微電網虛擬同步發電機之智慧型控制

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：79

、訪客IP：3.142.210.157

姓名

曾梓渝(Tzu-Yu Tseng) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

微電網虛擬同步發電機之智慧型控制
(Intelligent Control of Virtual Synchronous Generator for Microgrid)

相關論文

★ 機場地面燈光更新工程 -以桃園國際機場南邊跑滑道為例	★ 多功能太陽能微型逆變器之研製
★ 應用於儲能系統之智慧型太陽光電功率平滑化控制	★ 利用智慧型控制之三相主動式電力濾波器的研製
★ 應用於內藏式永磁同步馬達之智慧型速度控制及最佳伺服控制頻寬研製	★ 新型每安培最大轉矩控制同步磁阻馬達驅動系統之開發
★ 同步磁阻馬達驅動系統之智慧型每安培最大轉矩追蹤控制	★ 利用適應性互補式滑動模態控制於同步磁阻馬達之寬速度控制
★ 具智慧型太陽光電功率平滑化控制之微電網電能管理系統	★ 高性能同步磁阻馬達驅動系統之寬速度範圍控制器發展
★ 智慧型互補式滑動模態控制系統實現於X-Y-θ三軸線性超音波馬達運動平台	★ 智慧型同動控制之龍門式定位平台及應用
★ 利用智慧型滑動模式控制之五軸主動式磁浮軸承控制系統	★ 智慧型控制雙饋式感應風力發電系統之研製
★ 無感測器直流變頻壓縮機驅動系統之研製	★ 應用於模組化輕型電動車之類神經網路控制六相永磁同步馬達驅動系統

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

本論文提出一種虛擬同步發電機的控制策略，利用虛擬慣量估測器來估測虛擬同步發電機的虛擬慣量，並減少在負載變化下分散式發電機的實功率輸出和頻率的振盪。此外，為了提高虛擬慣量估測器的性能與分散式發電機的實功率輸出和頻率的暫態響應，提出了一種線上訓練的派翠機率小波模糊神經網絡（PPWFNN）控制器來取代傳統的比例積分(PI)控制器。本文也介紹了派翠機率小波模糊神經網絡的結構和倒傳遞調整的線上學習算法。另外，也提出了下垂控制與低頻負載卸除的控制策略。微電網由儲能系統、太陽能（PV）系統和負載組成。低頻負載卸除是防止連續頻率下降和防止停電的主要措施。本研究為了克服傳統下垂控制在負載變化下的暫態響應緩慢與抗擾性差等缺點，也應用了線上訓練的派翠機率小波模糊神經網絡控制器來取代傳統的PI控制器。最後，根據模擬和實驗結果，使用所提出的虛擬慣量估測器和派翠機率小波模糊神經網絡控制器，可以實現在負載變化下提升實功率輸出的暫態響應、頻率響應和快速卸載的性能。

摘要(英)

In this thesis, a novel virtual inertia estimation methodology is proposed to estimate the suitable virtual inertia of the virtual synchronous generator (VSG) and to reduce the oscillations of the active power output and frequency of the distributed generator (DG) under load variation. Moreover, to improve the performance of the proposed virtual inertia estimator and the transient responses of the active power output and frequency of the DG, an online trained Petri probabilistic wavelet fuzzy neural network (PPWFNN) controller is proposed to replace the conventional proportional-integral (PI) controller. The network structure and the online learning algorithm using backpropagation (BP) of the proposed PPWFNN are represented in detail. Furthermore, a microgrid based on droop control with under-frequency load shedding (UFLS) algorithm is also researched in this study. The microgrid is composed of a storage system, a photovoltaic (PV) system and the load. The UFLS is the principal measure to prevent successive frequency declination and blackouts. In this study, to overcome the drawbacks of the traditional droop control such as slow transient response and poor disturbance rejection under load variation. The online trained PPWFNN controller is also proposed to replace the conventional PI controller in microgrid. Finally, according to the simulation and experimental results, superior performance of the active power output, frequency response and rapid load shedding under load variation can be achieved by using the proposed virtual inertia estimator and the intelligent PPWFNN controller.

