發(fā)布時(shí)間：2018-11-07 09:57

【摘要】：近幾年,智能電網(wǎng)的研究成為電力系統(tǒng)的一個(gè)熱點(diǎn)問題,而保障優(yōu)質(zhì)的電能質(zhì)量是智能電網(wǎng)研究的重點(diǎn)問題。另一方面,大量電力電子設(shè)施的廣泛使用,新能源等技術(shù)的應(yīng)用,都需要高質(zhì)量的電能提供保障。所以,識(shí)別出電能質(zhì)量信號(hào)中的擾動(dòng)信息不僅有利于檢測出優(yōu)劣的電能質(zhì)量,而且還能減少或者控制由電能質(zhì)量擾動(dòng)產(chǎn)生的各種問題。在實(shí)際生活中出現(xiàn)的擾動(dòng)并不只是單一的擾動(dòng),而是經(jīng)常出現(xiàn)幾種擾動(dòng)共存的情況。因此,識(shí)別出擾動(dòng)是保障優(yōu)質(zhì)的電能質(zhì)量的基礎(chǔ)。本課題重點(diǎn)是圍繞復(fù)合擾動(dòng)的特征提取和分類識(shí)別兩部分展開探究。在特征提取方面,本文主要是應(yīng)用S變換和小波變換提取特征量。本文在探究復(fù)合擾動(dòng)的提取特征時(shí),用S變換對擾動(dòng)作深入探究,提出一種提高時(shí)間和頻率分辨率的S算法,提取每種擾動(dòng)的改進(jìn)的S矩陣的每列最大幅值的最大值、每列最大幅值的最小值和工頻幅值的均值三個(gè)特征量作為一部分特征量;對擾動(dòng)信號(hào)進(jìn)行小波變換,提取擾動(dòng)信號(hào)每層能量的差值作為另一部分,加上改進(jìn)S變換提取的一部分特征量作為總的特征量。在分類方面,應(yīng)用支持向量機(jī)識(shí)別出不同的擾動(dòng)。其中,高斯核函數(shù)是其辨識(shí)出擾動(dòng)信號(hào)的關(guān)鍵因子。本文對高斯核函數(shù)進(jìn)行改進(jìn),引入幅度調(diào)節(jié)參數(shù)和徑向?qū)挾日{(diào)節(jié)參數(shù),提高了電能質(zhì)量復(fù)合擾動(dòng)的識(shí)別準(zhǔn)確率;對于分類器中的參數(shù)選擇難的問題,用粒子群進(jìn)行參數(shù)尋優(yōu),并且深入研究粒子群,提出了指數(shù)型的慣性權(quán)重,快速準(zhǔn)確的求取參數(shù)的最優(yōu)組合,提高了擾動(dòng)識(shí)別的準(zhǔn)確率。仿真結(jié)果顯示,利用小波算法和提高時(shí)頻分辨率的S算法獲取特性向量用到支持向量機(jī)中,得到的識(shí)別準(zhǔn)確率比小波變換和S變換提取的特征量進(jìn)行識(shí)別的準(zhǔn)確率提高了3.7839%,比小波變換提取的特征量的識(shí)別準(zhǔn)確率提高了7.5758%;利用基于幅度調(diào)節(jié)和徑向?qū)挾日{(diào)節(jié)的高斯核函數(shù)算法,提高了支持向量機(jī)分類器的識(shí)別準(zhǔn)確率,降低了計(jì)算復(fù)雜度,使支持向量的個(gè)數(shù)變少,其整體識(shí)別準(zhǔn)確率比支持向量機(jī)的提高了1.8182%;利用指數(shù)型慣性權(quán)重的粒子群算法求取改進(jìn)的支持向量機(jī)中的參數(shù)的最優(yōu)值,得到的識(shí)別準(zhǔn)確率比粒子群得到的結(jié)果提升了0.3788%。
[Abstract]:In recent years, the research of smart grid has become a hot issue in power system, and the guarantee of high quality power quality is the key issue in the research of smart grid. On the other hand, the widespread use of a large number of power electronic facilities and the application of new energy technologies require high quality electrical energy to provide protection. Therefore, identifying the disturbance information in the power quality signal is not only helpful to detect the power quality, but also can reduce or control all kinds of problems caused by the power quality disturbance. The disturbance in real life is not only a single disturbance, but also the coexistence of several disturbances. Therefore, the identification of disturbances is the basis for ensuring high quality power quality. This thesis focuses on feature extraction and classification recognition of complex disturbances. In feature extraction, this paper mainly uses S transform and wavelet transform to extract feature quantity. In this paper, an S algorithm is proposed to improve the resolution of time and frequency in order to extract the maximum of the maximum value in each column of the improved S-matrix of each disturbance. The minimum value of the maximum value of each column and the mean value of the power frequency amplitude are taken as part of the eigenvalues. Wavelet transform is applied to the disturbance signal, the difference of energy in each layer is extracted as the other part, and a part of the characteristic quantity extracted by the improved S transform is taken as the total characteristic quantity. In classification, support vector machines (SVM) are used to identify different disturbances. Among them, Gao Si kernel function is the key factor to identify disturbance signal. In this paper, Gao Si kernel function is improved, amplitude adjustment parameter and radial width adjustment parameter are introduced to improve the accuracy of power quality complex disturbance identification. For the problem of difficult parameter selection in classifier, the particle swarm optimization is used to optimize the parameters, and the particle swarm optimization is deeply studied. The inertial weight of exponential type is put forward, the optimal combination of parameters is obtained quickly and accurately, and the accuracy of disturbance identification is improved. The simulation results show that the wavelet algorithm and the S algorithm to improve the time-frequency resolution are used to obtain the characteristic vector in the support vector machine. The recognition accuracy is 3.7839 higher than that of wavelet transform and S-transform, and 7.5758% higher than that of wavelet transform. Using Gao Si kernel function algorithm based on amplitude adjustment and radial width adjustment, the recognition accuracy of support vector machine classifier is improved, the computational complexity is reduced, and the number of support vectors is reduced. The overall recognition accuracy is 1.8182 higher than that of support vector machine. The particle swarm optimization algorithm of exponential inertia weight is used to obtain the optimal value of the parameters in the improved support vector machine, and the recognition accuracy is improved by 0.3788 compared with the result obtained by the particle swarm optimization.
【學(xué)位授予單位】：東北電力大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TM76;TP18

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本文編號(hào)：2316003