| DC 欄位 |
值 |
語言 |
| DC.contributor | 電機工程學系 | zh_TW |
| DC.creator | 李昀儒 | zh_TW |
| DC.creator | Yun-Ju Li | en_US |
| dc.date.accessioned | 2024-8-8T07:39:07Z | |
| dc.date.available | 2024-8-8T07:39:07Z | |
| dc.date.issued | 2024 | |
| dc.identifier.uri | http://ir.lib.ncu.edu.tw:444/thesis/view_etd.asp?URN=111521097 | |
| dc.contributor.department | 電機工程學系 | zh_TW |
| DC.description | 國立中央大學 | zh_TW |
| DC.description | National Central University | en_US |
| dc.description.abstract | 在本研究中，提出了一種智慧型控制之動態電壓調節器(Dynamic Voltage Restorer, DVR）來穩定電網電壓在電壓驟升、驟降和不平衡時之負載電壓。由於意外情況、負載變化以及基於再生能源之分散式發電機（Distributed Generators, DGs）高滲透率，異常的電網電壓將嚴重導致設備損壞和敏感負載跳脫。因此，開發了一種DVR來在異常電網電壓條件下以穩定負載電壓。所開發的DVR係基於同步旋轉座標軸方法，並採用雙二階廣義積分鎖相迴路（Dual Second-Order Generalized-Integrator-Phase-Locked-Loop, DSOGI-PLL）進行電網同步。此外，本文所發展的DVR在不同負載中使用兩種不同控制策略，分別為，同相補償策略和能量優化補償策略。再者，為了有效提高所開發DVR的電壓補償性能，首次提出了兩種新型柴比雪夫機率模糊神經網路（Chebyshev probabilistic fuzzy neural network, CPFNN）控制器和柴比雪夫派翠機率模糊神經網路（Chebyshev Petri probabilistic fuzzy neural network, CPPFNN）來取代傳統的比例積分諧振（Proportional-Integral-Resonant , PIR）、傳統比例積分（Proportional-Integral, PI）和模糊類神經網路（Fuzzy Neural Network, FNN）控制器。詳細推導了所提出的CPFNN和CPPFNN控制器的網絡結構和線上學習算法。最後，藉由實驗結果驗證使用所提出的CPFNN和CPPFNN控制器的智慧型DVR在電網電壓驟升、驟降和不平衡時對負載電壓改善的有效性。 | zh_TW |
| dc.description.abstract | An intelligent dynamic voltage restorer (DVR) is proposed in this study to stabilize and balance the load voltage during grid voltage swell, sag and imbalance. Owing to the contingency, unexpected load change and the high penetration rate of the renewable energy source-based distributed generators (DGs), the abnormal grid voltage conditions will severely lead to the equipment damage and sensitive loads tripping. Thus, a DVR is developed to stabilize the load voltage during the abnormal grid voltage conditions. The developed DVR is based on the synchronous reference frame and the dual second-order generalized integrator phase locked loop (DSOGI-PLL) is adopted for the grid synchronization in this study. Moreover, the developed DVR adapts the in-phase method and energy optimized method in different loads. To effectively improve the voltage compensation performance of the developed DVR, two novel Chebyshev probabilistic fuzzy neural network (CPFNN) controllers and Chebyshev Petri probabilistic fuzzy neural network (CPPFNN) are firstly proposed to replace the traditional proportional-integral-resonant (PIR), traditional proportional-integral (PI) and fuzzy neural network (FNN) controllers. The network structure and the online learning algorithm of the proposed CPFNN and CPPFNN controllers are derived in detailed. Finally, the effectiveness of the intelligent DVR using the proposed CPFNN or CPPFNN controllers for the load voltage improvement during grid voltage swell, sag and imbalance are certified by some experimental results. | en_US |
| DC.subject | 動態電壓調節器 | zh_TW |
| DC.subject | 電力系統故障 | zh_TW |
| DC.subject | 同相補償 | zh_TW |
| DC.subject | 能量優化補償 | zh_TW |
| DC.subject | 柴比雪夫派翠機率模糊類神經網路 | zh_TW |
| DC.subject | DVR | en_US |
| DC.subject | in-phase compensation | en_US |
| DC.subject | energy-optimized compensation | en_US |
| DC.subject | power system faults | en_US |
| DC.subject | Chebyshev Petri probabilistic fuzzy neural network(CPPFNN) | en_US |
| DC.title | 智慧型控制動態電壓調節器 改善負載電壓穩定性 | zh_TW |
| dc.language.iso | zh-TW | zh-TW |
| DC.title | Intelligent Controlled Dynamic Voltage Restorer for Improving Load Voltage Stability | en_US |
| DC.type | 博碩士論文 | zh_TW |
| DC.type | thesis | en_US |
| DC.publisher | National Central University | en_US |