CN114186407A - Wind power plant wake velocity field calculation method and system with parameters capable of being adjusted in self-adaptive mode - Google Patents
Wind power plant wake velocity field calculation method and system with parameters capable of being adjusted in self-adaptive mode Download PDFInfo
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Abstract
The invention belongs to the technical field of wind power, and particularly relates to a wind power plant wake velocity field calculation method and system with parameters capable of being adjusted in a self-adaptive mode. The method mainly comprises the following steps: firstly, acquiring wind speed and wind direction data of a whole year from an SCADA system of a wind turbine generator, and calculating according to the wind speed and wind direction data to obtain a wind speed average value of each unit position under determined wind speed and wind direction; then constructing a single machine wake velocity field calculation model with parameters capable of being adjusted in a self-adaptive mode and a superposition region wake velocity field calculation model as a wind power plant wake velocity field meter model; and then constructing a wake velocity field model parameter adaptive calculation model based on a long-short term memory network to obtain a wind power wake velocity field calculation model with optimal parameters by taking the minimum difference between the wind power field wake velocity field calculation model result and the real wake distribution as a target and taking single-machine wake velocity field calculation model parameters and stacking area wake velocity field calculation model parameters as optimization variables. The method can obviously improve the calculation precision of the wake velocity field of the wind power plant and provide technical support for power prediction and optimized operation of the wind power plant.
Description
Technical Field
The invention belongs to the technical field of wind power, and particularly relates to a wind power plant wake velocity field calculation method and system with parameters capable of being adjusted in a self-adaptive mode.
Background
With large-scale development and utilization of wind power, tens of wind power generation units or even hundreds of wind power generation units are generally constructed into a wind power plant according to a certain arrangement mode in order to save land resources and reduce investment cost. In a wind power plant, after the incoming wind speed passes through a wind turbine generator set positioned at the upstream, the wind speed is reduced, the turbulence degree is increased, and the wake effect is formed. The speed attenuation enables the output power of a downstream wind turbine generator to be reduced, the turbulence degree is increased, the pneumatic performance of the wind turbine generator is affected, and the fatigue load of the wind turbine generator is increased. Researches show that the generating efficiency loss of the wind turbine generator which completely works in the wake flow environment is up to 40 percent, and the load increase is up to 10 to 45 percent.
However, the parameters of the wake velocity model of the existing wind power plant are fixed, the wake velocity distribution calculation under different environments is difficult to adapt, the adaptability is not strong, and the accuracy is poor. Therefore, the method accurately calculates the wake distribution characteristics of the wind power plant, reduces the wake loss of the wind power plant, improves the output power, and becomes a key problem to be solved urgently in the optimization design and operation of the wind power plant.
Disclosure of Invention
The invention provides a wind power plant wake velocity field computing system with parameters capable of being adaptively adjusted, aiming at the defects and problems that the parameters of the conventional wind power plant wake distribution computing model are fixed and can not be adjusted, and the wake velocity under different environmental conditions can not be accurately computed.
The technical scheme adopted by the invention for solving the technical problems is as follows: a wind power plant wake velocity field calculation method with parameters capable of being adjusted in a self-adaptive mode comprises the following steps:
acquiring wind speed and wind direction data of a whole year from an SCADA system of a wind turbine generator, and calculating according to the wind speed and the wind direction data to obtain a wind speed average value of each unit position under determined wind speed and wind direction;
constructing a single machine wake flow velocity field calculation model with parameters capable of being adjusted in a self-adaptive mode and a superposition region wake flow velocity field calculation model as a wind power plant wake flow velocity field calculation model;
thirdly, constructing a target function by using the minimum difference value of the calculation model result of the wake velocity field of the wind power plant and the real wake distribution, and constructing a wake velocity field adaptive parameter calculation model based on the long-term and short-term memory network by using single-machine wake velocity field calculation model parameters and superposition area wake velocity field calculation model parameters as optimization variables;
and step four, taking the parameter value with the minimum objective function as the optimal parameter value, and substituting the optimal parameter value into the single-machine wake velocity field calculation model and the overlap region wake velocity field calculation model to obtain the wind power plant wake velocity field calculation model with the optimal parameter.