關鍵字(中)

★ 虛擬同步發電機
★ 下垂控制
★ 搖擺方程式
★ 虛擬慣量
★ 儲能系統
★ 太陽光發電系統
★ 派翠機率小波模糊類神經網路
★ 分散式能源

關鍵字(英)

★ virtual synchronous generator
★ droop control
★ swing equation
★ virtual inertia
★ storage system
★ photovoltaic
★ Petri probabilistic wavelet fuzzy neural network
★ distributed resources

論文目次

摘要 I
Abstract II
誌謝 III
目錄 IV
圖目錄 VIII
表目錄 XV
第一章緒論 1
1.1 研究背景與動機 1
1.2 文獻回顧 3
1.3 論文大綱 8
1.4 本文貢獻 9
第二章微電網規範與控制策略介紹 11
2.1 微電網規範 11
2.1.1 IEEE 929-2000規範 11
2.1.2 IEEE 1547-2003規範 12
2.1.3 電流諧波 13
2.1.4 電流諧波定義 13
2.1.5 諧波失真率公式及現行諧波管制標準 14
2.2 微電網控制策略 17
2.2.1 虛擬同步發電機策略之下垂控制方程式與曲線 17
2.2.2 虛擬同步發電機策略之下垂控制方程式設計 18
2.2.3 虛擬同步發電機 19
2.2.4 虛擬轉動慣量估測的限制器演算法 22
2.2.5 定功率控制[66] 24
2.2.6 電壓頻率控制 25
2.2.7 預同步控制策略 26
2.2.8 下垂控制策略之下垂控制方程式與曲線 30
2.2.9 下垂控制策略之下垂控制方程式設計 32
第三章系統架構與控制策略 34
3.1 簡介 34
3.2 三相座標軸轉換 34
3.3 鎖相迴路 41
3.3.1 以同步旋轉座標軸實現的鎖相迴路 41
3.3.2 以靜止座標軸實現的鎖相迴路 42
3.3.3 以同步旋轉座標軸實現的固定頻率式鎖相迴路 43
3.4 實功率與虛功率的計算 46
3.5 低通濾波器 54
3.5.1一階低通濾波器 54
3.5.2二階低通濾波器 55
3.5.3移動平均法 56
3.6 虛擬同步發電機控制架構 57
3.6.1 虛擬同步發電機之控制策略 57
3.7 下垂控制策略與負載卸除方案 61
3.7.1 下垂控制策略 61
3.7.2 負載卸除方案 65
第四章派翠機率小波模糊類神經網路 66
4.1 簡介 66
4.2 派翠機率小波模糊類神經網路架構 66
4.3 派翠機率小波模糊類神經網路線上學習法則 70
4.4 派翠機率小波模糊類神經網路收斂性分析 73
第五章模擬結果 76
5.1 虛擬同步發電機策略的模擬結果 76
5.1.1 情境一：固定的虛擬轉動慣量模擬結果 77
5.1.2 情境二：負載為0.25kW-2kW-1kW，PI、FNN與PPWFNN控制器估測虛擬轉動慣量之模擬結果 81
5.1.3 情境三：負載為0.5kW-2kW-1kW，PI、FNN與PPWFNN控制器估測虛擬轉動慣量之模擬結果 85
5.2 下垂控制策略與負載卸除策略之模擬結果 89
5.2.1 情境一：負載變化為1kW-2kW的模擬結果 90
5.2.2 情境二：負載變化為1kW-3kW-2kW的模擬結果 94
5.2.3 情境三：負載變化為1kW-3.5kW-3kW的模擬結果 98
第六章硬體與實驗結果 102
6.1 簡介 102
6.2 儲能系統硬體設備 103
6.2.1 儲能系統變流器 104
6.2.2 電阻負載之規劃 105
6.3 儲能系統週邊電路 106
6.3.1 交流電流回授電路 106
6.3.2 交流電壓回授電路 107
6.3.3 直流電壓回授電路 108
6.3.4 過電壓與過電流保護電路 109
6.3.5 開關互鎖電路 111
6.3.6 數位訊號處理器 114
6.3.7 DAC轉換電路 114
6.4 太陽能光電系統硬體設備 117
6.4.1 可程控直流電源供應器(具太陽能電池陣列模擬功能) 118
6.4.2 太陽能光電系統變流器 119
6.4.3 資料擷取卡 121
6.5 虛擬同步發電機策略之實驗結果 122
6.5.1 情境一：固定的虛擬轉動慣量實驗結果 122
6.5.2 情境二：負載為0.25kW-2kW-1kW，PI、FNN與PPWFNN控制器估測虛擬轉動慣量之實驗結果 127
6.5.3 情境三：負載為0.5kW-2kW-1kW，PI、FNN與PPWFNN控制器估測虛擬轉動慣量之實驗結果 131
第七章結論與未來展望 135
7.1 結論 135
7.2 未來展望 136
參考文獻 137
作者簡歷 146

