外文文獻引言分析

1、Harmonic PhasorAnalysisBased on ImprovedFFTAlgorithmBo Zeng, Zhaosheng Teng, Yulian Cai, Siyu Guo, and Baiyuan QingI. INTRODUCTIONNONLINEAR loads can introduce large harmonic current into power systems, which may lead to severe problems(e.g., meter malfunctions, equipment overheat, overvoltage, and

2、data loss) 1. In researches on eliminating or at least reducing the impacts of harmonics on power systems, harmonic phasor analysis has been one of the most vital problems that attract most attentions.譯文：電力系統(tǒng)中由于非線性負荷的存在產(chǎn)生大量的諧波電流，導致出現(xiàn)例如儀表故障、設(shè)備過熱、 過壓和數(shù)據(jù)丟失等嚴重問題 1。在研究消除或至少減少諧波對電力系統(tǒng)的影響時，諧波相量分析已經(jīng)成為較重要和熱門的

3、方法。分析：第一層簡述研究領(lǐng)域，確定研究對象電力系統(tǒng)諧波(harmonic of power systems )及研究的主流研究方法一諧波相量分析(harmonic phasor analysis).The existing harmonic phasor analysis methods utilize a variety of techniques, such as the least square algorithms 2, Kalman filter 3, artificial neural network 4, NewtonProny' m ethod 6, and stat

4、e estimation 7. However, when real-time performances are required, these methods do not give satisfactory outcome, and in these sit- uations, the fast Fourier transform (FFT)-based methods are preferable for its availability, understandability, simplicity, and easiness to implement in DSP and advanc

5、ed RISCmachineschips. Unfortunately, the fundamental frequency of a power system may vary, and fixed sampling rates are typical for most data acquisition systems 8. Though a number of sampling synchronization methods, such as the adoption of discrete phase-locked loop 9 or adjustable sampling freque

6、ncy 10, have been proposed, synchronous sampling is still difficult to achieve.譯文:現(xiàn)有的諧波相量分析理論有：最小二乘法2、卡爾曼濾波2、人工神經(jīng)網(wǎng)絡(luò) (ANNs)4、Newton法5、Prony法6及狀態(tài)估計法7。但是，對于動態(tài)信號這些方法難以有滿意的效果，在此情況 下，快速傅里葉變換(FFT)因其有效性，易懂，DSP及RISC芯片的輕松實現(xiàn)，得到廣泛應(yīng)用。然而，由于電力系統(tǒng)基波頻率實時變化，固定的采樣率僅對大多數(shù)數(shù)據(jù)采集系統(tǒng)具有典型性8。盡管已經(jīng)提出許多同步采樣方法，如使用采用離散鎖相環(huán)技術(shù)9,或修正采樣頻率法

7、10,同步采樣仍然很難實現(xiàn)。分析：第一層，綜述現(xiàn)有諧波相量分析方法理論，描述解決問題的方法一一快速傅里葉變換(FFT)。The FFT approaches under asynchronous sampling suffer from two serious drawbacks 11, namely, the spectral leakagedue to time limitation and the picket fence effect due to the frequency discretization of the calculated spectrum. As a con- se

8、quence,the harmonic phasor of a signal cannot be obtained accurately. The common strategy to cope with these drawbacks is the windowing of the signal sequence for reducing the spectral leakage 12 and spectrum interpolation for reducing the picket fence effect 13, 14.譯文：在非同步采樣條件下，F(xiàn)FT算法有兩個嚴重的缺點11,即數(shù)據(jù)截

9、斷引起頻譜泄漏，頻譜離散 化造成柵欄效應(yīng)，最終導致不能準確獲得諧波相量。解決這些缺點的最常見的方法是對信號序列加窗函數(shù) 和差值來減少頻譜泄露和柵欄效應(yīng)1314。分析：第一層陳述目前FFT算法存在的不足及研究的關(guān)鍵點一一減少頻譜泄露和柵欄效應(yīng)。Windows with great sidelobe attenuation and high sidelobe decaying rate can sufficiently reduce the spectral leakage and the harmonic interference 15. To achieve these desirable p

