靜態開關是微型電網中的重要設備,當微型電網系統發生故障,靜態開關須切離故障區域,以確保微型電網能正常運行。 為了能精確地判定故障發生,本論文提出一個結合派克轉換、小波轉換與Takagi-Sugeno-Kang(TSK)模糊理論之故障偵測方法,此方法亦可求得故障種類。同時使用Xilinx System Generator將提出之方法實現於FPGA晶片,最後將整個微型電網系統連同FPGA晶片放入RT-LAB環境下以實現硬體迴圈模擬。 從模擬結果可得知,本論文提出之方法可精確偵測到故障發生與判定故障種類,靜態開關也能正確切離,同時能即時模擬出微型電網各種情境與響應狀態。
A static switch is an important device in a micro-grid. In order to ensure the micro-grid can operate normally, the static switch must disconnect the faulted zone from the unfaulted zones. This thesis presents a fault detection/classification method which integrates Park transform, wavelet transform and Takagi-Sugeno-Kang fuzzy reasoning. This work uses an FPGA chip to realize the presented method by Xilinx System Generator. Finally, the developed FPGA incorporating with the micro-grid modeled using Simulink is studied in a hardware-in-the-loop simulation using RT-LAB. From simulation results, it is can be found that that the presented method can detect the fault occurrence and conduct the fault classification accurately. The static switch can disconnect the faulted zone correctly. All simulations are performed considering various situations and the results can be attained in real-time environment.