CN113408071B - Wind turbine generator tower attitude prediction method and system - Google Patents
Wind turbine generator tower attitude prediction method and system Download PDFInfo
- Publication number
- CN113408071B CN113408071B CN202110689904.XA CN202110689904A CN113408071B CN 113408071 B CN113408071 B CN 113408071B CN 202110689904 A CN202110689904 A CN 202110689904A CN 113408071 B CN113408071 B CN 113408071B
- Authority
- CN
- China
- Prior art keywords
- wind turbine
- prediction
- phase space
- space reconstruction
- attitude
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/10—Geometric CAD
- G06F30/17—Mechanical parametric or variational design
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/12—Timing analysis or timing optimisation
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/72—Wind turbines with rotation axis in wind direction
Abstract
The invention discloses a wind turbine generator tower attitude prediction method and system. The method comprises the following steps: acquiring multi-element time sequence data of the wind turbine tower attitude; performing phase space reconstruction on the multivariate time sequence data by using an improved C-C method; constructing a plurality of corresponding initial prediction models for the multivariate time sequence data after the phase space reconstruction; the initial prediction model is a support vector regression model; optimizing each initial prediction model by adopting a sequential minimum method; training each optimized prediction model based on the multivariate time series data after the phase space reconstruction; and predicting the attitude of the tower drum of the wind turbine through the trained prediction models. The time sequence is processed by utilizing a phase space reconstruction method, and a weighted SVR prediction model is established, namely, a sequential minimum method is adopted to optimize each support vector regression model, and meanwhile, the shutdown criterion and the model parameters are optimized; therefore, the attitude of the wind turbine tower can be predicted more accurately, and the fault prediction is realized.
Description
Technical Field
The invention relates to the field of wind turbine generator system fault prediction, in particular to a wind turbine generator system tower attitude prediction method and system.
Background
With the rapid development of science and technology, modern engineering systems become more and more complex, and the role of the modern engineering systems is more and more important. Complex systems generally work in a harsh environment, and because the system improves performance and increases uncertain factors influencing normal operation of the system, the potential possibility of system failure or malfunction is greatly increased. Often, a minor failure of a component will cause the performance of the whole system to gradually decrease, and even a failure of a critical component will cause the system to fail to operate properly or even to fail. Many catastrophic failures are caused by minor failures and result in significant property loss, casualties and social impact. In order to ensure that no fatal fault occurs in the operation process of a complex system, the requirements on the reliability and the safety of each subsystem and subsystem are extremely high. The reliability of the whole system is improved by improving the reliability of components and assemblies, and the requirement of a complex system on the reliability is difficult to meet, so the fault diagnosis and fault-tolerant control technology becomes an important technology for improving the reliability and the safety of the complex system.
The wind turbine installation and rotation platform online monitoring system is a closed-loop system with complex characteristics, and generally has the problems of data shortage, difficult establishment of a prediction model, serious uncertainty and the like, so that the fault prediction of the system has many difficulties, such as how to establish the prediction model, how to establish a prediction mechanism, how to ensure the prediction accuracy and real-time performance and the like, which need to be subjected to long-term deep analysis and discussion. In summary, the research on the failure prediction theory and method is a necessary way, and has a general meaning, the results of theoretical research and practical application of the method can bring endless benefits to complex systems with extremely high requirements on reliability and safety, and the research results can also be popularized and applied to various national economic fields, such as carrier rockets, space stations, satellites, manned spacecrafts, other complex industrial systems and the like, and have wide application prospects.
Disclosure of Invention
The invention aims to provide a wind turbine generator tower attitude prediction method and system, which are used for predicting the attitude of a wind turbine tower accurately and in real time so as to realize the prediction of faults.
In order to achieve the purpose, the invention provides the following scheme:
a wind turbine generator tower attitude prediction method comprises the following steps:
acquiring multi-element time sequence data of the wind turbine tower attitude;
performing phase space reconstruction on the multivariate time series data by using an improved C-C method;
constructing a plurality of corresponding initial prediction models for the multivariate time sequence data after the phase space reconstruction; the initial prediction model is a support vector regression model;
optimizing each initial prediction model by adopting a sequential minimum method;
training each optimized prediction model based on the multivariate time series data after the phase space reconstruction;
and predicting the attitude of the tower drum of the wind turbine through the trained prediction models.