10、roperties, different windows have been defined by adjusting coefficients of classical windows 16 -19 or by convoluting parent windows 15, 20, and have been used in replacement of the rectangular window. Although the design of windows is thought to be a quite matured research area, new methods are st

11、ill emerging, e.g., the new class of adjustable windows based on the cosine hyperbolic function proposed in 21 and the novel method for window parameterization in the frequency domain presented in 22. Nuttall 23 categorized classical windows according to optimal sidelobe behaviors and intro- duced t

12、he family of optimal cosine-type windows.To reduce the spectral error caused by the picket fence effect, windowed interpolation algorithms 12 -15 are employed. Among these algorithms, the multipoint interpolation discrete Fourier transform (DFT) algorithms 24 -29 are widely applied to promote the ha

13、rmonic phasor analysis accuracy, i.e., the measurementaccuracy of the harmonic frequencies, am- plitudes, and phases, or as called, the phasors. Unfortunately, for complicated windows such as the high-order combined cosine-type windows, the rectification is computationally ex- pensive due to the sol

14、ution of high-order equations. Approaches have been proposed to deal with the problem, e.g., Yang et al.30 proposed an accurate phasor estimation algorithm by using an FIR comb filter, and in 31, a method for exact calculation of harmonics using adaptive window width is presented.譯文：窗函數(shù)的旁瓣衰減和快速衰減特性可

15、有效減少頻譜泄露和間諧波15。為了獲得滿意的特性，不同的適應(yīng)經(jīng)典窗系數(shù)的窗函數(shù)16-19或卷積窗函數(shù)1520被提出且已經(jīng)取代了矩形窗。盡管窗函數(shù)已經(jīng)是一個相當成熟的研究領(lǐng)域，新的理論依舊不斷涌現(xiàn)，如在21基于余弦雙曲線函數(shù)的可調(diào)窗函數(shù)，在22提出在頻域中窗參數(shù)新方法，納托爾在23根據(jù)最優(yōu)窗旁瓣的行為將古典窗分類，并介紹了最優(yōu)的余弦窗函數(shù)族。為了減少柵欄效應(yīng)所造成的的頻譜誤差，引入的加窗插值的方法12-15。在這些方法中，多點插值離散傅里葉變換(DFT)算法24 129被廣泛應(yīng)用于提高諧波相量分析的精度，如測量諧波頻率的參數(shù)，振幅， 相位，即相量。然而，對于復(fù)雜的窗函數(shù)，例如高階的余弦型窗，為了

16、計算高階方程的解，這種改進方法 是計算復(fù)雜度很高的。 Yang et al.在文獻30中通過使用FIR濾波器提出了一個準確的參數(shù)估計算法，在 31中提出變矩形窗寬度的自適應(yīng)諧波分析算法。分析：第二層針對目前FFT算法出現(xiàn)的兩個突出問題，對現(xiàn)有研究進行文獻綜述。However, variations of the fundamental frequency are usually caused by faults in the power systems, which may leads to the uncertainty of the signal harmonics, and the ph

17、asor analysis of arbitrary harmonics under frequency variation is still an openproblem. Besides, interharmonics may exist in the measured signal. Accordingly, a frequency domain approach for power system harmonic phasor analysis under asynchronous sampling is thus proposed and discussed in this pape

18、r.譯文：基波頻率的變化通常是由電力系統(tǒng)故障引起的，這可能會導致該信號的諧波的不確定性，任意 變頻諧波的相量分析仍然是一個開放性問題。而且，間諧波也會存在于測量信號中。因此，本文提出并分 析在非同步采樣條件下，頻域內(nèi)的電力系統(tǒng)諧波相量分析問題。分析：第三層：提出目前尚未解決的問題，引出本研究動機。In this approach, the four-term fifth derivative Nuttall (FFDN) window, which ex-hibits better sidelobe behaviors compared with classic windows, is cho

