CN102478584A - Wind power station wind speed prediction method based on wavelet analysis and system thereof - Google Patents
Wind power station wind speed prediction method based on wavelet analysis and system thereof Download PDFInfo
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Abstract
The invention relates to a wind power station wind speed prediction method based on wavelet analysis and a system thereof. The method comprises the following steps: according to a specific prediction time interval, determining an input and an output variable of a prediction model; reading a historical wind speed value and correcting an incomplete point in the historical wind speed value so as to acquire a training sample value sequence of a wind speed prediction model; carrying out rapid wavelet decomposition to the training sample value sequence so as to acquire an approximation detail component value sequence; establishing the wind speed prediction model according to the approximation detail component value sequence so as to carry out the wind speed prediction. According to the wind power station wind speed prediction method based on the wavelet analysis and the system of the invention, through the wavelet decomposition, the training sample value sequence is decomposed into different layers according to a scale so that a trend term, a period term and a random term are separated. Each layer is individually analyzed and predicted and finally the corresponding prediction value can be obtained through reconstruction. By using the method, any prediction interval can be selected according to different demands. The wind speed prediction which is many steps ahead and has high precision can be performed.
Description
Technical field
The present invention relates to the predicting wind speed of wind farm method and system, more particularly, relate to a kind of predicting wind speed of wind farm method and system based on wavelet analysis.
Background technology
Wind-power electricity generation, as one of the most competitive in a short time generation mode, its advantage be renewable, pollution-free, take up an area of less, the construction period is short, investment flexibly, automatization level is high, managerial personnel are few etc.But wind is not to exist all the time, and it depends primarily on the variation of air flow, so the wind energy little randomness energy that is a kind of density.The energy size of its generation is unstable, receive geographic restriction serious, and conversion efficiency is low.
China is vast in territory, and the shore line is very long, the wind energy resources rich.Along with the continuous development of China's wind-power electricity generation industry, the wind-powered electricity generation total installation of generating capacity increases day by day.Big-and-middle-sized wind-powered electricity generation set grid-connection generates electricity, and has become the principal mode of world's Wind Power Utilization.Along with the unit sustainable growth of being incorporated into the power networks, single-machine capacity improves, machine group performance optimization, and failure rate reduces, and production cost descends, and wind-powered electricity generation slowly possesses the ability with the conventional energy resources competition.But because characteristics such as the wind-powered electricity generation randomness of exerting oneself, intermittence must leave enough subsequent use unit and peak during operation of power networks, system still can stable operation when guaranteeing that fluctuating widely appears in wind-powered electricity generation.This is the topmost characteristics that wind-power electricity generation is different from other generation modes, also becomes the topmost problem of restriction wind-power electricity generation large-scale application simultaneously.The solution of present stage is, the dispatching center is through being incorporated into the power networks with wind power plant, reading of data update system parameter at any time, thus be controlled at the fluctuation that causes in the scope that can tackle by wind-power electricity generation.But along with the increase of wind energy turbine set scale, wind-power electricity generation has brought bigger pressure to the influence of electric system also more and more significant to operation of power networks.Therefore; In order to improve the utilization ratio of wind energy, the prediction accurately of increasing wind-power electricity generation enterprise is served, thereby provides the curve that generates electricity more accurately through the prediction wind resource; So that regulation and control divide power distributing amount, realize modern wind-powered electricity generation and the operation of tradition generating combined optimization.Wind-force prediction accurately can also help the investor to confirm to build wherein wind energy turbine set, and the network operator's better maintaining and management wind-powered electricity generation unit that help wind energy turbine set.
