本文選題：電能質(zhì)量 + 擾動識別�。� 參考：《哈爾濱工業(yè)大學(xué)》2017年碩士論文

【摘要】：隨著科學(xué)技術(shù)與工業(yè)智能化的迅猛發(fā)展,電能質(zhì)量問題對于航空航天、汽車、電子制造等重要領(lǐng)域造成的影響日益顯著,因此電能質(zhì)量問題的治理得到廣泛的關(guān)注和研究。電能質(zhì)量問題治理的關(guān)鍵步驟是電能質(zhì)量擾動的識別,即電能質(zhì)量擾動檢測和分類識別。本文總結(jié)了由電能質(zhì)量問題所引起的危害,并根據(jù)電能質(zhì)量相關(guān)標(biāo)準(zhǔn)以及國內(nèi)外的發(fā)展現(xiàn)狀,從電能質(zhì)量擾動檢測和分類識別兩個方面深入研究電能質(zhì)量擾動識別算法。針對電能質(zhì)量擾動檢測問題,依據(jù)電力系統(tǒng)中常見的電能質(zhì)量問題,總結(jié)歸納了12種電能質(zhì)量擾動數(shù)學(xué)表達(dá)式。研究了常用的時頻域檢測算法(短時傅里葉變換、小波變換),并利用MATLAB對常用檢測算法進(jìn)行仿真,并闡述了各算法的優(yōu)缺點。針對常用時頻域法的不足,本文著重研究了利用S變換進(jìn)行電能質(zhì)量擾動檢測的方法,并在對S變換原理分析的基礎(chǔ)上,提出了一種分段改進(jìn)S變換檢測方法,克服了差分法、有效值法、傅里葉變換無法檢測所有電能質(zhì)量擾動類型、小波變換受噪聲影響大以及短時傅里葉變換窗寬固定的缺點。該方法將檢測信號分為低頻、中頻和高頻段,在每個頻段中存在相對應(yīng)的窗寬調(diào)節(jié)因子,通過改變窗寬調(diào)節(jié)因子,可以改變S變換的時頻分辨率。在研究過程中,分析檢測誤差和分段改進(jìn)S變換結(jié)果中擾動區(qū)域的峰度之間的關(guān)系,確定每個頻段的最優(yōu)窗寬調(diào)節(jié)因子。經(jīng)仿真對比得出分段改進(jìn)S變換比常用檢測算法的檢測能力更強(qiáng)。針對電能質(zhì)量擾動信號分類識別問題,利用分段改進(jìn)S變換對擾動信號進(jìn)行檢測,并對檢測結(jié)果進(jìn)行分析,分析得出時間-幅值和頻率-幅值兩種特征向量,其能夠反映擾動信號在時域和頻域的主要特征。從兩種特征向量中,提取了7種特征量。根據(jù)7種特征量構(gòu)建出具有14條分類規(guī)則的決策樹分類器,實現(xiàn)了擾動信號的自動分類。根據(jù)每種擾動信號的擾動參數(shù)隨機(jī)生成測試樣本對本文所提出的識別算法進(jìn)行仿真測試。測試結(jié)果表明新的識別算法具有較高的識別準(zhǔn)確率�；谏鲜鎏岢龅碾娔苜|(zhì)量擾動識別算法,設(shè)計了一種以DSP+ARM雙核CPU芯片OMAP-L138為平臺,具有基本電力參數(shù)測量、電能質(zhì)量擾動識別、電能質(zhì)量參數(shù)測量、歷史數(shù)據(jù)存儲和網(wǎng)絡(luò)通信等功能的電能質(zhì)量擾動識別裝置。試驗測試結(jié)果表明,該電能質(zhì)量擾動識別裝置基本達(dá)到所設(shè)計功能的要求。
[Abstract]:With the rapid development of science and technology and industrial intelligence, power quality problems have become increasingly significant in aerospace, automotive, electronic manufacturing and other important fields. The key step of power quality problem solving is the identification of power quality disturbance, that is, the detection and classification of power quality disturbance. In this paper, the harm caused by power quality problems is summarized. According to the relevant standards of power quality and the current situation of development at home and abroad, the power quality disturbance identification algorithm is deeply studied from two aspects: power quality disturbance detection and classification recognition. Aiming at the problem of power quality disturbance detection, 12 kinds of mathematical expressions of power quality disturbance are summarized according to the common power quality problems in power system. The common detection algorithms in time-frequency domain (short time Fourier transform, wavelet transform) are studied. The common detection algorithms are simulated by MATLAB, and the advantages and disadvantages of each algorithm are expounded. In order to overcome the shortcomings of time-frequency domain method, this paper mainly studies the method of power quality disturbance detection using S-transform. On the basis of analyzing the principle of S-transform, a piecewise improved S-transform detection method is proposed, which overcomes the difference method. Effective value method, Fourier transform can not detect all types of power quality disturbance, wavelet transform is greatly affected by noise and the window width of short time Fourier transform is fixed. In this method, the detected signals are divided into low frequency, intermediate frequency and high frequency bands. There are corresponding window width adjustment factors in each frequency band. By changing the window width adjustment factor, the time-frequency resolution of S-transform can be changed. In the research process, the relationship between the detection error and the kurtosis of the disturbed region in the modified S transform is analyzed, and the optimal window width adjustment factor for each frequency band is determined. The simulation results show that the improved S transform has better detection ability than the common detection algorithms. Aiming at the problem of classification and recognition of power quality disturbance signal, the disturbance signal is detected by using piecewise improved S transform, and the detection results are analyzed, and two characteristic vectors, time-amplitude and frequency-amplitude, are obtained. It can reflect the main characteristics of disturbance signal in time domain and frequency domain. Seven feature vectors were extracted from two kinds of feature vectors. A decision tree classifier with 14 classification rules is constructed according to the seven characteristic quantities, and the disturbance signal is classified automatically. According to the disturbance parameters of each disturbance signal, random test samples are generated to test the identification algorithm proposed in this paper. The test results show that the new recognition algorithm has high recognition accuracy. Based on the above proposed power quality disturbance identification algorithm, a DSP ARM dual-core CPU chip OMAP-L138 platform is designed, which has basic power parameter measurement, power quality disturbance identification, power quality parameter measurement, and power quality parameter measurement. Power quality disturbance recognition device for historical data storage and network communication. The test results show that the power quality disturbance identification device basically meets the requirements of the designed function.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TM711

