NL2036572A - Nacelle wind speed correction method and system based on mechanism model and neural network - Google Patents

Nacelle wind speed correction method and system based on mechanism model and neural network Download PDF

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NL2036572A
NL2036572A NL2036572A NL2036572A NL2036572A NL 2036572 A NL2036572 A NL 2036572A NL 2036572 A NL2036572 A NL 2036572A NL 2036572 A NL2036572 A NL 2036572A NL 2036572 A NL2036572 A NL 2036572A
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data
frequency
low
scada
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Xiao Zhao
Zhao Qiancheng
Deng Jiewen
Yin Xiangfeng
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Hunan Univ Of Science & Technology
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Abstract

The present invention discloses a nacelle wind speed correction method and system based on a mechanism model and a neural network. A wind speed data and a power data measured in a SCADA system are selected, and a SCADA wind speed is diVided into wind 5 speed datasets under different working conditions according to a rated wind speed. A theoretical wind speed is calculated through empirical formulas; a high-frequency residual and a low-frequency residual of a real wind speed and a theoretical wind speed are calculated by means of a wavelet transform, a relationship between the SCADA wind speed and the high-frequency residual and the low-frequency residual is established by the neural 10 network, a SCADA wind speed data from actual operating wind farms is input into the trained neural network to obtain a corresponding high-frequency residual and a corresponding low-frequency residual.

Description

NACELLE WIND SPEED CORRECTION METHOD AND SYSTEM BASED ON
MECHANISM MODEL AND NEURAL NETWORK
TECHNICAL FIELD
[0001] The present application relates to the field of data processing and data transmission, and in particular to a nacelle wind speed correction method and system based on a mechanism model and a neural network.
BACKGROUND
[0002] With the development of a wind power technology, it is necessary to obtain accurate wind speed data when performing economic benefit evaluation, power generation evaluation, fleet layout, and operation control in wind fields. Due to difficulty in directly measuring a real wind speed, a nacelle wind speed recorded by a Supervisory Control and
Data Acquisition System (SCADA) is usually used as an important parameter for analysis in evaluation calculations of wind turbines. However, due to factors such as airflow distortion caused by blade rotation and a nacelle, the wind speed values measured by the
SCADA often cannot directly reflect real wind speed of impellers and must be corrected before use. Such correction of the wind speed is called a nacelle transfer function.
[0003] At present, methods for correcting a SCADA wind speed mainly include a theoretical calculation correction method and a function fitting method. Here, a theoretical calculation correction method is mainly based on an aerodynamics theory, using multiple parameters of wind turbine units and operational data recorded by the SCADA to achieve correction of the nacelle wind speed through theoretical calculation. However, due to a large number of parameters and complex internal mechanisms, such method lacks calculation accuracy and cannot cover different models. The function fitting method corrects the nacelle wind speed by fitting a direct functional relationship between the wind speed of an anemometer tower and the nacelle wind speed; the method is simple and easy to use, but greatly relies on the data of the anemometer tower. A configuration site of the anemometer tower is harsh in demands and expensive. Generally, only one typical wind turbine unit is selected to be provided with the anemometer tower for characteristic evaluation, which cannot meet needs of all wind turbines in the entire field. Therefore, such method has high application costs and limited application scenarios.
SUMMARY
[0004] In order to overcome the shortcomings of the prior art, the present invention provides a nacelle wind speed correction method and system that utilize neural networks, theoretical characteristics, model evaluation and correction techniques, etc., and consider wind power working conditions. Specifically, the present invention provides a nacelle wind speed correction method and system based on a mechanism model and a neural network. A real wind speed is measured by installing a Light Detection and Ranging (LiDAR) at impellers, and then a physical mechanism is combined with a data model. The method and the system provided by the present invention have the characteristics of high calculation speed, high accuracy, and good generalization, and can be widely used for wind speed correction of different models of wind turbines.
[0005] In a first aspect, the present invention provides a nacelle wind speed correction method based on a mechanism model and a neural network, where the method includes the following steps:
[0006] selecting a wind speed data and a power data measured in a SCADA system, and dividing the wind speed data and the power data according to a preset rule to obtain a dataset under a variable pitch working condition and a dataset under a non-variable pitch working condition, where
[0007] the dividing based on the preset rule includes: judging whether the wind speed data 1s less than a rated wind speed data; if yes, determining that the wind speed data 1s a wind speed under the non-variable pitch working condition; else, determining that the wind speed data is a wind speed under the variable pitch working condition;
[0008] a resolution of the wind speed data and the power data in the SCADA system is 1s level;
[0009] the dataset under the variable pitch working condition and the dataset under the non- variable pitch working condition are converted into a data with a resolution of 30s level on average as a feature dataset;
[0010] obtaining air density information and sweeping area information, and calculating the air density information, the sweeping area information, the wind speed data, and the power data according to a preset empirical formula to obtain a theoretical wind speed ¥= under corresponding working conditions, where
[0011] the preset empirical formula is:
[0012] m= Poen PSV") Ya where
[0013] Fyn is the power data, ¥# is the wind speed data, # is the air density information, and S is the sweeping area information;
[0014] obtaining a real wind speed +, and then decomposing the theoretical wind speed ¥= and the real wind speed ¥: into corresponding high-frequency data and low-frequency data by means of a wavelet transform, respectively, to obtain corresponding high-frequency residuals ¥= and low-frequency residuals 7, and using the high-frequency residuals = and the low-frequency residuals ¥« as a target dataset, where the wind speed data is a feature dataset;
[0015] where the real wind speed ¥s is measured by LIDAR, and then the real wind speed *: and the theoretical wind speed ¥= are decomposed respectively by means of the wavelet transform to obtain corresponding high-frequency data Fx, ¥m and low-frequency data 2,
Vat, the Ys and ”»« are subject to subtraction to obtain a high-frequency residual ¥e between a real wind speed high-frequency data and a theoretical wind speed high-frequency data, and Y= and Ym are subject to subtraction to obtain a low-frequency residual ¥« between a real wind speed low-frequency data and a theoretical wind speed low-frequency data, specifically, after the real wind speed data is obtained, the real wind speed data is decomposed into a high-frequency data and a low-frequency data by means of the wavelet transform, corresponding time points are recorded, and then, based on the time points of the real wind speed, the theoretical wind speed is decomposed into a high-frequency data and a low-frequency data;
[0016] taking the feature dataset as input and the target dataset as output, performing training using the neural network, determining a time step by an optimization method, and establishing a wind speed-residual prediction model based on the working conditions and the high-frequency data and the low-frequency data;
[0017] inputting a SCADA wind speed data with a certain time step into a trained wind speed-residual prediction model to obtain high-frequency residual prediction value data and low-frequency residual prediction value data corresponding to the time step; and
[0018] performing linear addition on the SCADA wind speed data and the high-frequency residual prediction value data and the low-frequency residual prediction value data to obtain a final real wind speed correction value.
[0019] Alternatively, a real wind speed ”’ in front of a wind turbine hub is measured by the
LiDAR; and
[0020] a nacelle wind speed 2 is measured through a SCADA system.
