應用小波轉換與類神經網路於電力品質事故 之監測與辨識

Applications of Wavelet Transform and Neural Network to Recognize the Power Quality Events

林穎駿(Yin-Chun Lin)

指導教授 ： 楊宏澤

中原大學/工學院/電機工程研究所/碩士(2003年)

https://doi.org/10.6840/cycu200300663

摘要

隨著科技的進步發展，精密的生產設備與電子儀器被大量的使用，使得供電品質的要求日益昇高，特別是受電力暫態的影響。為了提升電力品質並找尋造成電力品質暫態之原因，需廣泛且長時間的監測資料，並對資料加以分析辨識，作為改善電力品質的參考。
信號分析的方法有很多種，小波轉換因具有在時域與頻域多重解析的能力，可藉由時-頻圖觀察不同頻率範圍在各時間點的分佈情形，可為電力品質事故辨識之重要依據。在特徵值擷取上，信號經小波轉換後所得之大量小波係數，將隨著電力品質事故發生點之不同而改變。為減少代表電力暫態信號之特徵值，且能解決事故發生點不同所造成的問題，本文針對各階小波係數計算其頻譜能量，以保留原始信號特徵，而不受時間發生點的影響。
為測試本文所採用電力暫態信號之特徵值擷取法於辨識系統之辨識率，本文利用Matlab軟體模擬電力系統多種不同型態電力品質事故，且收集實際現場量測波形資料，作為測試波形，並利用倒傳遞網路與類神經模糊分類系統分別作為信號辨識與建構模糊規則庫。同時，為測試系統對雜訊多寡的容忍度，本文亦模擬不同程度的訊雜比信號，測試本系統是否可成功的辨識出所發生之電力品質事故，以觀察所使用之方法在實測資料上的適用性。經測試後，使用本文所建構之辨識系統，其辨識電力事故的能力與雜訊忍受度均可獲得不錯的效果。

關鍵字

小波轉換 ； 電力品質 ； 辨識 ； 類神經模糊網路 ； 類神經網路

並列摘要

With rapid developments of technology and wide uses of precise equipment as well as delicate electronic devices, much higher power quality (PQ) is required nowadays, especially the influence of the power transient events. To improve the power quality and find out the causes of the power transients, it needs to monitor the power signals extensively for a long period of time. Results of analyzing and recognizing the monitored signals can therefore be used as references to ameliorate the power quality.
There is many a method to analyze the power signals. Among these methods, Wavelet Transform (WT) approach has the abilities of multi-resolution analysis for both the time and the frequency domains. We may obtain the frequency information for different time points through the time-frequency diagrams using the WT. However, features of plenty of the WT coefficients may vary with occurring points of the PQ events. To reduce the amount of the features representing the power transients and solve the recognizing problems caused by different occurring points of the PQ events, spectrum energies of different scales of WT coefficients are calculated in the thesis. Through the proposed approaches, features of the original power signals can be reserved and not influenced by occurring points of the PQ events.
In order to test the recognizing abilities of the proposed feature extraction method in the classification systems, diverse patterns of PQ events are simulated via the Matlab software tools; besides, practical field data are collected as testing data. The artificial neural networks and fuzzy neural classification systems are used for signal recognition and fuzzy rules construction, respectively. In the mean time, to test noise tolerance of the proposed systems, signals with different degrees of signal-to-noise ratios are also simulated. Success rates of recognizing the PQ events from the noise-riding signals are investigated for feasibility evaluation in the practical applications. The testing results show that the classification systems proposed in the thesis have promising performance in recognizing the PQ events and the tolerance to noise.

並列關鍵字

Recognition ； Power Quality ； Neuro-Fuzzy Network ； Neural Network ； Wavelet

參考文獻

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