According to the wind power plant wake velocity field calculation method with the parameters capable of being adjusted in a self-adaptive mode, in the step one, the complete annual wind speed and wind direction data comprise the wind speed and the wind direction of each unit position; the time frequency of wind speed and wind direction data acquisition was 1 minute.
According to the wind power plant wake velocity field calculation method with the adaptively adjustable parameters, in the step two, the single-machine wake velocity field calculation model is as follows:
in the formula: u (x) is the wake wind speed at x downstream of the wind turbine; psi is the pitch angle of the wind turbine; phi is the yaw angle of the wind turbine; s is the rotating speed of the wind turbine generator; u. of0The ambient incoming flow wind speed; theta, theta1、θ2、θ3、θ4、θ5、θ6And sigma is a correction coefficient which can be the rootDetermining the influence rule of the flow scene and the change of the unit operation parameters on the single machine wake flow velocity field according to the numerical simulation data, the SCADA system data and the measurement data;
the calculation model of the wake velocity field of the superposition area is as follows:
in the formula: alpha is alphaj,iThe weight value of intersection of the wake flow area of the upstream wind turbine generator j and the wind wheel area of the downstream wind turbine generator i is obtained; u. ofj,i(xi) The wake wind speed of the upstream wind turbine j at the downstream wind turbine i; u. ofjIs the incoming wind speed u of the upstream wind turbine generator jiThe wind speed is the incoming flow wind speed of the wind turbine generator i; deltajAnd ηjFor the correction coefficient, the correction coefficient can be determined according to numerical simulation data, SCADA system data and measurement data and by combining the numerical variation range of key elements of the wake velocity field of the interference region.
In the method for calculating the wake velocity field of the wind power plant with the adaptively adjustable parameters, the target function of the long-term and short-term memory network is constructed by the minimum difference between the results of the single-machine wake velocity field calculation model and the superimposed area wake velocity field calculation model and the real wake distribution in the step three,
in the formula:
the wind speed is the average wind speed of a certain incoming flow wind speed and the position of a wind generating set under the wind direction; and N is the number of the wind turbine generators.
In the third step, a wake velocity field adaptive parameter calculation model based on a long-short term memory network is constructed by taking single-machine wake velocity field calculation model parameters and superposition area wake velocity field calculation model parameters as optimization variables, wherein the optimization parameters of the long-short term memory network are as follows:
ζ＝(θ,θ1,θ2,θ3,θ4,θ5,θ6,σ,δj,ηj)
in the formula: ζ represents an optimization parameter of the long and short memory network.
The invention also provides a wind power plant wake velocity field computing system with adaptively adjustable parameters, which comprises communication equipment and a server, wherein the communication equipment is used for acquiring wind speed and wind direction information from the field wind power generator set; the server provides a support platform for the wake velocity field model parameter adaptive decision method, and is used for executing the steps of the calculation method.
The invention has the beneficial effects that: the wind power plant wake velocity field calculation system with the parameters capable of being adaptively adjusted can adaptively adjust model parameters according to different environments of different terrains and obtain model optimal parameters under different environments, and the constructed adaptive wake velocity field calculation model can accurately calculate the wake distribution characteristics of the wind power plant according to the environment conditions.
The wind power plant wake velocity field calculation model with the adaptively adjustable parameters can obviously improve the calculation precision of the wind power plant wake velocity field and provide technical support for wind power plant power prediction and optimized operation.
Drawings
FIG. 1 is a flow chart of a wind power plant wake velocity field calculation system with parameters adaptively adjustable.
FIG. 2 is a flow chart of wake velocity field model parameter adaptive decision making based on a long-short term memory network.
