combined into a multi-objective optimization model of a wind farm. According to the grid dispatch, the particle swarm algorithm is used to optimize the multi-objective optimization model of the wind farm to obtain the optimal power generation of each wind turbine and make the corresponding wind turbine operate at the optimal power generation.

2. A wind farm unit collaborative optimization method according to claim 1, characterized in that: The methods for obtaining fatigue damage degree include: Use the correlation analysis method to filter out the effective load signal from the load data, and then use the time domain method to convert the load signal into the time domain to obtain the load spectrum; The load spectrum is discretized using the rainflow counting method; According to the discretized load spectrum, the SN curve is established, and the load cycle damage degree of each cycle in the rain flow counting method is calculated according to the SN curve. Then, all load cycle damage degrees are accumulated to obtain the fatigue damage degree.

3. A wind farm unit collaborative optimization method according to claim 2, characterized in that: The calculation formula of the fatigue damage degree is as follows: where F i (k) is the fatigue coefficient of wind turbine i at time k, p i (t) is the output power of wind turbine i, P e is the rated power of wind turbine i, T ser is the design life of wind turbine i, D dis is the wind farm eddy current intensity equivalent coefficient, M rep To maintain the compensation coefficient, is the effective turbulence intensity of the wind turbine at time t.

4. A wind farm unit collaborative optimization method according to claim 1, characterized in that: The multi-objective optimization model of the wind farm is as follows: max f(x)=w1f1(x)-w2f2(x)-w3f3(x) s.t.γ min ≤γ i (k+hh)≤γ max C Tmin ≤C Ti (k+hh)≤C Tmax Among them, f1(x) is the full power characteristic model, f2(x) is the control constraint relationship function, f3(x) is the fatigue damage degree, T is the unit service time, C is the i power coefficient of the wind turbine, and γ is the blade rotation angle of the wind turbine.

5. The method for collaborative optimization of wind farm units according to claim 1, characterized in that: The method of using particle swarm algorithm to optimize the wind farm multi-objective optimization model and obtain the optimal power generation of each wind turbine is as follows: S1: Initialize a particle swarm, wherein the solution of each particle in the particle swarm corresponds to the optimal power of each wind turbine generator set; S2: Randomly select a particle and use the optimal power of the particle as the optimal power of all particles in the current particle swarm; S3: Update the parameters of all particles, then calculate the target vector of each particle and screen all particles with non-inferior solutions in the current particle swarm and add them to the elite set; S4: Calculate the fitness of each particle in the particle swarm using the fitness function formula, randomly select a particle from the elite set according to a probability method proportional to the fitness value, and use the particle as the optimal power of all particles in the current particle swarm; S5: Repeat steps S3-S4 until the number of iterations reaches the maximum number, and take the solution of the particles in the elite set at this time as the optimal power generation of the corresponding wind turbine. Then, clear the particles in the elite set, and repeat steps S2-S5 with the remaining particles until there are no more particles in the particle group.

6. A wind farm unit collaborative optimization method according to claim 5, characterized in that: If the number of particles in the elite set exceeds the maximum capacity, then twice the number of particles in excess is selected and 50% of them are randomly eliminated.

7. A wind farm unit collaborative optimization method according to claim 6, characterized in that: When selecting, select from low to high according to fitness.

8. A wind farm unit collaborative optimization system, characterized by: The system uses a wind farm unit collaborative optimization method as claimed in any one of claims 1 to 7, comprising: A collection module, which is used to collect power generation performance parameters and load data of the wind turbine; An analysis module, wherein the analysis module is used to construct a wind farm multi-objective optimization model according to power generation performance parameters and load data, and optimize the wind farm multi-objective optimization model to obtain the optimal power generation of each wind turbine; A control module is used to control the corresponding wind turbine group to operate at the optimal power generation power according to the optimal power generation power of each wind turbine group.

9. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes a wind farm unit collaborative optimization method according to any one of claims 1 to 7.

10. A computer storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a processor, the processor executes a wind farm unit collaborative optimization method according to any one of claims 1 to 7.

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