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 李天楚;伍智鵬;朱明星;;配電網(wǎng)諧波電流限值計算與評估的若干問題探討[J];電力電容器與無功補(bǔ)償;2016年06期

2 黃建明;瞿合祚;李曉明;;基于短時傅里葉變換及其譜峭度的電能質(zhì)量混合擾動分類[J];電網(wǎng)技術(shù);2016年10期

3 林海雪;;電網(wǎng)中電壓暫降和短時中斷問題及其標(biāo)準(zhǔn)介紹[J];建筑電氣;2015年06期

4 陳曉娟;鞏永穩(wěn);;基于138平臺的電能質(zhì)量檢測系統(tǒng)設(shè)計[J];電子器件;2014年05期

5 郭俊文;李開成;何順帆;張明;;基于改進(jìn)不完全S變換與決策樹的實時電能質(zhì)量擾動分類[J];電力系統(tǒng)保護(hù)與控制;2013年22期

6 張志禹;滿蔚仕;郗壘;黃璐瑤;;快速S變換在電能質(zhì)量分析中的應(yīng)用[J];電網(wǎng)技術(shù);2013年05期

7 陳華豐;張葛祥;;基于決策樹和支持向量機(jī)的電能質(zhì)量擾動識別[J];電網(wǎng)技術(shù);2013年05期

8 滿蔚仕;張志禹;康青;苗永康;席曉莉;;利用快速S變換的電能質(zhì)量擾動識別方法[J];西安交通大學(xué)學(xué)報;2013年08期

9 張逸;楊洪耕;林瑞星;王巍;謝康;;電能質(zhì)量數(shù)據(jù)交換格式文件通用解析方案[J];電力系統(tǒng)自動化;2012年11期

10 劉金鑫;李鵬;習(xí)朋;界金星;;基于數(shù)學(xué)形態(tài)學(xué)與后差分算法的微網(wǎng)暫態(tài)電能質(zhì)量擾動定位方法[J];電網(wǎng)與清潔能源;2011年09期

相關(guān)博士學(xué)位論文 前2條

1 易吉良;基于S變換的電能質(zhì)量擾動分析[D];湖南大學(xué);2010年

2 宋雪雷;基于小波變換和支持向量機(jī)的電能質(zhì)量擾動分析方法[D];哈爾濱工業(yè)大學(xué);2007年

相關(guān)碩士學(xué)位論文 前7條

1 吳字宇;電壓暫降在線檢測系統(tǒng)的研究[D];哈爾濱工業(yè)大學(xué);2016年

2 張衛(wèi)輝;基于多分辨率快速S變換的電能質(zhì)量擾動信號識別[D];東北電力大學(xué);2016年

3 張語R，

本文編號：1896833