Further, the multivariate time series data includes angular velocity of the wind turbine tower along the x-axis, the y-axis, and the z-axis, and an inclination angle of the wind turbine tower along the x-axis, the y-axis, and the z-axis.
Further, before performing a phase space reconstruction on the multivariate time series data by using an improved C-C method, the method further comprises:
and carrying out normalization processing on the multivariate time series data.
Further, the kernel function of the support vector regression model is a radial basis function.
Further, the training of each optimized prediction model based on the phase space reconstructed multivariate time series data specifically includes:
inputting the multivariate time sequence data after the phase space reconstruction into each optimized prediction model to obtain output data;
and optimizing the penalty factor and the nuclear parameter in each optimized prediction model based on the output data to complete the training process.
Further, the performing phase space reconstruction on the multivariate time series data by using the improved C-C method specifically includes:
determining delay time of each variable in the multivariate time series data;
calculating an embedding dimension;
performing a phase space reconstruction of the multivariate time series data based on the delay time and the embedding dimension.
The invention also provides a wind turbine generator tower attitude prediction system, which comprises:
the multivariate time sequence data acquisition module is used for acquiring multivariate time sequence data of the wind turbine tower attitude;
the phase space reconstruction module is used for performing phase space reconstruction on the multivariate time sequence data by utilizing an improved C-C method;
the model construction module is used for constructing a plurality of corresponding initial prediction models for the multivariate time series data after the phase space reconstruction; the initial prediction model is a support vector regression model;
the optimization module is used for optimizing each initial prediction model by adopting a sequential minimum method;
the training module is used for training each optimized prediction model based on the multi-element time sequence data after the phase space reconstruction;
and the prediction module is used for predicting the attitude of the tower drum of the wind turbine through the trained prediction models.
Further, the training module specifically includes:
the input unit is used for inputting the multivariate time sequence data after the phase space reconstruction into each optimized prediction model to obtain output data;
and the optimization unit is used for optimizing the penalty factors and the nuclear parameters in each optimized prediction model based on the output data to complete the training process.
Further, the phase space reconstruction module specifically includes:
a delay time determining unit for determining a delay time of each variable in the multivariate time series data;
an embedding dimension calculation unit for calculating an embedding dimension;
and the phase space reconstruction unit is used for performing phase space reconstruction on the multivariate time sequence data based on the delay time and the embedding dimension.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the time sequence is processed by utilizing a phase space reconstruction method, and a weighted SVR prediction model is established, namely, a sequential minimum method is adopted to optimize each support vector regression model, and meanwhile, the shutdown criterion and the model parameters are optimized; therefore, the attitude of the wind turbine tower can be predicted more accurately, and the fault prediction is realized.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a flow chart of a wind turbine tower attitude prediction method according to an embodiment of the invention;
FIG. 2 is a schematic diagram illustrating the principle of optimizing each of the initial prediction models by a sequential minimization method according to an embodiment of the present invention; a drawing;
FIG. 3 is an optimal parameter fitness curve for a first set of variables;
FIG. 4 is an optimal parameter fitness curve for a second set of variables;
FIG. 5 is an optimal parameter fitness curve for a third set of variables;
FIG. 6 is an optimal parametric fitness curve for a fourth set of variables;
FIG. 7 is an optimal parameter fitness curve for a fifth set of variables;
FIG. 8 is an optimal parameter fitness curve for a sixth set of variables;
FIG. 9 is a one-step prediction of 22s to 32s angular velocity p for the onset of a fault;
FIG. 10 is a one-step prediction of angular velocity q from 22s to 32s of fault onset;
FIG. 11 shows the angular velocities r from 22s to 32s at which a fault starts 0 Predicting the result in one step;
FIG. 12 is a one-step prediction of the fault onset 22s to 32s angle φ;
FIG. 13 is a graph of the results of a one-step prediction of the 22s to 32s angle θ for the onset of a fault;
FIG. 14 is a one-step prediction of the 22s to 32s angle ψ for the onset of a fault;
FIG. 15 shows the results of a one-step prediction of the angular velocity p to be predicted;
FIG. 16 is a one-step prediction result of the angular velocity q to be predicted;
FIG. 17 shows the angular velocity r to be predicted 0 Predicting the result in one step;
FIG. 18 shows the results of one-step prediction of the angle φ to be predicted
FIG. 19 shows the one-step prediction result of the angle θ to be predicted
Fig. 20 shows the result of one-step prediction of the angle ψ to be predicted.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a wind turbine generator tower attitude prediction method and system, which are used for predicting the attitude of a wind turbine tower accurately and in real time so as to realize the prediction of faults.
