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

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蘇子銘(ZI-MING SU) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

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)

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至系統瀏覽論文 (2029-8-1以後開放)

摘要(中)

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。

摘要(英)

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.

關鍵字(中)

★ 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

關鍵字(英)

★ microgrid clusters (MGCs)
★ Polynomial Petri Fuzzy Neural Network (PPFNN)
★ consensus control
★ operational resilience
★ voltage stability
★ frequency regulation

論文目次

目綠
摘要 i
ABSTRACT ii
誌謝 iii
目綠 iv
圖目綠 vii
表目錄 xiii
1 第一章緒論 1
1.1 研究動機 1
1.2 文獻回顧 1
1.3 論文大綱 3
2 第二章微電網與分散式電源介紹 4
2.1 介紹 4
2.2 微電網控制策略 4
2.2.1 定功率控制(P/Q control) 4
2.2.2 電壓頻率控制(V/F control) 4
2.2.3 主從控制 4
2.2.4 分級控制 5
2.2.5 共識性協調 7
2.3 微電網標準 7
2.3.1 IEEE 1547-2018 標準 7
2.4 分散式電源介紹 9
2.4.1 太陽能電池特性 9
2.4.2 儲能系統 12
3 第三章系統架構與控制策略 15
3.1 簡介 15
3.2 三相座標軸轉換 15
3.3 鎖相迴路 16
3.4 變流器之實、虛功控制與電流控制 17
3.5 電力系統架構與控制 18
3.5.1 系統架構與控制策略 18
3.5.2 主控制策略 20
3.5.3 從控制策略 22
3.5.4 預同步控制策略 24
4 第四章多項式派翠模糊類神經網路 25
4.1 簡介 25
4.2 多項式派翠模糊類神經網路架構 25
4.3 多項式派翠模糊類神經網路之線上學習法則 28
4.4 多項式派翠模糊類神經網路收斂性分析 31
5 第五章模擬情境 34
5.1 模擬情境 34
5.2 情境一之模擬結果與討論 34
5.3 情境二之模擬結果與討論 54
5.4 情境三之模擬結果與討論 60
6 第六章硬體迴圈與實驗結果 103
6.1 簡介 103
6.2 即時模擬系統 103
6.2.1 即時模擬介紹 103
6.2.2 OP4510及OP4512硬體 106
6.2.3 軟體介面 RT-LAB 109
6.2.4 模型分割 111
6.2.5 硬體迴圈規劃 114
6.3 IEC-61850 通訊協定 116
6.4 實驗結果 117
6.4.1 情境一之實驗結果 117
6.4.2 情境二之實驗結果 136
6.4.3 情境三之實驗結果 142
7 第七章結果與未來展望 176
7.1 結論 176
7.2 未來展望 176
參考文獻 177
作者簡歷 183