參考文獻

[1] Y. Hu, W. Wei, Y. Peng and J. Lei, " Fuzzy virtual inertia control for virtual synchronous generator," 2016 35th Chinese Control Conference (CCC), Chengdu, China, 2016, pp. 8523-8527.
[2] 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 Transactions on Smart Grid, vol. 7, no. 2, pp. 695-705, March 2016.
[3] J. Alipoor, Y. Miura and T. Ise, "Power system stabilization using virtual synchronous generator with alternating moment of inertia," IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 3, no. 2, pp. 451-458, June 2015.
[4] L. Lu and C. Chu, "Consensus-based secondary frequency and voltage droop control of virtual synchronous generators for isolated AC micro-grids," IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 5, no. 3, pp. 443-455, Sept. 2015.
[5] J. Fang, H. Li, Y. Tang and F. Blaabjerg, "Distributed power system virtual inertia implemented by grid-connected power converters," IEEE Transactions on Power Electronics, vol. 33, no. 10, pp. 8488-8499, Oct. 2018.
[6] I. J. Balaguer, Q. Lei, S. Yang, U. Supatti and F. Z. Peng, "Control for grid-connected and intentional islanding operations of distributed power generation," IEEE Transactions on Industrial Electronics, vol. 58, no. 1, pp. 147-157, Jan. 2011.
[7] IEEE Standard 929-2000, "IEEE Recommended Practice for Utility Interface of Photovoltaic(PV) Systems," IEEE Standard, New York, USA, pp. 1-26, 2000.
[8] IEEE Standard 1547-2003, "IEEE Standard for Interconnecting Distributed Resources with Electric Power Systems," IEEE Standard, New York, USA, pp. 1-16, 2003
[9] 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
[10] J. Liu, Y. Miura, T. Ise, "Comparison of dynamic characteristics between virtual synchronous generator and droop control in inverter-based distributed generators", IEEE Trans. Power Electron., vol. 31, no. 5, pp. 1-1, May 2016.
[11] C. Andalib-Bin-Karim, X.Liang, H. Zhang, “Fuzzy-secondary-controller-based virtual synchronous generator control scheme for interfacing inverters of renewable distributed generation in microgrids,” IEEE Transactions on Industry Applications, vol. 54, no. 2, pp. 1047-1061, Mar./Apr. 2018.
[12] B. Zhao, X. Zhang and J. Chen, "Integrated microgrid laboratory system," IEEE Transactions on Power Systems, vol. 27, no. 4, pp. 2175-2185, Nov. 2012.
[13] M. Chamana and S. B. Bayne, "Modeling and control of directly connected and inverter interfaced sources in a microgrid," 2011 North American Power Symposium, Boston, MA, 2011, pp. 1-7.
[14] 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 Transactions on Power Electronics, vol. 33, no. 8, pp. 6488-6508, Aug. 2018.
[15] D. E. Olivares et al., "Trends in microgrid control," IEEE Transactions on Smart Grid, vol. 5, no. 4, pp. 1905-1919, July 2014.
[16] J. Liu, Y. Miura, H. Bevrani, T. Ise, "Enhanced virtual synchronous generator control for parallel inverters in microgrids," IEEE Transactions on Smart Grid, vol. 8, no. 3, pp. 2268-2277, Sep. 2017.
[17] W.Zhang, W. Wang, H. Liu, Dianguo Xul, "A disturbance rejection control strategy for droop-controlled inverter based on super-twisting algorithm," IEEE Access, vol. 7, pp. 27037-27046, July 2019.
[18] J. M. Guerrero, L.G. de Vicuna, J. Matas, M. Castilla, J. Miret, "A wireless controller to enhance dynamic performance of parallel inverters in distributed generation systems," IEEE Transactions on Power Electronics, vol. 19, no. 5, pp. 1205-1213, Sep 2004.