[0021] Alternatively, the taking the feature dataset as input and the target dataset as output, performing training using the neural network, determining a time step by an optimization method, and establishing a wind speed-residual prediction model based on the working conditions and the high-frequency data and the low-frequency data includes:
[0022] performing training using the neural network, and establishing four wind speed- residual prediction models under two working conditions: a high-frequency residual prediction model using a SCADA wind speed as input and the high-frequency residual as output under the variable pitch working condition; a low-frequency residual prediction model using the SCADA wind speed as input and the low-frequency residual as output under the variable pitch working condition; a high-frequency residual prediction model using a SCADA wind speed as input and the high-frequency residual as output under the non-variable pitch working condition; and a low-frequency residual prediction model using the SCADA wind speed as input and the low-frequency residual as output under the non- variable pitch working condition.
[0023] Alternatively, in a process of establishing a wind speed-residual prediction model, training time steps and prediction time steps are subject to optimized solving by using a gamma detection technology, and parameters of each layer are subject to optimized combined solving by a layer-by-layer random jump method.
[0024] Alternatively, the layer-by-layer random jump method includes the following steps:
[0025] 1) determining parameters, jump ranges, and jump step sizes of random jumps:
[0026] determining the parameters that need to be optimized as the number of units, dropout value, and an activation function of a second layer, namely LSTM, and the number of units and an activation function of a fourth layer, namely a Dense layer; setting the range and jump step size of each parameter; and randomly selecting activation functions as a sigmoid function, a tanh function, and a relu function, where the parameter values are calculated using the following formula: 00271 Pi = Mia + step + rand (| "mm | Te mm where
[0028] ™ is a calculated value for a current round, +2 is a calculated value for a previous round, sx is a minimum range value, a is a maximum range value, “7 is the jump step size, and rand) is a function of randomly selecting integers within a given range, iz2ie NY
[0029] 2) in the first round, randomly initializing the number of units, the dropout value,
mapping dimensions, and the activation function of a selected layer within a given range; then dividing a certain quantity of wind speed and residual datasets into a training set and a testing set for training and testing, and calculating accuracy to obtain an accuracy value of the corresponding parameter; recording parameter combination and results; then adding a
S random positive integer or a random negative integer to the number of units and the dropout value of the selected layer, and then multiplying by the jump step size, to obtain a next set of parameter values, where the parameter values cannot exceed the given range, the activation function is also randomly selected, and the operation is repeated for five rounds; and
[0030] 3) calculating the accuracy of five sets of random parameters, selecting the parameter combination with the highest accuracy, then executing step 2) on the parameters of the fourth layer, similarly, after calculating the accuracy of the five sets of parameters, selecting the parameter combination with the highest accuracy, and finally, obtaining an optimal parameter combination.
[0031] Alternatively, after obtaining the SCADA wind speed data and determining the working conditions based on the SCADA wind speed data, the SCADA wind speed data is input into the corresponding high-frequency residual prediction model and low-frequency residual prediction model to obtain the high-frequency residual ¥= and the low-frequency residual ¥« of the corresponding time steps, finally, the high-frequency residual ¥es and the low-frequency residual ¥« of the corresponding time steps are added to the nacelle wind speed ”’z to obtain the corrected real wind speed Yo. Le.
[0032] Vp =F tb 3
[0033] In a second aspect, the present invention provides a nacelle wind speed correction system based on a mechanism model and a neural network, for realizing the nacelle wind speed correction method based on a mechanism model and a neural network, where the system includes:
[0034] a division module, where the division module is used for selecting a wind speed data and a power data measured in a SCADA system, and dividing the wind speed data and the power data according to a preset rule to obtain a dataset under a variable pitch working condition and a dataset under a non-variable pitch working condition;
[0035] a calculation module, where the calculation module 1s used for obtaining air density information and sweeping area information, and calculating the air density information, the sweeping area information, the wind speed data, and the power data according to a preset empirical formula to obtain a theoretical wind speed ¥» under corresponding working conditions;
[0036] a decomposition module, where the decomposition module is used for obtaining a real wind speed !+, and then decomposing the theoretical wind speed ¥» and the real wind speed ? into corresponding high-frequency data and low-frequency data by means of a wavelet transform, respectively, to obtain corresponding high-frequency residuals ¥e and low-frequency residuals *« | and using the high-frequency residuals Ye and the low- frequency residuals ¥« as a target dataset, where the wind speed data is a feature dataset;
[0037] an establishment module, where the establishment module is used for taking the feature dataset as input and the target dataset as output, performing training using the neural network, determining a time step by an optimization method, and establishing a wind speed-residual prediction model based on the working conditions and the high-frequency data and the low-frequency data,
[0038] an input module, where the input module is used for inputting a SCADA wind speed data with a certain time step into a trained wind speed-residual prediction model to obtain high-frequency residual prediction value data and low-frequency residual prediction value data corresponding to the time step; and
[0039] an output module, where the output module is used for performing linear addition on the SCADA wind speed data and the high-frequency residual prediction value data and the low-frequency residual prediction value data to obtain a final real wind speed correction value.
[0040] The present invention discloses a nacelle wind speed correction method and system based on a mechanism model and a neural network. A wind speed data and a power data measured in a SCADA system are selected, and a SCADA wind speed is divided into wind speed datasets under different working conditions according to a rated wind speed. A theoretical wind speed is calculated through empirical formulas; a high-frequency residual and a low-frequency residual of a real wind speed and a theoretical wind speed are calculated by means of a wavelet transform, a relationship between the SCADA wind speed and the high-frequency residual and the low-frequency residual is established by the neural network, a SCADA wind speed data from actual operating wind farms is input into the trained neural network to obtain a corresponding high-frequency residual and a corresponding low-frequency residual; and the SCADA wind speed data is in one-to-one correspondence with residual data, and linear addition is performed to obtain a real wind speed correction value. The method and the system provided by the present invention have the characteristics of high calculation speed, high accuracy, and good generalization, and can be widely used for wind speed correction of different models of wind turbines.
BRIEF DESCRIPTION OF DRAWINGS
[0041] FIG. 1 shows a flow diagram of steps of a nacelle wind speed correction method based on a mechanism model and a neural network of the present invention;
[0042] FIG. 2 shows an architecture diagram of the nacelle wind speed correction method based on a mechanism model and a neural network of the present invention;
[0043] FIG. 3 shows a structure diagram of a neural network of the present invention; and
[0044] FIG. 4 shows a module diagram of a nacelle wind speed correction system based on a mechanism model and a neural network of the present invention.
DETAILED DESCRIPTION OF THE EMBODIMENTS
[0045] In order to better understand the above objectives, features, and advantages of the present invention, a further detailed description of the present invention will be provided below in conjunction with the accompanying drawings and specific embodiments. It should be noted that, without conflict, the embodiments of the present application and the features in the embodiments can be combined with each other.
[0046] Many specific details are elaborated in the following description to facilitate a full understanding of the present invention. However, the present invention can also be implemented in other ways different from those described herein. Therefore, the scope of protection of the present invention is not limited by the specific embodiments disclosed below.
[0047] FIGs. 1 and 2 show a flow diagram and an architecture diagram of the steps of a nacelle wind speed correction method based on a mechanism model and a neural network of the present invention. The nacelle wind speed correction method 100 based on a mechanism model and a neural network includes the following steps:
[0048] S102: selecting a wind speed data and a power data measured in a SCADA system, and dividing the wind speed data and the power data according to a preset rule to obtain a dataset under a variable pitch working condition and a dataset under a non-variable pitch working condition, where
[0049] the step is used to establish an original dataset, the wind speed and power data in the
SCADA system are selected, and the wind speed and the power data are divided into a wind speed dataset under a variable pitch working condition and a wind speed dataset under a non-variable pitch working condition based on whether the wind speed reaches the rated wind speed, and specifically:
S [0050] when the wind speed data is divided based on different working conditions, it is necessary to first determine the model of wind turbines, then determine a corresponding rated wind speed value when reaching the rated power, and then use the rated wind speed as a basis for judgment. A 2MW direct-driven wind turbine in a certain wind field is taken as an example, the rated power is determined to be 2000kw and the rated wind speed is 12m/s.
