CN111222738A - Method for predicting power and optimizing parameters of wind power cluster - Google Patents
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Abstract
The invention discloses a method for predicting power and optimizing parameters of a wind power cluster, which divides historical NWP data and historical power data into two independent data sets and optimizes the parameters in three stages; performing principal component analysis on an original wind speed vector, taking the result of the principal component analysis as the input of a wind power cluster power prediction model, and dividing two independent data sets into a data set to be predicted and a historical data set respectively; calculating Euclidean characteristic distance between an input data matrix of the prediction point and a historical data set, comparing the Euclidean characteristic distance with a threshold value delta to obtain a data set with the highest matching degree and a prediction data set, judging whether optimization is finished, and otherwise, setting parameter values by a variable-scale network search method to continuously optimize to obtain four parameters with the minimum overall prediction error; and controlling three parameter values of the obtained four initial optimized values of the parameters to be unchanged, and changing the remaining one parameter value until the optimal four parameter combinations are obtained. The method has high prediction precision and popularization value.
Description
Technical Field
The invention relates to a method for predicting power and optimizing parameters of a wind power cluster, and belongs to the field of new energy power prediction.
Background
Wind energy is mainly influenced by natural factors and has the characteristics of randomness, volatility, intermittence and the like, so that the phenomena of voltage deviation, frequency deviation, voltage fluctuation, even grid disconnection and the like can occur after large-scale wind power grid connection, along with the rapid increase of the scale of the wind power grid connection, the influence of uncertainty of wind power on the stability, the abundance and the economy of a power system and a power market is more and more obvious, the wide attention of related researchers is attracted, and wind power prediction is a powerful measure and an effective means for solving the problem, so that the wind power prediction becomes one of main research topics in recent years at home and abroad.
At present, a wind power cluster power prediction method comprises an accumulation method and a statistical upscaling method. The cumulative method and the statistical upscaling method have obvious defects, the cumulative method has higher requirements on the completeness of historical data and numerical weather forecast data related to wind power prediction, the calculation is more complex, the time cost is higher, and more data storage resources are needed; the selection of the number of the reference wind power plants and the optimization of the reference wind power plants in the statistical scale-up method have large influence on the prediction accuracy of the total power of the cluster, and simultaneously comprise 2 prediction stages, and the calculation is complex. Therefore, in order to accurately predict the wind power, a relatively simple method with higher prediction precision for optimizing the wind power cluster power prediction parameters is needed.
Disclosure of Invention
The invention aims to provide a method for predicting power and optimizing parameters of a wind power cluster, so as to solve the problems in the background technology.
The purpose of the invention is realized by the following technical measures:
four parameters to be optimized in the method are four parameters of an improved space resource matching method-based wind power cluster power prediction technology in paper 7 of 'Power construction' volume 38, from dminAnd dmedWithin interval intercept close to dminthe percentage of data, namely a scale factor pr, a coefficient alpha to be determined for calculating a distance weight coefficient, a time factor lambda and a power weight coefficient α for calculating a time weight coefficient, and the specific calculation method and meaning are shown in the content of the paper;
the method is characterized by comprising the following steps:
step 1, dividing historical NWP data and historical power data into two independent data sets of 50% respectively; performing principal component analysis on each 50% of the divided historical NWP data by using original wind speed vectors with different heights, taking the result of the principal component analysis as the input of a wind power cluster power prediction model, dividing 30% of the principal component analysis into a data set to be predicted, dividing the remaining 70% of the principal component analysis into a historical data set, dividing 30% of each 50% of the divided historical power data into a data set to be predicted, and dividing the remaining 70% of the principal component analysis into a historical data set, so that each independent data set is divided into a corresponding data set to be predicted and a corresponding historical data set, and then performing parameter optimization in three stages;