參考文獻

[1] IEEE Power and Energy Society, IEEE Standard for the Specification of Microgrid Controllers IEEE Standard for the Specification of Microgrid Controllers, IEEE Standard 2030. 7/8, 2017, pp. 1–42.
[2] J. S. Gomez, D. Saez, J. W. Simpson-Porco, and R. Cardenas, “Distributed predictive control for frequency and voltage regulation in microgrids,” IEEE Trans. Smart Grid, vol. 11, no. 2, pp. 1319–1329, Mar. 2020.
[3] J. W. Simpson-Porco, Q. Shafiee, F. Dörfler, J. C. Vasquez, J. M. Guerrero, and F. Bullo, “Secondary frequency and voltage control of islanded microgrids via distributed averaging,” IEEE Trans. Ind.Electron., vol. 62, no. 11, pp. 7025–7038, May. 2015.
[4] Y. Han, K. Zhang, H. Li, E. A. A. Coelho, and J. M. Guerrero, “MAS-based distributed coordinated control and optimization in microgrid and microgrid clusters: A comprehensive overview,” IEEE Trans.Power Electron., vol. 33, no. 8, pp. 6488–6508, Aug. 2018.
[5] E. Espina, J. Llanos, C. Burgos-Mellado, R. Cardenas-Dobson, M. Martinez-Gomez, and D. Saez, “Distributed control strategies for microgrids: An overview,” IEEE Access, vol. 8, pp. 193412–193448, 2020.
[6] Y. Xia, W. Wei, M. Yu, X. Wang, and Y. Peng, “Power management for a hybrid AC/DC microgrid with multiple subgrids,” IEEE Trans. Power Electron., vol. 33, no. 4, pp. 3520–3533, Apr. 2018.
[7] M. Hosseinzadeh and F. R. Salmasi, “Robust optimal power management system for a hybrid AC/DC microgrid,” IEEE Trans. Sustain. Energy, vol. 6, no. 3, pp. 675–687, Jul. 2015.
[8] M. Manbachi and M. Ordonez, “AMI-based energy management for islanded AC/DC microgrids utilizing energy conservation and optimization,” IEEE Trans. Smart Grid, vol. 10, no. 1, pp. 293–304, Jan. 2019.
[9] P. Wang, X. Lu, X. Yang, W. Wang, and D. Xu, “An improved distributed secondary control method for DC microgrids with enhanced dynamic current sharing performance,” IEEE Trans. Power Electron., vol.31, no. 9, pp. 6658–6673, Sep. 2016.
[10] J. Llanos, D. E. Olivares, J. W. Simpson-Porco, M. Kazerani, and D. Saez, “A novel distributed control strategy for optimal dispatch of isolated microgrids considering congestion,” IEEE Trans. Smart Grid, vol.10, no. 6, pp. 6595–6606, Nov. 2019.
[11] A. Navas, J. S. Gómez, J. Llanos, E. Rute, D. Saez, and M. Sumner, “Distributed predictive control strategy for frequency restoration of microgrids considering optimal dispatch,” IEEE Trans. Smart Grid, vol. 12, no. 4, pp. 2748–2759, Jul. 2021.
[12] M. J. Rana and M. A. Abido, “Energy management in DC microgrid with energy storage and model predictive controlled AC–DC converter,” IET Gener., Transmiss. Distrib., vol. 11, no. 15, pp. 3694–3702, Oct. 2017.
[13] C. H. Lin, “Adaptive recurrent fuzzy neural network control for synchronous reluctance motor servo drive,” IET Electr. Power Appl., vol. 151, no. 6, pp. 711–724, Nov. 2004.
[14] S. Y. Chen, H. Chiang, T. Liu, and C. Chang, “Precision Motion Control of Permanent Magnet Linear Synchronous Motors Using Adaptive Fuzzy Fractional-Order Sliding-Mode Control,” IEEE/ASME Trans.Mechatronics, vol. 24, no. 2, pp. 741-752, Apr. 2019.
[15] Z. Zhang and Z. Yan, “An Adaptive Fuzzy Recurrent Neural Network for Solving the Nonrepetitive Motion Problem of Redundant Robot Manipulators,” IEEE Trans. Fuzzy Syst., vol. 28, no. 4, pp. 684-691, Apr. 2020.
[16] L. A. M. Riascos, F. G. Cozman, and P. E. Miyagi, ‘‘Detection and treatment of faults in automated machines based on Petri nets and Bayesian networks,’’ Proc. IEEE Int. Symp. Ind. Electron., Jun. 2003, pp. 729–734.
[17] T. Nishi and Y. Tanaka, ‘‘Petri net decomposition approach for dispatching and conflict-free routing of bidirectional automated guided vehicle systems,’’ IEEE Trans. Syst., Man, Cybern. A, Syst. Humans, vol. 42, no. 5, pp. 1230–1243, Sep. 2012.
