CN106875033B - Wind power cluster power prediction method based on dynamic self-adaption - Google Patents
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Abstract
The invention provides a wind power cluster power prediction method based on dynamic self-adaptation, which is carried out according to the following steps: step 1: collecting historical data, and dividing the wind power clusters; step 2: according to the divided wind power clusters, three prediction models, namely a time sequence prediction model, a numerical weather forecast prediction model and a space resource matching prediction model, are established, and power prediction of the three prediction models of the wind power clusters is trained; and step 3: selecting a prediction model with the best training error evaluation result according to the training error evaluation results of the three models; and 4, step 4: collecting real-time numerical weather forecast NWP data and real-time power measurement data; and 5: and substituting the real-time NWP data and the real-time power measurement data according to the prediction model selected in the training process to obtain a sub-cluster prediction result, and adding the power prediction results of the sub-clusters to obtain a cluster overall prediction result. According to the method, the optimal prediction model is selected for the wind power clusters under different working conditions, and the prediction precision is improved.
Description
Technical Field
The invention relates to the technical field of wind power generation, in particular to a wind power cluster power prediction method based on dynamic self-adaptation, which is suitable for power prediction of large-scale wind power clusters.
Background
In recent years, with the global energy problem becoming more severe, the development of renewable energy power generation, particularly wind power generation, has become more important. However, wind energy has inherent volatility, instability and intermittency, so that the output of wind power fluctuates with the change of wind speed. If the output of the wind power at the future moment can be correctly predicted, the safe and stable operation of the power grid can be positively influenced. By predicting the wind power generation amount at the future moment, the power grid side can adjust the dispatching plan in advance, so that the problems of unstable electric energy, shortage of supply and the like are avoided. The output value of the wind power plant in a certain day can be obtained in advance at the wind power plant side, so that equipment maintenance and fault maintenance are scientifically arranged.
Most of wind power prediction systems at home and abroad aim at a single wind power plant, and the adopted methods include a physical method, a time series method, an artificial intelligence method and the like. But the power prediction of a single wind farm cannot meet the requirements of grid dispatching. For power grid dispatching, the fluctuation significance of the total power of the wind power cluster formed by a plurality of wind power plants is more important. The wind power cluster power prediction system at home and abroad mainly adopts an overlay method and a statistical upscaling method. And accumulating the power prediction results of the single wind power plant by an superposition method to form the total power of the wind power cluster. The statistical scale-up method includes the steps of firstly selecting a reference wind power plant, predicting the power of the reference wind power plant, and obtaining the power of the wind power cluster through the scale-up of the power prediction result of the reference wind power plant. The methods have certain effect on the power prediction of the cluster, but have the problems of long model training time and low precision.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and improve the power prediction precision of a wind power cluster, provides a dynamic self-adaptive wind power cluster power prediction method, and aims at selecting an optimal prediction model for the wind power cluster under different working conditions to improve the prediction precision.
The technical scheme adopted by the invention is as follows:
a wind power cluster power prediction method based on dynamic self-adaptation is characterized by comprising the following steps:
step 1: collecting historical data of a wind power plant, and dividing wind power clusters according to local geographical positions and a power grid topological structure;
step 2: according to the divided wind power clusters, three prediction models, namely a time sequence prediction model, a numerical weather forecast prediction model and a space resource matching prediction model, are established, and power prediction of the three prediction models of the wind power clusters is trained;
and step 3: selecting a prediction model with the best training error evaluation result according to the training error evaluation results of the three models;
and 4, step 4: collecting real-time numerical weather forecast NWP data and real-time power measurement data;
and 5: and substituting the real-time NWP data and the real-time power measurement data according to the prediction model selected in the training process to obtain a sub-cluster prediction result, and adding the power prediction results of the sub-clusters to obtain a cluster overall prediction result.
The step 1 specifically comprises the following steps:
step 1-1: collecting historical weather forecast data of the wind power plant, wherein the historical weather forecast data contains parameters of wind speed, wind direction, temperature, humidity and air pressure;
step 1-2: collecting geographical position data of a wind power plant, and dividing wind power clusters according to a geographical position approach principle;
step 1-3: and collecting historical power data of each wind power plant.