[19] Y. Abdel-Rady Ibrahim Mohamed, E. F. El-Saadany, "Adaptive decentralized droop controller to preserve power sharing stability of paralleled inverters in distributed generation microgrids," IEEE Transactions on Power Electronics, vol. 23, no. 6, pp. 1205-1213, Nov 2008.
[20] B. de Nadai Nascimento, A. Carlos Zambroni de Souza, J. Guilherme de Carvalho Costa, M. Castilla, "Load shedding scheme with under-frequency and under-voltage corrective actions to supply high priority loads in islanded microgrids," IET Renewable Power Generation, vol. 13, pp. 1981-1989, Aug 2019.
[21] C. Li, Y. Wu, Y. Sun, H. Zhang, Y. Liu, Y. Liu, V. Terzija, "Continuous under-frequency load shedding scheme for power system adaptive frequency control," IEEE Transactions on Power Systems, vol. 35, no. 2, pp. 950-961, Mar 2020.
[22] M. Karimi, P. Wall, H. Mokhlis, V. Terzija, "A new centralized adaptive under frequency load shedding controller for microgrids based on a distribution state estimator," IEEE Transactions on Power Delivery, vol. 32, no. 1, pp. 370-380, Feb 2017.
[23] J. A. Laghari, H. Mokhlis, M. Karimi, A. Halim Abu Bakar, H. Mohamad, "A new under-frequency load shedding technique based on combination of fixed and random priority of loads for smart grid applications," IEEE Transactions on Power Systems, vol. 30, no. 5, pp. 2507-2515, Sep 2015.
[24] M. F. M. Arani and E. F. El-Saadany, "Implementing virtual inertia in DFIG-based wind power generation," IEEE Transactions on Power Systems, vol. 28, no. 2, pp. 1373-1384, May 2013.
[25] H. Yibin, Y. Xiangwu, L. Dongxue, L. Xinxin, Z. Bo "Experimental study of micro sources inverter based on virtual synchronous generator, " 2017 4th International Conference on Electrical and Electronic Engineering (ICEEE), Ankara, pp. 124-129, 2017.
[26] K. De Brabandere, B. Bolsens, J. Van den Keybus, A. Woyte, J. Driesen and R. Belmans, "A voltage and frequency droop control method for parallel inverters," IEEE Transactions on Power Electronics, vol. 22, no. 4, pp. 1107-1115, July 2007.
[27] Y. Li and Y. W. Li, "Decoupled power control for an inverter based low voltage microgrid in autonomous operation," 2009 IEEE 6th International Power Electronics and Motion Control Conference, Wuhan, 2009, pp. 2490-2496.
[28] Hadi Saadat, Power System Analysis, 3e, New York: McGraw-Hall, 2011.
[29] H. Zhao, Q. Yang and H. Zeng, "Multi-loop virtual synchronous generator control of inverter-based DGs under microgrid dynamics," IET Generation, Transmission & Distribution, vol. 11, no. 3, pp. 795-803, 16 2 2017.
[30] Y. Hu, W. Wei, Y. Peng, J. Lei, "Fuzzy virtual inertia control for virtual synchronous generator," 2016 35th Chinese Control Conference (CCC), Chengdu, pp. 8523-8527, 2016.
[31] J. Alipoor, Y. Miura, and T. Ise, "Distributed generation grid integration using virtual synchronous generator with adoptive virtual inertia," in Proc. IEEE Energy Convers. Congr. Expo. (ECCE), Sep. 2013, pp. 4546–4552.
[32] Jang, J. S. R., Sun, C. T., and Mizutani, E., "Neuro-fuzzy and soft computing: A computational approach to learning and machine intelligence," Prentice-Hall, New Jersey, pp. 1-614, 1997.
[33] F. J. Lin, W. J. Hwang and R. J. Wai, "A supervisory fuzzy neural network control system for tracking periodic inputs," IEEE Transactions on Fuzzy Systems, vol. 7, no. 1, pp. 41-52, Feb. 1999.
[34] Wang, L. X., A Course in Fuzzy Systems and Control, Prentice-Hall, New Jersey, pp. 1-424, 1997.
[35] Wang, L. X., Adaptive Fuzzy Systems and Control: Design and Stability Analysis, Prentice-Hall, Englewood Cliffs, New Jersey, pp. 1-352, 1994..