To achieve the above object, first, the present invention uses Lyapunov exponent to represent the motion behavior of chaotic attractors. Wherein, the time sequence refers to an observed value of a certain variable arranged at equal time intervals according to the time sequence; the Lyapunov exponent method can measure the average convergence or average divergence of the system between adjacent orbits in a phase space, and is sensitive to specific initial conditions and has strong immunity to noise; the chaotic attractors in the phase space can describe a chaotic system, the attractors are finite regions which the system tends to in the phase space, and the chaotic attractors are attractors which are formed by trajectories at different layers and have self-similar structures. Furthermore, the invention adopts a small data method to solve the maximum Lyapunov index of each state variable time sequence of the fan tower attitude online monitoring system, thereby judging the wonderness of the system. Wherein the ith Lyapunov index is defined as:
wherein n represents an n-dimensional phase space, p i (n) represents the spindle length.
According to the formula, the angular rates p, q and r of the fan tower barrel along the x axis, the y axis and the z axis can be respectively calculated 0 Further, the maximum Lyapunov index corresponding to the bias angle phi, theta, psi can be obtained as shown in the following table.
TABLE 1 maximum Lyapunov index values of various state variables of a wind turbine tower
Because the maximum Lyapunov index values of all state variable time sequences are greater than 0, the online monitoring system for the wind turbine tower attitude is a chaotic system, and the time sequences need to be analyzed and processed by using a relevant method of a chaotic theory.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
As shown in fig. 1, a wind turbine tower attitude prediction method includes the following steps:
step 101: and acquiring the multivariate time sequence data of the wind turbine tower attitude. The multivariate time series data comprises angular rates of the wind turbine tower along an x-axis, a y-axis, and a z-axis, and tilt angles of the wind turbine tower along the x-axis, the y-axis, and the z-axis.
Step 102: and performing phase space reconstruction on the multi-element time sequence data by using an improved C-C method. The method specifically comprises the following steps: determining delay time of each variable in the multivariate time sequence data; calculating an embedding dimension; performing a phase space reconstruction of the multivariate time series data based on the delay time and the embedding dimension.
Considering a multivariable discrete time sequence of the wind turbine tower attitude online monitoring system:
X(t p )＝[x 1 (t p ),…,x n (t p )] T
wherein time t p 1,2, …, N being the number of data in time series, x 1 (t p ),…,x n (t p ) N states of the corresponding system at t p The value of the time of day. The multivariate time series data needs to be normalized before phase space reconstruction.