At present, the prediction of wind speed is mainly depended on the physics forecast model, its calculated amount is big; The error accumulation rate is high; And need the personage of specialty to safeguard, and can not satisfy the demand of wind-powered electricity generation enterprise to the wind energy short-time forecast, more can not make meticulous forecast to the wind speed profile in the wind energy turbine set scope.In recent years, artificial neural network slowly is widely used in the wind-force prediction, and it can have very big advantage aspect the highly non-linear and serious uncertain recurrence of solution based on the direct modeling of input and output data.Neural network is of a great variety, is to adopt which kind of type neural network to particular problem still actually, and which kind of network weight learning algorithm does not all have clear conclusions at present.Problems such as simultaneously, calculated amount is big, speed of convergence is slow, local optimum also are the main difficulties that neural network faces.Based on above consideration, the present invention utilizes wavelet analysis technology to combine neural network model, sets up wind energy turbine set short-term wind-force forecast model, realizes wind speed is accurately estimated.
Summary of the invention
The technical matters that the present invention will solve is; To prior art the prediction of wind speed is mainly depended on the physics forecast model, its calculated amount is big, and the error accumulation rate is high; And need the personage of specialty to safeguard; Can not satisfy the demand of wind-powered electricity generation enterprise, more can not make meticulous defectives such as forecast, a kind of predicting wind speed of wind farm method and system based on wavelet analysis are provided the wind speed profile in the wind energy turbine set scope to the wind energy short-time forecast.
The technical solution adopted for the present invention to solve the technical problems is: construct a kind of predicting wind speed of wind farm method based on wavelet analysis, it may further comprise the steps:
According to a specific predicted time at interval, confirm the input and output variable of forecast model;
Read the historical wind speed value, revise the incomplete point in the said historical wind speed value, to obtain the training sample value sequence of forecasting wind speed model;
Said training sample value sequence is carried out quick wavelet decomposition, to obtain approximate details component value sequence;
According to said approximate details component value sequence, set up said forecasting wind speed model, to carry out forecasting wind speed.
In predicting wind speed of wind farm method of the present invention, also comprise:
Output result to said forecasting wind speed carries out wavelet reconstruction, to obtain corresponding forecasting wind speed value after the weighting.
In predicting wind speed of wind farm method of the present invention, also comprise:
The real-time wind speed observed reading and the said forecasting wind speed value of gathering are compared; In the comparison of a continuous specific times N, the average relative error between said real-time wind speed observed reading and the forecasting wind speed value is then adjusted the weighted value of said forecasting wind speed model all above 10%; Wherein, N is a natural number.
In predicting wind speed of wind farm method of the present invention,
Use calculating formula
Calculate the average relative error between said real-time wind speed observed reading and the forecasting wind speed value, wherein, v (t) is a t real-time wind speed observed reading constantly, v
*(t) be t forecasting wind speed value constantly.
In predicting wind speed of wind farm method of the present invention,
Use calculating formula
With
Adjust the weighted value of said forecasting wind speed model, wherein, α
j(t) be the t weight of i submodel constantly, e
j(t-u) be t-u model prediction constantly error, s is the cumulative errors time interval, and p is the predicted time interval.
In predicting wind speed of wind farm method of the present invention,
According to calculating formula
Revise the incomplete point in the said historical wind speed value, wherein, t is the incomplete points of data, and v (t) is revised historical wind speed value, t
1And t
2Be adjacent former and later two nearest effective observation stations with incompleteness point, and t
1＜t＜t
2, v (t
1) and v (t
2) be respectively and observation station t
1And t
2Corresponding historical wind speed value.
In predicting wind speed of wind farm method of the present invention, use many shellfishes west small echo to stating the training sample value sequence, carry out the quick wavelet decomposition of three layer depth, to obtain three groups of approximate details component value sequences.
According to another aspect of the present invention, a kind of predicting wind speed of wind farm system based on wavelet analysis is provided, it comprises:
The variable determination module is used for confirming the input and output variable of forecast model according to a specific predicted time at interval;
Read module is used to read the historical wind speed value, revises the incomplete point in the said historical wind speed value, to obtain the training sample value sequence of forecasting wind speed model;
The data decomposition module is used for said training sample value sequence is carried out quick wavelet decomposition, to obtain approximate details component value sequence;
The modeling and forecasting module is used for setting up said forecasting wind speed model, to carry out forecasting wind speed according to said approximate details component value sequence.
