基于小波變換的電能質量檢測與仿真分析
發(fā)布時間：2018-10-23 17:10
【摘要】：近年來，電能質量問題已引起電力部門以及用戶的廣泛關注。電能質量檢測是監(jiān)督、改善電能質量的一個非常必要的前提，對保證電力系統(tǒng)的安全經濟運行以及用電安全具有重要的理論和實際意義。本文重點研究了常見電能質量擾動信號的時間定位和分類問題。 本文首先對國內外電能質量檢測方面的研究進行總結，從不同角度描述了電能質量的定義以及分類方法，分析總結了電能質量的相關國家標準以及電能質量檢測新要求和發(fā)展趨勢，并給出了7種常見電能質量擾動的數(shù)學模型。然后詳細地介紹了小波理論及其性質，探討了小波在電能質量檢測中的應用。重點研究了基于小波變換的電能質量擾動信號奇異性檢測原理和分類特征向量的提取方法。通過仿真分析，在三維視角上直觀地呈現(xiàn)出所提取特征向量的區(qū)分空間，驗證了提取的分類特征向量的有效性。 電能質量擾動信號的檢測與定位為分析擾動產生的原因提供依據(jù)。文中提出了一種基于復小波的電能質量擾動檢測與定位方法。該方法利用離散復小波變換，提取擾動信號的復小波系數(shù)的幅值和相位信息，再利用幅值和相位的復合信息實現(xiàn)對5種暫態(tài)電能質量擾動信號的時間定位。在噪聲條件下該方法仍然適用，但是，當短時電能質量擾動的起止點發(fā)生在信號的幅值過零點附近時該方法將失效。針對這種情況，提出了一種輔助定位方法——對信號作小波分解與重構，獲取信號低頻波形，再對其使用復小波變換。仿真表明，該方法能在噪聲條件下實現(xiàn)對電能質量擾動信號的快速準確定位。 準確的識別和分類電能質量擾動對分析和綜合治理電能質量問題具有重要意義。文中提出了一種基于小波和改進神經樹的電能質量擾動分類方法，該方法利用小波分解擾動信號到各個頻帶，在基頻頻帶、諧波頻帶和高頻帶上分別計算其能量值和小波系數(shù)熵作為特征值，另計算基波頻帶擾動過程的均方根作為特征的補充，融合能量、熵和均方根值作為擾動分類的特征向量，規(guī)范化后輸入到改進神經樹分類器進行訓練和分類，改進神經樹分類器是由神經網絡和決策樹及其分類規(guī)則構成。仿真表明，該方法提取特征值的計算量小且融合后的特征向量能夠很好體現(xiàn)不同擾動信號之間的差異信息，，構造的改進神經樹分類器結合了神經網絡和決策樹在模式分類中各自的優(yōu)點，結構簡單且表現(xiàn)出良好的收斂性、全局最優(yōu)性和泛化性，且分類準確率較高，能夠有效地識別7種常見的電能質量擾動。
[Abstract]:In recent years, the power quality problem has aroused the widespread concern of the electric power department as well as the user. Power quality detection is a very necessary prerequisite for monitoring and improving power quality. It is of great theoretical and practical significance to ensure the safe and economical operation of power system and the safety of power consumption. This paper focuses on the time localization and classification of common power quality disturbance signals. Firstly, this paper summarizes the research on power quality detection at home and abroad, and describes the definition and classification of power quality from different angles. This paper analyzes and summarizes the relevant national standards of power quality, the new requirements and development trend of power quality detection, and gives seven mathematical models of power quality disturbances. Then the wavelet theory and its properties are introduced in detail, and the application of wavelet in power quality detection is discussed. The singularity detection principle of power quality disturbance signal based on wavelet transform and the extraction method of classification feature vector are studied. Through simulation analysis, the distinguishing space of the extracted feature vectors is presented intuitively from the three-dimensional perspective, and the validity of the extracted feature vectors is verified. The detection and location of power quality disturbance signal provide basis for analyzing the cause of disturbance. In this paper, a power quality disturbance detection and localization method based on complex wavelet is proposed. In this method, the amplitude and phase information of complex wavelet coefficients of disturbance signals are extracted by discrete complex wavelet transform, and the time localization of five kinds of transient power quality disturbance signals is realized by using the composite information of amplitude and phase. The method is still applicable under the noise condition, but it will fail when the starting and ending point of the short term power quality disturbance occurs near the zero crossing point of the signal amplitude. In order to solve this problem, an auxiliary localization method is proposed, which is to decompose and reconstruct the signal by wavelet transform, obtain the low frequency waveform of the signal, and then use complex wavelet transform. Simulation results show that the proposed method can locate the power quality disturbance signals quickly and accurately under noise conditions. Accurate identification and classification of power quality disturbances is of great significance in analyzing and synthesizing power quality problems. In this paper, a power quality disturbance classification method based on wavelet and improved neural tree is proposed. The energy value and wavelet coefficient entropy of harmonic band and high frequency band are calculated as eigenvalues respectively, and the root mean square (RMS) of fundamental frequency band perturbation process is calculated as the supplement of the feature, and the energy, entropy and RMS value are used as eigenvectors of disturbance classification. The improved neural tree classifier is composed of neural network, decision tree and its classification rules. Simulation results show that the proposed method can well represent the difference information between different disturbance signals, and the computation of the extracted eigenvalues is small and the fused Eigenvectors can well reflect the difference between different disturbance signals. The improved neural tree classifier combines the advantages of neural network and decision tree in pattern classification. It has the advantages of simple structure, good convergence, global optimality and generalization, and high classification accuracy. It can effectively identify seven common power quality disturbances.