The step 2 specifically comprises the following steps:
step 2-1: establishing a time series prediction model: the method comprises the steps that an autoregressive moving average model ARMA is used as a time sequence prediction model, parameter identification is conducted on the ARMA model through power data of a historical wind power cluster, and an upstream and downstream effect prediction model is formed; namely, it is
Wherein xtRepresenting the power at the time t to be predicted, xt-jRepresenting the measured power at the t-j moment; epsilont-kM and n are the order of ARMA model respectively,
θkand the ARMA model order m and n are obtained by a long self-regressive method;
is the coefficient of an autoregressive model, thetakIs the moving average model coefficient;
step 2-2: establishing a numerical weather forecast prediction model: the prediction model is based on a BP neural network, and is trained by taking the wind speed and the wind direction of all NWP forecast points in the cluster and the power of the cluster in the first 12 hours of prediction as input parameters and the actual power of the cluster as output parameters; in the training process, the number of hidden layer nodes of the BP neural network is obtained through traversal optimization;
step 2-3: establishing a space resource matching prediction model: the calculation method of the prediction model is shown in formula (2);
wherein,
the predicted value of the wind power cluster power after h hours; l represents that the weight coefficient of the total L matching sets and the t + h moment to be predicted is the highest by calculating the weight coefficient; p is a radical ofiThe measured value of the wind power cluster power in the matching set is obtained; omegai,t+hThe weight coefficient is a weight coefficient, and the larger the weight coefficient value is, the larger the weight value occupied by the set is; determination of L in equation (2), and weighting factor ωi,t+hThe method of calculation of (1); for prediction of a wind power cluster, the essence of a weight coefficient is to calculate the distance of a space resource parameter between two clusters; the distance di,t+hIs shown in the formula (3);
m in the formula (3) represents the number of wind power plants in the cluster, ηkThe weight coefficient of the importance degree of a certain space resource parameter to the whole measurement, for example, the wind speed is the most important parameter for wind power prediction, the weight coefficient can be set to be the highest, and the weight coefficient corresponding to the wind power plant with large capacity is higher than the weight coefficient corresponding to the wind power plant with small capacity; v. ofk,t+hA certain spatial resource parameter, v, for the moment to be predictedk,ia certain space resource parameter of history matching object, wherein β is the weight coefficient occupied by the power distance, Pi,Pt+h-1Representing the power measurements at time i and time t + h-1; drawing an example of a historical power and space resource distance scatter diagram according to the distance calculated by the formula (3); for the historical power and space resource distance scatter diagram, a threshold value delta is sets(ii) a Less than deltasThe historical power corresponding to the matching set of (d) is used for the prediction of real-time power, and is larger than deltasThe set of (b) is considered to be independent of the power to be predicted and can therefore be excluded. Threshold value deltasIs calculated as shown in equation (4), where dminIs the minimum distance value; dmedIs the median of the distance scatter distribution diagram; p is a radical ofrIs from dminAnd dmedWithin interval intercept close to dminThe percentage of data of (c);
δs＝dmin+pr·(dmed-dmin) (4)
for the model calculation formula (2), after the matching sets are determined, the weight coefficient ω of each set needs to be further determinedi,t+hIt is calculated as shown in formula (5), wherein
In order to be the distance-weighting factor,
is a time weight coefficient;
distance weight coefficient
The calculation is shown in formula (6), wherein di,t+hand (4) for the distance obtained by calculation of the formula (3), wherein mu is a median in a distance distribution scatter diagram, α is a undetermined coefficient, and optimization selection is carried out in training.
Time weight coefficient
The obvious effect of the time factor in wind power prediction is reflected, the more important the effect is in the historical data which is closer to the current prediction time point, and the time weight coefficient
τiIs a distance in time, τiT + h-i, λ is a time factor, 0<λ<1, optimization selection is needed in the training process. For different prediction time scales, the model corresponds to different optimal parameters.
The step 4 specifically comprises the following steps:
step 4-1: collecting real-time power output data in the SCADA system;
step 4-2: and collecting real-time NWP data of the numerical weather forecast center.
