CN109787295A - It is a kind of meter and wind power plant state ultrashort-term wind power predict calculation method - Google Patents
It is a kind of meter and wind power plant state ultrashort-term wind power predict calculation method Download PDFInfo
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Abstract
The present invention relates to technical field of wind power, be it is a kind of meter and wind power plant state ultrashort-term wind power prediction calculation method, its main feature is that: it includes the foundation of " space-time characterisation " data source, data analysis and processing, the status assessment of wind power plant, meter and the prediction of the ultrashort-term wind power of wind power plant state, simulation calculation and error analysis and etc., it is different that influence from the change of existing only consideration wind regime to precision of prediction carries out prediction, the change of meter of the present invention and wind power plant state, research wind power plant state simultaneously assesses it, physical significance is clear, and consider the dynamic characteristic of system.Meter and the ultrashort-term wind power of wind power plant state prediction calculation method precision of prediction are high, and prediction result is effective, applicability and practical.
Description
Technical field
It is the ultrashort-term wind power prediction calculating side of a kind of meter and wind power plant state the present invention relates to technical field of wind power
Method.
Background technique
Wind-powered electricity generation is the new energy of on the largest scaleization exploit condition, and power output feature is different from thermoelectricity, nuclear power, between being typical
Having a rest property power supply has stochastic volatility and uncontrollability.The grid-connected of large-scale wind power field can bring very important rush to power grid
It hits, frequent occurrence for protection power network safety operation phenomena such as abandonment, off-grid, leads to wind-power electricity generation enterprise investment return week
Phase increases.As can deep enough understanding output of wind electric field feature and carry out Accurate Prediction, then be beneficial to traffic department's frequency modulation, peak regulation,
It is spare etc., receive ability to be of great significance the wind-powered electricity generation for improving existing electric system.
Ultrashort-term wind power prediction refers to following 15 minutes to 4 hours from prediction time predictions, time point
Resolution is 15 minutes.The meaning of ultra-short term prediction is rolling amendment Plan Curve, adjusts active power output in time.
Existing prediction technique generally uses statistical model, by analysis wind speed, the statistical law of power historical sample, establishes
Nonlinear Mapping relationship between wind speed and power, power and power, this kind of algorithm are the Extrapolating model based on Statistics, object
It is clear to manage interrogatory, simultaneously because the rise of artificial intelligence and big data, research emphasis is partial to artificial intelligence prediction, but at present
The utilization of artificial intelligence prediction technique is mostly static method, can not reflect the dynamic characteristic of system.On the whole, existing
Prediction technique only considers influence of the change to precision of prediction of wind regime, and ignores the change of wind power plant state.
Summary of the invention
It is an object of the present invention to provide a kind of physical significances clearly, considers system dynamic characteristic, is scientific and reasonable, practical valence
Value is higher, precision is higher, can satisfy the meter of online requirement and the ultrashort-term wind power prediction of wind power plant state calculates
Method.
