本文選題：初始聚類(lèi)中心 + 天空?qǐng)D像　； 參考：《華北電力大學(xué)學(xué)報(bào)(自然科學(xué)版)》2017年06期

【摘要】：地基天空?qǐng)D像的云空辨識(shí)及云團(tuán)預(yù)測(cè)是研究區(qū)域電網(wǎng)光伏發(fā)電功率分布與變化的前提,對(duì)支撐調(diào)度提高光伏發(fā)電消納比例具有重要意義。首先分別采用較高的紅藍(lán)分量比值和較低的紅藍(lán)分量比值作為固定閾值分割地基天空?qǐng)D像,依次提取辨識(shí)結(jié)果中的天空像素點(diǎn)和云像素點(diǎn)的位置信息并獲取原圖像中對(duì)應(yīng)位置的天空像素點(diǎn)和云像素點(diǎn)的RGB值;其次對(duì)獲得的天空像素和云像素求均值并將各自均值中的紅藍(lán)分量相除獲取初始聚類(lèi)中心;然后使用K-means算法,利用加權(quán)歐式距離計(jì)算每一個(gè)聚類(lèi)樣本與聚類(lèi)中心之間的距離,通過(guò)數(shù)次迭代得到聚類(lèi)結(jié)果,進(jìn)而將聚類(lèi)結(jié)果還原成矩陣得到地基天空?qǐng)D像的云空辨識(shí)結(jié)果圖;最后利用云南某光伏電站全天空成像儀TSI-VIS-J1006采集的天空?qǐng)D像進(jìn)行仿真,結(jié)果表明該方法較固定閾值法的收斂速度更快、聚類(lèi)精度更高,能夠有效實(shí)現(xiàn)地基天空?qǐng)D像的云空辨識(shí)。
[Abstract]:The cloud space identification and cloud cluster prediction of ground-based sky image is the premise of studying the distribution and variation of photovoltaic power generation in regional power grid. It is of great significance to support dispatching and improve the absorption ratio of photovoltaic power generation. At first, the higher ratio of red and blue component and the lower ratio of red and blue component are used as fixed threshold to segment the ground-based sky image. The position information of sky pixel and cloud pixel in the identification result is extracted in turn, and the RGB value of sky pixel and cloud pixel of corresponding position in the original image is obtained. Secondly, the average value of sky pixel and cloud pixel is obtained and the initial clustering center is obtained by dividing the red and blue components of the respective mean. Then, the distance between each cluster sample and the clustering center is calculated by using the weighted Euclidean distance using K-means algorithm. The clustering results are obtained by several iterations, and then the clustering results are reduced to matrix to obtain the cloud-space identification results of the ground-based sky images. Finally, the sky images collected by the all-sky imager TSI-VIS-J1006 of a photovoltaic power station in Yunnan are simulated. The results show that the proposed method has faster convergence speed and higher clustering accuracy than the fixed threshold method, and it can effectively realize the cloud space identification of the ground-based sky image.
【作者單位】： 云南電網(wǎng)有限責(zé)任公司電力科學(xué)研究院;新能源電力系統(tǒng)國(guó)家重點(diǎn)實(shí)驗(yàn)室;美國(guó)伊利諾伊大學(xué)厄巴納-香檳分校;澳大利亞國(guó)立大學(xué)環(huán)境與社會(huì)學(xué)院;
【基金】：國(guó)家自然科學(xué)基金資助項(xiàng)目(51577067,51277075) 北京市自然科學(xué)基金資助項(xiàng)目(3162033) 河北省自然科學(xué)基金資助項(xiàng)目(E2015502060) 河北省科技支撐計(jì)劃重點(diǎn)項(xiàng)目(12213913D) 云南省新能源重大科技專(zhuān)項(xiàng)(2013ZB005) 新能源電力系統(tǒng)國(guó)家重點(diǎn)實(shí)驗(yàn)室開(kāi)放課題(LAPS15009,LAPS16007) 中央高�；究蒲袠I(yè)務(wù)費(fèi)重點(diǎn)項(xiàng)目(2014ZD29) 云南電網(wǎng)有限責(zé)任公司科技項(xiàng)目(YNKJQQ00000280)
【分類(lèi)號(hào)】：TM615

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