The step 5 specifically includes the following contents:
and (4) selecting a prediction model with the minimum training error according to the step (3), substituting the data in the step (4) into the selected prediction model to obtain a sub-cluster prediction result, and adding the power prediction results of the sub-clusters to obtain a cluster overall prediction result.
The prediction process of the three prediction models is different, and the following discussion is divided into three cases:
and if the upstream and downstream effect prediction model is selected, substituting the power data in the step 4-1 into the formula (1) to obtain a wind power cluster prediction result of 12 hours.
If the weather forecast prediction model is selected, NWP data is first corrected using equation (7).
yt＝x0,t+x1,tvt+x2,tvt 2+x3,tvt 3+qt(7)
Wherein v istIs the wind speed output of the NWP model at time t, ytIs the wind speed prediction error at time t. x is the number ofi,t(i ═ 0,1,2,3) are coefficients estimated using a kalman filter. And substituting the power data obtained in the step 4 and the corrected weather forecast data into a BP neural network model to obtain a 1 st hour prediction result. The predicted power of the 1 st hour is required to be substituted into the input parameter of the 2 nd hour, and so on.
And if the space resource matching prediction model is selected, substituting the NWP data and the predicted previous hour power data into the formulas (2) - (6) for prediction. It is noted that the input parameters contain the power one hour prior to the predicted point in the first 4 hours of prediction, and the input parameters do not contain the power one hour prior to the predicted point in the last several hours of prediction. Iterations of the parameters are entered in the first 4 hours of prediction.
Compared with the prior art, the invention has the following beneficial effects:
the method can realize the wind power cluster power prediction based on the dynamic self-adaptive technology, and further improve the power prediction precision. The method comprises the following specific steps:
(1) the method selects the optimal prediction model type according to the prediction error in the training stage, and avoids low precision caused by random selection of the prediction model.
(2) The invention provides an effective space resource matching model which is simple in modeling, low in calculation complexity, high in precision and strong in practicability.
(3) According to the space resource matching prediction model provided by the invention, the predicted input parameters in the first four hours contain the measured power at the previous moment of the prediction point, and the predicted input parameters in the last 8 hours do not contain the measured power at the previous moment, so that the prediction precision in the first 4 hours is improved, and the prediction precision in the last 4 hours is not influenced.
Drawings
FIG. 1 is sample distance data provided by the present invention;
FIG. 2 is a diagram of an iterative process of BP neural network input parameters;
FIG. 3 is an iterative process diagram of input parameters of a wind power cluster power space resource matching method.
FIG. 4 is a flow chart of overall prediction provided by the present invention.
Detailed Description
The prediction flow of the present invention is further illustrated with reference to the accompanying drawings, and the following examples are illustrative of the present invention but are not intended to limit the scope of the present invention.
As shown in fig. 4, a wind power cluster power prediction method based on dynamic self-adaptation is characterized by comprising the following steps:
step 1: collecting historical weather forecast data of wind speed, wind direction, temperature, humidity and air pressure of each wind power plant, collecting geographical position data of each wind power plant, dividing wind power clusters according to power grid topology, and collecting historical power data of each wind power plant;
step 2: according to the divided wind power clusters, three prediction models, namely a time sequence prediction model, a numerical weather forecast prediction model and a space resource matching prediction model, are established, and power prediction of the three prediction models of the wind power clusters is trained;
the method comprises the following specific steps:
step 2-1: establishing a time series prediction model: the method comprises the steps that an autoregressive moving average model ARMA is used as a time sequence prediction model, parameter identification is conducted on the ARMA model through power data of a historical wind power cluster, and an upstream and downstream effect prediction model is formed; namely, it is
Wherein xtRepresenting the power at the time t to be predicted, xt-jRepresenting the measured power at the t-j moment; epsilont-kM and n are the order of ARMA model respectively,
θkand the ARMA model order m and n are obtained by a long self-regressive method;
is the coefficient of an autoregressive model, thetakIs the moving average model coefficient;
step 2-2: establishing a numerical weather forecast prediction model: the prediction model is based on a BP neural network, and is trained by taking the wind speed and the wind direction of all NWP forecast points in the cluster and the power of the cluster in the first 12 hours of prediction as input parameters and the actual power of the cluster as output parameters; in the training process, the number of hidden layer nodes of the BP neural network is obtained through traversal optimization;