19、sen to truncate the harmonic signal. For applications such as weak harmonic analysis of power systems where the spectral leakage appears as the major difficulty, better performance can be achieved by the FFDN. Based on the FFDN, an improved FFT (IFFT) algorithm is presented for calculating the power

20、 system signal phasors. The proposed method has the major ad- vantages that it has shorter computation time and can get exact solutions, where white noise and interharmonics are present, frequency deviates from nominal frequency, and some harmonic components are extremely weak. The application of th

21、e new algorithm in a type of smart meter demonstrates good perfor-mance, suggesting that it be a preferable choice for real-world applications.譯文:在此法中，和經(jīng)典窗函數(shù)相比具有更好的旁瓣效應(yīng)的4項5階Nuttall窗(FFDN)被用來獲取諧波信號。使用FFDN可以實現(xiàn)弱化電力系統(tǒng)諧波分析中的頻率泄露問題。本文提出一種基于FFDN窗的改進FFT (IFFT)算法，此法用于電力系統(tǒng)諧波信號相量分析。這種方法的主要優(yōu)點是在白噪聲和間諧波存在頻 率偏離標稱頻

22、率時，一些諧波組件極度疲弱的情況下縮短計算時間，獲得準確結(jié)果，這種應(yīng)用于智能電表 的新算法預(yù)示它將在實際運算會是一個理想選擇。分析：第四層：介紹本研究，并概括研究的優(yōu)點或者意義所在。The remainder of the paper is organized as follows. Section II gives a brief review of the spectral leakage of FFT due to asyn-chronous sampling. Spectral characteristics of the Nuttall windows are analyzed in

23、 Section III. The FFDN-based IFFT algo-rithm is presented in Section IV, and in Section V are simula-tion results. Section VI shows the application result of the pro-posed method in a smart meter. Finally, conclusions are drawn in Section VII.譯文：本文的其余部分組織如下。第二節(jié)簡要回顧由于非同步采樣FFT的頻譜泄漏現(xiàn)象。第三節(jié)分析了 Nuttall窗的頻

24、率特性，第四節(jié)提出基于IFFT的FFDN算法，第五節(jié)為仿真結(jié)果，第六節(jié)做出此法在智能電表中的應(yīng)用效果，第七節(jié)得出結(jié)論。分析：第四層：簡述研究每部分章節(jié)劃分及研究內(nèi)容?；诟倪MFFTM法的諧波相量分析I引言電力系統(tǒng)中由于非線性負荷的存在產(chǎn)生大量的諧波電流，導致出現(xiàn)例如儀表故障、設(shè)備 過熱、過壓和數(shù)據(jù)丟失等嚴重問題1。在研究消除或至少減少諧波對電力系統(tǒng)的影響時，諧 波相量分析已經(jīng)成為較重要和熱門的方法?，F(xiàn)有的諧波相量分析理論有：最小二乘法2、卡爾曼濾波2、人工神經(jīng)網(wǎng)絡(luò)（ANNs）4、Newton法5、Prony法6及狀態(tài)估計法7。但是， 對于動態(tài)信號這些方法難以有滿意的效果，在此情況下，快速傅里葉變換（FFT）因其有效性，易懂，DSP及RISC芯片的輕松實現(xiàn)，得到廣泛應(yīng)用。然而，由于電力系統(tǒng)基波頻率實時 變化，固定的采樣率僅對大多數(shù)數(shù)據(jù)采集系統(tǒng)具有典型性8。盡管已經(jīng)提出許多同步采樣方法，如使用采用離散鎖相環(huán)技術(shù)9,或修正采樣頻率法10,同步采樣仍然很難實現(xiàn)。在非同步采樣條件下，F(xiàn)FT算法有兩個嚴重的缺點11,即數(shù)據(jù)截斷引起頻譜泄漏，頻 譜離散化造成柵欄效應(yīng)，最終導致不能準確獲得諧波相量。解決這些缺點的最常見的方法是 對信號序列加窗函數(shù)和差值來減少頻譜泄露和柵欄效應(yīng)1314。窗函數(shù)的旁瓣衰減和快速衰減特性可有效減少頻譜泄露和問諧波15。為了獲得