[0051] Alternatively, a real wind speed ¥: in front of a wind turbine hub is measured by the
LIDAR;
[0052] a nacelle wind speed ¥« is measured through a SCADA system;
[0053] further, by taking the rated wind speed as a limit, it is judged whether the data of the wind speed Fz of the wind turbine recorded by the SCADA system is less than the rated wind speed data,
[0054] if yes, it is determined that the wind speed data is a wind speed under the non- variable pitch working condition;
[0055] else, it is determined that the wind speed data is a wind speed under the variable pitch working condition.
[0056] Alternatively, a resolution of the wind speed data and the power data in the SCADA system is 1s level; the dataset under the variable pitch working condition and the dataset under the non-variable pitch working condition are converted into a data with a resolution of 30s level on average as a feature dataset;
[0057] S104: obtaining air density information and sweeping area information, and calculating the air density information, the sweeping area information, the wind speed data, and the power data according to a preset empirical formula to obtain a theoretical wind speed ¥» under corresponding working conditions, where
[0058] the step is used to process the dataset, a SCADA wind speed, power, environment and model parameters are input into empirical formulas to obtain a corresponding theoretical wind speed. Y= Specifically, air density information and sweeping area information are obtained, and the air density information, the sweeping area information, the wind speed data, and the power data are calculated according to a preset empirical formula to obtain a corresponding theoretical wind speed ¥=;
[0059] where the empirical formula used for calculating the theoretical wind speed ¥= is as follows: . mn / ; yy 2 po
[0060] Vm = Fen / PSV JFV here + r
[0061] where Fem is generator output power, ¥x is the wind speed recorded by the SCADA,
Pis the air density, and § is the sweeping area;
[0062] the derivation process of the empirical formula is as follows:
[0063] according to the aerodynamics theory, when air mass flows through a region $ at a speed ¥, the power of air motion is expressed as: > — 3 gE
[0064] Pina = PV 5/2 (1)
[0065] in formula (1), # is the air density and & is the sweeping area;
[0066] a power coefficient Co is usually defined as: + op / KE
[0067] C,=8 {p81 2)
[0068] in formula (2), = is the mechanical power of the wind turbine; . C . . . .
[0069] according to * equation (2), the relationship between the mechanical power of the wind turbine and the power coefficient can be written as: # EN = 3 100707 Pz =0.505C, (RI 20) 6)
[0071] where 25 9B/®
[0072] 4 is a tip speed ratio, ® is a blade radius, and ®« is a generator speed;
[0073] considering that it is difficult to directly measure the mechanical power of the wind turbine, actual measured power is generator power in the SCADA system, the above formula (3) can be rewritten as: u yy 3 4 3 (00747 Peen = 051PSC (RI y@,)' = ke, “
[0075] where # is an energy conversion rate.
[0076] According to the aerodynamics theory, the airflow passing through wind turbine blades can be divided into three categories: one category represents a wind speed °° in front of the blades; one category represents a wind speed *2 when passing through the blades; and one category represents a wind speed ¥z behind the blades. An anemometer is usually mounted near the wind turbine blades, so that the SCADA wind speed can be considered as
Va. According to the aerodynamics theory, air kinetic energy absorbed by the wind turbine blades can be expressed as:
[0077] Br = PSV (vv) =0.5p8v,(% —v;) (5)
[0078] ¥2 = Va = ¥% (6)
[0079] formula (6) is substituted into above formula (5) to obtain:
[0080] x= 2 psv jv vg) (7)
[0081] Differences between the mechanical power of the wind turbine and the generator power are ignored, and formula (4) is substituted into formula (7) to obtain:
[0082] ko, = 2pSv, (Vg) (8)
[0083] From formulas (4} and (8), it can be concluded that: (00847 Vi = Fo: [QpSVi)+v, =P, [(QpPSV3) + 4 (9)
[0085] Finally, the empirical formula for the real wind speed and the SCADA wind speed is obtained as follows:
[0086] Vi = En [sv + (10)
[0087] where Fo is generator output power, and ¥+ is the wind speed recorded by the
SCADA.
[0088] S106: obtaining a real wind speed ¥:, and then decomposing the theoretical wind speed Y= and the real wind speed ¥s into corresponding high-frequency data and low- frequency data by means of a wavelet transform, respectively, to obtain corresponding high-frequency residuals ¥e and low-frequency residuals ¥«, and using the high-frequency residuals Yer and the low-frequency residuals Fai as a target dataset, where the wind speed data is a feature dataset;
[0089] in this step, the real wind speed ”+ is measured by LiDAR, and then the real wind speed Pand the theoretical wind speed Vos are decomposed respectively by means of the wavelet transform to obtain corresponding high-frequency data ¥u, sa and low-frequency data ¥u, Va, the’ and Pas are subject to subtraction to obtain a high-frequency residual Ve between the real wind speed high-frequency data and the theoretical wind speed high- frequency data, and ¥s and ”= are subject to subtraction to obtain a low-frequency residual ¥4 between the real wind speed low-frequency data and the theoretical wind speed low- frequency data.
[0090] Specifically, after the real wind speed data is obtained, the real wind speed data is decomposed into a high-frequency data and a low-frequency data by means of the wavelet transform, corresponding time points are recorded, then, based on the time points of the real wind speed, the theoretical wind speed is decomposed into a high-frequency data and a low-frequency data, and a wavelet transform formula is shown in formula (11): da VN
WoO =a] y=)
[0091] a (11)
[0092] In the formula (11), ¥) represents a mother wavelet function, #¢€R represents a contraction-expansion factor and a translation factor, ¥ must satisfy two conditions:
Jews, [od st,
[0093] first: >= Tm 0< Cf “| wlan) Joos $9
[0094] second: °
[0095] where war) [0 od is the Fourier transform of VÒ. At this point, continuous wavelet transform of a signal FO is calculated by formula (12);
CET Go SO Had
[0096] it €} {ore JV t } (12)
[0097] where Wisla) is a wavelet coefficient, Val is a conjugate of Val)
[0098] To describe a wavelet transform of a time sequence x), 1=12.., N the continuous
Cg . . i Ai, u ee wavelet transform is discretized. Given @ = 2%; c=k£2 in formula (11), at this time, the formula (11) is changed into:
[0099] A =2"2 yk) (13)
[0100] The formula for calculating a discrete wavelet coefficient of the time sequence RC is:
WI, =WT(2,k2) => xO], 0)
[0101] = (14)
[0102] Therefore, an inverse transformation formula for reconstructing an original signal through the wavelet coefficient is: x{#) + > WT, ¥ (0)
[0103] fron (15)
[0104] S108: taking the feature dataset as input and the target dataset as output, performing training using the neural network, determining a time step by an optimization method, and establishing a wind speed-residual prediction model based on the working conditions and the high-frequency data and the low-frequency data;
[0105] FIG. 3 shows the structure diagram of a neural network of the present invention,
The neural network used is a self-attention mechanism based long short-term memory recurrent neural network (Self-Attention-LSTM). The neural network has a five-layer structure, a first layer is an input layer which inputs the feature dataset; a second layer is the
LSTM layer which calculates the data of the first layer in sequence in batches through
S LSTM neurons to obtain a corresponding hidden state as input of the next layer; a third layer is a self-attention layer which maps and recombines the hidden state input by the second layer using a self-attention mechanism, and dimensions of Q, K, and V after mapping are 10; a fourth layer is a Dense layer (Dense); and a fifth layer is an output layer which outputs values obtained by final calculation of the model. The weight and bias parameters of the neural network are a relationship model between the wind speed-residual, and a residual value can be obtained by inputting the wind speed into the neural network.