step 2-3: establishing a space resource matching prediction model: the calculation method of the prediction model is shown in formula (2);
wherein,
the predicted value of the wind power cluster power after h hours; l represents that the weight coefficient of the total L matching sets and the t + h moment to be predicted is the highest by calculating the weight coefficient; p is a radical ofiThe measured value of the wind power cluster power in the matching set is obtained; omegai,t+hThe weight coefficient is a weight coefficient, and the larger the weight coefficient value is, the larger the weight value occupied by the set is; determination of L in equation (2), and weighting factor ωi,t+hThe method of calculation of (1); for prediction of a wind power cluster, the essence of a weight coefficient is to calculate the distance of a space resource parameter between two clusters; the distance di,t+hIs calculated as shown in formula (3);
m in the formula (3) represents the number of wind power plants in the cluster, ηkThe weight coefficient of the importance degree of a certain space resource parameter to the whole measurement, for example, the wind speed is the most important parameter for wind power prediction, the weight coefficient can be set to be the highest, and the weight coefficient corresponding to the wind power plant with large capacity is higher than the weight coefficient corresponding to the wind power plant with small capacity; v. ofk,t+hA certain spatial resource parameter, v, for the moment to be predictedk,ia certain space resource parameter of history matching object, wherein β is the weight coefficient occupied by the power distance, Pi,Pt+h-1Representing the power measurements at time i and time t + h-1; an example of a plot of historical power and spatial resource distance scatter is shown in FIG. 2, based on the distances calculated by equation (3). For this figure, a threshold value δ is sets. Less than deltasThe historical power corresponding to the matching set of (d) is used for the prediction of real-time power, and is larger than deltasThe set of (b) is considered to be independent of the power to be predicted and can therefore be excluded. The dashed line in fig. 1 is the threshold value and the box of the solid line is the selected set. Threshold value deltasIs calculated as shown in equation (4), where dminIs the minimum distance value; dmedIs the median of the distance scatter distribution diagram; p is a radical ofrIs from dminAnd dmedWithin interval intercept close to dminThe percentage of data of (c).
δs＝dmin+pr·(dmed-dmin) (4)
For the model calculation formula (2), after the matching sets are determined, the weight coefficient ω of each set needs to be further determinedi,t+hWhich is represented by the calculation formula (5), wherein
In order to be the distance-weighting factor,
is a time weight coefficient;
distance weight coefficient
The calculation is shown in formula (6), wherein di,t+hfor the distance calculated by the formula (3), mu is the median in the distance distribution scatter diagram, α is the undetermined coefficient, and optimization selection is carried out in training;
time weight coefficient
The significant effect of the time factor in wind power prediction is reflected, and the effect of the historical data which is closer to the current prediction time point is more important; time weight coefficient
τiIs a distance in time, τiT + h-i, λ is a time factor, 0<λ<1, carrying out optimization selection in the training process. For different prediction time scales, the model corresponds to different optimal parameters.
And step 3: selecting a prediction model with the minimum training error according to the training error evaluation results of the three models;
and 4, step 4: and collecting real-time power output data in the SCADA system and real-time NWP data of a numerical weather forecast center.
And 5: substituting the real-time NWP data and the real-time power measurement data according to the prediction model selected in the training process to obtain a sub-cluster prediction result, and adding the power prediction results of the sub-clusters to obtain a cluster total prediction result; the method specifically comprises the following steps: selecting a prediction model with the minimum training error according to the step 3, substituting the data in the step 4 into the selected prediction model, wherein the prediction processes of the three prediction models are different, and the following discussion is carried out according to three cases:
if the upstream and downstream effect prediction model is selected, substituting the power data of the real-time power output data in the SCADA system collected in the step 4 into the formula (1) to obtain a 12-hour wind power cluster prediction result;
if a weather forecast prediction model is selected, NWP data correction is carried out by using a formula (7);
yt＝x0,t+x1,tvt+x2,tvt 2+x3,tvt 3+qt(7)
wherein v istIs the wind speed output of the NWP model at time t, ytIs the wind speed prediction error at time t. x is the number ofi,t(i ═ 0,1,2,3) are coefficients estimated using a kalman filter. And substituting the power data obtained in the step 4 and the corrected weather forecast data into a BP neural network model to obtain a 1 st hour prediction result. The predicted power of the 1 st hour is required to be substituted into the input parameter of the 2 nd hour, and so on. The detailed iterative process of the input parameters is shown in fig. 2.