[18] Y. Du, L. Qi and M. Zhou, "Analysis and Application of Logical Petri Nets to E-Commerce Systems,"IEEE Trans. Syst., Man, Cybern. A, Syst. Humans, vol. 44, no. 4, pp. 468-481, Apr. 2014.
[19] K. H. Tan and T. Tseng, “Seamless Switching and Grid reconnection of Microgrid using Petri recurrent wavelet fuzzy neural network,” IEEE Trans. Power Electron., vol. 36, no. 10, pp. 11847–11861, Oct. 2021.
[20] R. Davidrajuh, "Extracting Petri Modules from Large and Legacy Petri Net Models," IEEE Access, vol. 8, pp. 156539-156556, 2020.
[21] A. G. Ivakhnenko, “Polynomial theory of complex systems,” IEEE Trans. Syst., Man, Cybern., vol. SMC-1, no. 4, pp. 364–378, Oct. 1971.
[22] S. Oh, W. Pedrycz, and B. Park, “Polynomial neural networks architecture: Analysis and design,” Comput.Electr. Eng., vol. 29, no. 6, pp. 703–725, Aug. 2003.
[23] W. Huang, S. Oh, and W. Pedrycz, “Fuzzy wavelet polynomial neural networks: Analysis and design,”IEEE Trans. Fuzzy Syst., vol. 25, no. 5, pp. 1329–1341, Oct. 2017.
[24] Z. Wang, S. -K. Oh, E. -H. Kim, Z. Fu and W. Pedrycz, "Hierarchically Reorganized Multi-Layer Fuzzy Neural Networks Architecture Driven with the Aid of Node Selection Strategies and Structural Network Optimization," IEEE Access, vol. 10, pp. 7772-7792, 2022.
[25] R. A. Badwawi, W. R. Issa, T. K. Mallick, and M. Abusara, “Supervisory control for power management of an islanded AC microgrid using a frequency signalling-based fuzzy logic controller,” IEEE Trans. Sustainable Energy, vol. 10, no. 1, pp. 94-104, Jan. 2019.
[27] Y. Han, K. Zhang, H. Li, E. A. A. Coelho and J. M. Guerrero, “MAS-based distributed coordinated control and optimization in microgrid and microgrid clusters: a comprehensive overview,” IEEE Trans. Power Electronics, vol. 33, no. 8, pp. 6488-6508, Aug. 2018.
[26] D. E. Olivares, A. M. Sani, A. H. Etemadi, C. A. Cañizares, R. Iravani, M. Kazerani, A. H. Hajimiragha, O. G. Bellmunt, M. Saeedifard, R. P. Behnke, G. A. J. Estévez, and N. D. Hatziargyriou, “Trends in microgrid control,” IEEE Trans. Smart Grid, vol. 5, no. 4, pp. 1905-1919, Jul. 2014.
[27] Y. Han, K. Zhang, H. Li, E. A. A. Coelho and J. M. Guerrero, “MAS-based distributed coordinated control and optimization in microgrid and microgrid clusters: a comprehensive overview,” IEEE Trans. Power Electronics, vol. 33, no. 8, pp. 6488-6508, Aug. 2018.
[28] J. M. Guerrero, J. C. Vasquez, J. Matas, L. G. de Vicuna and M. Castilla, “Hierarchical control of droop-controlled AC and DC microgrids—a general approach toward standardization,” IEEE Trans. Ind. Electron., vol. 58, no. 1, pp. 158-172, Jan. 2011.
[29] K. Yu, Q. Ai, S. Wang, J. Ni, and T. Lv, “Analysis and optimization of droop controller for microgrid system based on small-signal dynamic model,” IEEE Trans. Smart Grid, vol. 7, no. 2, pp. 695-705, Mar. 2016.
[30] L. Meng, Q. Shafiee, G. F. Trecate, H. Karimi, D. Fulwani, X. Lu, and J. M. Guerrero, “Review on control of DC microgrids and multiple microgrid clusters,” IEEE J. Emerg. Sel. Top. Power Electron., vol. 5, no. 3, pp. 928–948, Sep. 2017.
[31] IEEE Standard 1547-2018, “IEEE Standard for interconnecting distributed resources with electric power systems,” IEEE Standard, New York, USA, pp. 1-16, 2018
[32] 談光雄:〈電網之運轉與智慧型控制〉。博士論文，電機電子工程學系，國防大學理工學院國防科學研究所，民國102年。
[33] 楊柏輝:〈應用單級轉換器之太陽能光電系統實虛功控制策略〉。碩士論文，電機工程學系，國立中央大學，民國105年。
[34] K. Ishaque, Z. Salam, M. Amjad, and S. Mekhilef, “An improved particle swarm optimization (PSO)-based MPPT for PV with reduced steady-state oscillation,” IEEE Trans. Power Electron., vol. 27, no. 8, pp. 3627-3638, Aug. 2012.
[35] 謝錦隆，薛康琳，鍾岳霖，戴志揚:〈臺灣風力發電與液流電池系統儲電情境模擬〉。臺灣能源期刊第三卷第一期第55-78頁，中華民國105年3月。
[36] X. Huang and B. Jiang, “Research on lithium battery energy storage system in wind power,” in Proc. International Conf. Electrical and Control Engineering, Yichang, pp. 1200-1203, 2011.
[37] 黃仲欽:〈交流電動機控制〉。交流電動機課程講義，民國97年。
[38] 石承民:〈結合虛擬慣量併網型微電網之智慧型控制〉。碩士論文，電機工程學系，國立中央大學，民國108年。