step 2, prediction in the first stage: drawing a scatter distribution diagram by taking the distance of m-dimensional parameters between 2 wind power clusters, namely the space resource distance as an abscissa and the historical power measured value as an ordinate, respectively using each data of a data set to be predicted and a historical data set in each independent data set to calculate the Euclidean characteristic distance between the data set to be predicted and the historical data set, comparing the Euclidean characteristic distance with a threshold delta, thereby obtaining the data set and the predicted data set with the highest matching degree from the historical data set, and weighting and summing to obtain a prediction result;
the calculation method for obtaining the prediction result through weighted summation is shown in formula (1) in section 2.2.1 of 'wind power cluster power prediction technology based on improved space resource matching method' paper in volume 38, No. 7 of the Power construction;
the threshold value delta is calculated by dmin+pr(dmed-dmin)
In the formula: dminIs the minimum distance value among the spatial resource distances, dmedIs the median of the scatter plot, prIs from dminAnd dmedWithin interval intercept close to dminPercentage of data, i.e. scale factor p in four parametersr；
Comparing with a threshold value delta to obtain an overall prediction error value of the data set to be predicted, wherein the data set to be predicted comprises 30% of divided 50% of historical NWP data; judging whether the optimization is finished or not, if the obtained error value is more than 10%, judging that the optimization is not finished, setting parameter values by a variable-scale network search method to continue the optimization, if the obtained error value is less than 10%, judging that the optimization is finished, and comparing four parameters prprediction accuracy under all combinations of α, λ and β, and four parameters p with minimum overall prediction errorrα, λ and β are initial optimized values;
step 3, second-stage prediction: four parameters p obtained for stage one predictionrα, lambda and β, controlling the values of three of the parameters to be constant, changing the remaining one of the parametersCalculating Euclidean feature distances between the points to be predicted and the historical data sets according to the data sets to be predicted and the historical data sets in the same independent data set, comparing the Euclidean feature distances with a threshold value delta to obtain matched data sets, and weighting and summing the matched data sets according to the step 2 to obtain an overall prediction error; analysis of four parameters prthe influence of alpha, lambda and β on the prediction accuracy, and respectively combining four parameters prand the influence rule of alpha, lambda and β on the prediction precision is analyzed, and four parameters p obtained in the first stage are adjustedrinitial optimized values of α, λ and β, with four parameters p for minimum overall prediction errorralpha, lambda and β as four parameters proptimal combined values of α, λ and β;
and 4, predicting in a third stage: and setting a key parameter combination optimization value according to the result of the stage two prediction, calculating the Euclidean characteristic distance between the point to be predicted and the historical data set according to the optimization value and the data set to be predicted and the historical data set in the other independent data set, comparing the Euclidean characteristic distance with a threshold value delta, obtaining the data set with the highest matching degree and the predicted data set from the historical data set, and obtaining the prediction result by weighting and summing to obtain the prediction result of each point.
The difference between the application and the paper "wind power cluster power prediction technology based on the improved spatial resource matching method" in the No. 7 article of the 38 th volume of the electric power construction "is the following three points: the application provides a detailed historical data set dividing method; the application provides a unique three-stage prediction optimization process; the present application proposes four key parameters prdetailed optimization procedures of α, λ and β.
The invention achieves the following beneficial effects: a wind power cluster power prediction and parameter optimization method is based on data mining of a large number of samples, and provides a novel multi-feature similarity matching method, and the data mining based on the large number of samples is an effective way for improving wind power prediction precision; through wind power plant cluster verification, the result shows that the improved multi-feature similarity matching method is superior to the traditional prediction method, the root mean square error in 0-2 hours is reduced by 3.65%, and the root mean square error in 0-4 hours is reduced by 1.56%.
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Fig. 1 is a block diagram of the overall flow structure of the present invention.
Detailed Description
The following detailed description is made with reference to the accompanying drawings in the embodiment of the invention, and the invention aims to provide a wind power cluster power prediction and parameter optimization method for improving wind power prediction accuracy based on data mining of a large number of samples.