(1) After the multivariate time sequence is normalized, respectively aiming at each variable, selecting proper delay time tau d The final embedding dimension m is calculated. The following 2 quantities were calculated:
wherein the content of the first and second substances,
in order to assist in calculating the value 1,
to assist in calculating quantity 2. r is a radical of hydrogen i I σ/2 is the search radius taken in the calculation, and σ is the standard deviation of the time series. By
T to calculate τ corresponding to the first local minimum of d ＝tτ s T is the time lag, τ s Is the sampling interval. The maximum and minimum two search radii are chosen, defining the delta Δ S (m, t) as:
ΔS(m,t)＝max{S(m,r i ,t)}-min{S(m,r i ,t)}
wherein S (m, r, t) is defined as:
wherein m is 2,3, …; the correlation integral of the embedded time series is:
since the length N of the actual time series cannot be infinite, the calculation is specifically performed
As an estimate of C (m, r, t). Wherein, M ═ N- (M-1) t is the embedding point number of the M-dimensional space, Θ (·) is the Heaviside function, Θ (x) is 1, x is not less than 0, Θ (x) is 0, and x is less than 0; i | · | purple wind ∞ Is 1 ∞ And (4) norm. At the same time with
As an estimate of S (m, r, t). Thus, the phase space reconstruction is performed on each variable to obtain a multivariate time series embedded delay vector as follows:
(2) the conventional C-C process in step 1 is modified as follows:
(1) replacing the original infinity norm with the 2-norm;
(2) σ ═ std (x) (1+ cv/3), cv ═ std (x)/mean (x) is defined so as to enlarge the search radius r;
(3) when r is defined as rlog (m +1) to enlarge the search radius and reduce oscillation, the correlation integral of the embedded time series is improved as follows:
wherein r (m) > 0, and when m ═ 1, r (m) > r; when m > 1, r (m) rlog (m + 1). R in other formulas is also changed into r (m);
(4) by using the idea of 'averaging' in spectrum estimation, the time series is divided into multiple segments, and tau of each segment of the time series is obtained d And τ w Take τ d Is taken as the final delay time and the final embedding dimension m is calculated. Solving for τ for each variable d And m is followed by taking
m * Max (m) as the final embedded delay parameter for the multivariate time series. Its embedded delay vector is as follows:
step 103: constructing a plurality of corresponding initial prediction models for the multivariate time sequence data after the phase space reconstruction; the initial prediction model is a support vector regression model.
Step 104: and optimizing each initial prediction model by adopting a sequential minimum method.
The principle of step 103-104 is as follows:
(1) standard support vector machine (SVR) method
1) Mapping the inclination angle and the angular rate measured by the on-line wind turbine tower attitude monitoring system to a high-dimensional space F and constructing a regression estimation function F (x) in the space to define as follows:
f(x)＝＜w·Φ(x)＞+b
wherein the dimension of w is the feature space dimension, phi represents the nonlinear mapping from the input space to the output space, and b is the threshold. Solving for w and b by minimizing the objective function, the formula is:
wherein epsilon is a loss function and is used for controlling the size of the regression approximation error pipeline; c is a penalty factor used for balancing the flatness degree and the number of sample points with the deviation larger than epsilon of the regression function f; xi i And
is a relaxation factor.
2) The regression function is very sensitive to isolated points due to the fixed penalty factor C, which in turn results in errors. The invention adopts a linear distribution method to obtain a multi-element time sequence weighting coefficient rho after phase space reconstruction, and the formula is as follows:
1≤i≤nm *
wherein eta is the initial degree of relationship, and n is the variable number of the time series. Thus, the rewritable optimization objective function is:
introducing a Lagrange multiplier, and constructing the following Lagrange function:
for parameters w, b, xi i ,
Calculating the partial derivatives, and making the partial derivatives of all parameters zero, the following dual optimization problem can be obtained:
at this time, the function regression problem of the support vector machine can be summarized as a quadratic programming problem, and the regression estimation function is as follows:
wherein, K (x, x) i ) Is a kernel function. Because of alpha i And
cannot be 0 at the same time, so b can be calculated as:
(2) the initial prediction model is optimized using Sequential Minimal Optimization (SMO).
According to the characteristics of the attitude monitoring data of the tower drum of the fan, the invention uses an SMO-SVR method to respectively establish prediction models of 6 state quantities, and the algorithm adopts two layers of circulation optimization parameters alpha 1 And alpha 2 As shown in fig. 2.
