本文關(guān)鍵詞：電力系統(tǒng)電能質(zhì)量的擾動(dòng)檢測(cè)與識(shí)別方法研究　出處：《吉林大學(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)量擾動(dòng) 小波包變換 HHT變換 S變換 多特征組合 概率神經(jīng)網(wǎng)絡(luò)

【摘要】：電能作為清潔環(huán)保,經(jīng)濟(jì)高效,易于控制和轉(zhuǎn)換的能源,廣泛的應(yīng)用到生產(chǎn)和生活的各個(gè)領(lǐng)域。電能質(zhì)量的優(yōu)劣不僅影響電能用戶利益,同樣會(huì)影響電網(wǎng)的安全運(yùn)行,所以對(duì)電能質(zhì)量問(wèn)題的檢測(cè)具有重要的意義。針對(duì)目前主要的電能質(zhì)量問(wèn)題進(jìn)行分類,并具體分析了各類型的電能質(zhì)量問(wèn)題的發(fā)生原因及其危害。根據(jù)常見(jiàn)的暫態(tài)和穩(wěn)態(tài)電能質(zhì)量擾動(dòng)問(wèn)題的特點(diǎn),建立擾動(dòng)數(shù)學(xué)模型,并分析了目前電能質(zhì)量擾動(dòng)的檢測(cè)方法和分類方法的研究現(xiàn)狀。本文主要工作內(nèi)容如下:1)采用小波包變換對(duì)電能質(zhì)量擾動(dòng)信號(hào)進(jìn)行分析,根據(jù)擾動(dòng)信號(hào)的特點(diǎn)研究小波包變換的采樣頻率、小波基函數(shù)和分解層數(shù)等參數(shù)的選取,分析得到各擾動(dòng)類型的小波包節(jié)點(diǎn)的歸一化能量分布,提出對(duì)能量分布進(jìn)行處理得到具有明顯變化的新小波包能量分布的方法。在新的能量分布中可以看出,含諧波的擾動(dòng)和暫態(tài)振蕩擾動(dòng)在對(duì)應(yīng)節(jié)點(diǎn)上的能量分布較大,可以提取對(duì)應(yīng)節(jié)點(diǎn)的新小波包節(jié)點(diǎn)能量作為表征這些擾動(dòng)的特征向量,并通過(guò)仿真對(duì)利用小波包變換提取擾動(dòng)特征向量的可行性進(jìn)行研究。2)首先,采用HHT變換對(duì)各種類型的電能質(zhì)量擾動(dòng)信號(hào)進(jìn)行分析,利用分析結(jié)果中的瞬時(shí)頻率對(duì)不同擾動(dòng)類型發(fā)生的起止時(shí)刻進(jìn)行估計(jì),并根據(jù)瞬時(shí)幅值和邊際譜提取擾動(dòng)信號(hào)的幅值特征和頻率特征;由于HHT變換后特征提取效果不明顯,為尋求更有效的提取特征值的方法,又采用S變換對(duì)各種類型的電能質(zhì)量擾動(dòng)信號(hào)進(jìn)行分析,利用分析結(jié)果中的最高頻率幅值變化對(duì)擾動(dòng)發(fā)生的起止時(shí)刻進(jìn)行估計(jì),并利用基頻幅值變化和時(shí)間幅值平方和均值變化提取擾動(dòng)信號(hào)的幅值特征和頻率特征,具體分析了S變換提取擾動(dòng)特征向量的過(guò)程。之后,通過(guò)對(duì)比兩種方法的擾動(dòng)時(shí)刻估計(jì)和特征提取的效果可以看出,利用S變換的擾動(dòng)起止時(shí)刻定位較準(zhǔn)確,對(duì)應(yīng)特征變化明顯,閾值選取方便,方法更易實(shí)現(xiàn)。3)針對(duì)單一特征向量不能有效的表征所有的電能質(zhì)量擾動(dòng)信號(hào)的差異性,分析了不同檢測(cè)方法特征提取的特點(diǎn),對(duì)電能質(zhì)量擾動(dòng)的多特征組合邏輯進(jìn)行了研究。根據(jù)小波包能量分布中諧波信號(hào)的明顯差異,以及S變換中的頻率特征對(duì)頻率特征明顯的含諧波和暫態(tài)振蕩進(jìn)行分類,并利用S變換得到的其它幅值特征和奇異性特征進(jìn)行后續(xù)的分類。研究了利用多個(gè)概率神經(jīng)網(wǎng)絡(luò)構(gòu)造多特征組合的分類器實(shí)現(xiàn)擾動(dòng)的分類的方法。仿真結(jié)果表明,在噪聲強(qiáng)度為40dB時(shí)平均分類準(zhǔn)確度可達(dá)99.25%,噪聲強(qiáng)度為20dB時(shí)平均分類準(zhǔn)確度也可達(dá)88.38%。
[Abstract]:Electric energy as clean and environmental protection, high efficiency, easy control and conversion of energy, widely used in various fields of production and life. The power quality of the electric energy quality not only affect the interests of users, will also affect the safe operation of power grid, so the detection of power quality problems has important significance. In view of the current classification the main power quality problems, and analyzes the causes and hazards of various types of power quality problems. According to the common transient and steady state characteristics of power quality disturbance, a disturbance model, and analyzes the current research status of power quality disturbance detection and classification. The main work of this paper the contents are as follows: 1) by using the wavelet packet transform of power quality disturbance signal is analyzed according to the disturbance signal characteristics of wavelet packet transform sampling frequency, wavelet function and decomposition level Select the number of parameters, analysis of normalized energy distribution of each disturbance type of wavelet packet, the energy distribution could be obtained by processing the energy distribution method has obvious changes of the new wavelet packet. The energy distribution in the novel can be seen in the disturbance of harmonic and transient oscillations in the corresponding node energy distribution large, node energy can be extracted from the corresponding node of the new wavelet packet as the characterization of these perturbations of the feature vector, and the simulation of using wavelet packet transform to extract the feature vector of disturbance feasibility study.2) firstly, mining quality disturbance signals by analyzing various types of power with HHT transform, the instantaneous frequency by using the analysis results of time to estimate the different disturbance types, and according to the instantaneous amplitude and amplitude of marginal spectrum extraction characteristics and frequency characteristics of disturbance signal due to the HHT transform; After feature extraction, the effect is not obvious, for feature extraction of more effective value, and using S transform to various types of power quality disturbance signal analysis, the highest frequency and amplitude changes with the result of analysis of the disturbance occurred in the starting and ending time estimation, and using the fundamental frequency amplitude change and amplitude and time square the mean changes in amplitude and frequency characteristics of feature extraction of disturbance signal, and detailed analysis of the S transform to extract the feature vectors. After the process disturbance, the disturbance moment estimation and feature extraction of two methods to contrast effect can be seen, using S transform to disturbance time accurate positioning, the corresponding characteristic changes obviously, the threshold is convenient the method is easier to realize.3) for effective characterization of single feature vector can't be all the difference power quality disturbance signal, analyzes the characteristics of different detection methods of extraction. Point of power quality disturbance logic combination of multiple features is studied. According to the difference of wavelet packet energy distribution in the harmonic signal, and S transform in frequency characteristics of frequency characteristic with harmonic and transient oscillation amplitude classification, other features and singularity for subsequent classification and use of S transform. Method of classification using a probabilistic neural network to construct multi feature combination classifier can achieve disturbance. The simulation results show that the noise intensity is 40dB the average classification accuracy of 99.25%, noise intensity is 20dB the average classification accuracy is up to 88.38%.

【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TM711;TM930

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