CN106960260A - A kind of wind power forecasting system for being easy to power scheduling - Google Patents
A kind of wind power forecasting system for being easy to power scheduling Download PDFInfo
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Abstract
The invention provides a kind of wind power forecasting system for being easy to power scheduling, including data extraction module, data preprocessing module, training module, wind power prediction module, the data extraction module is used to obtain multiple preliminary samples；The data preprocessing module is used to pre-process the data of preliminary sample, and determines training sample according to pretreated data；The training module is used for the parameter using modified particle swarm optiziation Support Vector Machines Optimized, and SVMs is trained using the parameter of the SVMs after training sample and optimization, supporting vector machine model is obtained；The wind power prediction module is used to carry out wind power prediction using obtained supporting vector machine model, and exports wind power prediction result.Modeling process of the present invention is simple and practical, can fast and effectively carry out wind power prediction, and the safety and stability and management and running for power system are significant, with extensive application value.
Description
Technical field
The present invention relates to technical field of electric power, and in particular to a kind of wind power forecasting system for being easy to power scheduling.
Background technology
Wind power plant brings severe challenge after access power network to the economic load dispatching and safety and stability of power system.If energy
Wind power is accurately and effectively predicted, electric dispatching department will be enable to shift to an earlier date according to output of wind electric field situation of change
It is rational in time to adjust operation plan.So as to mitigate the adverse effect that wind-electricity integration is caused to power network, the standby appearance of system is reduced
Amount, reduces the operating cost of wind-electricity integration on the whole.
The content of the invention
Regarding to the issue above, the present invention provides a kind of wind power forecasting system for being easy to power scheduling.
The purpose of the present invention is realized using following technical scheme：
There is provided a kind of wind power forecasting system for being easy to power scheduling, including data extraction module, data prediction
Module, training module, wind power prediction module, the data extraction module are used for from numerical weather forecast system or electric power
Data extraction is carried out in the relevant data acquisition and supervisor control of system, multiple preliminary samples are obtained；The data are located in advance
Reason module is used to pre-process the data of preliminary sample, and determines training sample according to pretreated data；The instruction
Practicing module is used for the parameter using modified particle swarm optiziation Support Vector Machines Optimized, using the branch after training sample and optimization
The parameter for holding vector machine is trained to SVMs, obtains supporting vector machine model；The wind power prediction module is used
The supporting vector machine model obtained in use carries out wind power prediction, and exports wind power prediction result.
Beneficial effects of the present invention are：Modeling process is simple and practical, can fast and effectively carry out wind power prediction, for
The safety and stability of power system and management and running are significant, with extensive application value.
Brief description of the drawings
Using accompanying drawing, the invention will be further described, but the embodiment in accompanying drawing does not constitute any limit to the present invention
System, for one of ordinary skill in the art, on the premise of not paying creative work, can also be obtained according to the following drawings
Other accompanying drawings.
The structure connection block diagram of Fig. 1 present invention；
Fig. 2 is the structure connection block diagram of data preprocessing module of the present invention.
Reference：
Data extraction module 1, data preprocessing module 2, training module 3, wind power prediction module 4, sample process list
Member 10, data screening unit 20.
Embodiment
The invention will be further described with the following Examples.
Referring to Fig. 1, a kind of wind power forecasting system for being easy to power scheduling that the present embodiment is provided, including data are extracted
Module 1, data preprocessing module 2, training module 3, wind power prediction module 4, the data extraction module 1 are used for from numerical value
Data extraction is carried out in weather forecast system or the relevant data acquisition and supervisor control of power system, obtains multiple first
Walk sample；The data preprocessing module 2 is used to pre-process the data of preliminary sample, and according to pretreated data
Determine training sample；The training module 3 is used for the parameter using modified particle swarm optiziation Support Vector Machines Optimized, using instruction
The parameter for practicing the SVMs after sample and optimization is trained to SVMs, obtains supporting vector machine model；Institute
Stating wind power prediction module 4 is used to using obtained supporting vector machine model progress wind power prediction, and export wind-powered electricity generation work(
Rate predicts the outcome.
Preferably, the data of extraction include wind speed, temperature and wind power plant actual measurement power output data, the wind speed, temperature
The input data as SVMs training sample is spent, the wind power plant actual measurement power output data are instructed as SVMs
Practice the output data of sample.
Preferably, when carrying out wind power prediction using obtained supporting vector machine model, using real-time wind speed and in real time
Temperature as prediction input.
The above embodiment of the present invention strong adaptability, can as general wind power plant power prediction model, modeling process is simple
Practicality, can fast and effectively carry out wind power prediction, and the safety and stability and management and running for power system have important meaning
Justice, with extensive application value.