Realize goal of the invention the technical solution adopted is that: it is a kind of meter and wind power plant state ultrashort-term wind power predict meter
Calculation method, it is characterised in that: it the following steps are included:
1) foundation of " space-time characterisation " data source
It is obtained by Wind turbines data collection system and grid dispatching center statistical data and is measured inside wind-driven generator
Data and history wind power data, select n kind metric data to arrange as space sample, and sequentially in time, obtain big
The data source of data analysis:
Xn×T=[x (t1),x(t2),…,x(tT)], x (ti)=[x1,x2,…,xn]T,
Wherein: tiFor sampling instant, x (ti) it is the column vector that metric data is constituted, n is metric data type, Xn×TIt is big
The data source of data analysis,
Metric data is sampled sequentially in time, different types of metric data has spatial character, and the two is combined
Get up and then constitutes the data source with space-time characterisation；
2) data analysis and processing
The variable for characterizing wind power plant state is numerous, while also being influenced by power grid and external environment, needs to step 1)
In data source processing:
1. the data source X obtained by step 1)n×T=[x (t1),x(t2),…,x(tT)], by matrix Xn×TIt is standardized
To obtain standard Hermitian matrix, so that each index is comparable,
2. window width is determined, to matrix X using separation window setting technique in real timen×TIt is analyzed and is extracted in real time, for reality
The real-time analysis of existing data can obtain raw data matrix using a kind of specifically separation window, referred to as separate window in real time:
Wherein: TωTo separate window window width in real time；
3) status assessment of wind power plant
The state of wind-driven generator is assessed based on average spectral radius:
Wherein: RMSRIt is averaged spectral radius for wind power plant,
rMSR,iFor the average spectral radius of i blower in wind power plant,
N is the number of units of blower in wind power plant,
1. the sample covariance matrix of 2. middle the taken time window of step 2) is calculated,
2. the average spectral radius of sample covariance matrix is calculated,
3. repeat step 2) 1. and 2., until window sliding to current time,
4. drawing average spectral radius tendency chart, the state of wind-driven generator is assessed；
4) meter and the prediction of the ultrashort-term wind power of wind power plant state
In order to better illustrate the ultrashort-term wind power prediction for realizing meter and wind power plant state, the continuity of state is proposed
It is assumed that prediction model uses BP neural network, 80% neural network model uses the change of BP neural network or BP neural network
Body form:
PWP=fNN(nwp,RMSR),
Wherein: PWPFor the prediction power of wind power plant, fNNIndicate the non-linear relation of input and output, nwp indicates Numerical Weather
Forecast information, RMSRFor the state of wind power plant,
Assuming that i+1, i+2 are predicted in moment i ..., the corresponding wind power of i+16；
5) simulation calculation
Input quantity: the NWP information (15min interval sampling) in wind power plant location is emulated, it is every that wind power plant surveys open state
Whole field wind power data under 15min interval sampling, for the data source (15min interval sampling) of wind power plant status assessment,
According to step 1) to step 4), the whole real-time prediction result of field wind power is obtained；
6) error analysis
" wind farm power prediction forecast management that error assessment standard executes National Energy Board, the People's Republic of China (PRC) 2011
Tentative Measures " specified in accuracy rate and qualification rate requirement, if PMkFor the actual average power of k period, PPkFor the k period
Predicting mean power, N is to examine total period day, and Cap is wind power plant booting capacity, then, real-time predictablity rate is defined as:
Qualification rate is defined as:
If whereinThen Bk=1, ifThen Bk=0
By step 5), input emulation input quantity, the prediction power that model is calculated is with measured power by step 6)
Error assessment standard carries out error calculation, provides the predictablity rate of the calculation method.
Meter proposed by the present invention and the ultrashort-term wind power of wind power plant state predict calculation method, have fully considered wind-powered electricity generation
Field state simultaneously assesses it, and physical significance is clear, and considers the dynamic characteristic of system, and practical value is higher, and precision is higher,
Multi-step prediction suitable for super short-period wind power；Also other wind power predictions can be evaluated.
Detailed description of the invention
Fig. 1 is output of wind electric field and averaging spectrum radius relationship figure；
Fig. 2 is the wind power prediction flow chart of meter and wind power plant state；
Fig. 3 is predicted value and true value comparison schematic diagram.
Specific embodiment
In the following with reference to the drawings and specific embodiments, to the ultrashort-term wind power of a kind of meter of the invention and wind power plant state
Prediction calculation method is described further.