FIG. 3 is a plot of a wind farm layout for Horns Rev, Denmark.
FIG. 4 is a graph comparing wind speed at 270 degrees.
FIG. 5 is a graph comparing wind speed at 220 degrees.
Detailed Description
Aiming at the problems that parameters of the conventional wind power plant wake velocity calculation method are fixed, the wake velocity distribution of different environments and different terrains is difficult to adapt, and the accuracy is poor, the invention provides a wind power plant wake velocity field calculation method and system with adaptively adjustable parameters. The invention is further illustrated with reference to the following figures and examples.
Example 1: the embodiment provides a wind power plant wake velocity field calculation method with parameters capable of being adjusted in a self-adaptive mode, and the method comprises the following steps:
acquiring wind speed and wind direction data of a whole year from an SCADA system of a wind turbine generator, wherein the wind speed and wind direction data of the whole year comprise wind speed and wind direction of each unit, and the time frequency of acquiring the wind speed and wind direction data is 1 minute; and then calculating and determining the wind speed and the annual average wind speed at the position of the wind generating set under the wind direction according to the obtained data.
Secondly, constructing a single-machine wake velocity field calculation model with parameters capable of being adaptively adjusted and a superposition region wake velocity field calculation model as a wind power plant wake velocity calculation model, and comprising the following steps of:
(1) constructing a single machine wake flow velocity field calculation model;
in the formula: u (x) is the wake wind speed at x downstream of the wind turbine; psi is the pitch angle of the wind turbine; phi is the yaw angle of the wind turbine; s is the rotating speed of the wind turbine generator; u. of0The ambient incoming flow wind speed; theta, theta1、θ2、θ3、θ4、θ5、θ6And sigma is a correction coefficient, and the correction coefficient can be determined according to numerical simulation data, SCADA system data and measurement data and by combining the flow scene and the change of unit operation parameters with the influence rule of the single-machine wake flow velocity field.
(2) Constructing a velocity field calculation model of the superposition area:
in the formula: alpha j, i is the wake flow area of the upstream wind turbine generator j and the downstream wind turbine generatorThe weight of the intersection of the areas of the wind wheels of the group i; u. ofj,i(xi) The wake wind speed of the upstream wind turbine j at the downstream wind turbine i; u. ofjIs the incoming wind speed u of the upstream wind turbine generator jiThe wind speed is the incoming flow wind speed of the wind turbine generator i; deltajAnd ηjFor the correction coefficient, the correction coefficient can be determined according to numerical simulation data, SCADA system data and measurement data and by combining the numerical variation range of key elements of the wake velocity field of the interference region.
Thirdly, constructing a parameter self-adaptive wake velocity field calculation model based on the long-short term memory network by taking the minimum difference between the results of the single-machine wake velocity field calculation model and the superimposed area wake velocity field calculation model and the real wake distribution as a target and taking parameters of the single-machine wake velocity field calculation model and parameters of the superimposed area wake velocity field calculation model as optimization variables, wherein the method comprises the following steps:
(1) constructing an objective function of the long-term and short-term memory network by using the minimum difference value between the calculation model result of the wind power station wake velocity field and the real wake distribution, wherein the objective function is as follows:
in the formula:
the average wind speed of a certain incoming flow at a wind-down position and at a wind turbine generator position is N, and N is the number of the wind turbine generators;
(2) the method comprises the following steps of constructing wake velocity field calculation model parameters based on a long-short term memory network by taking single-machine wake velocity field calculation model parameters and wake velocity field calculation model parameters of an overlap region as optimization variables, wherein the optimization parameters of the long-short term memory network are as follows:
ζ＝(θ,θ1,θ2,θ3,θ4,θ5,θ6,σ,δj,ηj)
in the formula: zeta represents the optimization parameter of the long and short memory network;
and taking the parameter value when the objective function is minimum as the optimal parameter value, and substituting the optimal parameter value into the single-machine wake velocity field calculation model and the superposition area wake velocity field calculation model to obtain the wind power plant wake velocity field calculation model with the optimal parameter.