The algorithm adopts two layers of circulation to optimize the parameter alpha 1 And alpha 2 . Outer layer circulation: for parameter alpha 2 Selection is performed. And finding out sample points violating the KKT condition from the non-boundary points of the sample data, if the sample points do not exist, searching the whole sample set, and if the sample points do not exist, finishing the algorithm. Inner layer circulation: finding out | E (alpha) from non-boundary sample points 1 )-E(α 2 ) | MaxIs optimized by the parameter alpha 1 Where E (-) is an error function. If such an alpha is not present 1 Then alpha is selected in the outer layer cycle 2 And if the parameters are invalid, the outer loop is performed again. The SMO algorithm process is described as follows:
1) for initially selected alpha 1 And alpha 2 To update these two values, the new values of the multipliers must be in a straight line in order not to violate the linear constraint, as follows:
in the formula (I), the compound is shown in the specification,
is alpha 1 And alpha 2 The average value of the corresponding weight coefficient. Thus, the argument of the objective function is limited to the plane (α) 1 ,α 2 ) The two-dimensional optimization problem becomes a one-dimensional problem, so that the solution can be analyzed.
2) First, solve for
Then it is used to solve
Is operable as
Wherein:
when y is 1 ≠y 2 ,
When y is 1 ＝y 2 ,
3) By solving for alpha 2 The optimized values of (A) are:
then, α can be solved 1 Let s be y 1 y 2 And then:
4) correcting the threshold b:
wherein the content of the first and second substances,
are respectively as
And
expressions when not on a boundary. If it is
And
are not on the boundary, then
If it is
And
all on the boundary, taking the average value of the two formulas,
5) the shutdown criteria for the KKT condition are improved. If (w, xi) * ) Is the solution of the original problem of the objective function, and is simultaneously (alpha ) * ) For the solution of the dual problem, then the corresponding objective function values are made equal, i.e. R (w, xi) * )＝W(α,α * ) From this, it is possible to obtain:
order to
Then:
for a given accuracy requirement, when
Below this precision, the algorithm terminates. Handle
The shutdown criteria are used in conjunction with the shutdown criteria for the KKT condition, which may be set to occur once after a number of iterations
Checking to improve the running efficiency of the algorithm.
Step 105: and training each optimized prediction model based on the multi-element time sequence data after the phase space reconstruction. The method specifically comprises the following steps: and inputting the multivariate time sequence data after the phase space reconstruction into each optimized prediction model to obtain output data, and optimizing penalty factors and nuclear parameters in each optimized prediction model based on the output data to finish the training process.
According to the measured state parameter characteristics of the wind turbine tower, the invention selects a radial basis function K (x) i ,x j )＝exp(-β||x i -x j || 2 ) As a kernel function, β represents a kernel parameter. At the moment, the penalty factor C and the nuclear parameter beta need to be optimized according to the actual collected data of the state quantity of the wind turbine tower, so that the best prediction performance is obtained.
Setting C to be the range of output sample data prevents C from being overly sensitive to singular values, in the form:
wherein the content of the first and second substances,
representing the mean value, En, of the output samples of the wind turbine tower y And outputting the entropy of the sample for the wind turbine tower calculated by utilizing the inverse cloud. Since β is the expansion coefficient of the radial basis kernel, and the expansion coefficient generally reflects the range of the wind turbine tower input samples, it can be said that:
in formula (II), En' x For En of the input samples from the tower of a wind turbine x And
the normally-distributed random number is calculated,
step 106: and predicting the attitude of the tower drum of the wind turbine through the trained prediction models.
And performing one-step prediction by using the trained prediction model, bringing a new prediction value into the initially acquired state quantity time sequence, removing the most original sample data, then training the new prediction model again, and repeating iterative prediction.
Compared with the prior art, the invention has the beneficial effects that:
(1) in order to solve the problems of nonlinearity and chaos existing in time sequence data, a quantitative time sequence prediction method based on machine learning is provided. The method is characterized in that deep research is carried out on a representative support vector regression method, a phase space reconstruction method is utilized to process a time sequence, a weighted SVR prediction model is established, and meanwhile, the shutdown criterion and model parameters of the model are optimized;
(2) in order to make up for the respective defects of the qualitative and quantitative prediction methods, the two methods are organically combined, and a time sequence prediction method based on qualitative/quantitative mixing is provided to solve the problems of nonlinearity, chaos and uncertainty at the same time, and mainly comprises a cloud network method. Furthermore, a machine learning method is used for replacing matrix calculation, a multi-factor high-order cloud network hybrid prediction model can be established, and the prediction performance of the hybrid model can be further improved.
