An Adaptive Optimization Method for the Power Curve of Wind Turbines Based on Sector Distribution

Abstract

The present invention relates to the field of wind power generation technology, and more specifically to a method for adaptively optimizing wind turbine power curves based on sector distribution. The method comprises the following steps: analyzing data from wind speed and direction meters on wind towers and wind turbine equipment to determine the Weibull distribution characteristics of a wind turbine in different sectors at different times; establishing a database of optimal power curves applicable to a wind turbine under different Weibull distribution characteristics; utilizing a genetic algorithm to determine the optimal power curve corresponding to the Weibull distribution characteristics of a wind turbine at a certain spatial position at a certain time; and utilizing sector management and a central control system to control the power output of all wind turbines in a wind farm. The method solves the problem of low coupling between optimal power output of wind turbines in both time and space scales, and is novel and highly operational.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power generation, and in particular to a method for adaptively optimizing a wind turbine power curve based on sector distribution. Background Art

[0002] In recent years, global climate change has significantly impacted wind resources across the country, with significant interannual variations. Furthermore, the increasing scarcity of land resources has placed higher demands on improving land resource utilization efficiency during wind resource development. Furthermore, China's wind power development began in 2003, spanning over a decade, and there are very few superior resources. Existing technologies generally improve the optimal power output capacity of wind turbines through techniques such as variable pitch adjustment and sector management to control wake losses. To further enhance the power output capacity of the entire wind farm cluster in different wind direction sectors, the present invention proposes a technical approach to optimizing the power curve of wind turbines. Summary of the invention

[0003] To address the challenges of the prior art, the present invention provides a method for adaptively optimizing wind turbine power curves based on sector distribution. This method, using an adaptive genetic algorithm, combines the optimal wind power output curves corresponding to different Weibull distribution characteristic curves, significantly improving the wind turbine's optimal power output capability under wind resource characteristics at different time and spatial scales. Furthermore, by leveraging existing sector management and control methods, the optimal power output coupling of wind turbines across both time and spatial scales is improved.

[0004] To achieve the above object, the present invention adopts the following technical solutions:

[0005] A method for adaptively optimizing a wind turbine power curve based on sector distribution includes the following steps:

[0006] S1: Based on the wind speed and direction data from the wind tower and the wind turbine generator equipment, the Weibull distribution characteristics of a wind turbine generator in different sectors at different times are obtained;

[0007] S2: Establish an optimal power curve database for a certain wind turbine generator set under different Weibull distribution characteristics;

[0008] S3: Genetic algorithm is used to obtain the optimal power curve corresponding to the Weibull distribution characteristics of the wind turbine generator set at a certain position in the space at a certain moment;

[0009] S4: Sector management and central control system are used to control the power output of all wind turbines in a wind farm.

[0010] Furthermore, the step S1 includes the following steps:

[0011] S101: Collect wind speed and wind direction data from wind towers and wind turbine generators within the wind farm to obtain wind speed V at different times. i and the number of wind speed data N;

[0012] S102: using the maximum likelihood method to calculate the shape parameter k and scale parameter c of the Weibull distribution characteristics of each sector at the location of the wind turbine generator equipment at different times;

[0013] The shape parameter k is calculated by the following formula:

[0014]

[0015] The scale parameter is calculated by the following formula:

[0016]

[0017] Where: V i is the wind speed at time i, N is the number of wind speeds;

[0018] S103: Calculating the Weibull distribution probability function F(V) and the probability density function f(V) at different times for each sector at the location of the wind turbine generator equipment;

[0019] The Weibull distribution probability function is:

[0020] The probability density function is:

[0021] Where k is the shape parameter of Weibull, c is the scale parameter of Weibull, and V is the wind speed.

[0022] Furthermore, step S2 includes the following steps:

[0023] S201: For a certain type of wind turbine, use different wind power curves to calculate the average wind power density WPD and the average effective wind power density

[0024] The average wind power density WPD is calculated by the following formula:

[0025]

[0026] The average effective wind power density Calculated by the following formula:

[0027]

[0028] Where: under the integral sign is the incomplete gamma function;

[0029] V1 and V2 are respectively the upper wind speed value and the lower wind speed value of the effective wind range of the wind turbine.