Wherein: constructing a wake velocity field model parameter self-adaptive decision model based on a long and short term memory network, training the long and short term memory network containing the wake velocity field model, judging whether the calculation precision of the wake velocity field of the wind power plant meets the requirement or not,
the method comprises the following steps: inputting an initial value of an optimization parameter, wind speed and direction data and average wind speed at each wind turbine position, constructing a long and short memory network according to input data and output requirements, judging whether the calculation precision of the wake velocity field of the wind power plant meets the requirements or not,
if not, training again until the calculation precision meets the requirement;
if so, outputting the optimal value of the model parameter, taking the optimal value as the parameter of the wind power plant wake velocity field calculation model, and outputting the wind power plant wake velocity field calculation model.
Example 2: the embodiment provides a wind power plant wake velocity field calculation system with adaptively adjustable parameters, which comprises: a communication device and a server; the communication equipment is used for acquiring wind speed and wind direction information from a field wind turbine generator, and the time frequency of acquiring wind speed and wind direction data is 1 minute; the server provides a support platform for the wake velocity field model parameter adaptive decision method and is used to perform the computational method steps of embodiment 1.
Example 3: in the embodiment, a danish Horns Rev wind farm is taken as a research object, the danish Horns Rev wind farm has 80 wind turbine generators, 8 rows and 10 columns are arranged according to a parallelogram, the distance between each row and column is 7D, the distance between each northeast direction is 10.4D, the distance between each southeast direction is 9.4D, and D is the diameter of a wind wheel. The wind turbine generator is Vestas V80, the single machine capacity is 2MW, the diameter of a wind wheel is 80m, the central height of a hub is 70m, and the whole wind power plant is arranged as shown in figure 3.
The method of the invention is adopted to respectively calculate and analyze the wind conditions of the incoming flow wind direction of 270 degrees and 220 degrees and the incoming flow wind speed of 8.5 m/s. In the calculation process, the wind turbine is assumed to face into the wind. The computer processor has a master frequency of 1.8GHz, a memory of 2GB and an operation software MATLAB 2011. The calculation results are shown in fig. 4 and 5, respectively.
As can be seen from the comparison graph of the measured result of the method and the actual wind speed under 270 degrees in FIG. 4, the maximum difference value between the calculated result of the method and the actual wind speed is 0.14m/s, which illustrates the wind power plant wake distribution characteristics which can be prepared by the method when the incoming wind direction is 270 degrees.
As can be seen from the comparison graph of the measured result of the method of the invention and the actual wind speed under 220 degrees in FIG. 5, the maximum difference between the model calculation result constructed by the method of the invention and the actual wind speed is 0.21 m/s, which shows that the wake distribution characteristic of the wind power plant can be accurately calculated by the method of the invention when the incoming wind direction is 220 degrees.
The comprehensive calculation method can be used for accurately calculating the wind power plant wake flow distribution characteristics of the wind power plant wake flow distribution characteristics under different wind speeds and wind directions.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and scope of the present invention are intended to be covered thereby.
Claims (6)
1. A wind power plant wake velocity field calculation method with parameters capable of being adaptively adjusted is characterized by comprising the following steps: the method comprises the following steps:
acquiring wind speed and wind direction data of a whole year from an SCADA system of a wind turbine generator, and calculating according to the wind speed and the wind direction data to obtain a wind speed average value of each unit position under determined wind speed and wind direction;
constructing a single machine wake flow velocity field calculation model with parameters capable of being adjusted in a self-adaptive mode and a superposition region wake flow velocity field calculation model as a wind power plant wake flow velocity field calculation model;
thirdly, constructing a target function by using the minimum difference value of the calculation model result of the wake velocity field of the wind power plant and the real wake distribution, and constructing a wake velocity field adaptive parameter calculation model based on the long-term and short-term memory network by using single-machine wake velocity field calculation model parameters and superposition area wake velocity field calculation model parameters as optimization variables;
and step four, taking the parameter value with the minimum objective function as the optimal parameter value, and substituting the optimal parameter value into the single-machine wake velocity field calculation model and the overlap region wake velocity field calculation model to obtain the wind power plant wake velocity field calculation model with the optimal parameter.