[39] C. Dufour and J. Bélanger, “On the use of real-time simulation technology in smart grid research and development,” in Proc. IEEE Energy Conversion Congress and Exposition, Denver, CO, 2013, pp. 2982-2989.
[40] A. Subburaj, A. R. Arra, and S. Bayne, “Stability analysis of A.C. and D.C. microgrids using OPAL-real time digital simulator,” in Proc. Ninth Annual IEEE Green Technologies Conf. (GreenTech), Denver, CO, pp. 39-45, 2017.
[41] O. Krishan and S. Suhag, “Power management control strategy for hybrid energy storage system in a grid-independent hybrid renewable energy system: a hardware-in-loop real-time verification,” IET Renewable Power Generation, vol. 14, no. 3, pp. 454-465, 24 2 2020.
[42] S. Sharma, V. Verma and R. K. Behera, “Real-time implementation of shunt active power filter with reduced sensors,” IEEE Trans. Industry Applications, vol. 56, no. 2, pp. 1850-1861, Mar.-Apr. 2020.
[43] C. A. Caldeira, A. D. D. de Almeida, H. R. Schlickmann, C. S. Gehrke, and F. Salvadori, “Impact analysis of the BESS insertion in electric grid using real-time simulation,” in Proc. IEEE PES Innovative Smart Grid Technologies Conf. - Latin America (ISGT Latin America), Gramado, Brazil, pp. 1-6, 2019.
[44] C. Dufour, S. Cense, and J. Belanger, “An FPGA HIL reconfigurable testing platform for vehicular traction systems,” in Proc. IEEE Vehicle Power and Propulsion Conf. (VPPC), Coimbra, pp. 1-4, 2014.
[45] S. Abourida, J. Belanger, and C. Dufour, “Real-time HIL simulation of a complete PMSM drive at 10 /spl mu/s time step,” in Proc. European Conf. Power Electronics and Applications, Dresden, pp. 9 pp.-P. 9, 2005.
[46] D. Bian, M. Kuzlu, M. Pipattanasomporn, S. Rahman, and Y. Wu, “Real-time co-simulation platform using OPAL-RT and OPNET for analyzing smart grid performance,” in Proc. IEEE Power & Energy Society General Meeting, Denver, CO, pp. 1-5, 2015.
[47] 劉邦威:〈應用即時模擬技術於交流微電網之建模與模擬〉。碩士論文，電機工程學系，國立中正大學，民國101年。
[48] Opal-RT Technologies Inc., RT LAB Version 11.3, User’s Guide.
[49] OP4510使用教學，思渤科技，2019。
[64] Opal-RT Technologies Inc., ARTEMIS Version 7.3, User’s Guide.
[50] Opal-RT Technologies Inc., eHS User’s Guide.
[51] C. Dufour, S. Cense, T. Ould-Bachir, L. Grégoire, and J. Bélanger, “General-purpose reconfigurable low-latency electric circuit and motor drive solver on FPGA,” in Proc. IECON 2012 - 38th Annual Conf. IEEE Industrial Electronics Society, Montreal, QC, pp. 3073-3081, 2012.
[52] 柯廷翰:〈考慮配電系統三相故障之具低電壓穿越能力之智慧型太陽光電系統〉。碩士論文，電機工程學系，國立中央大學，民國102年。
[53] Opal-RT Technologies Inc., ARTEMIS Version 7.3, User’s Guide.
[54] IEC-61850使用教學，思渤科技，2023。
[55] L. Qi, W. Luan, X. S. Lu, and X. Guo, "Shared P-Type Logic Petri Net Composition and Property Analysis: A Vector Computational Method," IEEE Access, vol. 8, pp. 34644-34653, 2020.
[56] K. H. Tan, "Squirrel Cage Induction Generator System Using Wavelet Petri Fuzzy Neural Network Control for Wind Power Applications," IEEE Transactions on Power Electronics, vol. 31 pp. 1-1, 2015.
[57] W. Luan, L. Qi, Z. Zhao, J. Liu, and Y. Du, "Logic Petri Net Synthesis for Cooperative Systems," IEEE Access, vol. 7, pp. 161937-161948, 2019.
[58] E. A. Alzalab, A. M. El-Sherbeeny, M. A. El-Meligy, and H. T. Rauf, "Trust-Based Petri Net Model for Fault Detection and Treatment in Automated Manufacturing Systems," IEEE Access, vol. 9, pp. 157997-158009, 2021.
[59] X. Gao and X. Hu, "A Petri Net Neural Network Robust Control for New Paste Backfill Process Model," IEEE Access, vol. 8, pp. 18420-18425, 2020.
[60] R. J. Rodriguez, S. Bernardi, and A. Zimmermann, "An Evaluation Framework for Comparative Analysis of Generalized Stochastic Petri Net Simulation Techniques," IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 50, no. 8, pp. 2834-2844, 2020.
[61] 蕭果登:〈以OPAL-RT硬體迴圈實現微電網之智慧型控制〉。碩士論文，電機工程學系，國立中央大學，民國109年。

指導教授

林法正(Faa-Jeng Lin)

審核日期

2024-8-7

推文