Preferably, as shown in Fig. 2 the data preprocessing module 2 includes being used for carrying out Screening Treatment to preliminary sample
Sample process unit 10 and the data screening unit 20 that Screening Treatment is carried out for the data in the preliminary sample to filtering out；
Wherein Screening Treatment of the sample process unit 10 to preliminary sample, be specially：
(1) mahalanobis distance between each preliminary sample is calculated：
Wherein
In formula, Φ (xA,xB) represent preliminary sample xAWith preliminary sample xBBetween mahalanobis distance,Represent just
Walk sample xAA-th data and preliminary sample xBBetween mahalanobis distance, sqrt represents sqrt,It isTransposition,For preliminary sample xBMean data, SB -1Represent preliminary sample xBCovariance matrix, WaRepresent just
Walk sample xAData number；
(2) if meeting lower column filter formula, preliminary sample x is deletedA：
Wherein ρ1、ρ2For the adjusting thresholds factor of setting,For between all preliminary samples
The average value of mahalanobis distance, max Φ (xA,xB) be all preliminary samples between mahalanobis distance maximum, min Φ (xA,xB) be
The minimum value of mahalanobis distance between all preliminary samples.
This preferred embodiment preliminary sample higher to similarity is screened, and can ensure to retain effective preliminary sample
On the premise of reduce training time of supporting vector machine model on the whole, improve the efficiency of wind power prediction.
Preferably, the data in the preliminary sample that data screening unit 20 is filtered out according to lower column filter function pair are sieved
Choosing is handled：
Kα={ Kα(β),Kα(β)=1, β=1 ..., Wα}
Wherein
In formula, KαRepresent the training sample of the α preliminary sample of correspondence, Kα(β) represents β numbers in the α preliminary sample
According to WαThe number for the data having for the α preliminary sample；μαFor the desired value of the data of the α preliminary sample, vαFor α
The standard deviation of the data of preliminary sample, η1、η2For the Dynamic gene of setting；F [x] is decision function, as x >=0, f [x]=1,
Work as x<When 0, f [x]=0.
This preferred embodiment can optimize the data in preliminary sample, so that using the preliminary sample of optimization to supporting vector
Machine is trained, and on the one hand reduces the training time of supporting vector machine model, on the other hand results in more accurate training
Effect, so as to improve the precision of prediction of wind power, obtains predicting the outcome for the higher wind power of precision.
Preferably, the parameter of the use modified particle swarm optiziation Support Vector Machines Optimized, is specifically included：
(1) kernel function for defining SVMs is：
Γ=ε2xTxα+(1-ε2)exp(g‖x-xα‖2)
In formula, ε is weight coefficient, xTxαFor linear kernel function, exp (g ‖ x-xα‖2) it is gaussian kernel function, wherein g is height
This kernel function width.
(2) using support vector regression penalty coefficient C, kernel function width g, tri- parameters of weight coefficient δ as ginseng need to be optimized
Number, needs Optimal Parameters to be set as the particle in population this；
(3) Optimal Parameters are needed to optimize this using modified particle swarm optiziation.
This preferred embodiment linear kernel function is combined accordingly with gaussian kernel function after as final kernel function,
And support vector regression penalty coefficient C therein, kernel function width g, tri- parameters of weight coefficient δ are optimized, Neng Gou
Preliminary sample is preferably expressed in high-dimensional feature space；
In addition, the parameter optimized in this preferred embodiment is few, relative to other multi-kernel functions, SVMs was trained
Journey is relatively simple, and the SVMs of training has preferable regression accuracy and generalization ability, so as to improve supporting vector
The precision of prediction of machine model, obtains more excellent wind power prediction effect.
Preferably, the use modified particle swarm optiziation needs Optimal Parameters to optimize this, is specially：
1) particle cluster algorithm is initialized, setting number of particles, iterations, Studying factors, simulated annealing coefficient are selected just
Experimental design table is handed over, orthogonal test designs table columns is more than the dimension of particle, setting support vector regression penalty coefficient C, core letter
SerComm degree g, the hunting zone of tri- parameters of weight coefficient δ and translational speed bound；
2) speed of each particle is calculated, judges whether the speed of each particle crosses the border, if crossed the border, by the speed of the particle
It is taken as critical value；
3) position of each particle, each particle of fitness evaluation calculated with following fitness function are updated：
In formula, WtFor training sample total number, YkFor training sample actual value, Yk' it is training sample predicted value；
4) respective dimension is chosen from optimal particle and suboptimum particle according to orthogonal test table, carries out orthogonal test, evaluate each
Test particle；
5) according to the quality for judging the factor level in each dimension, design final particle and evaluate the particle；
6) fitness highest particle is chosen from final particle and test particle, and is compared with group's history optimal particle work
Compared with if better than group's history optimal particle, substituting group's history optimal particle, and annealing search is simulated with certain probability；
If 7) reach maximum iteration, terminate the power supply circuit functional value of search, output optimal particle and optimal particle.