[0106] Alternatively, in one embodiment, the taking the feature dataset as input and the target dataset as output, performing training using the neural network, determining a time step by an optimization method, and establishing a wind speed-residual prediction model based on the working conditions and the high-frequency data and the low-frequency data includes:
[0107] performing training using the neural network, and establishing four wind speed- residual prediction models under two working conditions: a high-frequency residual prediction model using a SCADA wind speed as input and the high-frequency residual as output under the variable pitch working condition; a low-frequency residual prediction model using the SCADA wind speed as input and the low-frequency residual as output under the variable pitch working condition; a high-frequency residual prediction model using a SCADA wind speed as input and the high-frequency residual as output under the non-variable pitch working condition; and a low-frequency residual prediction model using the SCADA wind speed as input and the low-frequency residual as output under the non- variable pitch working condition.
[0108] in a process of establishing a wind speed-residual prediction model, training time steps and prediction time steps are subject to optimized solving by using a gamma detection technology, and parameters of each layer are subject to optimized combined solving by a layer-by-layer random jump method.
[0109] Specifically, for different wind turbines, input and output time steps are determined using a gamma detection algorithm, and the parameters of each layer are determined using a layer-by-layer random jump method, but the overall structure of the neural network remains unchanged.
[0110] Gamma detection directly estimates a minimum mean square error present in a nonlinear model through observation data, and the parameters of input data can be determined in this way.
[0111] Assuming that the form of a sample data is 6,3) = Caan} 12, N} % represents the input in the {4 row, each %: may contain multiple features, ix represents n feature, and ¥: represents the ’ output. Assuming that a vector contains useful factors that affect the output, the relationship between the vector and the factors can be expressed as:
[0112] Y= f{x}+¢ (16)
[0113] J is a smooth unknown regression function, and § represents a noise random variable. Because any constant deviation can be contained in the unknown function, expectation is £&)=9 and variance is Vag) < to
[0114] If two points * and 3 UISLJEN) are very close in distance in input space, their corresponding outputs +: and Ys should also be very close in distance in the output space. If not, we believe that this difference is caused by noise $. 40) function calculates the average value of squared values of proximity distances of each point in the input space, as shown in formula (17):
Sk) = LS gf
[0115] NE (17)
[0116] where *l&]is the &u proximity point of %, i.e, *:l&lis the uw point closest to *,
Pd¥lis the ky, proximity point of :, i.e, YA&lis the ky point closest to , Il represents an Euclidean distance, and in the output space, a corresponding (3 function is shown in the following formula: 1 & os 2
FB) = lk] 3,
[0117] ING (18)
[0118] Gamma detection calculates gamma statistic F by drawing a linear regression line ata point, using the following formula:
[0119] 7 =A8+T (19)
[0120] In formula (19), a vertical intercept (when & is zero) describes a value of the gamma statistic represented by I". If F is very small, the regression function J exists, an output value ¥ highly depends on an input variable x, and close correlativity exists between input and output. If I" is large, it indicates that the input is independent of the output.
[0121] Since the value of the gamma statistic is affected by the size of the sample value, the result is normalized by considering the statistic ¥ratie The statistic ¥ratio is defined as:
Fratio= wd
[0122] a(y) (20)
[0123] In formula (20), #0)" represents a variance of the output ¥. A value ratio close to 0 indicates that the output ¥ has a high degree of predictability, while a value Fratie close to 1 indicates that the output ¥ is random and independent of the input variable x.
[0124] Relevant explanation on the selection of embedding dimension for gamma detection: T is a value of the gamma statistic corresponding to the situation when M is taken for the embedding dimension;
[0125] the smaller the gamma statistic, the more reasonable the corresponding embedding dimension, i.e., the closer the value of the statistic ¥rafiv is to 0, the more reasonable the corresponding embedding dimension is; and therefore, gamma detection can be performed respectively based on progressive increase of the embedding dimension, the statistics ratie are used for making judgments, and the appropriate embedding dimension is selected.
[0126] In the process of determining the parameters of each layer using a layer-by-layer random jump method, the layer-by-layer random jump method includes the following steps: 1) determining parameters, jump ranges, and jump step sizes of random jumps:
[0127] determining the parameters that need to be optimized as the number of units, dropout value, and an activation function of a second layer, namely LSTM, and the number of units and an activation function of a fourth layer, namely a Dense layer; setting the range and jump step size of each parameter; and randomly selecting activation functions as a sigmoid function, a tanh function, and a relu function, where the parameter values are calculated using the following formula: (0128) Me = Mh + 1p * rand (| Pe |e [mm (21)
[0129] where # is a calculated value for a current round, #1 is a calculated value for a previous round, Peis is a minimum range value, aa is a maximum range value, ¥ is the jump step size, and ¥ and() is a function of randomly selecting integers within a given range, iz2ie NT
[0130] 2) in the first round, randomly initializing the number of units, the dropout value, mapping dimensions, and the activation function of a selected layer within a given range; then dividing a certain quantity of wind speed and residual datasets into a training set and a testing set for training and testing, and calculating accuracy to obtain an accuracy value of the corresponding parameter; recording parameter combination and results; then adding a random positive integer or a random negative integer to the number of units and the dropout value of the selected layer, and then multiplying by the jump step size, to obtain a next set of parameter values, where the parameter values cannot exceed the given range, the
S activation function is also randomly selected, and the operation is repeated for five rounds; and
[0131] 3) calculating the accuracy of five sets of random parameters, selecting the parameter combination with the highest accuracy, then executing step 2) on the parameters of the fourth layer, similarly, after calculating the accuracy of the five sets of parameters, selecting the parameter combination with the highest accuracy, and finally, obtaining an optimal parameter combination.
[0132] S110: inputting a SCADA wind speed data with a certain time step into a trained wind speed-residual prediction model to obtain high-frequency residual prediction value data and low-frequency residual prediction value data corresponding to the time step; and
[0133] the SCADA second level wind speed is extracted once every 10 to 20 times the resolution multiplied by the time step according to the input time step and resolution during training. The SCADA wind speed is divided into a wind speed under a variable pitch working condition and a wind speed under a non-variable pitch working condition based on whether it reaches the rated wind speed, then, an average value is calculated based on a set resolution, the wind speed under the variable pitch working condition after resolution reduction is input into the trained high-frequency residual prediction model and the trained low-frequency residual prediction model under the variable pitch working condition to obtain the high-frequency residual prediction value and the low-frequency residual prediction value at the wind speed under the variable pitch working condition; and the wind speed under the non-variable pitch working condition after resolution reduction is input into the trained high-frequency residual prediction model and the trained low-frequency residual prediction model under the non-variable pitch working condition to obtain the high- frequency residual prediction value and the low-frequency residual prediction value at the wind speed under the non-variable pitch working condition.
[0134] S112: performing linear addition on the SCADA wind speed data and the high- frequency residual prediction value data and the low-frequency residual prediction value data to obtain a final real wind speed correction value.
