利用智慧型估測之可變係數下垂控制微電網

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姓名

翁祥瑀(Xiang-Yu Weng) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

利用智慧型估測之可變係數下垂控制微電網
(Variable Coefficient Droop Controlled Microgrid Using Intelligent Estimation)

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至系統瀏覽論文 (2028-8-1以後開放)

摘要(中)

本文提出一種變係數下垂控制策略應用於儲能系統及太陽能光電系統並聯運行的情況下，利用儲能系統之實功率/角頻率P-w下垂控制方程式進行反推的方式來估測下垂係數，以此改善微電網實功率及頻率在負載變化時之暫態響應。此外，為了更有效改善微電網實功率及頻率在負載變化下之暫態響應，本文提出了具有線上訓練能力的柴比雪夫派翠模糊神經網路(Chebyshev Petri Fuzzy Neural Network, CPFNN)以用於取代傳統的比例積分(Proportional-Integral, PI)控制器，並且本文詳細推導所提出的CPFNN之網路架構和線上學習策略。最後，本文利用模擬及實驗以驗證所提出之變係數下垂控制策略結合CPFNN於微電網中改善實功率及頻率之暫態響應的有效性

摘要(英)

A variable coefficient droop control strategies applied to the parallel connection of energy storage system and solar system in this study, and uses the real power/angular frequency P-w droop control equation of the energy storage system to estimate the droop coefficient , so as to improve the transient response of the real power output and frequency of the microgrid when the load changes. In addition, in order to more effectively improve the transient response of the real power output and frequency of the microgrid under load changes, this paper proposes a Chebyshev Petri Fuzzy Neural Network (CPFNN) with online training capabilities for replaces the traditional Proportional-Integral (PI) controller, and this paper deduces the network architecture and online learning strategy of the proposed CPFNN in detail. Finally, this paper uses simulation and experiments to verify the effectiveness of the proposed variable coefficient droop control strategy combined with CPFNN to improve the transient response of real power and frequency in microgrids.

關鍵字(中)

★ 變係數下垂控制
★ 並聯變流器
★ 柴比雪夫派翠模糊神經網路
★ 負載變動

關鍵字(英)

★ variable coefficient droop control
★ parallel inverter
★ Chaybyshev petri fuzzy neural network
★ load change

論文目次

摘要 I
Abstract II
誌謝 III
目錄 IV
圖目錄 VIII
表目錄 XIII
第一章緒論 1
1.1 研究背景與動機 1
1.2 文獻回顧 2
1.3 論文大綱 6
1.4 本文貢獻 7
第二章微電網規範與控制策略介紹 8
2.1 微電網規範 8
2.1.1 IEEE 929-2000規範 8
2.1.2 IEEE 1547-2018規範 9
2.2 微電網控制策略 10
2.2.1 主從控制 10
2.2.2 下垂控制 11
第三章系統架構與控制策略 13
3.1 簡介 13
3.2 三相座標軸轉換 13
3.3 鎖相迴路 16
3.4 實功率與虛功率之計算 17
3.5 PI控制器 18
3.6 低通濾波器 19
3.7 下垂控制策略與變係數下垂控制策略 21
3.7.1 下垂控制策略 22
3.7.2 變係數下垂控制策略 26
第四章柴比雪夫派翠模糊類神經網路 30
4.1 簡介 30
4.2 柴比雪夫派翠模糊類神經網路架構 30
4.3 柴比雪夫派翠模糊類神經網路線上學習法則 35
4.4 柴比雪夫派翠模糊類神經網路收斂性分析 37
第五章模擬結果 40
5.1 變係數下垂控制策略之模擬結果 40
5.1.1 情境一:固定的下垂係數，負載變化為1 kW-2 kW之模擬結果 42
5.1.2 情境二:固定的下垂係數與估測的下垂係數，負載變化為1 kW-2 kW之模擬結果 46
5.1.3 情境三:固定的下垂係數與估測的下垂係數，負載變化為1.25 kW-3.25 kW-2.25 kW之模擬結果 53
第六章硬體規劃與實驗結果 60
6.1 簡介 60
6.2 儲能系統介紹 61
6.2.1 磷酸鋰鐵電池 61
6.2.2 電池保護裝置 62
6.2.3 電池平衡裝置 63
6.3 儲能系統硬體設備 65
6.3.1 儲能系統變流器 66
6.3.2 電阻負載之規劃 68
6.4 儲能系統週邊電路 69
6.4.1 交流電流回授電路 70
6.4.2 交流電壓回授電路 71
6.4.3 直流電壓回授電路 72
6.4.4 過電壓與過電流保護裝置 73
6.4.5 開關互鎖電路 74
6.4.6 數位訊號處理器 78
6.4.7 DAC電路 81
6.5 太陽能光電系統硬體設備 84
6.5.1 可程控直流電源供應器 85
6.5.2 太陽能光電系統變流器 85
6.5.3 太陽能光電系統電流控制迴路 88
6.5.4 資料擷取卡 89
6.6 變係數下垂控制策略之實驗結果 91
6.6.1 情境一:固定的下垂係數，負載變化為1 kW-2 kW之實驗結果 94
6.6.2 情境二:固定的下垂係數與估測的下垂係數，負載變化為1 kW-2 kW之實驗結果 98
6.6.3 情境三:固定的下垂係數與估測的下垂係數，負載變化為1.25 kW-3.25 kW-2.25 kW之實驗結果 105
第七章結論與未來展望 112
7.1 結論 112
7.2 未來展望 113
參考文獻 114
作者簡歷 120

