由於科技產業的蓬勃發展，使得數位或類比訊號處理裝置所需分析的資料量大幅增加。然受到儀器故障、設備元件特性等作用，電力訊號內含異常干擾成分的機率卻大幅升高。因異常干擾訊號所影響之層面相當廣泛，故為了增加分析的精確度，異常干擾訊號應予以辨識，以便採取預警措施，以掌握系統運轉的可靠度。
本論文係應用現場可規劃邏輯閘陣列(Field-programmable gate array, FPGA)完成以箱形計數法為輔之碎形幾何技術之硬體實現。由於碎形幾何技術可將動態渾沌特性予以量化，因此文中係嵌入碎形維度計算於整個偵測過程，並藉由硬體描述語言將其實現於FPGA晶片，進而提昇干擾訊號之檢測能力。文中將所提偵測模式測試於電力系統波動之檢測，藉由模擬實測結果，證實該方法在訊號檢測上確具高可行性及實用價值。

Because of the fast development of high-tech industry, the amount of data generated either from digital device or analog devices has been increased significantly. Particularly, the data to be analyzed are often affected by the characteristics of faulted equipment or elements. In other words, the unexpected disturbances may largely downgrade the accuracy of the data analysis. Hence, an effective approach that can help distinguish different disturbances would be useful to forewarn the operators, increasing the reliability of the system operation.
In this paper, a box counting-based fractal geometry approach is proposed for the disturbance detection, which was hardware-implemented by using the field programmable gate array. In the proposed approach, because the fractal geometry help quantize the behaviors of chaotics, a fractal dimension computation procedures has been embedded into the FPGA chips such that the disturbance-detection capability can be improved to a large scale. This detection approach has been tested on several power system disturbances. With the test results, they have validated the feasibility and practical values presented by the method for the application investigated.

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