[36] W. Yu and X. Li, "Fuzzy identification using fuzzy neural networks with stable learning algorithms," IEEE Transactions on Fuzzy Systems, vol. 12, no. 3, pp. 411-420, June 2004.
[37] F. J. Lin, H. J. Shieh, P. K. Huang and L. T. Teng, "Adaptive control with hysteresis estimation and compensation using RFNN for piezo-actuator," IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 53, no. 9, pp. 1649-1661, Sept. 2006.
[38] Y. Gao and M. J. Er, "An intelligent adaptive control scheme for postsurgical blood pressure regulation," IEEE Transactions on Neural Networks, vol. 16, no. 2, pp. 475-483, March 2005.
[39] F. J. Lin, P. Huang, C. Wang and L. Teng, "An induction generator system using fuzzy modeling and recurrent fuzzy neural network," IEEE Transactions on Power Electronics, vol. 22, no. 1, pp. 260-271, Jan. 2007.
[40] F. J. Lin, I. Sun, K. Yang and J. Chang, "Recurrent fuzzy neural cerebellar model articulation network fault-tolerant control of six-phase permanent magnet synchronous motor position servo drive," IEEE Transactions on Fuzzy Systems, vol. 24, no. 1, pp. 153-167, Feb. 2016.
[41] X. Jin, D. Srinivasan and R. L. Cheu, "Classification of freeway traffic patterns for incident detection using constructive probabilistic neural networks," IEEE Transactions on Neural Networks, vol. 12, no. 5, pp. 1173-1187, Sept. 2001.
[42] S. J. Hart, R. E. Shaffer, S. L. Rose-Pehrsson and J. R. McDonald, "Using physics-based modeler outputs to train probabilistic neural networks for unexploded ordnance (UXO) classification in magnetometry surveys," IEEE Transactions on Geoscience and Remote Sensing, vol. 39, no. 4, pp. 797-804, April 2001.
[43] M. Tripathy, R. P. Maheshwari and H. K. Verma, "Power transformer differential protection based on optimal probabilistic neural network," IEEE Transactions on Power Delivery, vol. 25, no. 1, pp. 102-112, Jan. 2010.
[44] F. J. Lin, Y. S. Huang, K. H. Tan, Z. H. Lu, and Y. R. Chang, "Intelligent-controlled doubly fed induction generator system using PFNN," Neural Computing and Applications, vol. 22, no. 7, pp. 1695-1712, June 2013.
[45] F. J. Lin and K. Tan, "Squirrel-cage induction generator system using probabilistic fuzzy neural network for wind power applications," 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Istanbul, 2015, pp. 1-8.
[46] F. J. Lin, M. Huang, P. Yeh, H. Tsai and C. Kuan, "DSP-based probabilistic fuzzy neural network control for Li-Ion battery charger," IEEE Transactions on Power Electronics, vol. 27, no. 8, pp. 3782-3794, Aug. 2012.
[47] J. Zhang, G. G. Walter, Y. Miao and W. N. W. Lee, "Wavelet neural networks for function learning," IEEE Transactions on Signal Processing, vol. 43, no. 6, pp. 1485-1497, June 1995.
[48] Q. Zhang, "Using wavelet network in nonparametric estimation," IEEE Transactions on Neural Networks, vol. 8, no. 2, pp. 227-236, March 1997.
[49] F. Lin, K. Tan, D. Fang and Y. Lee, "Intelligent controlled three-phase squirrel-cage induction generator system using wavelet fuzzy neural network for wind power," IET Renewable Power Generation, vol. 7, no. 5, pp. 552-564, Sept. 2013.
[50] 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」，中央大學，碩士論文，民國102年。
[51] R. Zurawski and M. Zhou, "Petri nets and industrial applications: a tutorial," IEEE Tran. Industrial Electronics, vol. 41, no. 6, pp. 567 -583, December 1994.