參考文獻

[1] D. E. Olivares, A. Mehrizi-Sani, A. H. Etemadi, C. A. Canizares, R. Iravani, M. Kazerani, A. H. Hajimiragha, O. Gomis-Bellmunt, M. Saeedifard, R. Palma-Behnke, G. A. Jimenez-Estevez, and N. D. Hatziargyriou, “Trends in microgrid control,” IEEE Trans. Smart Grid, vol. 5, no. 4, pp. 1905-1919, Jul. 2014.
[2] 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.
[3] B. Zhao, X. Zhang, and J. Chaen, “Integrated microgrid laboratory system,” IEEE Trans. Power Syst., Vol. 27, No. 4, pp. 2175-2185, Nov. 2012.
[4] M. Chamana and B. Bayne, “Modeling and control of directly connected and inverter interfaced sources in a microgrid,” North American Power Symposium, pp. 1-7, Aug. 2011.
[5] 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.
[6] W. Zhang,W. Wang, H. Liu, and Dianguo Xu1, “A disturbance re-jection control strategy for droop-controlled inverter based on super-twisting algorithm,” IEEE Access, vol. 7, pp.27037-27046, Jul. 2019.
[7] P. M. Juan, F. Andrade, J. M. Guerrero, and C. Vasquez, “A linear quadratic regulator with optimal reference tracking for three-phase inverter-based islanded microgrids,” IEEE Trans. Power Electron., vol. 36, no. 6, pp. 7112-122, Jun. 2021.
[8] 曾梓渝，「微電網虛擬同步發電機之智慧型控制」，國立中央大學，碩士論文，民國110年。
[9] F. J. Lin, K. H. Tan, C. F. Chang, M. Y. Li, and T. Y. Tseng, “Development of intelligent controlled microgrid for power sharing and load shedding,” IEEE Trans Power Electron., vol. 37, no. 7, pp. 7928-7940, Jul. 2022.
[10] Surya. P, V. Nougain and S. Mishra, “Adaptive droop-based control for active power sharing in autonomous microgrid for improved transient performance,” IEEE J.Emerg. Sel. Top. Power Electron., vol. 9 no. 3 pp. 3010-3018, Jun. 2021.
[11] Y. Lin, Y. Li, and J. Xiang, “Load support by droop-controlled distributed generations,” IEEE Trans. Ind Electron., vol. 68, no. 9, pp. 8345-8355, Sep. 2021.
[12] Z. Li, K. W. Chen, J. Hu, and J. M. Guerrero, “Adaptive droop control using adaptive virtual impedance for microgrids with variable PV outputs and load demands ,” IEEE Trans. Ind Electron., vol. 68, no. 10, pp. 9630-9640, Oct. 2021.
[13] Y. A-R. I. Mohamed and F. E-S. Ehab, “Adaptive decentralized droop controller to preserve power sharing stability of paralleled inverters in distributed generation microgrids,” IEEE Trans. Power Electron., vol. 23, no. 6, pp. 1205-1213, Nov. 2008.
[14] T. Yamamoto, K. Takao and T. Yamada, “ Design of a data-driven PID controller ,” IEEE Trans. Control Syst. Technol, vol. 17, no. 1, pp. 29-39, Jan. 2009.
[15] P. Kasinathan, R. Vairamani, and S. Sundramoorthy, “Dynamic performance investigation of dq model with PID controller-based unified powerflow controller,” IET Power Electron., vol. 6, pp. 843-850, May 2013.
[16] J. Zhuo, C. An, and J. Fei, “Fuzzy multiple hidden layer neural sliding mode control of active power filter with multiple feedback loop,” IEEE Access, vol. 9, pp. 114294-114307, Aug. 2021.
[17] S-B. Armin, and M. M. Ebadzadeh, “Novel self-organizing fuzzy neural network to learn and mimic habitual sequential tasks,” IEEE T. Cybernetics, vol. 52, no. 1, pp. 323-332, Jan. 2022.
[18] L. Liu, J. Fei, and C. An, “Adaptive sliding mode long short-term nemory fuzzy beural control for harmonic suppression,” IEEE Access, vol. 9, pp. 69724-69734, May 2021.
