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  本文選題：暫態(tài)電能質(zhì)量 + 擾動檢測��； 參考：《東北石油大學(xué)》2017年碩士論文

【摘要】：近年來,電能質(zhì)量問題引起了電力部門和用戶的高度重視,在現(xiàn)代社會中已逐漸成為使用最廣泛且不可或缺的一種能源。由于大量精密儀器和電力電子設(shè)備的廣泛使用,使得電網(wǎng)電能質(zhì)量日益惡化,必可避免的將造成電能質(zhì)量的下降。分析研究電能質(zhì)量擾動信號,已漸漸變成現(xiàn)代電力系統(tǒng)的首要研究課題,具有重要的探究意義。首先,本文歸納了國內(nèi)外在電能質(zhì)量檢測方面所做的研究,對電能質(zhì)量的概念以及分類方式進(jìn)行不同角度的描述,剖析并總結(jié)了電能質(zhì)量擾動信號的特征,并給出了擾動信號的數(shù)學(xué)模型以及對應(yīng)的波形圖。深入分析傅立葉變換、短時傅立葉變換以及小波變換的基本原理,探究并對比了其在電能質(zhì)量分析范疇的應(yīng)用。其次,本文考慮到在實際的工程應(yīng)用中,由于外界因素的干擾,檢測到的信號通常包含噪聲。在采用小波去噪時,由于存在少許的區(qū)域,其中的原信號與噪聲信號相對更靠近,容易錯誤的將原信號的部分細(xì)節(jié)看做是噪聲信號,從而給去除掉。為了更好的保留原始信號的有用信息,本文提出一種基于系數(shù)變換的改進(jìn)的去噪算法。通過MATLAB仿真,證實了該算法能夠在去除噪聲的同時,更好的重現(xiàn)原信號。再次,由于小波變換擁有很好的時頻局部化特性,同時依據(jù)暫態(tài)電能質(zhì)量信號的不平穩(wěn)特征,則信號的奇異性能夠利用小波變換模極大值來體現(xiàn)。本文在小波變換的前提下,提出一種動態(tài)窗模極大值擾動定位算法,針對傳統(tǒng)模極大值在檢測信號突變點(diǎn)時存在的突變點(diǎn)遺漏和過零點(diǎn)檢測不明顯的問題進(jìn)行算法改進(jìn),通過對單一和復(fù)合擾動的仿真實驗,證明了本文所提方法的可行性。最后,運(yùn)用S變換法對這些擾動信號進(jìn)行檢測分析,從S變換結(jié)果矩陣中我們能夠提取出擾動信號起止時間、信號的擾動幅值和頻率特征量,繪制S變換模矩陣列向量平方和均值曲線、列向量極大值曲線和行向量平方和均值曲線,通過三維網(wǎng)絡(luò)圖和三種曲線,可以清晰看到信號的擾動特征,并檢測出信號的擾動時間、幅值和頻率,證實了S變換在暫態(tài)電能質(zhì)量擾動檢測方面的優(yōu)勢。
[Abstract]:In recent years, the power quality problem has attracted the attention of the power department and the users, and has gradually become the most widely used and indispensable energy in modern society. Because of the extensive use of a large number of precision instruments and power electronic equipment, the power quality of the power grid is becoming worse and worse, and the quality of power can be avoided. The analysis and study of power quality disturbance signal has gradually become the primary research topic of modern power system, which has important research significance. Firstly, this paper summarizes the research on power quality detection at home and abroad, describes the concept and classification of power quality in different angles, and analyzes and summarizes the power quality disturbance. The mathematical model of the disturbance signal and the corresponding waveform diagram are given. The basic principles of Fu Liye transform, short time Fu Liye transform and wavelet transform are deeply analyzed, and their application in the field of energy quality analysis is explored and compared. Secondly, this paper considers that the external factors are dry in practical engineering applications. The signal detected usually contains noise. In the use of wavelet de-noising, the original signal is relatively close to the noise signal due to the existence of a few regions, and it is easy to mistake the partial details of the original signal as a noise signal. The improved denoising algorithm of coefficient transformation. Through MATLAB simulation, it is proved that the algorithm can better reproduce the original signal while removing the noise. Thirdly, the wavelet transform has a good time-frequency localization characteristic, and the singularity of the signal can make use of the wavelet transform mode pole according to the unstable characteristic of the transient power quality signal. In this paper, a dynamic window mode maximum disturbance localization algorithm is proposed on the premise of wavelet transform. The algorithm improves the problem of the missing point mutation point and the zero crossing point detection of the traditional mode maximum value when detecting the signal mutation point. Finally, the S transform method is used to detect and analyze the disturbance signals. From the result matrix of the S transformation, we can extract the time of the disturbance signal, the amplitude and the frequency characteristic of the signal, and draw the vector square and mean curve of the S transform matrix array, the maximum curve of the column vector and the mean square and the mean of the row vector. The curve, through the three-dimensional network graph and the three kinds of curves, can clearly see the disturbance characteristic of the signal, and detect the disturbance time, amplitude and frequency of the signal, and confirm the advantage of S transform in the detection of transient power quality disturbance.
【學(xué)位授予單位】：東北石油大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TM930

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本文編號：2072443