本文選題：圖像處理 + 閾值分割�。� 參考：《天津大學(xué)》2016年碩士論文

【摘要】：近些年來,隨著太陽能并網(wǎng)容量不斷地增加,其帶來的問題也開始引起人們的關(guān)注。其主要問題是光伏輸出功率具有間歇性等特點會對電網(wǎng)造成沖擊,因此需要在光伏發(fā)電功率預(yù)測方面進(jìn)行研究。而隨著地基遙感測云儀器研制成功,能很好的對光伏電站上空的天氣情況進(jìn)行監(jiān)控,結(jié)合圖像處理技術(shù)的發(fā)展,使得利用地基云圖判斷光伏電站是否受到云遮擋而進(jìn)行光伏功率預(yù)測的方式成為可能。同時,經(jīng)實踐證明:基于地基云圖的光伏預(yù)測方法在短時內(nèi)具有良好的精度和實用效果。利用云圖外推的方式進(jìn)行光伏功率的預(yù)測時,其精度主要依賴于云圖中云團(tuán)識別和外推的準(zhǔn)確程度。而目前的研究都主要關(guān)注于整個預(yù)測模型的建立,而忽視了云圖中云團(tuán)識別和外推的準(zhǔn)確程度。因此本文利用全天空成像儀TSI采集的云圖數(shù)據(jù),首先提取云圖的有效區(qū)域并對云圖進(jìn)行畸變校正完成云圖的預(yù)處理階段。之后利用閾值分割的方法進(jìn)行云團(tuán)的識別。最后在識別的基礎(chǔ)上利用云圖序列進(jìn)行云團(tuán)的外推。本文主要的做如下工作:(1)對原始地基云圖進(jìn)行預(yù)處理。提取遮光帶和鏡頭支臂影像區(qū)域,并對其缺失的云圖有效信息進(jìn)行修復(fù),還原了云圖的有效區(qū)域,為后續(xù)識別和外推的工作打下了堅實的基礎(chǔ)。(2)完成云團(tuán)的畸變校正工作。通過對全天空成像儀TSI采集的云圖的畸變特點進(jìn)行分析,根據(jù)其特點完成了云圖的畸變校正。在校正后的圖像存在缺失信息的區(qū)域進(jìn)行修補(bǔ),還原真實天空情況。(3)根據(jù)云團(tuán)識別原理對云圖進(jìn)行灰度化,并分析幾種傳統(tǒng)的閾值分割的方法對云圖中云團(tuán)識別的問題,通過對其改進(jìn),提出了基于分塊插值的閾值分割方法進(jìn)行云團(tuán)的識別方法。(4)利用識別后的圖像,結(jié)合基于最大互相關(guān)法的云團(tuán)的匹配的方法分塊計算云團(tuán)的運動矢量,并完成云團(tuán)的短時外推工作。
[Abstract]:In recent years, with the increasing of solar power grid capacity, the problems caused by solar power grid are becoming more and more important. The main problem is that the intermittent characteristics of photovoltaic output power will impact the power grid, so it is necessary to study the power prediction of photovoltaic power generation. With the successful development of ground-based remote sensing cloud measuring instruments, it can monitor the weather conditions over photovoltaic power stations well, and combine with the development of image processing technology. It is possible to predict the photovoltaic power by using the ground-based cloud map to determine whether the photovoltaic power station is blocked by the cloud. At the same time, it is proved by practice that the photovoltaic prediction method based on ground-based cloud map has good accuracy and practical effect in a short period of time. The accuracy of photovoltaic power prediction by extrapolation depends on the accuracy of cloud cluster identification and extrapolation. The present research focuses on the establishment of the whole prediction model, while neglecting the accuracy of cloud cluster identification and extrapolation in cloud images. Therefore, in this paper, the cloud image data collected by the all-sky imager TSI is used to extract the effective region of the cloud image, and the distortion correction of the cloud image is carried out to complete the pre-processing stage of the cloud image. Then the method of threshold segmentation is used for cloud cluster recognition. Finally, the cloud cluster extrapolation is carried out by using cloud image sequence on the basis of recognition. The main work of this paper is as follows: 1) preprocessing the cloud map of the original foundation. Extracting the image region of shading band and lens arm and repairing the missing effective information of cloud image, reducing the effective area of cloud image, laying a solid foundation for the subsequent work of recognition and extrapolation. 2) completing the distortion correction of cloud cluster. Based on the analysis of the distortion characteristics of the cloud image collected by the all-sky imager TSI, the distortion correction of the cloud image has been completed according to its characteristics. In the corrected image there is missing information in the region to repair, restore the real sky. 3) according to the principle of cloud recognition for the gray cloud image, and analysis of several traditional threshold segmentation of cloud image recognition problems, By improving the method, a threshold segmentation method based on block interpolation is proposed for cloud cluster recognition. (4) using the recognized image and the matching method of cloud cluster based on maximum cross-correlation method, the motion vector of cloud cluster is calculated in blocks. And the short-time extrapolation of the cloud cluster is completed.
【學(xué)位授予單位】：天津大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TP391.41

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1 陳靖;地基云圖中云團(tuán)的識別和短時外推方法研究[D];天津大學(xué);2016年

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本文編號：1977185