The specific embodiment is as follows:
firstly, the normalized variable time sequence to be predicted is subjected to phase space reconstruction by utilizing an improved C-C average method, and the embedding dimension is obtained as
A delay time of
And optimizing the penalty factor C and the kernel parameter gamma aiming at input and output samples consisting of the 6 variables to be predicted and the auxiliary prediction factors thereof, wherein the fitness curve is shown in figures 3 to 8.
The optimum parameters obtained are shown in table 1:
TABLE 1 optimal parameters table
Finally, the prediction model is also trained by using 60 sets of time series, and fig. 9 to 14 show the one-step prediction simulation results of 22s to 32s from the beginning of the fault.
As can be seen from fig. 9 to 14, the SVR model has better short-term prediction capability, and the prediction accuracy thereof is closely related to the reliability of the training samples and the determination of the initial parameters.
Further, as with the cloud fuzzy method, a main factor domain U and k auxiliary predictor domains V are defined k . Still taking the variable phi as an example, the domain of argument for the main factor phi is defined as U [ -0.0065,0.0065]The argument field of the auxiliary predictor r is V [ -0.0030,0.0025]And dividing U and V into 10 domain subintervals, and setting parameters C and gamma, such as C, of SVR1 and SVR2 1 ＝2.43,C 2 ＝19,γ 1 ＝0.086,γ 2 0.0014. According to the above process, the simulation results of one-step prediction from 22s to 32s for each variable to be predicted are shown in fig. 15 to 20.
As can be seen from the simulation results of the state variables, the cloud-SVR time series prediction method has a very good effect on short-term prediction, and can accurately track various fluctuation changes of time series data.
The invention also provides a wind turbine generator tower attitude prediction system, which comprises:
and the multivariate time sequence data acquisition module is used for acquiring multivariate time sequence data of the wind turbine tower attitude.
And the phase space reconstruction module is used for performing phase space reconstruction on the multi-element time sequence data by utilizing an improved C-C method.
The model construction module is used for constructing a plurality of corresponding initial prediction models for the multivariate time series data after the phase space reconstruction; the initial prediction model is a support vector regression model.
And the optimization module is used for optimizing each initial prediction model by adopting a sequential minimum method.
And the training module is used for training each optimized prediction model based on the multi-element time sequence data after the phase space reconstruction.
And the prediction module is used for predicting the attitude of the tower drum of the wind turbine through the trained prediction models.
Wherein, the training module specifically includes:
the input unit is used for inputting the multivariate time sequence data after the phase space reconstruction into each optimized prediction model to obtain output data;
and the optimization unit is used for optimizing the penalty factors and the nuclear parameters in each optimized prediction model based on the output data to complete the training process.
Wherein, the phase space reconstruction module specifically comprises:
a delay time determining unit for determining a delay time of each variable in the multivariate time series data;
an embedding dimension calculation unit for calculating an embedding dimension;
and the phase space reconstruction unit is used for performing phase space reconstruction on the multivariate time sequence data based on the delay time and the embedding dimension.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the description of the method part.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (9)
1. A wind turbine generator tower attitude prediction method is characterized by comprising the following steps:
acquiring multi-element time sequence data of the wind turbine tower attitude;
performing phase space reconstruction on the multivariate time series data by using an improved C-C method;
constructing a plurality of corresponding prediction models for the multivariate time sequence data after the phase space reconstruction; the prediction model is a support vector regression model;
optimizing each prediction model by adopting a sequential minimum method;
training each optimized prediction model based on the multivariate time series data after the phase space reconstruction;
and predicting the attitude of the tower drum of the wind turbine through the trained prediction models.
2. The wind turbine tower attitude prediction method of claim 1, wherein the multivariate time series data comprises angular velocity of the wind turbine tower along the x-axis, the y-axis, and the z-axis, and an inclination angle of the wind turbine tower along the x-axis, the y-axis, and the z-axis.
3. The wind turbine tower attitude prediction method of claim 1, further comprising, prior to performing a phase-space reconstruction on the multivariate time series data using an improved C-C method:
and carrying out normalization processing on the multivariate time series data.
