以幅狀基底函數(shù)類神經(jīng)網(wǎng)路為基礎的諧波檢測演算法

1、以幅狀基底函數(shù)類神經(jīng)網(wǎng)路為基礎的諧波檢測演算法研究生：鄧淯峰 指導教授：張文恭 博士國立中正大學電機工程研究所摘要由於電力系統(tǒng)中非線性負載的使用大量增加，使得諧波對於電力品質(zhì)的污染日趨嚴重，過量的諧波可能會導致電力系統(tǒng)過電壓或是過電流，而使得電力設備壽命減短，電子儀器亦會因受到諧波干擾而產(chǎn)生不準確的結果。因此，關於抑制諧波的問題已逐漸成為電力公司與用戶間所關心的議題。在信號處理方面，由於快速傅立葉轉換(Fast Fourier Transform, FFT)計算速度快，方法簡易，因此應用上最為廣泛，大多數(shù)的量測儀器皆以FFT演算法作為基礎。但是，若信號特性不符合FFT的使用限制，在頻率變動以及

2、頻率解析度不足的情況下，分析結果將受到?jīng)┬c欄柵效應影響而產(chǎn)生誤差。雖然可以增加分析訊號的取樣值來改善上述的影響，但這樣會降低運算效能。所以，如何達到高解析度及提昇效能，值得加以深入探討與研究。近年來以人工類神經(jīng)網(wǎng)路(Artificial Neural Network, ANN)為基礎的方法，如Adaptive Linear Element (ADALINE)，倒傳遞神經(jīng)網(wǎng)路 (Back Propagation Neural Network, BPN)等，廣泛應用在信號處理方面。時域的分析方法除了具有速度快的優(yōu)點，也解決了頻域分析方法的限制。因此本文提出應用幅狀基底函數(shù)神經(jīng)網(wǎng)路 (Radi

3、al Basis Function Neural Network, RBFNN)為基礎的諧波監(jiān)測演算法。最後，利用LabVIEW軟體，並配合相關硬體設備，經(jīng)由模擬信號與實際量測分析來加以驗證本文方法之有效性與實用性。Radial Basis Function Based Neural Network for Power System Harmonics DetectionStudent: Y. F. Teng Advisor: G. W. Chang, Ph. D.Institute of Electrical EngineeringCollege of EngineeringNational

4、 Chung Cheng UniversityABSTRACTWith the widespread use of nonlinear loads in the power system, harmonic distortion causes a serious deterioration of power quality. Excessive harmonics may introduce over-voltage or over-current problems that will reduce the life of power system equipment. The equipme

5、nt performance also will become inaccurate due to harmonic disturbances. Therefore, mitigating harmonics has become a great concern for both utilities and customers.The Fast Fourier Transform (FFT) has been widely used for the signal processing because of its computational efficiency. In addition, m

6、ost power meters adopt FFT-based algorithm to analyze the harmonics and to show the frequency spectra. However, the FFT-based algorithm is less accurate if the system frequency varies and the frequency resolution decreases, and the analytic results will be inaccurate caused by the leakage and picket

7、-fence effects. Although increasing the sampling frequency can mitigate the undesired effects, this will impede the computational efficiency. Therefore, how to achieve both the high resolution and efficiency is worth investigating.In recent years, the Artificial Neural Network (ANN) based methods, A

8、daptive Linear Element (ADALINE) and Back Propagation Neural Network (BPN), have been widely used for the signal processing. The time-domain methods not only reduce the calculation time, but also avoid the restriction of the frequency domain methods. For this reason, this thesis proposes the RBFNN (Radial basis Function Neural Network) based algorithm for harmonics detection. Finally, the thesis applies Lab