本文選題：相關(guān)性分析 + 最優(yōu)延位法��； 參考：《電力系統(tǒng)自動化》2017年21期

【摘要】：傳統(tǒng)的相關(guān)性分析方法局限于獲得明顯的相關(guān)關(guān)系,難以挖掘序列之間潛在的相關(guān)性信息,使得電力系統(tǒng)調(diào)度運行的參考信息受損。針對此問題,提出一種用于分析廣義負(fù)荷序列之間潛在相關(guān)關(guān)系的最優(yōu)延位法,該方法通過對序列進(jìn)行適當(dāng)延位,挖掘出廣義負(fù)荷序列之間的間接相關(guān)關(guān)系。首先,以獲得序列間最大Pearson相關(guān)系數(shù)為目標(biāo)函數(shù),將位移范圍與序列數(shù)據(jù)的時間單調(diào)性作為約束條件,建立最優(yōu)延位模型;然后,提出了模型的解算策略;最后,以德國2016年區(qū)域數(shù)據(jù)為例,分別對單日、每月和全年數(shù)據(jù)的相關(guān)關(guān)系進(jìn)行分析計算,統(tǒng)計得到最大相關(guān)系數(shù)與需要的位移時間。分析結(jié)果表明,與傳統(tǒng)的相關(guān)性分析方法相比,所提最優(yōu)延位法可以發(fā)現(xiàn)序列之間潛在的延位相關(guān)關(guān)系,完善了風(fēng)電、光伏和負(fù)荷等廣義負(fù)荷序列之間的相關(guān)性分析方法。
[Abstract]:The traditional correlation analysis method is limited to obtaining the obvious correlation relation, and it is difficult to mine the potential correlation information between the sequences, which makes the reference information of power system dispatching and operation damaged.In order to solve this problem, an optimal continuation method is proposed to analyze the potential correlation between generalized load sequences.Firstly, taking the maximum Pearson correlation coefficient between sequences as the objective function, taking the monotonicity of the displacement range and the sequence data as the constraint condition, the optimal continuation model is established. Then, the solution strategy of the model is proposed.Taking the regional data of Germany in 2016 as an example, the correlation between the data of one day, a month and a whole year is analyzed and calculated, and the maximum correlation coefficient and the required displacement time are obtained by statistics.The results show that, compared with the traditional correlation analysis method, the proposed optimal continuation method can find the potential correlation relationship between sequences, and improve the correlation analysis method between wind power, photovoltaic and load generalized load sequences.
【作者單位】： 廣西大學(xué)廣西電力系統(tǒng)最優(yōu)化與節(jié)能技術(shù)重點實驗室;
【基金】：國家重點研發(fā)計劃資助項目(2016YFB0900100) 國家自然科學(xué)基金資助項目(51377027)~~
【分類號】：TM732;TM74

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本文編號：1743760