2. The wind farm wake velocity field calculation method with adaptively adjustable parameters according to claim 1, characterized in that: the wind speed and wind direction data of the whole year in the step one comprise the wind speed and the wind direction of each unit position; the time frequency of wind speed and wind direction data acquisition was 1 minute.
3. The wind farm wake velocity field calculation method with adaptively adjustable parameters according to claim 1, characterized in that: in the second step, the calculation model of the single machine wake flow velocity field is as follows:
in the formula: u (x) is the wake wind speed at x downstream of the wind turbine; psi is the pitch angle of the wind turbine; phi is the yaw angle of the wind turbine; s is the rotating speed of the wind turbine generator; u. of0The ambient incoming flow wind speed; theta, theta1、θ2、θ3、θ4、θ5、θ6And sigma is a correction coefficient, and the correction coefficient can be determined according to numerical simulation data, SCADA system data and measurement data by combining the flow scene and the influence rule of unit operation parameter change on the single-machine wake flow velocity field;
the calculation model of the wake velocity field of the superposition area is as follows:
in the formula: alpha is alphaj,iIs as followsThe weight value of intersection of the wake flow area of the downstream wind turbine generator j and the wind wheel area of the downstream wind turbine generator i; u. ofj,i(xi) The wake wind speed of the upstream wind turbine j at the downstream wind turbine i; u. ofjIs the incoming wind speed u of the upstream wind turbine generator jiThe wind speed is the incoming flow wind speed of the wind turbine generator i; deltajAnd ηjFor the correction coefficient, the correction coefficient can be determined according to numerical simulation data, SCADA system data and measurement data and by combining the numerical variation range of key elements of the wake velocity field of the interference region.
4. The wind farm wake velocity field calculation method with adaptively adjustable parameters according to claim 1, characterized in that: in the third step, the difference between the results of the single machine wake velocity field calculation model and the overlap region wake velocity field calculation model and the real wake distribution is minimum to construct the objective function of the long-term and short-term memory network,
in the formula:
the wind speed is the average wind speed of a certain incoming flow wind speed and the position of a wind generating set under the wind direction; and N is the number of the wind turbine generators.
5. The wind farm wake velocity field calculation method with adaptively adjustable parameters according to claim 4, characterized in that: in the third step, a wake velocity field adaptive parameter calculation model based on a long-short term memory network is constructed by taking single-machine wake velocity field calculation model parameters and wake velocity field calculation model parameters of an overlap region as optimization variables, wherein the optimization parameters of the long-short term memory network are as follows:
ζ＝(θ,θ1,θ2,θ3,θ4,θ5,θ6,σ,δj,ηj)
in the formula: ζ represents an optimization parameter of the long and short memory network.
6. A wind power plant wake velocity field computing system with parameters capable of being adjusted in a self-adaptive mode is characterized in that: the system comprises communication equipment and a server, wherein the communication equipment is used for collecting wind speed and wind direction information from a field wind turbine; the server provides a support platform for a wake velocity field model parameter adaptive decision method for performing the steps of the calculation method of any one of claims 1 to 5.
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| CN116667344A (en) * | 2023-07-31 | 2023-08-29 | 浙江大学 | Self-adaptive local fatigue load balance scheduling method and device for offshore wind farm |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116667344A (en) * | 2023-07-31 | 2023-08-29 | 浙江大学 | Self-adaptive local fatigue load balance scheduling method and device for offshore wind farm |
| CN116667344B (en) * | 2023-07-31 | 2023-10-10 | 浙江大学 | Self-adaptive local fatigue load balance scheduling method and device for offshore wind farm |
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