中文摘要
儘管科技日新月異，使得電力品質相關研究始終為電力系統安全運轉之重要議題。且於現今電力系統中，大量再生能源併入電網、智慧電網高速發展以及電力電子設備的大量使用導致了許多電力品質問題。其中電壓及電流諧波、電壓驟降與驟升等皆會對敏感性負載造成影響，而當系統電壓改變時亦會導致敏感性負載受到影響甚至毀壞。綜上所述，電力品質問題對於電力系統可靠性與安全運轉至關重要。因此為提高具有非線性與敏感性負載之配電系統運轉效率，本文提出基於人工神經網路及PI控制之動態電壓恢復器(DVR)。DVR為一分散式彈性交流輸電系統設備，用於解決因電壓、電流或頻率不穩定導致之電網問題。以提高電壓之方式保持配電網中電壓之穩定度，是解決電力品質問題中效益最高之方式。
本文使用MATLAB/Simulink軟體進行模擬，並驗證所提出DVR技術於改善因諧波導致電壓失真之可行性。使用具有可程式電源之電力系統模型以緩解電壓驟降、驟升、3階及5階諧波。並評估具有DVR與不具有DVR之可程式電源之電力系統模型對負載電壓之響應。而本文亦使用人工神經網路調整電壓信號之d-q軸及DVR中之電壓源轉換器(VSI)之絕緣閘極雙極性電晶體(IGBT)脈衝信號。加入兩個PI控制器並透過DVR-VSI脈衝於不同操作狀況下之負載電壓管理，並將蒐集之負載電壓數據提供人工神經網路(ANN)之模型訓練輸入/輸出。當電壓訊號分別存在3階及5階諧波時，其總諧波失真率(THD)分別為25%及36%，而在本文研究中，使用基於人工神經網路之DVR時可成功將上述之情形之總諧波失真率降低至2%以下。

Abstract
Power quality is an important topic in today’s power system that can have an impact on consumers and utilities. The integration of renewable energy sources, smart grid technologies, and extensive use of power electronics equipment in the recent electric power system causes a massive of problems. Harmonics in current and voltage, voltage sag, and swell can all affect sensitive loads. Interference with other elements of the system can cause input voltage variations, making these devices vulnerable. As a result, in the present day, with the expansion of sensitive and expensive electronic equipment, power quality is critical for the reliable and safe operation of the power system. To improve the performance of a distribution power system, which contains non-linear and sensitive load, this paper offers an Artificial Neural Network (ANN) and PI controllers for a Dynamic Voltage Restorer (DVR). The DVR is a prospective Distribution Flexible AC Transmission System (D-FACTS) device that is frequently used to solve difficulties in the distribution grid caused by non-standard voltage, current, or frequency. It injects voltages via the injection transformer into the distribution line to keep the voltage profile and the load voltage constant. The DVR is the most cost-effective way to solve voltage-related PQ issues.
The simulations were run in MATLAB/Simulink to demonstrate the effectiveness of the DVR-based proposed technique in smoothing the distorted voltage caused by harmonics. To accommodate voltage sag, swell, 3rd and 5th harmonics, a power system model with a programmable power source was used. The response of the systems to load voltage is assessed in both DVR and non-DVR scenarios. The suggested DVR-based controller was found to be successful in managing voltage distortion and achieving a smooth compensated load voltage.
Two ANNs are applied to regulate the D-Q axes voltage signals and adjust the IGBT pulses of the voltage source inverter (VSI) used to operate DVR. Two PI controllers also, introduced for managing the load voltage by DVR-VSI pulses at distinct abnormal operation circumstances, provide the input/output data necessary to train ANNs. With insertion 3rd and 5th harmonics in the supply voltage, the load voltage THD percentage was around 25% and 36%, respectively. THD was decreased by less than 2% in both cases when the suggested DVR based ANN was included. So the system performance with the proposed ANN-DVR controller is enhanced.

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