電力系統在正常運轉時，常常會因不穩定之暫態現象導致系統出現過電壓之情形，而其中又以鐵磁共振現象所帶給電力設備之危害較為嚴重。鐵磁共振是一種複雜性高且難以預知之暫態現象，常被相關運轉人員及維護人員忽略。本研究應用小波轉換來分析鐵磁共振現象，並發展出一套將電壓經由小波分析後之高頻成分找出適當之能量門檻值，且僅需三個週期之短暫時間即可偵測系統有無發生鐵磁共振現象之演算法。最後利用Matlab/Simulink建立超高壓變電所之降壓變壓器、發電廠之升壓變壓器及一次變電所之比壓器等三種範例系統來驗證其鐵磁共振偵測演算法之可靠度，並模擬智慧型電子裝置(Intelligent Electronic Device, IED)具有其演算法。經由模擬結果可得當系統發生鐵磁共振時，三個週期後IED即發出跳脫信號至斷路器，使斷路器進行跳脫之動作。另外，此演算法亦不會因系統電源含有諧波成分或一般系統之正常開關切換等暫態現象導致偵測錯誤。

In power system operation, unstable transient phenomena can cause overvoltage circumstances. Among these transient phenomena, ferroresonance is the most severe one that harms power equipments. The operation and maintenance people usually do not know or ignore it because that is a little bit complicated and hard to predict. This thesis mainly applied wavelet transform to analyze ferroresonance phenomena. The algorithm is developed and based on the high frequency voltage component of wavelet analysis. Furthermore, the threshold energy is further calculated in order to identify the ferroresonance transient as soon as possible. Finally, three example systems, including the step-down transformer in Extra High Voltage (EHV) substation, the step-up transformer in power plant and the Potential Transformer (PT) in Primary Substation (P/S), are established to verify the validity of algorithms by Matlab/Simulink. It is found that in case of the Intelligent Electronic Device (IED) embedded with the proposed algorithm, the IED can exactly sent a tripping signal to circuit breaker within three cycles once the ferroresonance phenomenon was detected. Moreover, the proposed algorithm can differentiated the transients other than ferroresonance such as harmonics or normal switching transients to avoid the mistripping of the IED.

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