In conjunction with Fig. 1-Fig. 3, the ultrashort-term wind power of meter of the invention and wind power plant state prediction calculation method include with
Lower step:
It is a kind of meter and wind power plant state ultrashort-term wind power predict calculation method, it is characterised in that: it includes following
Step:
1) foundation of " space-time characterisation " data source
It is obtained by Wind turbines data collection system and grid dispatching center statistical data and is measured inside wind-driven generator
Data and history wind power data, select n kind metric data to arrange as space sample, and sequentially in time, obtain big
The data source of data analysis:
Xn×T=[x (t1),x(t2),…,x(tT)], x (ti)=[x1,x2,…,xn]T,
Wherein: tiFor sampling instant, x (ti) it is the column vector that metric data is constituted, n is metric data type, Xn×TIt is big
The data source of data analysis,
Metric data is sampled sequentially in time, different types of metric data has spatial character, and the two is combined
Get up and then constitutes the data source with space-time characterisation；
2) data analysis and processing
The variable for characterizing wind power plant state is numerous, while also being influenced by power grid and external environment, needs to step 1)
In data source processing:
1. the data source X obtained by step 1)n×T=[x (t1),x(t2),…,x(tT)], by matrix Xn×TIt is standardized
To obtain standard Hermitian matrix, so that each index is comparable,
2. window width is determined, to matrix X using separation window setting technique in real timen×TIt is analyzed and is extracted in real time, for reality
The real-time analysis of existing data can obtain raw data matrix using a kind of specifically separation window, referred to as separate window in real time:
Wherein: TωTo separate window window width in real time；
3) status assessment of wind power plant
The state of wind-driven generator is assessed based on average spectral radius:
Wherein: RMSRIt is averaged spectral radius for wind power plant,
rMSR,iFor the average spectral radius of i blower in wind power plant,
N is the number of units of blower in wind power plant,
2. the sample covariance matrix of 2. middle the taken time window of step 2) is calculated,
2. the average spectral radius of sample covariance matrix is calculated,
3. repeat step 2) 1. and 2., until window sliding to current time,
5. drawing average spectral radius tendency chart, the state of wind-driven generator is assessed；
4) meter and the prediction of the ultrashort-term wind power of wind power plant state
In order to better illustrate the ultrashort-term wind power prediction for realizing meter and wind power plant state, the continuity of state is proposed
It is assumed that prediction model uses BP neural network, 80% neural network model uses the change of BP neural network or BP neural network
Body form:
PWP=fNN(nwp,RMSR),
Wherein: PWPFor the prediction power of wind power plant, fNNIndicate the non-linear relation of input and output, nwp indicates Numerical Weather
Forecast information, RMSRFor the state of wind power plant,
Assuming that i+1, i+2 are predicted in moment i ..., the corresponding wind power of i+16；
5) simulation calculation
Input quantity: the NWP information (15min interval sampling) in wind power plant location is emulated, it is every that wind power plant surveys open state
Whole field wind power data under 15min interval sampling, for the data source (15min interval sampling) of wind power plant status assessment,
According to step 1) to step 4), the whole real-time prediction result of field wind power is obtained；
6) error analysis
" wind farm power prediction forecast management that error assessment standard executes National Energy Board, the People's Republic of China (PRC) 2011
Tentative Measures " specified in accuracy rate and qualification rate requirement, if PMkFor the actual average power of k period, PPkFor the k period
Predicting mean power, N is to examine total period day, and Cap is wind power plant booting capacity, then, real-time predictablity rate is defined as:
Qualification rate is defined as:
If whereinThen Bk=1, ifThen Bk=0
By step 5), input emulation input quantity, the prediction power that model is calculated is with measured power by step 6)
Error assessment standard carries out error calculation, provides the predictablity rate of the calculation method.
Specific sample calculation analysis
The present invention is analyzed by taking the measured data in northeast wind power plant in December, 2018 as an example, sampling interval 15min.
The installed capacity of the wind power plant is 49.5MW, and blower quantity is 33, and the rated capacity of separate unit blower is 1.5MW；Prediction result
Evaluation index is selected as National Energy Board in the administrative provisions to the existing real-time prediction of wind power plant wind power, the finger of proposition
(National Energy Board is about the notice [EB/ for printing and distributing wind farm power prediction forecast management Tentative Measures to carry out error assessment for mark
OL], 2011).
1 precision of prediction of table statistics
Tab.1 prediction accuracy statistics
The particular embodiment of the present invention has made detailed explanation to the contents of the present invention, but does not limit to the present embodiment,
Those skilled in the art are according to the present invention to enlighten any obvious change done, and belongs to rights protection of the present invention
Range.