In predicting wind speed of wind farm of the present invention system, also comprise:
The reconstruct weighting block is used for the output result of said forecasting wind speed is carried out wavelet reconstruction, to obtain corresponding forecasting wind speed value after the weighting.
In predicting wind speed of wind farm of the present invention system, also comprise:
The weight adjusting module; The real-time wind speed observed reading and the said forecasting wind speed value that are used for gathering compare; In the comparison of a continuous specific times N, the average relative error between said real-time wind speed observed reading and the forecasting wind speed value is then adjusted the weighted value of said forecasting wind speed model all above 10%; Wherein, N is a natural number.
The predicting wind speed of wind farm method and system based on wavelet analysis of embodiment of the present invention; Have following beneficial effect: wavelet decomposition is passed through in (1); The training sample value sequence that will have the historical wind speed value to constitute resolves into different levels according to yardstick, and trend term, periodic term and random entry are separated; (2) each layer is carried out separate analysis and prediction, can improve prediction accuracy; (3) can select the predicting interval arbitrarily according to the different application demand, carry out leading multistep high precision forecasting wind speed; (4), check the validity of forecast model in real time, on-line study and new model weight more according to actual observed value and model checking index; (5) Optimization Dispatching decision-making accurately provides reliably, otherwise effective technique supports for the yardman makes.
Description of drawings
Below in conjunction with accompanying drawing and embodiment the present invention is described further, in the accompanying drawing:
Fig. 1 is the block diagram that the present invention is based on the predicting wind speed of wind farm system of wavelet analysis;
Fig. 2 is the process flow diagram that the present invention is based on predicting wind speed of wind farm method first embodiment of wavelet analysis;
Fig. 3 is the process flow diagram that the present invention is based on predicting wind speed of wind farm method second embodiment of wavelet analysis;
Fig. 4 is the process flow diagram that the present invention is based on predicting wind speed of wind farm method the 3rd embodiment of wavelet analysis;
Fig. 5 is the synoptic diagram of training sample value sequence and approximate details component value sequence among the present invention;
Fig. 6 is the predicting wind speed of wind farm method practical application one hour in advance that the present invention is based on wavelet analysis and three hours in advance forecasting wind speeds figure as a result.
Embodiment
In order to make the object of the invention, technical scheme and advantage clearer,, the present invention is further elaborated below in conjunction with accompanying drawing and embodiment.Should be appreciated that specific embodiment described herein only in order to explanation the present invention, and be not used in qualification the present invention.
Fig. 1 illustrates the block diagram of the predicting wind speed of wind farm system 1 that the present invention is based on wavelet analysis; Should comprise modeling and forecasting module 11, variable determination module 12, read module 13, data decomposition module 14, reconstruct weighting block 15 and weight adjusting module 16 based on the predicting wind speed of wind farm system 1 of wavelet analysis; Wherein, Variable determination module 12, data decomposition module 14 and reconstruct weighting block 15 all link to each other with modeling and forecasting module 11; Read module 13 links to each other with data decomposition module 14, and weight adjusting module 16 links to each other with reconstruct weighting block 15.Should be noted that the annexation between each equipment is for the needs of clear its information interaction of explaination and control procedure in all diagrams of the present invention, therefore should be regarded as annexation in logic, and should not only limit to physical connection.
At work, variable determination module 12 is confirmed the input and output variable of forecasting wind speed model according to this predicting wind speed of wind farm system 1 desired predicted time interval based on wavelet analysis; Simultaneously, read module 13 will read the historical wind speed value from the historical data base of wind energy turbine set data acquisition and supervisor control, and revise the incomplete point in the historical wind speed value, thereby obtain the required training sample value sequence of forecasting wind speed model.Then, read module 13 outputs to data decomposition module 13 with the training sample value sequence, and data decomposition module 13 then uses the discrete wavelet analysis that the training sample value sequence is carried out quick wavelet decomposition, obtains approximate details component value sequence.Thus; Modeling and forecasting module 11 just can receive the input and output variable of the forecast model of confirming of variable determination module 12 outputs; And the approximate details component value sequence of data decomposition module 14 outputs; Thereby can set up the forecasting wind speed model, wind speed is predicted, and output predicts the outcome accordingly.