[0030] Further, the step S3 includes the following steps:

[0031] S301: Initialization, set the evolution generation counter t = 0, set the maximum evolution generation T, and randomly generate M individuals as the initial population P(0);

[0032] S302: Individual evaluation: Calculate the fitness of each individual in the population P(t);

[0033] S303: Selection operation: Apply the selection operator to the population;

[0034] S304: Crossover operation: Apply the crossover operator to the population;

[0035] S305: Mutation operation: Apply the mutation operator to the population to obtain the next generation population P(t + 1);

[0036] S306: Termination condition judgment: If t = T, then output the individual with the maximum fitness obtained during the evolution process as the optimal solution, and obtain the wind power curve corresponding to the maximum average wind power density and the average effective wind power density.

[0037] Further, the step S4 includes the following steps:

[0038] S401: The wind power generation equipment monitors the incoming wind direction sector of the nacelle of the wind power generation equipment at a certain moment;

[0039] S402: For the Weibull distribution characteristics at this moment, the central control system calls the time-domain optimal power curve and the space-domain optimal power curve;

[0040] S403: Through the sector management system, analyze the better power curves among the time-domain optimal power curves and the space-domain optimal power curves of all wind turbine generator equipment in the wind farm;

[0041] S404: All wind turbine generator equipment in the wind farm completes collaborative output through the central control system, so that the power output of the entire wind farm reaches the optimum.

[0042] Compared to existing technologies, the adaptive optimization method for wind turbine power curves presented in this invention has the following beneficial effects: It addresses the issue of low coupling between optimal power output of wind turbines across both temporal and spatial scales. This novel and highly operational method combines the optimal wind power output curves corresponding to different Weibull distribution characteristic curves using an adaptive genetic algorithm, significantly improving the optimal power output capability of wind turbines under wind resource characteristics at different temporal and spatial scales. Furthermore, by leveraging existing sector management and control methods, the optimal power output coupling of wind turbines across both temporal and spatial scales is improved, providing important guidance for the development of wind power projects in various regions. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 Flowchart of the adaptive optimization method for wind turbine power curve;

[0044] Figure 2 It is an adaptive optimization method for wind turbine power curve based on sector distribution. DETAILED DESCRIPTION

[0045] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments. The following embodiments are only used to illustrate the present invention but are not intended to limit the scope of the present invention.

[0046] like Figure 1 As shown, the present invention proposes a method for adaptively optimizing a wind turbine power curve based on sector distribution, comprising the following steps:

[0047] S1: Based on the wind speed and direction data from the wind tower and the wind turbine generator equipment, the Weibull distribution characteristics of a wind turbine generator in different sectors at different times are obtained;

[0048] S2: Establish an optimal power curve database for a certain wind turbine generator set under different Weibull distribution characteristics;

[0049] S3: Genetic algorithm is used to obtain the optimal power curve corresponding to the Weibull distribution characteristics of the wind turbine generator set at a certain position in the space at a certain moment;

[0050] S4: Sector management and central control system are used to control the power output of all wind turbines in a wind farm.

[0051] The specific steps of step S1 to step S4 are as follows:

[0052] The step S1 comprises the following steps:

[0053] S101: Collect the wind speed and wind direction data measured by the anemometers and wind turbines within the wind farm to obtain the wind speed V at different times i and the number N of wind speed data;

[0054] S102: Calculate the shape parameter k and scale parameter c of the Weibull distribution at different times for each sector at the location of the wind turbine equipment using the maximum likelihood method;

[0055] The shape parameter k is calculated by the following formula:

[0056]

[0057] The scale parameter is calculated by the following formula:

[0058]

[0059] where: V i is the wind speed at the i-th moment, and N is the number of wind speeds;

[0060] S103: Calculate the Weibull distribution probability function F(V) and probability density function f(V) at different times for each sector at the location of the wind turbine equipment;

[0061] The Weibull distribution probability function is:

[0062] The probability density function is:

[0063] where k is the shape parameter of Weibull, c is the scale parameter of Weibull, and V is the wind speed.

[0064] Furthermore, the step S2 includes the following steps:

[0065] S201: For a certain inherent wind turbine model, use different wind power curves to calculate the average wind power density WPD and the average effective wind power density

[0066] The average wind power density WPD is calculated by the following formula:

[0067]

[0068] The average effective wind power density is calculated by the following formula:

[0069]

[0070] where: under the integral sign is the incomplete gamma function;

[0071] V1 and V2 are respectively the upper wind speed value and the lower wind speed value of the effective wind power range of the wind turbine generator set.