As shown in fig. 1, four parameters to be optimized in the method are four parameters of "wind power cluster power prediction technology based on improved spatial resource matching method" in paper No. 7 of "power construction" volume 38, from dminAnd dmedWithin interval intercept close to dminPercentage of data, i.e. scale factor prthe specific calculation method and meaning are shown in the paper, and the method is characterized by comprising the following steps of:
step 1, dividing historical NWP data and historical power data into two independent data sets of 50% respectively; performing principal component analysis on each 50% of the divided historical NWP data by using original wind speed vectors with different heights, taking the result of the principal component analysis as the input of a wind power cluster power prediction model, dividing 30% of the principal component analysis into a data set to be predicted, dividing the remaining 70% of the principal component analysis into a historical data set, dividing 30% of each 50% of the divided historical power data into a data set to be predicted, and dividing the remaining 70% of the principal component analysis into a historical data set, so that each independent data set is divided into a corresponding data set to be predicted and a corresponding historical data set, and then performing parameter optimization in three stages;
step 2, prediction in the first stage: drawing a scatter distribution diagram by taking the distance of m-dimensional parameters between 2 wind power clusters, namely the space resource distance as an abscissa and the historical power measured value as an ordinate, respectively using each data of a data set to be predicted and a historical data set in each independent data set to calculate the Euclidean characteristic distance between the data set to be predicted and the historical data set, comparing the Euclidean characteristic distance with a threshold delta, thereby obtaining the data set and the predicted data set with the highest matching degree from the historical data set, and weighting and summing to obtain a prediction result;
the calculation method for obtaining the prediction result through weighted summation is shown in formula (1) in section 2.2.1 of 'wind power cluster power prediction technology based on improved space resource matching method' paper in volume 38, No. 7 of the Power construction;
the threshold value delta is calculated by
δ＝dmim+pr(dmed-dmin)
In the formula: dminIs the minimum distance value among the spatial resource distances, dmedIs the median of the scatter plot, prIs from dminAnd dmedWithin interval intercept close to dminPercentage of data, i.e. scale factor p in four parametersr；
Comparing with a threshold value delta to obtain an overall prediction error value of the data set to be predicted, wherein the data set to be predicted comprises 30% of divided 50% of historical NWP data; judging whether the optimization is finished or not, if the obtained error value is more than 10%, judging that the optimization is not finished, setting parameter values by a variable-scale network search method to continue the optimization, if the obtained error value is less than 10%, judging that the optimization is finished, and comparing four parameters prprediction accuracy under all combinations of α, λ and β, and four parameters p with minimum overall prediction errorrα, λ and β are initial optimized values;
step 3, second-stage prediction: four parameters p obtained for stage one predictionrinitial optimization values of alpha, lambda and β, controlling the values of three parameters to be unchanged, changing the value of the remaining parameter, calculating the Euclidean characteristic distance between the point to be predicted and the historical data set according to the data set to be predicted and the historical data set in the same independent data set, comparing the Euclidean characteristic distance with a threshold value delta to obtain matching data sets, weighting and summing the matching data sets according to the step 2 to obtain an overall prediction error, and analyzing the fourth stepA parameter prthe influence of alpha, lambda and β on the prediction accuracy, and respectively combining four parameters prand the influence rule of alpha, lambda and β on the prediction precision is analyzed, and four parameters p obtained in the first stage are adjustedrinitial optimized values of α, λ and β, with four parameters p for minimum overall prediction errorralpha, lambda and β as four parameters proptimal combined values of α, λ and β;
and 4, predicting in a third stage: and setting a key parameter combination optimization value according to the result of the stage two prediction, calculating the Euclidean characteristic distance between the point to be predicted and the historical data set according to the optimization value and the data set to be predicted and the historical data set in the other independent data set, comparing the Euclidean characteristic distance with a threshold value delta, obtaining the data set with the highest matching degree and the predicted data set from the historical data set, and obtaining the prediction result by weighting and summing to obtain the prediction result of each point.