[0135] After obtaining the high-frequency residual prediction value Ya and the low- frequency residual prediction value ¥s at the wind speed under the variable pitch working condition, as well as the high-frequency residual prediction value and the low-frequency residual prediction value at the wind speed under the non-variable pitch working condition, the SCADA wind speed under the variable pitch working condition, the high-frequency residual prediction value at the wind speed under the variable pitch working condition, and
S the low-frequency residual prediction value at the wind speed under the variable pitch working condition are linearly added to obtain a final real variable pitch wind speed correction value, and the SCADA wind speed under the non-variable pitch working condition, the high-frequency residual prediction value at the wind speed under the non- variable pitch working condition, and the low-frequency residual prediction value at the wind speed under the non-variable pitch working condition are linearly added to obtain a final real non-variable pitch wind speed correction value.
[0136] That is to say, a corrected real wind speed Ve is obtained by adding the high- frequency residual ”: and the low-frequency residual ¥s of the corresponding time steps to the nacelle wind speed 4, i.e.
[0137] eat Vata (22)
[0138] According to the present embodiment, the present invention discloses the nacelle wind speed correction method based on a mechanism model and a neural network. The wind speed data and the power data measured in a SCADA system are selected, and a
SCADA wind speed is divided into wind speed datasets under different working conditions according to a rated wind speed. A theoretical wind speed is calculated through empirical formulas; a high-frequency residual and a low-frequency residual of a real wind speed and a theoretical wind speed are calculated by means of a wavelet transform, a relationship between the SCADA wind speed and the high-frequency residual and the low-frequency residual is established by the neural network, a SCADA wind speed data from actual operating wind farms is input into the trained neural network to obtain a corresponding high-frequency residual and a corresponding low-frequency residual; and the SCADA wind speed data is in one-to-one correspondence with residual data, and linear addition is performed to obtain a real wind speed correction value. The method and the system provided by the present invention have the characteristics of high calculation speed, high accuracy, and good generalization, and can be widely used for wind speed correction of different models of wind turbines.
[0139] FIG. 4 shows a module diagram of a nacelle wind speed correction system based on a mechanism model and a neural network of the present invention. The nacelle wind speed correction system 200 based on a mechanism model and a neural network, for realizing the nacelle wind speed correction method based on a mechanism model and a neural network, includes:
[0140] a division module 210, where the division module is used for selecting a wind speed
S data and a power data measured in a SCADA system, and dividing the wind speed data and the power data according to a preset rule to obtain a dataset under a variable pitch working condition and a dataset under a non-variable pitch working condition;
[0141] a calculation module 220, where the calculation module is used for obtaining air density information and sweeping area information, and calculating the air density information, the sweeping area information, the wind speed data, and the power data according to a preset empirical formula to obtain a theoretical wind speed Y= under corresponding working conditions;
[0142] a decomposition module 230, where the decomposition module is used for obtaining a real wind speed ”s, and then decomposing the theoretical wind speed ¥= and the real wind speed ¥s into corresponding high-frequency data and low-frequency data by means of a wavelet transform, respectively, to obtain corresponding high-frequency residuals ¥a and low-frequency residuals ¥«, and using the high-frequency residuals ¥e and the low- frequency residuals ¥« as a target dataset, where the wind speed data is a feature dataset;
[0143] an establishment module 240, where the establishment module is used for taking the feature dataset as input and the target dataset as output, performing training using the neural network, determining a time step by an optimization method, and establishing a wind speed-residual prediction model based on the working conditions and the high-frequency data and the low-frequency data;
[0144] an input module 250, where the input module is used for inputting a SCADA wind speed data with a certain time step into a trained wind speed-residual prediction model to obtain high-frequency residual prediction value data and low-frequency residual prediction value data corresponding to the time step; and
[0145] an output module 260, where the output module is used for performing linear addition on the SCADA wind speed data and the high-frequency residual prediction value data and the low-frequency residual prediction value data to obtain a final real wind speed correction value.
[0146] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working process of an electronic device of the present invention can refer to the corresponding processes in the aforementioned embodiments, as well as the same parts and beneficial effects as the aforementioned embodiments, which will not be repeated here.
[0147] According to the present embodiment, the present invention discloses the nacelle 5S wind speed correction system based on a mechanism model and a neural network. The wind speed data and the power data measured in a SCADA system are selected, and SCADA wind speed is divided into wind speed datasets under different working conditions according to rated wind speed. Theoretical wind speed is calculated through empirical formulas; a high-frequency residual and a low-frequency residual of a real wind speed and a theoretical wind speed are calculated by means of a wavelet transform, a relationship between the SCADA wind speed and the high-frequency residual and the low-frequency residual is established by the neural network, a SCADA wind speed data from actual operating wind farms is input into the trained neural network to obtain a corresponding high-frequency residual and a corresponding low-frequency residual; and the SCADA wind speed data is in one-to-one correspondence with residual data, and linear addition is performed to obtain a real wind speed correction value. The method and the system provided by the present invention have the characteristics of high calculation speed, high accuracy, and good generalization, and can be widely used for wind speed correction of different models of wind turbines.
[0148] In the several embodiments provided in the present application, it should be understood that the disclosed method and system can be implemented in other ways. The device embodiments described above are only illustrative. For example, the division of the modules is only a logical functional division. In actual implementation, there may be other division methods, for example: multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, coupling, or direct coupling, or communication connection between the various components displayed or discussed can be indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical, or in other forms.
[0149] The modules mentioned above as separate components can be, or may not be physically separated, and the components displayed as modules can be, or may not be physical units; the modules or the components can be located in one place or distributed across multiple network units; and some or all of the units can be selected according to actual needs to achieve the purpose of the present embodiment.
[0150] In addition, in each embodiment of the present invention, all functional units can be integrated into one processing unit, or each unit can be used as a separate unit, or two or more units can be integrated into one unit; and the integrated units mentioned above can be implemented in the form of hardware, or can be implemented in the form of hardware and software functional units.
S [0151] Those of ordinary skill in the art can understand that all or part of the steps to implement the above method embodiments can be completed through hardware related to program instructions. The aforementioned program can be stored in computer readable storage media. When executed, the program executes the steps including the above method embodiments. The aforementioned storage media include: mobile storage devices, Read-
Only Memory (ROM), Random Access Memory (RAM), disks or CDs, and other media that can store program codes.
[0152] Alternatively, if the integrated units of the present invention are implemented in the form of software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, essentially or in other words, the technical solution of the embodiments of the present invention or the part contributing to the prior art can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions to enable a computing device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage media include various media that can store program codes, such as mobile storage devices, ROMs, RAMs, disks, or CDs.
[0153] Although some specific embodiments of the present invention have been explained in detail through examples, those skilled in the art should understand that the above examples are only for illustrative purposes but not to limit the scope of the present invention. Those skilled in the art should also understand that multiple modifications can be made to the embodiments without departing from the scope and spirit of the present invention. The scope of the present invention is limited by the appended claims.

