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    請使用永久網址來引用或連結此文件: http://ir.lib.ncu.edu.tw/handle/987654321/95789


    題名: 基於共識性協調之智慧型控制策略於多聚落式微電網之韌性強化;Resilience Enhancement of Multiple Microgrid Clusters by Intelligent Control Strategy Based on Consensus Coordination
    作者: 蘇子銘;SU, ZI-MING
    貢獻者: 電機工程學系
    關鍵詞: 微電網集群;多項式派翠模糊類神經網路;共識性協調;韌性;電壓穩定性;頻率調節;microgrid clusters (MGCs);Polynomial Petri Fuzzy Neural Network (PPFNN);consensus control;operational resilience;voltage stability;frequency regulation
    日期: 2024-08-07
    上傳時間: 2024-10-09 17:16:57 (UTC+8)
    出版者: 國立中央大學
    摘要: 現代電力系統日益複雜,分散式能源(DERs)的日益整合,需要先進的控制策略來管理微電網集群(MGCs)。本研究探討了在MGCs中採用多項式派翠模糊神經網絡(PPFNN)控制器,以應對這些挑戰。PPFNN控制器結合了多項式理論、派翠網和模糊神經網絡的優勢,提供了一個強大的框架,用於互連微電網之間的動態共識和協調。傳統的控制方法往往難以應對微電網系統的動態和隨機特性。PPFNN控制器結合了模糊邏輯的強健性和神經網絡的學習能力,為維持電壓穩定、頻率調節和高效電力分配提供了優越的解決方案。本研究證明,採用PPFNN控制器不僅提高了微電網集群的運行韌性,還維持了電壓和頻率的穩定性,增強了系統對抗干擾的強健性。通過利用神經網絡的自適應學習能力和派翠網的邏輯結構,PPFNN控制器為現代微電網集群的實時運行需求提供了一個先進的解決方案,確保了一個具有韌性和高效的電力系統。通過實時模擬,本研究強調了控制器在處理各種情景時的有效性,從而提供了一個可擴展且可靠的現代能源網挑戰解決方案。;The growing complexity of modern power systems and the increasing integration of distributed energy resources (DERs) necessitate advanced control strategies for microgrid clusters (MGCs). This study investigates the adoption of Polynomial Petri Fuzzy Neural Network (PPFNN) based controller in MGCs to address these challenges. The PPFNN based controller combines the strengths of polynomial theory, Petri nets, and fuzzy neural networks, providing a robust framework for dynamic consensus and coordination among interconnected microgrids. Traditional control methods often fall short in dealing with the dynamic and stochastic nature of microgrid systems. The PPFNN based controller, with its ability to combine the robustness of fuzzy logic and the learning capabilities of neural networks, offers a superior solution for maintaining voltage stability, frequency regulation, and efficient power sharing. This study demonstrates that adopting PPFNN based controller not only improves the operational resilience of microgrid clusters but also maintains voltage and frequency stability, enhances system robustness against disturbances. By leveraging the adaptive learning capabilities of neural networks and the logical structuring of Petri nets, PPFNN based controller provides a sophisticated solution for the real-time operational demands of modern microgrid clusters, ensuring a resilient and efficient power system. Through real-time simulation, the research highlights the controller′s effectiveness in handling various scenarios, thus providing a scalable and reliable approach to modern energy grid challenges.
    顯示於類別:[電機工程研究所] 博碩士論文

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