Claims (1)
1. Four parameters to be optimized in the method are four parameters of an improved space resource matching method-based wind power cluster power prediction technology in paper 7 of 'Power construction' volume 38, from dminAnd dmedWithin interval intercept close to dminPercentage of data, i.e. scale factor pra to-be-determined coefficient α for calculating a distance weight coefficient, a time factor lambda for calculating a time weight coefficient and a power weight coefficient beta, wherein the specific calculation method and meaning are shown in the content of the paper;
the method is characterized by comprising the following steps:
step 1, dividing historical NWP data and historical power data into two independent data sets of 50% respectively; performing principal component analysis on each 50% of the divided historical NWP data by using original wind speed vectors with different heights, taking the result of the principal component analysis as the input of a wind power cluster power prediction model, dividing 30% of the principal component analysis into a data set to be predicted, dividing the remaining 70% of the principal component analysis into a historical data set, dividing 30% of each 50% of the divided historical power data into a data set to be predicted, and dividing the remaining 70% of the principal component analysis into a historical data set, so that each independent data set is divided into a corresponding data set to be predicted and a corresponding historical data set, and then performing parameter optimization in three stages;
step 2, prediction in the first stage: drawing a scatter distribution diagram by taking the distance of m-dimensional parameters between 2 wind power clusters, namely the space resource distance as an abscissa and the historical power measured value as an ordinate, respectively using each data of a data set to be predicted and a historical data set in each independent data set to calculate the Euclidean characteristic distance between the data set to be predicted and the historical data set, comparing the Euclidean characteristic distance with a threshold delta, thereby obtaining the data set and the predicted data set with the highest matching degree from the historical data set, and weighting and summing to obtain a prediction result;
the calculation method for obtaining the prediction result through weighted summation is shown in formula (1) in section 2.2.1 of 'wind power cluster power prediction technology based on improved space resource matching method' paper in volume 38, No. 7 of the Power construction;
the threshold value delta is calculated by
δ＝dmin+pr(dmed-dmin)
In the formula: dminIs the minimum distance value among the spatial resource distances, dmedIs the median of the scatter plot, prIs from dminAnd dmedWithin interval intercept close to dminPercentage of data, i.e. scale factor p in four parametersr；
Comparing with a threshold value delta to obtain an overall prediction error value of the data set to be predicted, wherein the data set to be predicted comprises 30% of divided 50% of historical NWP data; judging whether the optimization is finished or not, if the obtained error value is more than 10%, judging that the optimization is not finished, setting parameter values by a variable-scale network search method to continue the optimization, if the obtained error value is less than 10%, judging that the optimization is finished, and comparing four parameters prprediction accuracy under all combinations of α, λ and β, and four parameters p with minimum overall prediction errorrα, λ and β are initial optimized values;
step 3, second orderSegment prediction: four parameters p obtained for stage one predictionrinitial optimization values of alpha, lambda and β, controlling the values of three parameters to be unchanged, changing the value of the remaining parameter, calculating the Euclidean characteristic distance between the point to be predicted and the historical data set according to the data set to be predicted and the historical data set in the same independent data set, comparing the Euclidean characteristic distance with a threshold value delta to obtain matching data sets, weighting and summing the matching data sets according to the step 2 to obtain an overall prediction error, analyzing four parameters prthe influence of alpha, lambda and β on the prediction accuracy, and respectively combining four parameters prand the influence rule of alpha, lambda and β on the prediction precision is analyzed, and four parameters p obtained in the first stage are adjustedrinitial optimized values of α, λ and β, with four parameters p for minimum overall prediction errorralpha, lambda and β as four parameters proptimal combined values of α, λ and β;
and 4, predicting in a third stage: and setting a key parameter combination optimization value according to the result of the stage two prediction, calculating the Euclidean characteristic distance between the point to be predicted and the historical data set according to the optimization value and the data set to be predicted and the historical data set in the other independent data set, comparing the Euclidean characteristic distance with a threshold value delta, obtaining the data set with the highest matching degree and the predicted data set from the historical data set, and obtaining the prediction result by weighting and summing to obtain the prediction result of each point.
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