[0072] Further, the step S3 includes the following steps:

[0073] S301: Initialization, set the evolution generation counter t = 0, set the maximum evolution generation T, and randomly generate M individuals as the initial population P(0);

[0074] S302: Individual evaluation: Calculate the fitness of each individual in the population P(t);

[0075] S303: Selection operation: Apply the selection operator to the population;

[0076] S304: Crossover operation: Apply the crossover operator to the population;

[0077] S305: Mutation operation: Apply the mutation operator to the population to obtain the next generation population P(t + 1);

[0078] S306: Termination condition judgment: If t = T, then output the individual with the maximum fitness obtained during the evolution process as the optimal solution, and obtain the wind power curve corresponding to the maximum average wind power density and the average effective wind power density.

[0079] Further, the step S4 includes the following steps:

[0080] S401: The wind power generation equipment monitors the incoming flow wind direction sector of the nacelle of the wind power generation equipment at a certain moment;

[0081] S402: For the Weibull distribution characteristics at this moment, the central control system calls the time-domain optimal power curve and the space-domain optimal power curve;

[0082] S403: Through the sector management system, analyze the better power curve among the time-domain optimal power curve and the space-domain optimal power curve of all wind turbine generator set equipment in the wind farm;

[0083] S404: All wind turbine generator set equipment in the wind farm complete collaborative output through the central control system, so that the power output of the entire wind farm reaches the optimal.

[0084] Embodiment 1

[0085] As Figure 1 shown, this embodiment proposes a method for adaptively optimizing the power curve of a wind turbine generator set based on sector distribution, including the following steps:

[0086] S1: Based on the data analysis of the wind speed and wind direction instruments installed on the wind measurement towers and wind turbine generator sets, obtain the Weibull distribution characteristics of a certain wind turbine generator set in different sectors at different times, which specifically includes the following steps:

[0087] S101: Collect the wind measurement data of the wind speed and wind direction instruments installed on the wind measurement towers and each wind turbine generator set within the wind farm, and obtain the wind speed V at different times i and the number N of wind speed data;

[0088] S102: Use the maximum likelihood method to calculate the shape parameter k and scale parameter c of the Weibull distribution characteristics at different times in each sector at the location of the wind turbine generator set;

[0089] The shape parameter k is calculated by the following formula:

[0090]

[0091] The scale parameter is calculated by the following formula:

[0092]

[0093] In the formula: V i is the wind speed at the i-th moment, and N is the number of wind speeds;

[0094] S103: Calculate the Weibull distribution probability function F(V) and probability density function f(V) at different times in each sector at the location of the wind turbine generator set;

[0095] The Weibull distribution probability function is:

[0096] The probability density function is:

[0097] In the formula, k is the shape parameter of Weibull, c is the scale parameter of Weibull, and V is the wind speed.

[0098] S2: Establish an optimal power curve database applicable to a certain wind turbine generator set under different Weibull distribution characteristics. Specifically: for a certain inherent wind turbine generator set model, use different wind power curves to calculate the average wind power density WPD and the average effective wind power density

[0099] Among them, the average wind power density WPD is calculated by the following formula:

[0100]

[0101] The average effective wind power density is calculated by the following formula:

[0102]

[0103] In the formula: under the integral sign is the incomplete gamma function;

[0104] V1 and V2 are respectively the upper wind speed value and the lower wind speed value of the effective wind power range of the wind turbine.

[0105] S3: Use the genetic algorithm to obtain the optimal power curve corresponding to the Weibull distribution characteristics of the wind turbine at a certain position in space at a certain moment. The specific algorithm is as follows:

[0106] S301: Initialization, set the evolution generation counter t = 0, set the maximum evolution generation T, and randomly generate M individuals as the initial population P(0);

[0107] S302: Individual evaluation: Calculate the fitness of each individual in the population P(t);

[0108] S303: Selection operation: Apply the selection operator to the population;

[0109] S304: Crossover operation: Apply the crossover operator to the population;

[0110] S305: Mutation operation: Apply the mutation operator to the population to obtain the next generation population P(t + 1);

[0111] S306: Termination condition judgment: If t = T, then output the individual with the maximum fitness obtained during the evolution process as the optimal solution, and obtain the wind power curve corresponding to the maximum average wind power density and the average effective wind power density.