【學位授予單位】：湖南大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TM711
本文編號：2289926
[Abstract]:In recent years, the power quality problem has aroused the widespread concern of the electric power department as well as the user. Power quality detection is a very necessary prerequisite for monitoring and improving power quality. It is of great theoretical and practical significance to ensure the safe and economical operation of power system and the safety of power consumption. This paper focuses on the time localization and classification of common power quality disturbance signals. Firstly, this paper summarizes the research on power quality detection at home and abroad, and describes the definition and classification of power quality from different angles. This paper analyzes and summarizes the relevant national standards of power quality, the new requirements and development trend of power quality detection, and gives seven mathematical models of power quality disturbances. Then the wavelet theory and its properties are introduced in detail, and the application of wavelet in power quality detection is discussed. The singularity detection principle of power quality disturbance signal based on wavelet transform and the extraction method of classification feature vector are studied. Through simulation analysis, the distinguishing space of the extracted feature vectors is presented intuitively from the three-dimensional perspective, and the validity of the extracted feature vectors is verified. The detection and location of power quality disturbance signal provide basis for analyzing the cause of disturbance. In this paper, a power quality disturbance detection and localization method based on complex wavelet is proposed. In this method, the amplitude and phase information of complex wavelet coefficients of disturbance signals are extracted by discrete complex wavelet transform, and the time localization of five kinds of transient power quality disturbance signals is realized by using the composite information of amplitude and phase. The method is still applicable under the noise condition, but it will fail when the starting and ending point of the short term power quality disturbance occurs near the zero crossing point of the signal amplitude. In order to solve this problem, an auxiliary localization method is proposed, which is to decompose and reconstruct the signal by wavelet transform, obtain the low frequency waveform of the signal, and then use complex wavelet transform. Simulation results show that the proposed method can locate the power quality disturbance signals quickly and accurately under noise conditions. Accurate identification and classification of power quality disturbances is of great significance in analyzing and synthesizing power quality problems. In this paper, a power quality disturbance classification method based on wavelet and improved neural tree is proposed. The energy value and wavelet coefficient entropy of harmonic band and high frequency band are calculated as eigenvalues respectively, and the root mean square (RMS) of fundamental frequency band perturbation process is calculated as the supplement of the feature, and the energy, entropy and RMS value are used as eigenvectors of disturbance classification. The improved neural tree classifier is composed of neural network, decision tree and its classification rules. Simulation results show that the proposed method can well represent the difference information between different disturbance signals, and the computation of the extracted eigenvalues is small and the fused Eigenvectors can well reflect the difference between different disturbance signals. The improved neural tree classifier combines the advantages of neural network and decision tree in pattern classification. It has the advantages of simple structure, good convergence, global optimality and generalization, and high classification accuracy. It can effectively identify seven common power quality disturbances.
【學位授予單位】：湖南大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TM711
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