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基于圖譜特征的復(fù)合絕緣子老化神經(jīng)網(wǎng)絡(luò)評估方法

發(fā)布時(shí)間:2019-01-11 14:58
【摘要】:復(fù)合絕緣子隨著運(yùn)行年限的增加會逐漸老化,且各項(xiàng)性能指標(biāo)降低。為研究在線運(yùn)行復(fù)合絕緣子老化程度定量分析的檢測方法,引入高光譜遙感技術(shù)通過非接觸方式獲取復(fù)合絕緣子的圖譜特征,采用主成分分析法構(gòu)建了特征空間。結(jié)果表明:不同老化程度復(fù)合絕緣子的光譜曲線不同;同一老化程度復(fù)合絕緣子樣本點(diǎn)在特征空間集中在1個(gè)點(diǎn)簇,不同老化程度的點(diǎn)簇基本無交叉,可用于定量判斷其老化程度。進(jìn)一步建立了老化神經(jīng)網(wǎng)絡(luò)模型,該模型預(yù)測結(jié)果與實(shí)際值比較接近,交叉驗(yàn)證均方根誤差為0.014 1。研究結(jié)果為在線非接觸定量評估復(fù)合絕緣子老化程度提供了依據(jù)。
[Abstract]:The composite insulator will aging gradually with the increase of service life, and the performance index will decrease. In order to study the quantitative analysis method of aging degree of composite insulators in on-line operation, hyperspectral remote sensing technology was introduced to obtain the atlas features of composite insulators by non-contact method. The feature space was constructed by principal component analysis. The results show that the spectral curves of composite insulators with different aging degrees are different. The sample points of composite insulators with the same aging degree are concentrated in one cluster in the feature space, and the clusters with different aging degrees have no crossover, which can be used to quantitatively judge the aging degree of insulators. Furthermore, the aging neural network model is established. The prediction results of the model are close to the actual values, and the root-mean-square error of cross-validation is 0.014. The results provide a basis for quantitative evaluation of aging degree of composite insulators.
【作者單位】: 中國電力科學(xué)研究院;
【基金】:國家自然科學(xué)基金(51277167)~~
【分類號】:TM216

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本文編號:2407254


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