[0112] S4: Use the sector management and central control system to achieve the control of the power output of all wind turbines in a certain wind farm, as Figure 2 shown, specifically as follows:

[0113] S401: The wind power generation equipment monitors the incoming wind direction sector of the nacelle of the wind power generation equipment at a certain moment;

[0114] S402: For the Weibull distribution characteristics at this moment, the central control system calls the optimal power curve in the time domain and the optimal power curve in the space domain;

[0115] S403: Through the sector management system, analyze the better power curve among the optimal power curves in the time domain and the optimal power curves in the space domain of all wind turbine equipment in the wind farm;

[0116] S404: All wind turbine equipment in the wind farm completes coordinated output through the central control system, so that the power output of the entire wind farm reaches the optimum.

[0117] The preferred embodiments of the present invention have been described in detail above. However, the present invention is not limited to the specific details in the above embodiments. Within the scope of the technical concept of the present invention, various transformations can be made to the technical solutions of the present invention, and these simple modifications all fall within the protection scope of the present invention.

Claims

1. An adaptive optimization method for the power curve of a wind turbine based on sector distribution, characterized in that The following steps are involved: S1: Based on the wind speed and direction data from the wind tower and the wind turbine generator equipment, the Weibull distribution characteristics of a wind turbine generator in different sectors at different times are obtained; S2: Establish an optimal power curve database for a certain wind turbine generator set under different Weibull distribution characteristics; S3: Genetic algorithm is used to obtain the optimal power curve corresponding to the Weibull distribution characteristics of the wind turbine generator set at a certain position in the space at a certain moment; S4: Sector management and central control system are used to control the power output of all wind turbines in a wind farm.

2. The adaptive optimization method for the power curve of a wind turbine based on sector distribution according to claim 1, wherein: The step S1 comprises the following steps: S101: Collect the wind speed and wind direction data measured by the anemometers and the built-in wind speed and wind direction sensors of each wind turbine within the wind farm to obtain the wind speed V at different times i and the number N of wind speed data; S102: using the maximum likelihood method to calculate the shape parameter k and scale parameter c of the Weibull distribution characteristics of each sector at the location of the wind turbine generator equipment at different times; The shape parameter k is calculated by the following formula: The scale parameter is calculated by the following formula: Where: V i is the wind speed at the i-th moment, and N is the number of wind speeds; S103: Calculating the Weibull distribution probability function F(V) and the probability density function f(V) at different times for each sector at the location of the wind turbine generator set; The Weibull distribution probability function is as follows: The probability density function is as follows: Where k is the shape parameter of Weibull, c is the scale parameter of Weibull, and V is the wind speed.

3. The adaptive optimization method for the power curve of a wind turbine based on sector distribution according to claim 1, wherein: The step S2 comprises the following steps: S201: For a certain type of fixed wind turbine generator, different wind power curves are adopted to calculate the average wind power density WPD and the average effective wind power density The average wind power density WPD is calculated by the following formula: The average effective wind power density is calculated by the following formula: Where: under the integral sign is the incomplete gamma function; V1 and V2 are the upper and lower wind speed limits of the effective wind power range of the wind turbine respectively.

4. A method for adaptively optimizing the power curve of a wind turbine based on sector distribution according to claim 1, characterized in that: The step S3 comprises the following steps: S301: Initialization, setting the evolutionary generation counter t=0, setting the maximum evolutionary generation T, and randomly generating M individuals as the initial population P(0); S302: Individual evaluation: Calculate the fitness of each individual in the population P(t); S303: Selection operation: applying the selection operator to the group; S304: Crossover operation: applying the crossover operator to the group; S305: Mutation operation: Apply the mutation operator to the population to obtain the next generation population P(t+1); S306: Termination condition judgment: If t=T, the individual with the maximum fitness obtained in the evolution process is output as the optimal solution to obtain the wind power curve corresponding to the maximum average wind power density and the average effective wind power density.

5. The adaptive optimization method for the power curve of a wind turbine based on sector distribution according to claim 1, wherein: The step S4 comprises the following steps: S401: The wind turbine generator device detects the wind direction sector of the incoming flow from the wind turbine generator device nacelle at a certain moment; S402: Based on the Weibull distribution characteristics at this moment, the central control system calls the time domain optimal power curve and the space domain optimal power curve; S403: Analyzing the optimal power curve of the time domain and the optimal power curve of the space domain for all wind turbine generators in the wind farm through the sector management system; S404: All wind turbine generators in the wind farm complete coordinated output through the central control system, so that the power output of the entire wind farm reaches the optimal level.

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