[52] K. H. Tan, "Squirrel-cage induction generator system using wavelet Petri fuzzy neural network control for wind power applications, " IEEE Trans.
Power Electronics, vol. 31, no. 7, pp. 5242-5254, July 2016.
[53] H. Huang and H. Kirchner, "Formal specification and verification of modular security policy based on colored Petri nets, " IEEE Trans. Dependable and Secure Computing, vol. 8, no. 6, pp. 852-865, November - December 2011.
[54] Y. Y. Du, L. Qi, and M. C. Zhou, "Analysis and application of logical Petri nets to e-commerce systems," IEEE Trans. Systems, Man, and Cybernetics: System, vol. 44, no. 4, pp. 468-481, April 2014.
[55] T. Nishi and Y. Tanaka, "Petri net decomposition approach for dispatching and conflict-free routing of bidirectional automated guided vehicle systems," IEEE Trans. Systems, Man, and Cybernetics - Part A: Systems and Humans, vol. 42, no. 2, pp. 1230-1243, September 2012.
[56] Y. Y. Du, C. J. Jiang, and M. C. Zhou, "A petri-net-based correctness analysis of internet stock trading systems, " IEEE Trans. Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 38, no.1, pp. 93-99, Jan. 2008.
[57] Y. S. Huang, Y. S. Weng, and M. C. Zhou, "Modular design of urban traffic-light control systems based on synchronized timed Petri nets," IEEE Trans. Intelligent Transportation Systems, vol. 15, no. 2, pp. 93-99, April 2014.
[58] IEEE Recommended Practices and Requirements for Harmonic Control in Electrical Power Systems, IEEE Std 519-2014, 2014.
[59] IEEE Standard Definitions for the Measurement of Electric Power Quantities Under Sinusoidal, Nonsinusoidal, Balanced, or Unbalanced Conditions, IEEE Standards Association, IEEE Std.1459-2010, 2010.
[60] IEEE Recommended Practice and Requirements for Harmonic Control in Electric Power Systems, IEEE Standards Association, 2014.
[61] 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 Transactions on Industrial Electronics, vol. 58, no. 1, pp. 158-172, Jan. 2011.
[62] S. Teimourzadeh, F. Aminifar, M. Davarpanah and M. Shahidehpour, "Adaptive control of microgrid security," IEEE Transactions on Smart Grid, vol. 9, no. 4, pp. 3909-3910, July 2018.
[63] C. Yan and D. Xu, "Design study of MW photovoltaic inverter," 2018 IEEE International Power Electronics and Application Conference and Exposition (PEAC), Shenzhen, 2018, pp. 1-6.
[64] P. M. Ashton, C. S. Saunders, G. A. Taylor, A. M. Carter and M. E. Bradley, "Inertia estimation of the GB power system using synchrophasor measurements," IEEE Transactions on Power Systems, vol. 30, no. 2, pp. 701-709, March 2015.
[65] H. P. Beck and R. Hesse, “Virtual synchronous machine,” 2007 9th International Conference on Electrical Power Quality and Utilisation, 2007, pp. 1–6.
[66] 石承民，「結合虛擬慣量併網型微電網之智慧型控制」，中央大學，碩士論文，民國108年。
[67] 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」，中央大學，碩士論文，民國100年。
[68] Current Transducers, HY50-P
[69] Voltage Transducer LV 25-P
[70] TMS320F28335, TMS320F28334, TMS320F28332, TMS320F28235,
TMS320F28234, TMS320F28232 Digital Signal Controllers (DSCs) Data Manual, Texas Instruments, Jun. 2007.
[71] TMS320x2833x, 2823x System Control and Interrupts Reference Guide
[72] TMS320x2833x, 2823x Enhanced Pulse Width Modulator (ePWM) Module Reference Guide
[73] TMS320x2833x Analog-to-Digital Converter (ADC) Module Reference Guide
[74] TMS320x2833x, 2823x Serial Peripheral Interface (SPI) Reference Guide
[75] 8/10/12-Bit Dual Voltage Output Digital-to-Analog Converter with SPI Interface
[76] 使用手冊，可程控直流電源供應器(具太陽能電池陣列模擬) 62000H系列使用手冊，Chroma，2012。

指導教授

林法正

審核日期

2021-8-19

推文