And if the space resource matching prediction model is selected, substituting the NWP data and the predicted previous hour power data into the formulas (2) - (6) for prediction. It is noted that the input parameters contain the power one hour prior to the predicted point in the first 4 hours of prediction, and the input parameters do not contain the power one hour prior to the predicted point in the last several hours of prediction. The iterative process of inputting parameters during the first 4 hours of prediction is shown in fig. 3.
Claims (7)
1. A wind power cluster power prediction method based on dynamic self-adaptation is characterized by comprising the following steps:
step 1: collecting historical data of a wind power plant, and dividing wind power clusters according to local geographical positions and a power grid topological structure;
step 2: according to the divided wind power clusters, three prediction models, namely a time sequence prediction model, a numerical weather forecast prediction model and a space resource matching prediction model, are established, and power prediction of the three prediction models of the wind power clusters is trained; the method specifically comprises the following steps:
step 2-1: establishing a time series prediction model: an autoregressive moving average model ARMA is used as a time sequence prediction model, and parameter identification is carried out on the ARMA model by using power data of a historical wind power cluster to form an upstream and downstream effect prediction model; namely, it is
Wherein xtRepresenting the power at the time t to be predicted, xt-jRepresenting the measured power at the t-j moment; epsilont-kM and n are the order of ARMA model respectively,
θkand the ARMA model order m and n are obtained by a long self-regressive method;
is the coefficient of an autoregressive model, thetakIs the moving average model coefficient;
step 2-2: establishing a numerical weather forecast prediction model: the prediction model is based on a BP neural network, and is trained by taking the wind speed and the wind direction of all NWP forecast points in the cluster and the power of the cluster in 12 hours before prediction as input parameters and the actual power of the cluster as output parameters; in the training process, the number of hidden layer nodes of the BP neural network is obtained through traversal optimization;
step 2-3: establishing a space resource matching prediction model: the calculation method of the prediction model is shown in formula (2);
wherein,
the predicted value of the wind power cluster power after h hours; l represents that the weight coefficient of the total L matching sets and the t + h moment to be predicted is the highest by calculating the weight coefficient; p is a radical ofiThe measured value of the wind power cluster power in the matching set is obtained; omegai,t+hThe weight coefficient is a weight coefficient, and the larger the weight coefficient value is, the larger the weight value occupied by the set is; determination of L in equation (2), and weighting factor ωi,t+hThe method of calculation of (1); for prediction of a wind power cluster, the essence of a weight coefficient is to calculate the distance of a space resource parameter between two clusters; the distance di,t+hIs shown in the formula (3);
m in the formula (3) represents the number of wind power plants in the cluster, ηkThe weight coefficient of the importance degree of a certain space resource parameter to the whole measurement is the weight coefficient, for example, the wind speed is the most important parameter for wind power prediction, the weight coefficient is set to be the highest, and the weight coefficient corresponding to the wind power plant with large capacity is higher than the weight coefficient corresponding to the wind power plant with small capacity; v isk,t+hA certain spatial resource parameter, v, for the moment to be predictedk,ia certain space resource parameter of history matching object, wherein β is the weight coefficient occupied by the power distance, Pi,Pt+h-1Representing the power measurements at time i and time t + h-1; drawing an example of a historical power and space resource distance scatter diagram according to the distance calculated by the formula (3); for the historical power and space resource distance scatter diagram, a threshold value delta is sets(ii) a Less than deltasThe historical power corresponding to the matching set of (d) is used for the prediction of real-time power, and is larger than deltasThe set of (a) is regarded as being independent of the power to be predicted and excluded; threshold value deltasIs shown in the formula (4), wherein dminIs the minimum distance value; dmedIs the median of the distance scatter distribution diagram; p is a radical ofrIs from dminAnd dmedWithin interval intercept close to dminThe percentage of data of (c);