In order further to improve this work based on the predicting wind speed of wind farm system 1 of wavelet analysis; Reconstruct weighting block 15 will carry out wavelet reconstruction to modeling and forecasting module 11 predicting the outcome of output; To obtain corresponding forecasting wind speed value after the weighted calculation; Thereby can the forecasting wind speed value be returned modeling and forecasting module 11, thus, predicting the outcome that modeling and forecasting module 11 is exported also can comprise the forecasting wind speed value.
Again further, weight adjusting module 16 also can compare the real-time wind speed observed reading and the forecasting wind speed value of gathering, in the comparison of a continuous specific times N; Average relative error between said real-time wind speed observed reading and the forecasting wind speed value is all above 10%; Then adjust the weighted value of said forecasting wind speed model, thereby make that the forecasting wind speed model in the modeling and forecasting module is more accurate, realize the forecasting wind speed of precision; Wherein, N is a natural number.
Fig. 2 shows the flow process of method first embodiment of the predicting wind speed of wind farm that the present invention is based on wavelet analysis, and this method flow is based on system architecture shown in Figure 1, and detailed process is following:
S11: according to a specific predicted time at interval, confirm the input and output variable of forecast model, understandable, for this specific predicted time flexible design according to actual needs at interval,, this predicted time is limited at interval accordingly at this;
S12: read the historical wind speed value from the historical data base of wind energy turbine set data acquisition and supervisor control, revise the incomplete point in the said historical wind speed value, to obtain the training sample value sequence of forecasting wind speed model;
In real work, can be according to calculating formula
Revise the incomplete point in the said historical wind speed value, wherein, t is the incomplete points of data, and v (t) is revised historical wind speed value, t
1And t
2Be adjacent former and later two nearest effective observation stations with incompleteness point, and t
1＜t＜t
2, v (t
1) and v (t
2) be respectively and observation station t
1And t
2Corresponding historical wind speed value.
S13: select the discrete wavelet analysis that the training sample value sequence is carried out quick wavelet decomposition, to obtain approximate details component value sequence;
In real work, can use many shellfishes west small echo to stating the training sample value sequence, carry out the quick wavelet decomposition of three layer depth, to obtain three groups of approximate details component value sequences.The formula L that discrete wavelet wherein decomposes:
Wherein, n representes the number of list entries;
It is the low frequency component after decomposing;
It is the high fdrequency component after decomposing; J represents j level wavelet decomposition, when j=0,
It is exactly the discrete series of original input signal; h
N-2kIt is the scale coefficient of multiresolution analysis; g
N-2kIt is the wavelet coefficient of multiresolution analysis.
Like Fig. 5 is the training sample value sequence and the synoptic diagram of carrying out three groups of approximate details component value sequences after three layers of decomposition of many shellfishes west small echos, and wherein, a is the training sample value sequence; B is the approximate details component value sequence after one deck wavelet decomposition; C is the approximate details component value sequence after two layers of wavelet decomposition; D is the approximate details component value sequence after three layers of wavelet decomposition.
S14: according to approximate details component value sequence, set up said forecasting wind speed model, carrying out forecasting wind speed, and the output result.
Fig. 3 shows the flow process of method second embodiment of the predicting wind speed of wind farm that the present invention is based on wavelet analysis, and this method flow is based on system architecture shown in Figure 1, and detailed process is following:
Step S21 among second embodiment, S22, S23, S24 respectively with first embodiment in S11, S12, S13, S14 identical; Wherein the difference of second embodiment and first embodiment is, has increased step S25, in step S25, the output result of forecasting wind speed is carried out wavelet reconstruction, to obtain corresponding forecasting wind speed value after the weighting.