Claims (7)

CONCLUSIESCONCLUSIONS 1. Een werkwijze voor het corrigeren van windsnelheid op een gondel die gebaseerd is op een mechanismemodel en een neuraal netwerk, waarbij de werkwijze de volgende stappen omvat: het selecteren van windsnelheidsgegevens en vermogengegevens gemeten In een Toezichthoudend en Gegevensverzamelend systeem (Supervisory Control and Data Acquisition System, SCADA), en het verdelen van de windsnelheidsgegevens en de vermogensgegevens volgens een vooraf ingestelde regel om een gegevensset te verkrijgen onder een werkingstoestand met veranderlijke helling en een gegevensset onder een werkingstoestand met niet veranderlijke helling, waarbij het verdelen gebaseerd op de vooraf ingestelde regel het volgende omvat: het beoordelen of de windsnelheidsgegevens kleiner zijn dan nominale windsnelheidsgegevens; zo ja, te bepalen dat de windsnelheidsgegevens windsnelheid zijn onder de werkingstoestand met niet veranderlijke helling; anders te bepalen dat de windsnelheidsgegevens windsnelheid zijn onder de werkingstoestand met veranderlijke helling; waarbij een resolutie van de windsnelheidsgegevens en de vermogensgegevens in het SCADA-systeem op het niveau van 1 s is; waarbij de gegevensset onder de werkingstoestand met veranderlijke helling en de gegevensset onder de werkingstoestand met niet veranderlijke helling worden omgezet in gegevens met een resolutie op een niveau van gemiddeld 30s als een kenmerkengegevensset; het verkrijgen van informatie over luchtdichtheid en informatie over bestreken oppervlak, en het berekenen van de informatie over luchtdichtheid, de informatie over bestreken oppervlak de windsnelheidsgegevens en de vermogensgegevens volgens een vooraf ingestelde empirische formule om de overeenkomstige theoretische windsnelheid Vm te verkrijgen onder overeenstemmende werkingscondities, waarbij de vooraf ingestelde empirische formule als volgt is: Vy = Poen) (QpSV) + Va waarbij Pen de vermogensgegevens zijn, Va de windsnelheidsgegevens zijn, p de informatie over luchtdichtheid is, en S de informatie over bestreken oppervlak is; het verkrijgen van de werkelijke windsnelheid V‚, en dan het ontbinden van de theoretische windsnelheid Vm en de werkelijke windsnelheid V,; in overeenkomstige hoogfrequente gegevens en laagfrequente gegevens door middel van wavelet- transformatie, respectievelijk, voor het verkrijgen van overeenstemmende hoogfrequente residuen Ven en laagfrequente residuen Va, en het gebruiken van de hoogfrequente residuen Ven en de laagfrequente residuen Vg als een doelgegevensset, waarbij de windsnelheidsgegevens de kenmerkengegevensset zijn, waarbij de werkelijke windsnelheid V; gemeten wordt door middel van Licht Detectie en Inschatten (Light Detection and Ranging, LiDAR), en dan de werkelijke windsnelheid V; en de theoretische windsnelheid Vn respectievelijk ontbonden worden door gebruik te maken van de wavelet-transformatie om overeenkomstige hoogfrequente gegevens Vin, Vin en laagfrequente gegevens Vy, Vm te verkrijgen, waarbij de Va en Vmn afgetrokken worden om een hoogfrequent residu Ven te verkrijgen tussen hoogfrequente gegevens over de werkelijke windsnelheid en hoogfrequente gegevens over de theoretische windsnelheid, en waarbij Vi en Vm afgetrokken worden om een laagfrequent residu Vg te verkrijgen tussen laagfrequente gegevens over de werkelijke windsnelheid en laagfrequente gegevens over de theoretische windsnelheid, waarbij specifiek, nadat de werkelijke windsnelheidsgegevens verkregen zijn, de werkelijke windsnelheidsgegevens ontbonden zijn in hoogfrequente gegevens en laagfrequente gegevens door middel van de wavelet-transformatie, overeenkomstige tijdspunten worden geregistreerd, en waarbij dan, gebaseerd op de tijdspunten van de werkelijke windsnelheid, de theoretische windsnelheid ontbonden wordt in hoogfrequente gegevens en laag frequente gegevens; het nemen van de kenmerkengegevensset als input en de doelgegevensset als output, het uitvoeren van een training doorheen het neuraal netwerk, het bepalen van een tijdsstap met behulp van een optimalizeringsmethode, en het opstellen van een voorspellingsmodel voor windsnelheidsresidu gebaseerd op de werkingscondities en de hoogfrequente gegevens en de laagfrequente gegevens, het invoeren van SCADA-windsnelheidsgegevens met een bepaalde tijdsstap in een getraind voorspellingsmodel voor windsnelheidsresidu om hoogfrequente residu voorspellingswaardegegevens en laagfrequente residu voorspellingswaardegegevens in overeenstemming met de tijdsstap te verkrijgen; en het uitvoeren van lineaire optelling op de SCADA-windsnelheidsgegevens en de hoogfrequente residu voorspellingswaardegegevens en de laagfrequente residu voorspellingswaardegegevens om een uiteindelijke werkelijke windsnelheidscorrectiewaarde te verkrijgen.1. A method for correcting wind speed on a nacelle based on a mechanism model and a neural network, the method comprising the following steps: selecting wind speed data and power data measured in a Supervisory Control and Data Acquisition System, SCADA), and dividing the wind speed data and the power data according to a preset rule to obtain a data set under a variable slope operating condition and a data set under a non-changing slope operating condition, dividing based on the preset rule includes: assessing whether the wind speed data is smaller than nominal wind speed data; if so, determine that the wind speed data is wind speed under the non-variable slope operating condition; otherwise determine that the wind speed data is wind speed under the variable slope operating condition; wherein a resolution of the wind speed data and the power data in the SCADA system is at the level of 1 s; wherein the data set under the variable slope operating condition and the data set under the non-changing slope operating condition are converted into 30s average resolution data as a feature data set; obtaining air density information and swept area information, and calculating the air density information, swept area information, wind speed data and power data according to a preset empirical formula to obtain the corresponding theoretical wind speed Vm under corresponding operating conditions, where the preset empirical formula is as follows: Vy = Poen) (QpSV) + Va where Pen is the power data, Va is the wind speed data, p is the air density information, and S is the area covered information; obtaining the actual wind speed V‚, and then decomposing the theoretical wind speed Vm and the actual wind speed V‚; into corresponding high-frequency data and low-frequency data by wavelet transform, respectively, to obtain corresponding high-frequency residuals Ven and low-frequency residuals Va, and using the high-frequency residuals Ven and the low-frequency residuals Vg as a target data set, where the wind speed data is the feature dataset, where the actual wind speed is V; is measured by means of Light Detection and Ranging (LiDAR), and then the actual wind speed V; and the theoretical wind speed Vn are respectively decomposed by using the wavelet transform to obtain corresponding high-frequency data Vin, Vin and low-frequency data Vy, Vm, subtracting the Va and Vmn to obtain a high-frequency residual Ven between high-frequency data on the actual wind speed and high-frequency data on the theoretical wind speed, and where Vi and Vm are subtracted to obtain a low-frequency residual Vg between low-frequency actual wind speed data and low-frequency data on the theoretical wind speed, where specifically, after the actual wind speed data have been obtained , the actual wind speed data is decomposed into high-frequency data and low-frequency data by the wavelet transform, corresponding time points are recorded, and then, based on the time points of the actual wind speed, the theoretical wind speed is decomposed into high-frequency data and low-frequency data ; taking the feature dataset as input and the target dataset as output, performing training through the neural network, determining a time step using an optimization method, and constructing a wind speed residual prediction model based on the operating conditions and the high-frequency data and the low-frequency data, inputting SCADA wind speed data with a certain time step into a trained wind speed residual prediction model to obtain high-frequency residual prediction value data and low-frequency residual prediction value data according to the time step; and performing linear addition on the SCADA wind speed data and the high frequency residual prediction value data and the low frequency residual prediction value data to obtain a final actual wind speed correction value. 