本文關(guān)鍵詞：電能質(zhì)量擾動檢測與識別研究　出處：《廣西大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 電能質(zhì)量 改進S變換 檢測 特征提取 分類識別

【摘要】：近年,隨著現(xiàn)代工業(yè)技術(shù)的快速發(fā)展,電力電子技術(shù)也發(fā)展迅速,越來越多的新能源裝置和沖擊性負荷被接入電網(wǎng),使得電網(wǎng)的電能質(zhì)量擾動問題日益變得嚴重,給國民生產(chǎn)和生活帶來一系列問題,造成了巨大的經(jīng)濟損失。因此必須對這些電能質(zhì)量擾動問題進行分析和治理,而快速、準確檢測出這些電能質(zhì)量問題以及準確識別出這些電能質(zhì)量問題的種類是提高電能質(zhì)量的關(guān)鍵。本文將電能質(zhì)量問題的檢測和識別分別進行研究,分析了目前已有的檢測和識別的方法,著重對S變換的原理和計算方法進行了分析,在此基礎(chǔ)上了提出了一種改進S變換方法。在電能質(zhì)量擾動檢測方面,利用改進S變換實現(xiàn)了對諧波、電壓暫升以及電壓暫降等電能質(zhì)量擾動的檢測,并與利用S變換的檢測結(jié)果進行了比較分析,實驗證明了基于改進S變換的檢測方法的有效性,且檢測結(jié)果更準確。在電能質(zhì)量擾動識別方面,首先利用改進S變換對諧波、暫升、暫降、振蕩、脈沖、含諧波的暫升以及含諧波的暫降等7種擾動進行了分析,選擇了信號改進S變換模矩陣中提取的幅值包絡(luò)曲線、基頻幅值曲線以及時間幅值平方和均值曲線等三種曲線作為特征曲線,然后選用兩種方法分別進行識別分析。其一,利用二分類支持向量機對上述擾動進行識別,構(gòu)建了二分類支持向量機樹,實現(xiàn)了擾動分類;其二,利用極限學(xué)習(xí)機進行分類,用均值、標準差、偏度、峭度以及均方根值分別去刻畫三種特征曲線,得到15個特征量作為極限學(xué)習(xí)機輸入量,得到了良好的分類效果。以上兩種分類識別結(jié)果證明了基于改進S變換提取的電能質(zhì)量擾動特征量的有效性。
[Abstract]:In recent years, with the rapid development of modern industrial technology, power electronics technology has also developed rapidly, more and more new energy devices and impact load are connected to the power grid. The power quality disturbance problem of power network becomes more and more serious, which brings a series of problems to the national production and daily life, resulting in huge economic losses. Therefore, it is necessary to analyze and deal with these power quality disturbance problems. The key to improve power quality is to detect these power quality problems quickly and accurately and to identify the types of power quality problems accurately. In this paper, the detection and identification of power quality problems are studied separately. The existing methods of detection and recognition are analyzed, and the principle and calculation method of S-transform are analyzed. Based on this, an improved S-transform method is proposed to detect the disturbance of power quality. The improved S-transform is used to detect the power quality disturbances, such as harmonic, voltage rise and voltage sag, and the results are compared with those of S-transform. Experiments show that the detection method based on improved S-transform is effective and the detection results are more accurate. In power quality disturbance identification, the improved S-transform is first used to detect harmonics, suspensions, sags, oscillations and pulses. Seven kinds of disturbances, such as the rise of harmonics and the sag of harmonics, are analyzed, and the amplitude envelope curves extracted from the improved S transform modulus matrix are selected. The fundamental frequency amplitude curve and the time amplitude square sum mean curve are taken as characteristic curves, and then two methods are selected to identify and analyze them. The disturbance is identified by two-classification support vector machine, and the tree of two-classification support vector machine is constructed, and the disturbance classification is realized. Secondly, using the extreme learning machine to classify, using the mean, standard deviation, deviation, kurtosis and root mean square value to describe the three characteristic curves, get 15 characteristics as the input of the learning machine. The above two classification and recognition results prove the effectiveness of the power quality disturbance feature extraction based on the improved S-transform.
【學(xué)位授予單位】：廣西大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TM711;TM930

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本文編號：1406914