本文選題：小波變換　切入點(diǎn)：主成分分析　出處：《華北電力大學(xué)(北京)》2016年碩士論文

【摘要】：雷電過電壓產(chǎn)生于電力系統(tǒng)外部,具有沖擊電流大、沖擊電壓高等特點(diǎn),相對(duì)于內(nèi)部過電壓對(duì)電力系統(tǒng)的危害更大。我國電力系統(tǒng)分布廣,復(fù)雜度高,運(yùn)行穩(wěn)定性要求高,為了保證電網(wǎng)建設(shè)的經(jīng)濟(jì)性,針對(duì)不同雷擊過電壓的類型所采取的防雷措施也不同,雷電過電壓相對(duì)于內(nèi)部過電壓防護(hù)難度更大,防護(hù)效果更加局限。因此,雷電過電壓的識(shí)別可以為輸電線路雷擊故障的監(jiān)測、檢測、處理與維修提供了重要的參考價(jià)值。本文以小波分析、主成分分析和神經(jīng)網(wǎng)絡(luò)等理論為基礎(chǔ),針對(duì)輸電線路的雷電過電壓分類識(shí)別問題,深入研究了基于小波-主成分分析的數(shù)據(jù)分析和特征量提取方法,以及基于改進(jìn)的神經(jīng)網(wǎng)絡(luò)的分類識(shí)別方法,并介紹了基于小波-主成分分析的雷電過電壓識(shí)別系統(tǒng)的軟件設(shè)計(jì)與實(shí)現(xiàn)。本論文的工作主要體現(xiàn)在以下三個(gè)方面：1.對(duì)雷電過電壓數(shù)據(jù)的處理：針對(duì)雷電過電壓高頻、瞬變等特點(diǎn),利用小波變換良好的時(shí)頻域局部性能和多分辨率分析等特點(diǎn)和主成分分析法降低數(shù)據(jù)維度來提取數(shù)據(jù)特征的思想,提出了一種基于小波變換和主成分分析方法的雷電過電壓分析和特征量提取方法。2.基于神經(jīng)網(wǎng)絡(luò)的分類模型的改進(jìn)：針對(duì)傳統(tǒng)BP網(wǎng)絡(luò)對(duì)數(shù)據(jù)的分類速度慢,模型訓(xùn)練效率低的缺點(diǎn),對(duì)BP神經(jīng)網(wǎng)絡(luò)的訓(xùn)練過程進(jìn)行改進(jìn),提高了訓(xùn)練的收斂速度,并在此基礎(chǔ)上進(jìn)一步提高了分類準(zhǔn)確率。3.系統(tǒng)的設(shè)計(jì)與實(shí)現(xiàn)：采用Java-web技術(shù),結(jié)合MVC框架思想,實(shí)現(xiàn)了一個(gè)具有對(duì)雷電過電壓數(shù)據(jù)的處理、分析、識(shí)別等功能的軟件系統(tǒng)。本論文中已經(jīng)驗(yàn)證了該分類識(shí)別模型的有效性,并且實(shí)現(xiàn)了基于該分類識(shí)別模型的軟件系統(tǒng),在接下來的研究中將繼續(xù)完善該系統(tǒng)。
[Abstract]:Lightning overvoltage is produced from the outside of power system and has the characteristics of large impulse current and high impulse voltage. It is more harmful to power system than internal overvoltage.In order to ensure the economy of power grid construction, different lightning protection measures are adopted for different types of lightning overvoltage in order to ensure the economy of power grid construction, because of the wide distribution, high complexity and high operational stability of power system in China.Lightning overvoltage is more difficult to protect than internal overvoltage, and the protective effect is more limited.Therefore, the identification of lightning overvoltage can provide an important reference value for the monitoring, detection, processing and maintenance of lightning strike faults of transmission lines.Based on the theories of wavelet analysis, principal component analysis and neural network, this paper deeply studies the method of data analysis and feature extraction based on wavelet principal component analysis, aiming at the problem of lightning overvoltage classification and recognition of transmission lines.The classification and recognition method based on improved neural network and the software design and implementation of lightning overvoltage recognition system based on wavelet principal component analysis are introduced.The work of this paper is mainly reflected in the following three aspects: 1.Processing of lightning overvoltage data: aiming at the characteristics of lightning overvoltage high frequency, transient, etc.,Based on the advantages of wavelet transform in time-frequency domain local performance and multi-resolution analysis, and the idea of reducing the dimension of data by principal component analysis (PCA), the idea of extracting data features is presented.A method of lightning overvoltage analysis and feature extraction based on wavelet transform and principal component analysis (PCA) is proposed.The improvement of classification model based on neural network: aiming at the shortcomings of traditional BP neural network in data classification and low efficiency of model training, the training process of BP neural network is improved, and the convergence speed of training is improved.On this basis, the classification accuracy is further improved.The design and implementation of the system: a software system with the functions of processing, analyzing and recognizing lightning overvoltage data is realized by using Java-web technology and MVC framework.This paper has verified the validity of the classification and recognition model, and has implemented the software system based on the classification and recognition model, and will continue to improve the system in the following research.
【學(xué)位授予單位】：華北電力大學(xué)(北京)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TM863

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本文編號(hào)：1717961