2. De werkwijze voor het corrigeren van windsnelheid op een gondel die gebaseerd 1s op een mechanismemodel en een neuraal netwerk overeenkomstig conclusie 1, waarbij werkelijke windsnelheid Vi vóór een naaf van een windturbine wordt gemeten door de LIDAR; en gondelwindsnelheid V4 wordt gemeten met behulp van het SCADA-systeem.2. The method for correcting wind speed on a nacelle based on a mechanism model and a neural network according to claim 1, wherein actual wind speed Vi in front of a hub of a wind turbine is measured by the LIDAR; and nacelle wind speed V4 is measured using the SCADA system. 3. De werkwijze voor het corrigeren van windsnelheid op een gondel die gebaseerd is op een mechanismemodel en een neuraal netwerk overeenkomstig conclusie 1, waarbij het nemen van de kenmerkengegevensset als input en de doelgegevensset als output, het uitvoeren van een training doorheen het neuraal netwerk, het bepalen van een tijdsstap met behulp van een optimalizeringsmethode, en het opstellen van een voorspellingsmodel voor windsnelheidsresidu gebaseerd op de werkingscondities en de hoogfrequente gegevens en de laagfrequente gegevens het volgende omvat: het uitvoeren van een training doorheen het neuraal netwerk en het opstellen van vier voorspellingsmodellen voor windsnelheidsresidu onder twee werkingscondities: een hoogfrequent residuvoorspellingsmodel dat SCADA windsnelheid als een input en het hoogfrequente residu als een output gebruikt in de werkingstoestand met veranderlijke helling; een laagfrequent residuvoorspellingsmodel dat SCADA windsnelheid als een input en het laagfrequente residu als een output gebruikt in de werkingstoestand met veranderlijke helling, een hoogfrequent residuvoorspellingsmodel dat SCADA windsnelheid als een input en het hoogfrequente residu als een output gebruikt in de werkingstoestand met niet veranderlijke helling; en een laagfrequent residuvoorspellingsmodel dat SCADA windsnelheid als een input en het laagfrequente residu als een output gebruikt in de werkingstoestand met niet veranderlijke helling.3. The method of correcting wind speed on a nacelle based on a mechanism model and a neural network according to claim 1, wherein taking the feature data set as input and the target data set as output, performing training through the neural network, determining a time step using an optimization method, and establishing a prediction model for wind speed residual based on the operating conditions and the high-frequency data and the low-frequency data involves: performing a training through the neural network and establishing four prediction models for wind speed residual under two operating conditions: a high-frequency residual prediction model that uses SCADA wind speed as an input and the high-frequency residual as an output in the variable slope operating condition; a low-frequency residual prediction model that uses SCADA wind speed as an input and the low-frequency residual as an output in the variable slope operating condition, a high-frequency residual prediction model that uses SCADA wind speed as an input and the high-frequency residual as an output in the non-variable slope operating condition; and a low-frequency residual prediction model that uses SCADA wind speed as an input and the low-frequency residual as an output in the unchanging slope operation condition. 4. De werkwijze voor het corrigeren van windsnelheid op een gondel die gebaseerd is op een mechanismemodel en een neuraal netwerk overeenkomstig conclusie 1, waarbij: in een proces voor het opstellen van het voorspellingsmodel voor windsnelheidsresidu trainingstijdsstappen en voorspellingstijdsstappen onderworpen aan geoptimaliseerd oplossen door gebruik te maken van een gammadetectietechnologie, en waarbij parameters van elke laag onderworpen aan geoptimaliseerd combinatie-oplossen door een laag per laag springmethode met willekeurige sprong.The method of correcting wind speed on a nacelle based on a mechanism model and a neural network according to claim 1, wherein: in a process for establishing the wind speed residual prediction model, training time steps and prediction time steps are subjected to optimized solving by using of a gamma detection technology, and where parameters of each layer are subjected to optimized combination solving by a layer-by-layer random-hop method. 5. De werkwijze voor het corrigeren van windsnelheid op een gondel die gebaseerd is op een mechanismemodel en een neuraal netwerk overeenkomstig conclusie 4, waarbij: laag per laag springmethode met willekeurige sprong de volgende stappen omvat:The method for correcting wind speed on a nacelle based on a mechanism model and a neural network according to claim 4, wherein: layer-by-layer random-jump jumping method includes the following steps: 1) het bepalen van parameters, sprongbereiken en sprongstapgroottes van willekeurige sprongen:1) determining parameters, jump ranges and jump step sizes of arbitrary jumps: het bepalen van parameters die geoptimaliseerd moeten worden als aantal eenheden, uitvalwaarde en een activeringsfunctie van een tweede laag, namelijk LSTM, en aantal eenheden en een activeringsfunctie van een vierde laag, namelijk een Dichte laag; het instellen van het bereik en de sprongstapgrootte van elke parameter; en het willekeurig selecteren van activeringsfuncties als een sigmoid-functie, een tanh-functie en een relu-determining parameters to be optimized such as number of units, dropout value and an activation function of a second layer, namely LSTM, and number of units and an activation function of a fourth layer, namely a Dense layer; setting the range and jump step size of each parameter; and randomly selecting activation functions such as a sigmoid function, a tanh function and a relu- functie, waarbij de parameterwaarden worden berekend met behulp van de volgende formule:function, where the parameter values are calculated using the following formula: y= step rand TV ag mE waarbij m; een berekende waarde is voor een huidige ronde, m;-1 een berekende waarde is voor een vorige ronde, Mmin een minimale bereikwaarde is, mma een maximale bereikwaarde is, step de sprongstapgrootte is en rand() een functie ! >2icN'js voor het willekeurig selecteren van gehele getallen binnen een bepaald bereik;y= step edge TV ag mE where m; is a calculated value for a current round, m;-1 is a calculated value for a previous round, Mmin is a minimum range value, mma is a maximum range value, step is the jump step size and rand() is a function ! >2icN'js for randomly selecting integers within a given range; 2) het in de eerste ronde willekeurig initialiseren van het aantal eenheden, de uitvalwaarde, dimensies van het in kaart brengen, en de activeringsfunctie van een geselecteerde laag binnen een gegeven bereik; het vervolgens opdelen van een bepaald aantal windsnelheidsgegevenssets en residugegevenssets in een trainingsset en een testset voor het trainen en testen, en het berekenen van nauwkeurigheid om een nauwkeurigheidswaarde van de overeenkomstige parameter te verkrijgen; het registreren van parameter combinatie en resultaten; het vervolgens optellen van een willekeurig positief geheel getal of een willekeurig negatief geheel getal bij het aantal eenheden en bij de uitvalwaarde van de geselecteerde laag, en het vervolgens het vermenigvuldigen met de sprongstapgrootte, om een volgende set van parameterwaarden te verkrijgen, waarbij de parameterwaarden het gegeven bereik niet kunnen overschrijden, waarbij de activeringsfunctie ook willekeurig geselecteerd wordt, en waarbij de handeling wordt herhaald voor vijf rondes; en2) in the first round, randomly initializing the number of units, dropout value, mapping dimensions, and activation function of a selected layer within a given range; then dividing a certain number of wind speed data sets and residual data sets into a training set and a test set for training and testing, and calculating accuracy to obtain an accuracy value of the corresponding