4. The method for predicting the attitude of a tower of a wind turbine generator as claimed in claim 1, wherein the kernel function of the support vector regression model is a radial basis function.
5. The wind turbine tower attitude prediction method according to claim 1, wherein the training of each optimized prediction model based on the multivariate time series data after the phase space reconstruction specifically comprises:
inputting the multivariate time sequence data after the phase space reconstruction into each optimized prediction model to obtain output data;
and optimizing the penalty factor and the nuclear parameter in each optimized prediction model based on the output data to complete the training process.
6. The wind turbine tower attitude prediction method according to claim 1, wherein the performing phase-space reconstruction on the multivariate time series data by using an improved C-C method specifically comprises:
determining delay time of each variable in the multivariate time sequence data;
calculating an embedding dimension;
performing a phase space reconstruction of the multivariate time series data based on the delay time and the embedding dimension.
7. A wind turbine tower attitude prediction system, comprising:
the multivariate time sequence data acquisition module is used for acquiring multivariate time sequence data of the wind turbine tower attitude;
the phase space reconstruction module is used for performing phase space reconstruction on the multivariate time sequence data by utilizing an improved C-C method;
the model construction module is used for constructing a plurality of corresponding prediction models for the multivariate time series data after the phase space reconstruction; the prediction model is a support vector regression model;
the optimization module is used for optimizing each prediction model by adopting a sequential minimum method;
the training module is used for training each optimized prediction model based on the multi-element time sequence data after the phase space reconstruction;
and the prediction module is used for predicting the attitude of the tower drum of the wind turbine through the trained prediction models.
8. The wind turbine tower attitude prediction system of claim 7, wherein the training module specifically comprises:
the input unit is used for inputting the multivariate time sequence data after the phase space reconstruction into each optimized prediction model to obtain output data;
and the optimization unit is used for optimizing the penalty factors and the nuclear parameters in each optimized prediction model based on the output data to complete the training process.
9. The wind turbine tower attitude prediction system of claim 7, wherein the phase space reconstruction module specifically comprises:
a delay time determining unit for determining delay time of each variable in the multivariate time series data;
an embedding dimension calculation unit for calculating an embedding dimension;
and the phase space reconstruction unit is used for performing phase space reconstruction on the multivariate time sequence data based on the delay time and the embedding dimension.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110689904.XA CN113408071B (en) | 2021-06-22 | 2021-06-22 | Wind turbine generator tower attitude prediction method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110689904.XA CN113408071B (en) | 2021-06-22 | 2021-06-22 | Wind turbine generator tower attitude prediction method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113408071A CN113408071A (en) | 2021-09-17 |
| CN113408071B true CN113408071B (en) | 2022-09-16 |
Family
ID=77682218
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110689904.XA Active CN113408071B (en) | 2021-06-22 | 2021-06-22 | Wind turbine generator tower attitude prediction method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113408071B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103839280A (en) * | 2014-03-21 | 2014-06-04 | 武汉科技大学 | Method for tracking human body posture based on visual information |
| CN109034223A (en) * | 2018-07-16 | 2018-12-18 | 湖南城市学院 | A kind of Study on prediction technology of chaotic series and system |
Family Cites Families (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102750542A (en) * | 2012-05-29 | 2012-10-24 | 北京交通大学 | Support vector regression machine wind speed combination forecast method with interpolation being smoothed and optimized |
| CN105865514A (en) * | 2016-03-22 | 2016-08-17 | 国网黑龙江省电力有限公司哈尔滨供电公司 | Wind power system running state grey prediction system based on chaos phase space reconstruction and method thereof |