Claims (1)
1. a kind of meter and the ultrashort-term wind power of wind power plant state predict calculation method, it is characterised in that: it includes following step
It is rapid:
1) foundation of " space-time characterisation " data source
Metric data inside wind-driven generator is obtained by Wind turbines data collection system and grid dispatching center statistical data
And history wind power data, it selects n kind metric data to arrange as space sample, and sequentially in time, obtains big data
The data source of analysis:
Xn×T=[x (t1),x(t2),…,x(tT)], x (ti)=[x1,x2,…,xn]T,
Wherein: tiFor sampling instant, x (ti) it is the column vector that metric data is constituted, n is metric data type, Xn×TFor big data
The data source of analysis,
Metric data is sampled sequentially in time, different types of metric data has spatial character, and the two is combined
Then constitute the data source with space-time characterisation；
2) data analysis and processing
The variable for characterizing wind power plant state is numerous, while also being influenced by power grid and external environment, needs in step 1)
Data source processing:
1. the data source X obtained by step 1)n×T=[x (t1),x(t2),…,x(tT)], by matrix Xn×TIt is standardized to obtain
To standard Hermitian matrix, so that each index is comparable,
2. window width is determined, to matrix X using separation window setting technique in real timen×TIt is analyzed and is extracted in real time, in order to realize number
According to real-time analysis, raw data matrix can be obtained using a kind of specific separation window, referred to as separation window in real time:
Wherein: TωTo separate window window width in real time；
3) status assessment of wind power plant
The state of wind-driven generator is assessed based on average spectral radius:
Wherein: RMSRIt is averaged spectral radius for wind power plant,
rMSR,iFor the average spectral radius of i blower in wind power plant,
N is the number of units of blower in wind power plant,
1. the sample covariance matrix of 2. middle the taken time window of step 2) is calculated,
2. the average spectral radius of sample covariance matrix is calculated,
3. repeat step 2) 1. and 2., until window sliding to current time,
4. drawing average spectral radius tendency chart, the state of wind-driven generator is assessed；
4) meter and the prediction of the ultrashort-term wind power of wind power plant state
In order to better illustrate the ultrashort-term wind power prediction for realizing meter and wind power plant state, the vacation of the continuity of state is proposed
If prediction model uses BP neural network, 80% neural network model uses the variant of BP neural network or BP neural network
Form:
PWP=fNN(nwp,RMSR),
Wherein: PWPFor the prediction power of wind power plant, fNNIndicate the non-linear relation of input and output, nwp indicates numerical weather forecast
Information, RMSRFor the state of wind power plant,
Assuming that i+1, i+2 are predicted in moment i ..., the corresponding wind power of i+16；
5) simulation calculation
Input quantity: the NWP information in wind power plant location, 15min interval sampling are emulated, wind power plant surveys the every 15min of open state
Whole field wind power data under interval sampling, for the data source of wind power plant status assessment, 15min interval sampling, according to step
It is rapid 1) to step 4), obtain the whole real-time prediction result of field wind power；
6) error analysis
Error assessment standard executes National Energy Board, the People's Republic of China (PRC) 2011, and " wind farm power prediction forecast management is provisional
Method " specified in accuracy rate and qualification rate requirement, if PMkFor the actual average power of k period, PPkFor the prediction of k period
Mean power, N are to examine total period day, and Cap is wind power plant booting capacity, then, real-time predictablity rate is defined as:
Qualification rate is defined as:
If whereinThen Bk=1, ifThen Bk=0
By step 5), input emulation input quantity, the prediction power that model is calculated is with measured power by the error in step 6)
Evaluation criterion carries out error calculation, provides the predictablity rate of the calculation method.
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| CN113642784B (en) * | 2021-07-27 | 2023-08-18 | 西安理工大学 | Wind power ultra-short-term prediction method considering fan state |
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