發(fā)布時(shí)間：2018-02-25 04:14

  本文關(guān)鍵詞： 非侵入負(fù)荷分解 稀疏性 欠定求解 相似系數(shù)　出處：《電網(wǎng)技術(shù)》2017年09期 　論文類型：期刊論文

【摘要】：非侵入式負(fù)荷監(jiān)測是實(shí)現(xiàn)智能用電的關(guān)鍵技術(shù),負(fù)荷分解是實(shí)現(xiàn)該技術(shù)的重要環(huán)節(jié)。立足于非侵入采集模式下電流信號(hào)的欠定求解,研究了一種負(fù)荷分解算法。利用居民用戶電器啟動(dòng)時(shí)間存在時(shí)間差的特點(diǎn),將求解模型建立為單位時(shí)間間隔內(nèi)僅從采集信號(hào)中分離兩路信號(hào),一路為新投入運(yùn)行負(fù)荷的獨(dú)立電流,一路為上一時(shí)刻的混合電流。該模型使每個(gè)投入運(yùn)行的負(fù)荷均可獨(dú)立分解,同時(shí)保證了欠定維度小,從而可有效求解。求解過程結(jié)合負(fù)荷電流信號(hào)的頻域稀疏性,將欠定方程轉(zhuǎn)化為最優(yōu)化約束問題,通過兩步迭代收縮閾值算法從混合信號(hào)中恢復(fù)出兩路電流信號(hào),并通過相似系數(shù)判斷分離是否有效。利用實(shí)測用電數(shù)據(jù)驗(yàn)證了算法的有效性,通過正迭代方法得到當(dāng)前參與運(yùn)行的用電負(fù)荷,并根據(jù)相似系數(shù)確定負(fù)荷類型,實(shí)現(xiàn)了負(fù)荷辨識(shí)。
[Abstract]:Non-intrusive load monitoring is the key technology to realize intelligent power consumption, and load decomposition is an important link to realize this technology. In this paper, a load decomposition algorithm is studied. Based on the characteristic of time difference in the start-up time of household electrical appliances, the solution model is established to separate only two signals from the collected signals in unit time interval. An independent current for a new operational load and a mixed current for the last time. The model allows each load to be decomposed independently, while ensuring a small underdetermined dimension. The solution process combines the frequency domain sparsity of the load current signal, transforms the underdetermined equation into an optimization constraint problem, and recovers two current signals from the mixed signal by the two-step iterative shrinkage threshold algorithm. The validity of the algorithm is verified by the measured power consumption data, and the current load is obtained by the positive iteration method, and the load type is determined according to the similarity coefficient, and the load identification is realized.
【作者單位】： 華北電力大學(xué)電氣與電子工程學(xué)院;
【基金】：國家重點(diǎn)研究發(fā)展計(jì)劃項(xiàng)目(2016YFB0901104) 中央高�；究蒲袠I(yè)務(wù)費(fèi)專項(xiàng)資金資助項(xiàng)目(2016MS13)~~
【分類號(hào)】：TM714;TM73
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本文編號(hào)：1532963