應用小波轉換及人工智慧進行 配電系統電容切換暫態位置之判斷

Application of Wavelet Transform and Artificial Intelligence for Identifying Locations of Switching Capacitor Transients

陳柏元(Po-Yuan Chen)

指導教授 ： 洪穎怡

中原大學/工學院/電機工程研究所/碩士(2005年)

https://doi.org/10.6840/cycu200500265

摘要

近幾年來，由於高科技產業的蓬勃發展，這些高科技產業大量使用精密的生產設備與測試儀器，使得供電品質的要求日益昇高，因此如何改善電力品質是電力公司與用戶皆要認真面對的問題。一般而言，電力品質問題中，包括了電壓驟昇、驟降、電力諧波、三相不平衡、頻率飄移等問題。此外，電力系統中的電磁暫態現象，如電容器切換會造成電壓暫態事故，電壓振幅可能因共振現象而產生過電壓暫態，這些暫態高電壓大電流可能會導致系統設備之誤動作及損毀，甚至會經由配電饋線之傳導而造成臨近網路的元件及暫態靈敏性負載中保護設備損毀或誤動作並造成重大損失，進而關係到責任歸屬及賠償等問題。因此電力公司在處理此類電力事故之原因釐清及責任歸屬等問題上，正確的暫態事故發生時間、確實位置與嚴重程度等資訊便相當重要。所以，對於電力公司要如何去得知暫態來源位置，並針對事故原因及責任歸屬問題做有效釐清，因此暫態源位置的判別將是一個刻不容緩的課題
本文將針對電容器暫態源位置辨識方法做研究並提出一個新方法。此法首先結合小波轉換與巴賽瓦定理做代表特徵值之擷取，然後運用模糊分類來討論量測儀表之數量及適當位置。最後利用類神經網路進行代表特徵值訓練並做暫態源位置之判斷。
最後本文利用十八個匯流排之電力系統作為測試對象，以Matlab6.5/NeuroSolutions 專業軟體來進行範例系統模擬、數理分析，模擬結果將可驗證本文方法之有效性及準確性。

關鍵字

巴賽瓦定理 ； 類神經網路 ； 小波轉換 ； 電容切換暫態現象 ； 模糊分類 ； 電力品質

並列摘要

In recent years, due to the rapid developments of the hi-tech industry as well as much more usages of the precise production equipments and test instruments, the far high power quality (PQ) is demanded nowadays. Hence, improvement of power quality is an important task for utility companies and their customers. Generally, power quality problems include voltage swell, voltage sag, power harmonic, three-phase imbalance, frequency variation and voltage flicker. Besides, the electromagnetic transient phenomenon of the power system, such as capacitor switching, can cause incident of the voltage and current transients that would result in over voltage transient due to the resonance phenomenon. The high voltage and current transient may result in damage of devices in the power systems and malfunction of protection equipment of sensitive loads. Therefore, capacitor switching transient is a serious threat to power electronic equipments in the viewpoint of PQ. Actually, the accurate location and time of PQ problem are useful for responsibility authority and accident correction Therefore, identifying and locating the locations of transient sources have attracted more attention of utility engineers and scholars.
This thesis presents a new method for efficiently locating the sources associated for utility capacitor switching transients. The proposed method first combines wavelet transform and Parserval theorem to extract the features of the transients. Then proper location number for metering measurements by fuzzy clustering is determined. Finally, the features and transient source location is trained by neural networks. Diverse patterns of PQ events are simulated by Matlab6.5/NeuroSolutions software
Finally, an 18-bus power system is used for testing, Simulation results obtained by using Matlab6.5/NeuroSolutions show that the proposed approach is effective and relatively accurate in comparison with existing approaches.

並列關鍵字

Fuzzy clustering ； Parserval Theorem ； Neural Network ； Power Quality ； Wavelet Transform ； Capacitor Switching Transients

參考文獻

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