電力品質監測之研究已躍為當今供電產業與高科技工業之發展重點，若能有效掌握各種電力品質污染源，並即時提出因應對策，將可大幅減少電費支出，且有助於機器運轉壽命之延續。故本文之研究主旨即在於提出一個具有高度計算能力之時頻辨識方法，整合小波轉換與Hilbert轉換於電力訊號之時頻分析，同時提出相對應之VLSI架構，並輔以心臟收縮陣列架構予以設計，期能透過場規劃邏輯陣列將其硬體電路實現，並結合類比數位轉換模組，完整創新建構電力品質監測系統；又為驗證本研究方法之實用性，本文分別應用所提演算法及硬體電路評估驗證電壓中斷、電壓驟降驟昇、諧波及電容切換暫態等電力品質訊號之監測效能，測試結果輔以佐證本文所提方法確具良好之時頻定位能力，且應有助於電力訊號之分析與檢測。

Electric power quality monitoring has emerged to be a critical issue from perspectives of power producers and high-tech industries. By grasping the polluted source as well as adopting efficient strategies, electricity cost can be significantly reduced, while the life of equipment will become longer. In view of this importance, a novel method with high-efficiency time-frequency computation is proposed in this thesis, in which the wavelet transform is integrated with Hilbert transform such that the monitoring performance is significantly improved. Meanwhile, a systolic array-based design of field programmable gate array (FPGA) is also employed to reach the hardware realization, where the analog-to-digital circuit is combined such that a platform of electric power quality monitoring is overall realized. In order to assess the performance of this prototype, signals including voltage interruption, voltage sag, voltage swell, voltage harmonics, and capacitor-switching transients are all tested. Software simulations and experimental results help support the proposed method for the application considered.

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