δs＝dmin+pr·(dmed-dmin) (4)
for the model calculation formula (2), after the matching sets are determined, the weight coefficient ω of each set needs to be further determinedi,t+hWhich is represented by the calculation formula (5), wherein
In order to be the distance-weighting factor,
is a time weight coefficient;
distance weight coefficient
The calculation is shown in formula (6), wherein di,t+hfor the distance calculated by the formula (3), mu is the median in the distance distribution scatter diagram, α is the undetermined coefficient, and optimization selection is carried out in training;
time weight coefficient
The significant effect of the time factor in wind power prediction is reflected, and the effect of the historical data which is closer to the current prediction time point is more important; time weight coefficient
τiIs a distance in time, τiT + h-i, λ is a time factor, 0<λ<1, carrying out optimization selection in a training process; for different prediction time scales, the model corresponds to noThe same optimal parameters;
and step 3: selecting a prediction model with the best training error evaluation result according to the training error evaluation results of the three models;
and 4, step 4: collecting real-time numerical weather forecast NWP data and real-time power measurement data;
and 5: and substituting the real-time NWP data and the real-time power measurement data according to the prediction model selected in the training process to obtain a sub-cluster prediction result, and adding the power prediction results of the sub-clusters to obtain a cluster overall prediction result.
2. The dynamic self-adaptive wind power cluster power prediction method according to claim 1, wherein the step 1 specifically comprises the following steps:
step 1-1: collecting historical weather forecast data of the wind power plant, wherein the historical weather forecast data contains parameters of wind speed, wind direction, temperature, humidity and air pressure;
step 1-2: collecting geographical position data of a wind power plant, and dividing wind power clusters according to a geographical position approach principle;
step 1-3: and collecting historical power data of each wind power plant.
3. The dynamic self-adaptive wind power cluster power prediction method according to claim 1, wherein the step 4 specifically comprises the following steps:
step 4-1: collecting real-time power output data in the SCADA system;
step 4-2: and collecting real-time NWP data of the numerical weather forecast center.
4. The wind power cluster power prediction method based on dynamic self-adaptation according to claim 1, wherein the step 5 specifically comprises: and (4) selecting a prediction model with the minimum training error according to the step (3), substituting the data in the step (4) into the selected prediction model to obtain a sub-cluster prediction result, and adding the power prediction results of the sub-clusters to obtain a cluster overall prediction result.
5. The wind power cluster power prediction method based on dynamic self-adaptation as claimed in claim 3, wherein if an upstream and downstream effect prediction model is selected, the power data in step 4-1 is substituted into formula (1) to obtain a 12-hour wind power cluster prediction result.
6. The wind power cluster power prediction method based on dynamic self-adaptation as claimed in claim 4, characterized in that if a weather forecast prediction model is selected, NWP data correction is performed by using formula (7) first;
yt＝x0,t+x1,tvt+x2,tvt 2+x3,tvt 3+qt(7)
wherein v istIs the wind speed output of the NWP model at time t, ytIs the wind speed prediction error at time t; x is the number ofi,t(i is 0,1,2,3) is a coefficient estimated by adopting a Kalman filter, and then the power data obtained in the step 4 and the corrected weather forecast data are substituted into a BP neural network model to obtain a prediction result at the 1 st hour; the predicted power of the 1 st hour is required to be substituted into the input parameter of the 2 nd hour, and so on.
7. The wind power cluster power prediction method based on dynamic self-adaptation as claimed in claim 4, wherein if a space resource matching prediction model is selected, the NWP data and the power data predicted in the previous hour are substituted into equations (2) - (6) for prediction; it is noted that the input parameters contain the power one hour before the predicted point in the first 4 hours of prediction, the input parameters do not contain the power one hour before the predicted point in the last several hours of prediction, and the input parameters iterate in the first 4 hours of prediction.
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| CN111222738B (en) * | 2019-10-18 | 2022-04-15 | 华中科技大学 | Method for predicting power and optimizing parameters of wind power cluster |
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