【摘要】：為準(zhǔn)確估計(jì)變壓器熱狀態(tài),提出了一種基于核極限學(xué)習(xí)機(jī)和Bootstrap方法的變壓器頂層油溫區(qū)間預(yù)測(cè)模型。首先通過(guò)Bootstrap采樣得到L組訓(xùn)練樣本,分別訓(xùn)練L個(gè)核極限學(xué)習(xí)機(jī)模型對(duì)頂層油溫進(jìn)行擬合回歸點(diǎn)預(yù)測(cè);然后訓(xùn)練一個(gè)核極限學(xué)習(xí)機(jī)模型對(duì)頂層油溫觀測(cè)噪聲方差進(jìn)行回歸估計(jì);最后根據(jù)這L+1個(gè)核極限學(xué)習(xí)機(jī)模型的結(jié)果估計(jì)在某置信水平上的頂層油溫的預(yù)測(cè)區(qū)間。算例仿真結(jié)果表明,該方法可以較好地考慮變壓器頂層油溫預(yù)測(cè)模型的不確定性,得到較為準(zhǔn)確可靠的頂層油溫預(yù)測(cè)區(qū)間;采用核極限學(xué)習(xí)機(jī)算法的頂層油溫區(qū)間預(yù)測(cè)結(jié)果的不確定性小于BP神經(jīng)網(wǎng)絡(luò)和極限學(xué)習(xí)機(jī),與采用支持向量機(jī)算法區(qū)間預(yù)測(cè)模型相當(dāng),但計(jì)算速度明顯優(yōu)于支持向量機(jī)。相比于傳統(tǒng)的頂層油溫點(diǎn)預(yù)測(cè)方法,所提區(qū)間預(yù)測(cè)方法可以為變壓器的熱狀態(tài)估計(jì)、安全運(yùn)行等提供更為合理和充分的輔助依據(jù)。
[Abstract]:In order to accurately estimate the thermal state of the transformer, a prediction model of the top oil temperature interval of the transformer based on the kernel limit learning machine and the Bootstrap method is proposed. First, L group training samples are obtained by Bootstrap sampling, then L kernel extreme learning machine models are trained to predict the top oil temperature, then a kernel extreme learning machine model is trained to estimate the noise variance of the top layer oil temperature observation. Finally, the prediction interval of the top oil temperature at a certain confidence level is estimated according to the results of the L 1 nuclear extreme learning machine model. The simulation results show that this method can take into account the uncertainty of the oil temperature prediction model of the top layer of transformer, and obtain a more accurate and reliable prediction interval of the top oil temperature. The uncertainty of the prediction results of the top oil temperature interval using the kernel extreme learning machine algorithm is less than that of the BP neural network and the ultimate learning machine, which is similar to the interval prediction model using the support vector machine algorithm, but the calculation speed is obviously better than that of the support vector machine. Compared with the traditional top-layer oil temperature point prediction method, the proposed interval prediction method can provide more reasonable and sufficient auxiliary basis for transformer thermal state estimation and safe operation.
【作者單位】： 山東大學(xué)電氣工程學(xué)院;
【基金】：山東省科技發(fā)展計(jì)劃項(xiàng)目(2014GGH204002) 國(guó)家電網(wǎng)公司科技項(xiàng)目(SGTYHT/15-JS-191)~~
【分類號(hào)】：TM41

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本文編號(hào)：2291666