Fig. 4 shows the flow process of method second embodiment of the predicting wind speed of wind farm that the present invention is based on wavelet analysis, and this method flow is based on system architecture shown in Figure 1, and detailed process is following:
Step S31 among the 3rd embodiment, S32, S33, S34, S35 respectively with second embodiment in S21, S22, S23, S24, S25 identical; Wherein the difference of the 3rd embodiment and second embodiment is, has increased step S36, in step S36; The real-time wind speed observed reading and the said forecasting wind speed value of gathering are compared; In the comparison of a continuous specific times N, the average relative error between said real-time wind speed observed reading and the forecasting wind speed value is then adjusted the weighted value of said forecasting wind speed model all above 10%; Wherein, N is a natural number.
In real work, can use calculating formula
Calculate the average relative error between said real-time wind speed observed reading and the forecasting wind speed value, wherein, v (t) is a t real-time wind speed observed reading constantly, v
*(t) be t forecasting wind speed value constantly.Preferably; N can be 10; Promptly in continuous ten times comparison; Average relative error between wind speed observed reading and the forecasting wind speed value is then adjusted the weighted value of forecasting wind speed model all above 10% in real time, and this moment, computing formula was
In addition, can use calculating formula:
Thus, realized, thereby can carry out forecasting wind speed more accurately the forecasting wind speed Model Optimization.
As shown in Figure 6, be example with certain large-scale wind power field, adopt the historical wind speed Value Data of this wind energy turbine set, one hour in advance and prediction in three hours in advance, checking is based on the validity of the predicting wind speed of wind farm method of wavelet analysis.The practical implementation process is following:
1), the systematic perspective measured value is spaced apart one hour, predicts with three hours according to desired one hour in advance of system, confirms the input and output variable of forecasting wind speed model;
2), adopt the historical wind speed Value Data in 6 years of a certain large-scale wind power field, revise the incomplete point in the historical wind speed Value Data, obtain the required training sample value sequence of forecasting wind speed model;
3), select the discrete wavelet analysis training sample value sequence to be carried out quick wavelet decomposition, the approximate details component value sequence that obtains;
4) the approximate details component value sequence of, utilizing the multilayer perceptron neural network that training sample value sequence and wavelet decomposition are returned is set up forecast model respectively, the prediction of the sector-style of going forward side by side speed;
5), the result of forecasting wind speed model output is carried out wavelet reconstruction, obtain corresponding forecasting wind speed value after the weighting;
6), in order to test this robustness based on the method for the predicting wind speed of wind farm of wavelet analysis, adopt mean absolute error (MAE) that prediction effect is estimated, computing formula is following:
In the formula, v (t) is a t observed reading constantly, v
*(t) be t predicted value constantly, l is the number of future position, and resulting statistical value is more little, explains that prediction effect is good more, and precision of prediction is high more.In this example, l=24, resulting statistics such as following table 1
Table 1 test data performance relatively
| Forecast model | Neural network model (MAE) | The inventive method (MAE) |
| One hour in advance | 0.4813 | 0.3229 |
| Three hours in advance | 0.5536 | 0.4800 |
As above shown in the table, adopt short-term forecasting wind speed method proposed by the invention, precision of prediction is greatly improved, and has explained that this predicting wind speed of wind farm method based on wavelet analysis has higher accuracy and reliability.
The above is merely preferred embodiment of the present invention, not in order to restriction the present invention, all any modifications of within spirit of the present invention and principle, being done, is equal to and replaces and improvement etc., all should be included within protection scope of the present invention.
Claims (10)
1. predicting wind speed of wind farm method based on wavelet analysis is characterized in that: may further comprise the steps:
According to a specific predicted time at interval, confirm the input and output variable of forecast model;
Read the historical wind speed value, revise the incomplete point in the said historical wind speed value, to obtain the training sample value sequence of forecasting wind speed model;
Said training sample value sequence is carried out quick wavelet decomposition, to obtain approximate details component value sequence;
According to said approximate details component value sequence, set up said forecasting wind speed model, to carry out forecasting wind speed.
2. predicting wind speed of wind farm method according to claim 1 is characterized in that, also comprises:
Output result to said forecasting wind speed carries out wavelet reconstruction, to obtain corresponding forecasting wind speed value after the weighting.
