本文選題：經(jīng)驗?zāi)B(tài)分解　切入點：局部積分均值分解　出處：《中國礦業(yè)大學》2017年碩士論文

【摘要】：HHT作為現(xiàn)如今較為常用的時頻分析方法之一,與FFT分析、小波變換以及S變換等處理方法相比較,在非線性、非穩(wěn)態(tài)信號的處理以及自適應(yīng)性等方面建立了一定的優(yōu)勢。但HHT在信號處理分析過程中依舊存在一些弊端,因此本文的工作主要是對HHT方法存在的缺點進行分析和改進,并將改進的HHT方法運用到電能質(zhì)量擾動檢測與分析領(lǐng)域。首先,針對HHT方法存在的均值曲線擬合、模態(tài)混疊、虛假分量以及端點效應(yīng)等問題,提出了一種新的自適應(yīng)時頻分析方法——局部積分均值分解(Local Integral Mean Decomposition,LIMD)方法。該方法針對單分量信號的特點,重新定義了具有瞬時物理意義的單分量信號,提高了分解固有模態(tài)函數(shù)的有效性。針對EMD方法插值擬合效果差以及耗時時間長的缺點,改進了均值曲線的擬合方式,將積分中值定理引入到均值曲線的擬合過程中,運用極值點間的全部數(shù)據(jù)作為特征時間尺度,且只采用一次插值擬合的方式。通過仿真實驗表明該方法提高了算法的計算效率以及在端點效應(yīng)和虛假分量的抑制上取得了一定的改進效果。其次,針對LIMD方法在模態(tài)混疊效應(yīng)以及分辨率低等問題還需進一步改進,提出一種自適應(yīng)集總局部積分均值分解(Adaptively Ensemble Local Integral Mean Decomposition,AELIMD)方法。該方法分析不同頻率形式噪聲對極值點分布的影響,確定加噪頻率采用高頻輔助分解的優(yōu)勢,并以極值點分布特性作為評價指標自適應(yīng)選擇最優(yōu)加噪頻率。通過對EEMD加噪準則的研究,推導(dǎo)出加噪幅值和分解次數(shù)采取固定值:0.01 SD和2次,且以正負成對的形式加入到原始信號中。通過仿真實驗驗證了所提方法的自適應(yīng)性和計算性能。最后,將改進的HHT方法(AELIMD方法與Hilbert變換)運用到電能質(zhì)量擾動檢測分析中。運用AELIMD方法對含噪的電能質(zhì)量信號進行去噪分解,采用二階導(dǎo)數(shù)的方式求得模極大值點,提高了通過模極大值點定位擾動時刻的準確性。針對高頻復(fù)合擾動采取兩次AELIMD分解方法去除噪聲與虛假分量有效提取出擾動成分;針對穩(wěn)態(tài)擾動提出先去除諧波再提取閃變包絡(luò)的檢測方法;針對未知復(fù)合擾動給出了基于HHT方法的檢測思路。仿真結(jié)果表明了所提方法的有效性和可行性。并搭建了電能質(zhì)量擾動實驗平臺,通過實驗裝置模擬產(chǎn)生真實的電網(wǎng)擾動故障,采用實測數(shù)據(jù)驗證所提方法的檢測效果,綜合檢驗了AELIMD方法在電能質(zhì)量擾動檢測工程領(lǐng)域具有一定優(yōu)勢。
[Abstract]:Compared with FFT analysis, wavelet transform and S transform, HHT is one of the most commonly used time-frequency analysis methods. Some advantages have been established in the unsteady signal processing and adaptive analysis, but there are still some drawbacks in the signal processing and analysis of HHT, so the main work of this paper is to analyze and improve the shortcomings of the HHT method. The improved HHT method is applied to the field of power quality disturbance detection and analysis. Firstly, aiming at the problems of mean curve fitting, modal aliasing, false component and endpoint effect of HHT, etc. A new adaptive time-frequency analysis method, Local Integral Mean decomposition method (LIMD), is proposed, which redefines a single component signal with instantaneous physical meaning according to the characteristics of a single component signal. The efficiency of decomposing inherent mode function is improved. Aiming at the disadvantages of poor interpolation effect and time-consuming time of EMD method, the fitting method of mean value curve is improved, and the integral mean value theorem is introduced into the fitting process of mean curve. Using all the data between extremum points as the characteristic time scale, The simulation results show that the proposed method improves the computational efficiency of the algorithm and improves the endpoint effect and the suppression of false components. Secondly, the simulation results show that the proposed method improves the efficiency of the algorithm. Aiming at the problems of modal aliasing effect and low resolution of LIMD method, further improvement is needed. An adaptive Ensemble Local Integral Mean decomposition (AELIMD) method is proposed, in which the influence of noise in different frequency forms on the distribution of extreme points is analyzed, and the advantages of high frequency auxiliary decomposition are determined. The optimal noise frequency is adaptively selected by using the distribution of extreme points as the evaluation index. Through the study of the EEMD noise adding criterion, the amplitude and decomposition times of the noise adding are deduced by the fixed value: 0. 01 SD and 2 times. The proposed method is added to the original signal in the form of positive and negative pairs. The self-adaptability and computational performance of the proposed method are verified by simulation experiments. Finally, The improved HHT method and Hilbert transform are applied to the detection and analysis of power quality disturbance. The noised power quality signal is de-noised by AELIMD method, and the modulus maximum is obtained by the second-order derivative. The accuracy of locating disturbance time by modulus maximum is improved. The disturbance components are effectively extracted from noise and false components by twice AELIMD decomposition method for high frequency complex disturbance. Aiming at steady state disturbance, a detection method of flicker envelope is proposed, which removes harmonics first and then extracts flicker envelopes. The detection method based on HHT method is presented for unknown complex disturbances. The simulation results show that the proposed method is effective and feasible. A power quality disturbance experimental platform is built to simulate the real power grid disturbance faults. By using the measured data to verify the detection effect of the proposed method, the AELIMD method has some advantages in the field of power quality disturbance detection engineering.
【學位授予單位】：中國礦業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TM711;TM930

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