parameter; registering parameter combination and results; then adding any positive integer or any negative integer to the number of units and to the dropout value of the selected layer, and then multiplying by the jump step size, to obtain another set of parameter values, where the parameter values are the cannot exceed a given range, where the activation function is also randomly selected, and where the operation is repeated for five rounds; and 3) het berekenen van de nauwkeurigheid van vijf sets willekeurige parameters, het selecteren van de parametercombinatie met de hoogste nauwkeurigheid en het vervolgens uitvoeren van stap 2) op de parameters van de vierde laag, op dezelfde manier, na het berekenen van de nauwkeurigheid van de vijf sets parameters, het selecteren van de parametercombinatie met de hoogste nauwkeurigheid, en tenslotte het verkrijgen van een optimale parametercombinatie.3) calculating the accuracy of five sets of arbitrary parameters, selecting the parameter combination with the highest accuracy, and then performing step 2) on the parameters of the fourth layer, in the same way, after calculating the accuracy of the five sets of parameters, selecting the parameter combination with the highest accuracy, and finally obtaining an optimal parameter combination. 6. De werkwijze voor het corrigeren van windsnelheid op een gondel die gebaseerd is op een mechanismemodel en een neuraal netwerk overeenkomstig conclusie 3, waarbij: na het verkrijgen van de SCADA-windsnelheidsgegevens en het bepalen van de werkingscondities op basis van de SCADA-windsnelheidsgegevens, het invoeren van de SCADA-windsnelheidsgegevens in het overeenkomstige hoogfrequente residu voorspellingsmodel en het laagfrequente residu voorspellingsmodel om het hoogfrequente residu Ve en het laagfrequente residu Vi van de overeenstemmende tijdstappen, het vervolgens optellen van het hoogfrequente residu Ven en het laagfrequente residu Ven en het laagfrequente residu Vi van de overeenstemmende tijdstappen bij de gondelwindsnelheid Va om de gecorrigeerde werkelijke windsnelheid Vy, d.w.z. Vy mV + Fy +¥,The method of correcting wind speed on a nacelle based on a mechanism model and a neural network according to claim 3, wherein: after obtaining the SCADA wind speed data and determining the operating conditions based on the SCADA wind speed data, inputting the SCADA wind speed data into the corresponding high-frequency residual prediction model and the low-frequency residual prediction model to calculate the high-frequency residual Ve and the low-frequency residual Vi of the corresponding time steps, then adding the high-frequency residual Ven and the low-frequency residual Ven and the low-frequency residual Vi of the corresponding time steps at the nacelle wind speed Va to get the corrected actual wind speed Vy, i.e. Vy mV + Fy +¥, 7. Een systeem voor het corrigeren van gondelwindsnelheid die gebaseerd is op een mechanismemodel en een neuraal netwerk, voor het realiseren van een werkwijze voor het corrigeren van windsnelheid op een gondel die gebaseerd is op een mechanismemodel en een neuraal netwerk overeenkomstig één van de conclusies 1 - 6, waarbij het systeem het volgende bevat: een verdeelmodule, waarbij de verdeelmodule gebruikt wordt voor het selecteren van windsnelheidsgegevens en vermogengegevens gemeten in een SCADA-systeem en voor het verdelen van de windsnelheidsgegevens en de vermogensgegevens volgens een vooraf ingestelde regel om een gegevensset onder een werkingstoestand met veranderlijke helling en een gegevensset onder een werkingstoestand met niet veranderlijke helling te verkrijgen, een rekenmodule, waarbij de rekenmodule gebruikt wordt voor het verkrijgen van informatie over luchtdichtheid en informatie over bestreken oppervlak, en voor het berekenen van de informatie over luchtdichtheid, de informatie over bestreken oppervlak de windsnelheidsgegevens en de vermogensgegevens volgens een vooraf ingestelde empirische formule om de overeenkomstige theoretische windsnelheid Vm te verkrijgen onder overeenstemmende werkingscondities, een ontbindingsmodule, waarbij de ontbindingsmodule gebruikt wordt voor het verkrijgen van de werkelijke windsnelheid Vy, en voor het vervolgens ontbinden van de theoretische windsnelheid Vm en de werkelijke windsnelheid Vin overeenstemmende hoogfrequente gegevens en laagfrequente gegevens door middel van wavelet- transformatie, respectievelijk, voor het verkrijgen van overeenkomstige hoogfrequente residuen Vg en laagfrequente residuen Vi, en voor het gebruiken van de hoogfrequente residuen Vg, en de laagfrequente residuen Vy als een doelgegevensset, waarbij de windsnelheidsgegevens een kenmerkengegevensset zijn, een opstelmodule, waarbij de opstelmodule gebruikt wordt voor het nemen van de kenmerkengegevensset als input en de doelgegevensset als output, het uitvoeren van een training doorheen het neuraal netwerk, het bepalen van een tijdsstap met behulp van een optimalizeringsmethode, en het opstellen van een voorspellingsmodel voor windsnelheidsresidu gebaseerd op de werkingscondities en de hoogfrequente gegevens en de laagfrequente gegevens, een invoermodule, waarbij de invoermodule gebruikt wordt voor het invoeren van SCADA-windsnelheidsgegevens met een bepaalde tijdsstap in een getraind voorspellingsmodel voor windsnelheidsresidu om hoogfrequente residu voorspellingswaardegegevens en laagfrequente residu voorspellingswaardegegevens in overeenstemming met de tijdsstap te verkrijgen; en een uitvoermodule, waarbij de uitvoermodule gebruikt wordt voor het uitvoeren van lineaire optelling op de SCADA-windsnelheidsgegevens en de hoogfrequente residu voorspellingswaardegegevens en de laagfrequente residu voorspellingswaardegegevens om een uiteindelijke werkelijke windsnelheidscorrectiewaarde te verkrijgen.7. A system for correcting nacelle wind speed based on a mechanism model and a neural network, for realizing a method for correcting wind speed on a nacelle based on a mechanism model and a neural network according to any one of claims 1 - 6, wherein the system includes: a distribution module, the distribution module being used to select wind speed data and power data measured in a SCADA system and to distribute the wind speed data and power data according to a preset rule to distribute a data set under to obtain an operating condition with variable slope and a data set under an operating condition with non-changing slope, a calculation module, wherein the calculation module is used to obtain information about air density and information about covered area, and to calculate the information about air density, the area covered information, the wind speed data and the power data according to a preset empirical formula to obtain the corresponding theoretical wind speed Vm under corresponding operating conditions, a decomposition module, where the decomposition module is used to obtain the actual wind speed Vy, and to subsequently decompose the theoretical wind speed Vm and the actual wind speed Vin corresponding high-frequency data and low-frequency data by wavelet transform, respectively, to obtain corresponding high-frequency residuals Vg and low-frequency residuals Vi, and to use the high-frequency residuals Vg, and the low-frequency residuals Vy as a target data set, where the wind speed data is a feature data set, a setup module, where the setup module is used to take the feature data set as input and the target data set as output, perform training through the neural network, determine a time step using an optimization method, and establishing a wind speed residual prediction model based on the operating conditions and the high-frequency data and the low-frequency data, an input module, where the input module is used to input SCADA wind speed data at a certain time step into a trained prediction model for wind speed residual to obtain high-frequency residual prediction value data and low-frequency residual prediction value data according to the time step; and an output module, wherein the output module is used to perform linear addition on the SCADA wind speed data and the high-frequency residual prediction value data and the low-frequency residual prediction value data to obtain a final actual wind speed correction value.
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