[19] K. Zhou, S-K. Oh, J. Qiu, W. Pedrycz, and K. Seo, “Reinforced two-stream fuzzy neural networks architecture realized with the aid of one-dimensional/twodimensionaldatafeatures,” IEEE Trans. Fuzzy Syst., vol. 17, no. 1, pp. 707-721, Mar. 2023.
[20] 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.
[21] B. Cao, J. Zhao, Z. Lv, Y. Gu, P. Yang, and K. H. Saman, “Multiobjective evolution of fuzzy rough neural network via distributed parallelism for stock prediction,” IEEE Trans. Fuzzy Syst., vol. 28, no. 5, pp. 939-952, May 2020.
[22] J. Xiao, J. Cheng, K. Shi, and R. Zhang, “A general approach to fixed-time synchronization problem for fractional-order mul-tidimension-valued fuzzy neural networks based on memristor,” IEEE Trans. Fuzzy Syst., vol. 30, no. 4, pp. 968-977, Apr. 2022.
[23] P. Chittora, A. Singh and M. Singh, “Chebyshev functional expansion based artificial neural network controller for shunt compensation ,” IEEE Trans. Ind Inform., vol. 14, no.9, pp. 3792-3800, Sep. 2018.
[24] L. Jin, Z. Huang, Y. Li, Z. Sun, H. Li, and J.Zhang, “On modified multi-output Chebyshev-polynomial feed-forward neural network for pattern classification of wine regions,” IEEE Access, vol. 7, pp. 1973-1980, Dec. 2019.
[25] B. Wang, H. Jahanshahi , H. Dutta, Z-S Ernesto, V. Grebenyuk, S. Bekiros and Ayman A.Aly, “Incorporating fast and intelligent control technique into ecology: A Chebyshev neural network-based terminal sliding mode approach for fractional chaotic ecological systems ,” Inf. Sci., vol. 47, pp. 1-13, Sep. 2021.
[26] 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, Feb. 2020.
[27] K. H. Tan, "Squirrel-cage induction generator system using wavelet Petri fuzzy neural network control for wind power applications,” IEEE Trans. Power Electron., vol. 31, no. 7, pp. 5242-5254, Jul. 2016.
[28] W. Luan, L. Qi, Z. Zhao, J. L, and Y. Du, “Logic petri net synthesis for cooperative systems,” IEEE Access, vol. 7, pp. 161937-161948, Nov. 2019.
[29] 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, Nov. 2021.
[30] R. J. Rodríguez, S. Bernardi, and A. Zimmermann, “An evaluation framework for comparative analysis of generalized stochastic petri net simulation techniques,” IEEE Trans. Syst, Man Cybern, Part C -Appl. Rev, vol. 50, no.8, pp. 2834-2844, Aug. 2020.
[31] 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, Jan. 2020.
[32] IEEE recommended practice for utility interface of photovoltaic (PV) systems, IEEE Standard 929-2000, 2000.
[33] IEEE standard for interconnecting distributed resources with electric power systems, IEEE Standard 1547-2018, 2018
[34] 談光雄，「微電網之運轉與智慧型控制」，國防大學理工學院國防科學研究所，博士論文，民國102年
[35] 石承民，「結合虛擬慣量併網型微電網之智慧型控制」，國立中央大學，碩士論文，民國108年。
[36] 官啟玄，「以TSK機率模糊類神經網路控制之磷酸鋰鐵電池儲能系統之研製」，國立中央大學，碩士論文，民國100年。
[37] 陳俊豪，「利用智慧型控制之三相主動式電力濾波器的研製」，國立中央大學，碩士論文，民國106年。
[38] Current Transducers, HY50-P. 檢自:
https://www.alldatasheet.com/datasheet-
pdf/pdf/81505/LEM/HY50-P.html?gad
[39] Voltage Transducer LV 25-P. 檢自:
https://www.alldatasheet.com/view.jsp?Searchword=Lv25-p
[40] TMS320F28335, TMS320F28334, TMS320F28332, TMS320F28235,
TMS320F28234, TMS320F28232 Digital Signal Controllers
(DSCs)
Manual, Texas Instruments, Jun. 2007. 檢自:
https://www.ti.com/product/TMS320F28335
[41] 8/10/12-Bit Dual Voltage Output Digital-to-Analog
Converter with SPI Interface. 檢自:
https://www.alldatasheet.com/view.jsp?
Searchword=Mcp4922
[42] 可程控直流電源供應器(具太陽能電池陣列模擬) 6200H系列使用手
冊，Chroma，2018。

指導教授

林法正(Faa-Jeng Lin)

審核日期

2023-8-3

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