本文選題：混合電能質(zhì)量擾動　切入點：CEEMD　出處：《西南交通大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著國民經(jīng)濟的飛速發(fā)展,電力電子設(shè)備得到廣泛的應(yīng)用,各種沖擊性、非線性、波動性的負載也大幅增加,導(dǎo)致了一系列的電能質(zhì)量問題,使得電能質(zhì)量污染日趨嚴重。同時,電力用戶對電力的可靠性及質(zhì)量的要求都在不斷提高,許多精密儀器對電能質(zhì)量越來越敏感,電能質(zhì)量的下降,將對人們的生活生產(chǎn)造成很大的危害。因此,準確的對電能質(zhì)量擾動參數(shù)進行檢測、識別電能質(zhì)量擾動類別,對于電能質(zhì)量污染的抑制和治理具有重要意義。本文在前人研究的基礎(chǔ)上,對電能質(zhì)量擾動檢測和混合電能質(zhì)量擾動的分類問題進行了分析。在電能質(zhì)量擾動參數(shù)檢測方面,本文分別采用了CEEMD、IELMD和HVD這三種方法對電能質(zhì)量擾動進行檢測。方法一,采用CEEMD對電能質(zhì)量擾動進行分解,對所得到的IMF分量進行Hilbert變換得到原始信號的瞬時頻率和瞬時幅值。仿真結(jié)果表明,該方法對5種單一擾動和3種簡單的混合擾動都具有較好的檢測效果。方法二,采用IELMD方法將電能質(zhì)量擾動信號分解成多個PF分量,對得到的PF分量進行Hilbert變換得到對應(yīng)的瞬時頻率。仿真結(jié)果表明,IELMD方法對5種常見單一擾動具有較好的檢測效果。方法三,通過對擾動信號進行HVD分解,得到擾動的瞬時頻率和瞬時幅值。仿真結(jié)果表明,HVD分解對諧波和間諧波具有較好的檢測效果。在暫態(tài)電能質(zhì)量擾動分類方面,根據(jù)譜峭度的定義和Cohen類時頻分布的特點,將基于CWD的譜峭度與有效值這兩種算法結(jié)合起來應(yīng)用于暫態(tài)電能質(zhì)量擾動分類,仿真結(jié)果表明,該算法具有良好的抗噪性能,不僅對單一擾動的分類精度較高,對簡單的雙重擾動的分類效果也具有較高的分類精度。除此之外,該算法直接通過閾值分類,不需要采集大量的訓(xùn)練數(shù)據(jù)對分類器進行訓(xùn)練,具有較好的可行性。在混合電能質(zhì)量擾動分類方面,根據(jù)混合電能質(zhì)量擾動在時域和頻域的特點,將多種時頻分析算法融合起來提取多個特征,采用決策樹對混合電能質(zhì)量擾動進行分類。該算法從基頻幅值、頻率點個數(shù)、瞬時分量等多個角度出發(fā),采用CEEMD、不完全S變換、動態(tài)測度聯(lián)合提取了9個特征量,考慮到混合擾動中單一擾動之間的相互干擾,對9個特征量的進行合理的組合,設(shè)計了一個決策樹對混合擾動進行識別分類。通過仿真數(shù)據(jù)和實測數(shù)據(jù)對算法進行驗證,表明該算法對于單一擾動和混合擾動都具有較高的識別精度。
[Abstract]:With the rapid development of the national economy, power electronic equipment has been widely used. The load of impact, nonlinearity and volatility has increased significantly, resulting in a series of power quality problems. At the same time, the reliability and quality requirements of electric power users are constantly improving. Many precision instruments are more and more sensitive to power quality, and the power quality is declining. Therefore, accurate detection of power quality disturbance parameters, identification of power quality disturbance types, It is of great significance to restrain and control the power quality pollution. On the basis of previous studies, this paper analyzes the problems of power quality disturbance detection and hybrid power quality disturbance classification. In the aspect of power quality disturbance parameter detection, In this paper, three methods, CEEMD IELMD and HVD, are used to detect the disturbance of power quality. Method 1, CEEMD is used to decompose the disturbance of power quality. The instantaneous frequency and amplitude of the original signal are obtained by the Hilbert transform of the obtained IMF component. The simulation results show that the proposed method is effective for the detection of five single disturbances and three simple mixed disturbances. The IELMD method is used to decompose the power quality disturbance signal into several PF components, and the corresponding instantaneous frequency is obtained by Hilbert transformation of the PF component. The simulation results show that the proposed method has a better detection effect on five common single disturbances. The instantaneous frequency and amplitude of the disturbance are obtained by HVD decomposition of the disturbance signal. The simulation results show that the decomposition has a good effect on the detection of harmonics and interharmonics. According to the definition of spectral kurtosis and the characteristics of time-frequency distribution of Cohen class, two algorithms based on spectral kurtosis and effective value based on CWD are combined to classify transient power quality disturbances. The simulation results show that the algorithm has good anti-noise performance. Not only the classification accuracy of single disturbance is high, but also the classification effect of simple double disturbance is higher. In addition, the algorithm directly classifies by threshold value, and does not need to collect a large amount of training data to train the classifier. In the classification of hybrid power quality disturbances, according to the characteristics of hybrid power quality disturbances in time domain and frequency domain, a variety of time-frequency analysis algorithms are fused to extract multiple features. The decision tree is used to classify the hybrid power quality disturbance. The algorithm extracts nine characteristic quantities from the angle of fundamental frequency amplitude, frequency point number, instantaneous component and so on, using CEEMD, incomplete S transform and dynamic measure. Considering the mutual interference between the single disturbance in the mixed disturbance, a decision tree is designed to identify and classify the mixed disturbance. The algorithm is verified by the simulation data and the measured data. It is shown that the algorithm has high recognition accuracy for both single and mixed perturbations.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TM711

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本文編號：1631616