【摘要】：風(fēng)電的隨機(jī)波動性影響電力系統(tǒng)的安全、穩(wěn)定和經(jīng)濟(jì)運(yùn)行,是大規(guī)模風(fēng)電并網(wǎng)的主要挑戰(zhàn)。風(fēng)電功率預(yù)測是解決該問題的必要手段之一�，F(xiàn)階段風(fēng)電功率預(yù)測最主要的問題是精度不足,除數(shù)值天氣預(yù)報(Numerical Weather Prediction,NWP)精度的原因外,預(yù)測模型也存在兩方面問題:一是模型深度不足、映射結(jié)構(gòu)簡單;另一是訓(xùn)練樣本維度少、規(guī)模小、未經(jīng)嚴(yán)格清洗。上述問題導(dǎo)致預(yù)測模型學(xué)習(xí)能力不足、難以適應(yīng)復(fù)雜風(fēng)場景和描述復(fù)雜地形中流場關(guān)系,極大地限制了風(fēng)電功率預(yù)測精度的提升。因此,本文中提出了基于堆疊降噪自動編碼機(jī)(Stack Denoising Auto-Encoder,SDAE)的短期風(fēng)電功率預(yù)測方法,建立了以多點(diǎn)數(shù)值天氣預(yù)報為輸入、多臺機(jī)組功率為輸出的深度學(xué)習(xí)模型。主要工作包括:1)提出了風(fēng)電功率預(yù)測數(shù)據(jù)清洗方法通過對NWP、實測風(fēng)速、實測功率等風(fēng)電功率預(yù)測建模數(shù)據(jù)進(jìn)行質(zhì)量分析,提出了兩種實測風(fēng)速插補(bǔ)算法:基于流場相似狀態(tài)的K最近鄰插補(bǔ)(K Nearest Neighbor,KNN)和基于相關(guān)性排序的插補(bǔ)算法;提出了基于槳距角上臨界曲線的功率數(shù)據(jù)篩選方法和全場功率數(shù)據(jù)篩選規(guī)則,實現(xiàn)對實測功率數(shù)據(jù)的篩選。結(jié)果表明:基于流場相似狀態(tài)的KNN插補(bǔ)算法精度更高,更適合后續(xù)的建模工作;基于槳距角上臨界曲線的功率數(shù)據(jù)篩選方法能快速準(zhǔn)確的篩選出正常狀態(tài)下的功率數(shù)據(jù)。2)提出了基于SDAE的多點(diǎn)NWP誤差修正方法分析了NWP誤差的時空分布模式;提出了一種基于SDAE的多點(diǎn)NWP誤差修正方法,并建立了以多點(diǎn)NWP為輸入、多臺風(fēng)電機(jī)組風(fēng)速為輸出的三隱層SDAE網(wǎng)絡(luò)模型。結(jié)果表明:SDAE模型比3種現(xiàn)有模型的修正精度更高,且無需分月、分機(jī)組進(jìn)行大批量建模。3)建立了基于NWP修正風(fēng)速和SDAE的風(fēng)電功率預(yù)測模型提出了基于多對多映射結(jié)構(gòu)的風(fēng)電功率預(yù)測建模方法,建立了基于修正NWP風(fēng)速和SDAE的風(fēng)電功率預(yù)測模型,模型以多點(diǎn)修正NWP風(fēng)速作為模型輸入、多臺機(jī)組功率作為模型輸出。結(jié)果表明:與8種主流的預(yù)測模型相比,NWP修正對功率預(yù)測精度的提升效果明顯,采用多點(diǎn)NWP輸入有助于提高預(yù)測精度,三隱層SDAE網(wǎng)絡(luò)優(yōu)于淺層網(wǎng)絡(luò)。
[Abstract]:The stochastic volatility of wind power affects the safety, stability and economic operation of power system, which is the main challenge of large-scale wind power grid. Wind power prediction is one of the necessary methods to solve this problem. At present, the main problem of wind power prediction is lack of precision. Besides the accuracy of numerical weather forecast (Numerical Weather Prediction,NWP), the prediction model also has two problems: first, the depth of the model is insufficient, and the mapping structure is simple; The other is the training sample dimension is small, without strict cleaning. The above problems lead to a lack of learning ability of the prediction model, which is difficult to adapt to the complex wind scene and describe the relationship between the flow field in the complex terrain, which greatly limits the improvement of the prediction accuracy of wind power. Therefore, in this paper, a short-term wind power prediction method based on stack noise reduction automatic coding machine (Stack Denoising Auto-Encoder,SDAE) is proposed, and a depth learning model with multi-point numerical weather forecast as input and power output as output is established. The main works are as follows: 1) the cleaning method of wind power prediction data is put forward. The modeling data of wind speed and measured power of NWP, are analyzed by quality analysis. Two interpolation algorithms are proposed: K nearest neighbor interpolation (K Nearest Neighbor,KNN) based on similar state of flow field and interpolation algorithm based on correlation ranking. The power data screening method based on the critical curve of pitch angle and the full-field power data screening rule are proposed to screen the measured power data. The results show that the KNN interpolation algorithm based on the similar state of the flow field is more accurate and more suitable for the subsequent modeling work. The power data screening method based on the critical curve of pitch angle can quickly and accurately screen the power data under normal condition. 2) the multipoint NWP error correction method based on SDAE is proposed to analyze the temporal and spatial distribution of NWP error. A multi-point NWP error correction method based on SDAE is proposed, and a three-layer SDAE network model with multi-point NWP as input and wind speed as output is established. The results show that the correction accuracy of the SDAE model is higher than that of the three existing models, and it does not need to be divided into months. The wind power prediction model based on NWP modified wind speed and SDAE is established. The wind power prediction modeling method based on many-to-many mapping structure is proposed. The wind power prediction model based on modified NWP wind speed and SDAE is established. The model takes the multi-point modified NWP wind speed as the input and the power of several units as the model output. The results show that compared with the 8 main prediction models, the NWP correction can improve the power prediction accuracy obviously, the multi-point NWP input is helpful to improve the prediction accuracy, and the three-hidden layer SDAE network is better than the shallow one.
【學(xué)位授予單位】：華北電力大學(xué)(北京)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TM614;TP181

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