3. predicting wind speed of wind farm method according to claim 2 is characterized in that, also comprises:
The real-time wind speed observed reading and the said forecasting wind speed value of gathering are compared; In the comparison of a continuous specific times N, the average relative error between said real-time wind speed observed reading and the forecasting wind speed value is then adjusted the weighted value of said forecasting wind speed model all above 10%; Wherein, N is a natural number.
4. predicting wind speed of wind farm method according to claim 3 is characterized in that,
Use calculating formula
Calculate the average relative error between said real-time wind speed observed reading and the forecasting wind speed value, wherein, v (t) is a t real-time wind speed observed reading constantly, v
*(t) be t forecasting wind speed value constantly.
5. predicting wind speed of wind farm method according to claim 3 is characterized in that,
Use calculating formula
With
Adjust the weighted value of said forecasting wind speed model, wherein, α
j(t) be the t weight of i submodel constantly, e
j(t-u) be t-u model prediction constantly error, s is the cumulative errors time interval, and p is the predicted time interval.
6. according to the arbitrary described predicting wind speed of wind farm method of claim 1～5, it is characterized in that,
According to calculating formula
Revise the incomplete point in the said historical wind speed value, wherein, t is the incomplete points of data, and v (t) is revised historical wind speed value, t
1And t
2Be adjacent former and later two nearest effective observation stations with incompleteness point, and t
1＜t＜t
2, v (t
1) and v (t
2) be respectively and observation station t
1And t
2Corresponding historical wind speed value.
7. according to the arbitrary described predicting wind speed of wind farm method of claim 1～5, it is characterized in that,
Use many shellfishes west small echo to stating the training sample value sequence, carry out the quick wavelet decomposition of three layer depth, to obtain three groups of approximate details component value sequences.
8. predicting wind speed of wind farm system based on wavelet analysis is characterized in that: comprising:
The variable determination module is used for confirming the input and output variable of forecast model according to a specific predicted time at interval;
Read module is used to read the historical wind speed value, revises the incomplete point in the said historical wind speed value, to obtain the training sample value sequence of forecasting wind speed model;
The data decomposition module is used for said training sample value sequence is carried out quick wavelet decomposition, to obtain approximate details component value sequence;
The modeling and forecasting module is used for setting up said forecasting wind speed model, to carry out forecasting wind speed according to said approximate details component value sequence.
9. predicting wind speed of wind farm according to claim 8 system is characterized in that, also comprises:
The reconstruct weighting block is used for the output result of said forecasting wind speed is carried out wavelet reconstruction, to obtain corresponding forecasting wind speed value after the weighting.
10. predicting wind speed of wind farm according to claim 9 system is characterized in that, also comprises:
The weight adjusting module; The real-time wind speed observed reading and the said forecasting wind speed value that are used for gathering compare; In the comparison of a continuous specific times N, the average relative error between said real-time wind speed observed reading and the forecasting wind speed value is then adjusted the weighted value of said forecasting wind speed model all above 10%; Wherein, N is a natural number.
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| CN111288973A (en) * | 2020-01-23 | 2020-06-16 | 中山大学 | Method and device for obtaining flow rate of sea surface, computer equipment and storage medium |
| CN111814101A (en) * | 2020-07-10 | 2020-10-23 | 北京无线电测量研究所 | Flight path prediction method and system and electronic equipment |
| CN112072702A (en) * | 2020-09-09 | 2020-12-11 | 南京工业职业技术大学 | Power control system and method for wind turbine generator |
| CN112613674A (en) * | 2020-12-29 | 2021-04-06 | 国能日新科技股份有限公司 | Medium-and-long-term wind power generation capacity prediction method and device, electronic equipment and storage medium |
| CN112613674B (en) * | 2020-12-29 | 2024-03-08 | 国能日新科技股份有限公司 | Medium-and-long-term wind power generation capacity prediction method and device, electronic equipment and storage medium |
| CN117313927A (en) * | 2023-09-19 | 2023-12-29 | 华能澜沧江水电股份有限公司 | Wind power generation power prediction method and system based on wavelet neural network |
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