| CN108589764A (en) * | 2018-07-02 | 2018-09-28 | 天津大学 | Bucket foundation wind turbine entirety deep water sinking attitude control method |
| CN109653962B (en) * | 2018-12-21 | 2022-04-15 | 鲁能新能源(集团)有限公司 | Wind turbine generator set online monitoring system and monitoring method |
| CN110414031B (en) * | 2019-05-07 | 2021-10-22 | 深圳大学 | Method and device for predicting time sequence based on volterra series model, electronic equipment and computer readable storage medium |
| CN111340061A (en) * | 2020-01-18 | 2020-06-26 | 中国人民解放军国防科技大学 | Multi-mode data fusion and classification method based on SVM model parameter optimization |
| CN111754034A (en) * | 2020-06-15 | 2020-10-09 | 西安工业大学 | Time sequence prediction method based on chaos optimization neural network model |
| CN111914470B (en) * | 2020-06-17 | 2024-05-07 | 西安交通大学 | Multi-element monitoring time series regression prediction method for energy chemical production system |
-
2021
- 2021-06-22 CN CN202110689904.XA patent/CN113408071B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103839280A (en) * | 2014-03-21 | 2014-06-04 | 武汉科技大学 | Method for tracking human body posture based on visual information |
| CN109034223A (en) * | 2018-07-16 | 2018-12-18 | 湖南城市学院 | A kind of Study on prediction technology of chaotic series and system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113408071A (en) | 2021-09-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114564000B (en) | Active fault tolerance method and system based on intelligent aircraft actuator fault diagnosis | |
| Toscano et al. | A new heuristic approach for non-convex optimization problems | |
| CN112099351B (en) | Distributed fault diagnosis method based on centrosymmetric polyhedron | |
| CN109066651B (en) | Method for calculating limit transmission power of wind power-load scene | |
| Wen et al. | Data‐driven transient frequency stability assessment: A deep learning method with combined estimation‐correction framework | |
| CN114741946A (en) | Reliability simulation method related to failure of multiple components of typical mechanism of aircraft engine | |
| CN114363195A (en) | Network flow prediction early warning method for time and spectrum residual convolution network | |
| CN111723982A (en) | Medium-and-long-term power load combined prediction method based on gray-Markov chain | |
| CN110929835B (en) | Novel silicon carbide-based aviation power converter fault diagnosis method and system | |
| Khan et al. | Improvement on state estimation for discrete-time LTI systems with measurement loss | |
| CN109599866B (en) | Prediction-assisted power system state estimation method | |
| Huang et al. | A modified fusion model-based/data-driven model for sensor fault diagnosis and performance degradation estimation of aero-engine | |
| Shouman et al. | A hybrid machine learning model for reliability evaluation of the reactor protection system | |
| Dewallef et al. | Combining classification techniques with Kalman filters for aircraft engine diagnostics | |
| Liu et al. | A new inherent reliability modeling and analysis method based on imprecise Dirichlet model for machine tool spindle | |
| CN113408071B (en) | Wind turbine generator tower attitude prediction method and system | |
| Xu et al. | An adaptive on-board real-time model with residual online learning for gas turbine engines using adaptive memory online sequential extreme learning machine | |
| Lu et al. | Novel extreme learning machine using Kalman filter for performance prediction of aircraft engine in dynamic behavior | |
| Sun et al. | Research on Satellite Fault Detection Method Based on MSET and SRPRT | |
| Jin et al. | A novel analytical redundancy method based on decision-level fusion for aero-engine sensors | |
| Li et al. | Application of combination forecasting model in aircraft failure rate forecasting | |
| Liu et al. | An intelligent prediction method of aero-engine gas path performance parameters | |
| Huang et al. | Gas path deterioration observation based on stochastic dynamics for reliability assessment of aeroengines | |
| CN112667957A (en) | Intelligent electric energy meter failure rate prediction method based on deep neural network | |
| CN111340647A (en) | Power distribution network data adjustment method and system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20220826 Address after: Building 2, No. 18 Lishi Hutong, Dongcheng District, Beijing 100010 Applicant after: China lvfa Investment Group Co.,Ltd. Applicant after: LUNENG GROUP Co.,Ltd. Applicant after: DUCHENG WEIYE GROUP Co.,Ltd. Address before: No.14, jingsan Road, Shizhong District, Jinan City, Shandong Province Applicant before: LUNENG GROUP Co.,Ltd. Applicant before: DUCHENG WEIYE GROUP Co.,Ltd. |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |