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Prediction method of wind power ramp event based on feature adaptive selection and wdnn

A technology of self-adaptive selection and forecasting methods, applied in forecasting, neural learning methods, data processing applications, etc., can solve problems such as low forecasting accuracy and single influencing factors, and achieve the effect of improving forecasting accuracy

Active Publication Date: 2022-04-01
NORTHEAST DIANLI UNIVERSITY
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Problems solved by technology

However, the influencing factors of the wind power ramp event considered in the prior art are relatively single, resulting in low prediction accuracy

Method used

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  • Prediction method of wind power ramp event based on feature adaptive selection and wdnn
  • Prediction method of wind power ramp event based on feature adaptive selection and wdnn
  • Prediction method of wind power ramp event based on feature adaptive selection and wdnn

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Embodiment Construction

[0053] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0054] Such as figure 1 As shown, the wind power climbing event prediction method based on feature adaptive selection and WDNN of the present invention comprises the following steps:

[0055] Step 1: Extract the time L from the data acquisition and monitoring control system in the wind farm in time series with the sampling period Δt 1 The original data of the active power of the fan and the air temperature in the fan constitute the original data set PT of the fan operation 0 ={(P(t n ) 0 ,T(t n ) 0 )|n=1,2,...,N}; among them, t n is the time corresponding to the nth sampling point, N is the total number of sampling points, t n+1 -t n =Δt, L 1 =(N-1)Δt, P(t n ) 0 for t n Raw data of fan active power at time, T(t n ) 0 for t n Raw data of air temperature at time.

[0056] Step 2: Run the original dataset PT on the turbine 0 Perfo...

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Abstract

The invention relates to the technical field of wind power ramp event prediction, and provides a wind power ramp event prediction method based on feature adaptive selection and WDNN. Firstly, the original data set of wind turbine operation is collected and normalized; then the wind power ramp event sample set PTr is constructed; then, based on the feature adaptive selection method, the relevant sample set is selected from the sample set PTr, and each relevant sample set Wavelet decomposition is performed on the signal composed of medium active power, and the decomposed signal is combined with temperature and category label data to obtain the decomposed sample set; For output, build and train a DNN-based wind power climbing event prediction model; finally collect and process the raw data of wind turbine operation in real time, and use the trained prediction model to output the corresponding category labels. The invention can optimize the input variables of the wind power climbing event prediction model and improve the prediction accuracy.

Description

technical field [0001] The invention relates to the technical field of wind power ramp event prediction, in particular to a wind power ramp event prediction method based on feature adaptive selection and WDNN. Background technique [0002] With the gradual depletion of non-renewable energy sources in the world, renewable energy sources have gradually occupied a dominant position in industry and life. In my country, electric energy, as an indispensable form of energy in life, is also facing tremendous changes, which is mainly reflected in the increasing proportion of wind energy and other new energy generation connected to the grid. According to the information released by China Energy Network, China's wind power generation in 2017 was 305.6 billion kWh, an increase of 26.8% compared with 2016, and the proportion rose from 4.0% to 4.8%. However, with the rapid development of wind power and large-scale grid connection, some problems brought about by wind power have gradually ...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q10/04G06Q50/06G06K9/62G06N3/04G06N3/08
CPCG06Q10/04G06Q50/06G06N3/084G06N3/045G06F18/214Y04S10/50Y02E40/70
Inventor 唐振浩孟庆煜曹生现
Owner NORTHEAST DIANLI UNIVERSITY
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