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Short-term wind speed combined forecasting method for wind turbine cabin of wind power plant

A technology for wind turbines and wind farms, applied in instruments, biological neural network models, data processing applications, etc., and can solve problems such as errors and large forecasts

Active Publication Date: 2016-07-06
重庆市晔文数据科技有限公司
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  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The biggest disadvantage of relying solely on the wind tower data for forecasting is that wind farms are affected by terrain, turbulence, etc., and there may be significant differences between the wind speed at the nacelle at the wind turbine and the wind speed at the wind tower. Therefore, only the wind speed measured at the wind tower is used for forecasting The output of the entire wind farm will inevitably lead to large forecast errors, which has nothing to do with the specific forecast method

Method used

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  • Short-term wind speed combined forecasting method for wind turbine cabin of wind power plant
  • Short-term wind speed combined forecasting method for wind turbine cabin of wind power plant
  • Short-term wind speed combined forecasting method for wind turbine cabin of wind power plant

Examples

Experimental program
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Effect test

example 1

[0068] For the collected wind speed data of #7 wind turbine, the starting point of simulation forecast N={620, 626, 632, 638, 644, 650, 656, 662, 668, 674, 680} and the forecast step size L f = 1, construct two simulation experiment sets to determine the optimal parameters of the two sub-models, the construction method of the simulation experiment set is: the sub-model established by the DTW method or the PCC method, for the length L of the wind speed sequence for similarity comparison, the The wind speed formed by the first I wind speed sequences most similar to {v(#7,N-L+1),v(#7,N-L+2),...,v(#7,N)} sequence set Q P , the output of the training set P is {v(#7,N-L+1),v(#7,N-L+2),...,v(#7,N)}; the test set T and the training set P The same; by using the simulation error of {v(#7,N-L+1),v(#7,N-L+2),…,v(#7,N)} as the standard, let I∈[ 2,6], L∈[4,36], S∈[0.1,0.5], L, I, and S are optimized using the particle swarm optimization algorithm, the maximum number of iterations is 30, a...

example 2

[0074]In order to verify the universality of this application, a total of 31 wind turbines from #7 to #37 are used to simulate the 11 starting points of the forecast in Experiment 1, and when the forecast step size is 1 to 6, the method provided by this patent includes two sub-models The combined model is compared with BP neural network extrapolation method, GRNN neural network extrapolation method and ARIMA time series method, and the results are listed in Table 3. It can be seen from Table 3 that the forecast accuracy of COM-PSO-GRNN is the highest regardless of the error standard of MSE or MAE, and the accuracy of the three extrapolation methods from high to low is GRNN neural network extrapolation method and BP neural network Extrapolation and ARIMA time series methods, stating:

[0075] 1) Short-term wind speed forecast based on similarity principle is feasible, and its effect is better than that based on extrapolation;

[0076] 2) The DTW-PSO-GRNN sub-model is superior ...

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Abstract

The invention discloses a short-term wind speed combined forecasting method for a wind turbine cabin of a wind power plant. The method comprises the steps of analyzing the similarity of wind speed subsequences of a forecasted wind turbine cabin and all wind turbine cabins within a certain time period day by day by adopting a dynamic time warping method and a correlation coefficient method separately, extracting wind speed data of a plurality of subsequences with the most similar evolution, separately establishing a generalized regression neural network sub-model forecasting unit based on the dynamic time warping method and the correlation coefficient method, and globally optimizing specific parameters of each sub-model by adopting a particle swarm optimization, wherein the mean of the forecasting results of the two sub-models is used as a final forecasting result of the combined forecasting method. The method realizes fine forecasting of the cabin wind speed of each wind turbine in the wind power plant, thereby effectively improving the short-term output forecasting level of the whole wind power plant.

Description

technical field [0001] The invention belongs to the technical field of wind power generators in wind farms, and in particular relates to a combined short-term wind speed forecasting method for a wind motor nacelle in a wind farm. Background technique [0002] In order to effectively integrate wind energy into the grid, it is extremely necessary and critical to accurately forecast the output of wind farms. Among them, the short-term forecast of 0 to 6 hours is necessary for real-time scheduling of the grid, ensuring grid frequency, power and voltage balance, etc. The technical parameters of grid security are of great significance. [0003] As a renewable clean energy, wind energy has the advantages of flexible installed capacity, high reliability of wind power generating units, low cost, and simple operation and maintenance. According to the "2014 Wind Power Industry Monitoring Situation" published by the National Energy Administration in February 2015, by the end of 2014, t...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q50/06
CPCG06N3/02G06Q50/06
Inventor 杜杰彭丽霞孙泓川张琛代刊谌芸毛冬艳曹一家陆金桂刘玉宝潘林林刘月巍
Owner 重庆市晔文数据科技有限公司
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