This preferred embodiment carries out parameter optimization by the way of orthogonal test and simulated anneal algritym are combined, and solves
Premature convergence problem and convergence concussion problem that conventional particle group's algorithm is present, strengthen the energy that group optimal particle jumps out local best points
Power；
In addition, this preferred embodiment be effectively extracted from group's optimal particle and suboptimum particle using orthogonal test it is valuable
Information, can be lifted particle cluster algorithm the average value of Dissatisfied hitch fruits, standard deviation, evaluate number of times, success rate and success table
Performance in terms of existing, and this preferred embodiment orthogonal test relative to traditional orthogonal particle cluster algorithm, greatly reduce
The operand of information extraction.
Finally it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention, rather than to present invention guarantor
The limitation of scope is protected, although being explained with reference to preferred embodiment to the present invention, one of ordinary skill in the art should
Work as understanding, technical scheme can be modified or equivalent, without departing from the reality of technical solution of the present invention
Matter and scope.
Claims (7)
1. a kind of wind power forecasting system for being easy to power scheduling, it is characterized in that, including data extraction module, data prediction
Module, training module, wind power prediction module, the data extraction module are used for from numerical weather forecast system or electric power
Data extraction is carried out in the relevant data acquisition and supervisor control of system, multiple preliminary samples are obtained；The data are located in advance
Reason module is used to pre-process the data of preliminary sample, and determines training sample according to pretreated data；The instruction
Practicing module is used for the parameter using modified particle swarm optiziation Support Vector Machines Optimized, using the branch after training sample and optimization
The parameter for holding vector machine is trained to SVMs, obtains supporting vector machine model；The wind power prediction module is used
The supporting vector machine model obtained in use carries out wind power prediction, and exports wind power prediction result.
2. a kind of wind power forecasting system for being easy to power scheduling according to claim 1, it is characterized in that, the number of extraction
Power output data are surveyed according to including wind speed, temperature and wind power plant, the wind speed, temperature are used as SVMs training sample
Input data, wind power plant actual measurement power output data as SVMs training sample output data.
3. a kind of wind power forecasting system for being easy to power scheduling according to claim 2, it is characterized in that, using obtaining
Supporting vector machine model when carrying out wind power prediction, using the input of real-time wind speed and real time temperature as prediction.
4. a kind of wind power forecasting system for being easy to power scheduling according to claim 3, it is characterized in that, the data
Pretreatment module includes being used for carrying out preliminary sample the sample process unit of Screening Treatment and for preliminary to what is filtered out
Data in sample carry out the data screening unit of Screening Treatment.
5. a kind of wind power forecasting system for being easy to power scheduling according to claim 4, it is characterized in that, it is described preliminary
Sample process module includes preliminary sample processing unit and data screening unit, and the preliminary sample screening unit is used for preliminary
Sample carries out Screening Treatment, and the data screening unit is used to sieve the data in the remaining preliminary sample after Screening Treatment
Choosing is handled, and the data filtered out are built into corresponding training sample.
6. a kind of wind power forecasting system for being easy to power scheduling according to claim 5, it is characterized in that, sample process
Screening Treatment of the unit to preliminary sample, be specially：
(1) mahalanobis distance between each preliminary sample is calculated：
Wherein
In formula, Φ (xA,xB) represent preliminary sample xAWith preliminary sample xBBetween mahalanobis distance,Represent preliminary sample
This xAA-th data and preliminary sample xBBetween mahalanobis distance, sqrt represents sqrt,It isTransposition,For preliminary sample xBMean data, SB -1Represent preliminary sample xBCovariance matrix, WαRepresent just
Walk sample xAData number；
(2) if meeting lower column filter formula, preliminary sample x is deletedA：
Wherein ρ1、ρ2For the adjusting thresholds factor of setting,For the geneva between all preliminary samples away from
From average value, max Φ (xA,xB) be all preliminary samples between mahalanobis distance maximum, min Φ (xA,xB) it is all first
Walk the minimum value of the mahalanobis distance between sample.
7. a kind of wind power forecasting system for being easy to power scheduling according to claim 5, it is characterized in that, data screening
Data in the preliminary sample that unit 20 is filtered out according to lower column filter function pair carry out Screening Treatment：
Kα={ Kα(β),Kα(β)=1, β=1 ..., Wα}
Wherein
In formula, KαRepresent the training sample of the α preliminary sample of correspondence, Kα(β) represents the β data in the α preliminary sample,
WαThe number for the data having for the α preliminary sample；μαFor the desired value of the data of the α preliminary sample, vαAt the beginning of α
Walk the standard deviation of the data of sample, η1、η2For the Dynamic gene of setting；F [x] is decision function, and as x >=0, f [x]=1 works as x
<When 0, f [x]=0.
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Cited By (3)
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| CN108549220A (en) * | 2018-03-29 | 2018-09-18 | 广东电网有限责任公司电力调度控制中心 | Coal unit operating status real time evaluating method and its system |
| CN113239638A (en) * | 2021-06-29 | 2021-08-10 | 百维金科(上海)信息科技有限公司 | Overdue risk prediction method for optimizing multi-core support vector machine based on dragonfly algorithm |
| CN116266258A (en) * | 2022-12-15 | 2023-06-20 | 天津大学 | Wind farm arrangement and yaw control method and electronic equipment |
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