應用於儲能系統之智慧型風場功率平滑化之控制

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姓名

蔡居甫(Chi-fu Tsai) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

應用於儲能系統之智慧型風場功率平滑化之控制
(Wind Farm Power Smoothing Using Energy Storage System by Intelligent Control)

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摘要(中)

本論文提出以遞歸模糊類神經網路為架構之智慧型控制器應用於電池儲能系統之中來實現風場功率平滑化之控制。電池儲能系統為兩級式架構，由雙向直流至直流轉換器和三階層變流器組成。風場功率平滑化控制的目的是減緩風場功率的波動性，解決風場輸出功率因變化劇烈而不適合直接導入市電的問題，以維持電力系統之供電品質與穩定性。另外，本論文所提出的風場功率平滑化控制方法也能減少所需電池儲能系統所需容量之大小，進而節省所需成本。在不同風速變化之情況下，本論文所提出之風場功率平滑化控制方法能達成以減緩風場功率的波動性以及減少所需電池儲能系統所需容量之大小。本論文將詳細推導遞歸模糊類神經網路控制器之網路架構與線上學習法則，另一方面也利用PSIM軟體進行電池儲能系統之相關模擬，以證明其在實作時之可行性，最後本論文透過實作結果以驗證所提出控制方法之有效性。

摘要(英)

This thesis presents an intelligent controller based on the recurrent fuzzy neural network (RFNN) algorithm for the battery energy storage system (BESS) using in the wind farm power smoothing application. A two-stage BESS is composed of a bidirectional DC/DC converter and a three-level inverter. The purpose of wind farm power smoothing control is to mitigate the wind farm power fluctuation problem when it is fed directly to the grid. The proposed wind farm power smoothing control method can maintain the quality and stability of the power system and reduce the required BESS capacity and the investment cost. Moreover, the network structure and on-line learning algorithm of the RFNN are introduced in detail. Additionally, some simulation results are given to verify the design of the BESS via PSIM. Finally, the feasibility of the proposed control scheme is verified using some experiment results.

關鍵字(中)

★ 電池儲能系統
★ 風場功率平滑化控制
★ 遞歸模糊類神經網路
★ 雙向直流至直流轉換器
★ 三階層變流器

關鍵字(英)

★ battery energy storage system (BESS)
★ wind farm power smoothing control
★ recurrent fuzzy neural network (RFNN)
★ bidirectional DC/DC converter
★ three-level inverter

論文目次

目錄

中文摘要 I
Abstract II
誌謝 III
目錄 IV
圖目錄 VI
表目錄 X
第一章緒論 1
1.1 研究背景 1
1.2 相關文獻 2
1.3 研究動機與目的 3
1.4 論文大綱 4
第二章儲能系統硬體架構介紹 5
2.1 簡介 5
2.2 雙向直流至直流轉換器 6
2.3 三階層三相四線式變流器 9
2.3.1 三階層變流器模型 10
2.3.2 三階層三相四線式變流器控制原理 14
2.3.3 鎖相迴路設計 18
2.4 外接式風力發電機 18
第三章應用智慧型控制實現風機功率平滑化控制 23
3.1 簡介 23
3.2 遞歸模糊類神經網絡 23
3.2.1 遞歸模糊類神經網絡架構 24
3.2.2 遞歸模糊類神經網絡線上學習演算法 26
3.3 功率平滑化控制方法介紹 27
3.3.1 平均輸出法 28
3.3.2 移動平均法 28
3.3.3 一階低通濾波器 29
3.3.4 所提出控制方法對於功率平滑化之應用 30
第四章兩級式儲能系統模擬及相關理論驗證 33
4.1 簡介 33
4.2 兩級式儲能系統 33
4.3 功率平滑化控制方法驗證 40
4.4 和其他平滑化方法的比較 49
第五章實作結果與討論 51
5.1 簡介 51
5.2 硬體電路介紹 53
5.2.1 TMS320F28335控制電路板 53
5.2.2 市電電壓偵測電路 53
5.2.3 直流電壓偵測電路 54
5.2.4 電流感測電路 54
5.3 實作程式介紹 55
5.3.1 兩級式儲能系統 56
5.3.2 風機模擬器 58
5.4 實驗結果 59
第六章結論與未來研究方向 73
參考文獻 74
作者簡歷 78

參考文獻

[1] G. M. Shafiullah, A. M. T. Oo, A. B. M. S. Ali, and P. Wolfs, “Potential challenges of integrating large-scale wind energy into the power grid – A review,” Renewable and Sustainable Energy Reviews, vol. 20, pp. 306–321, Jan. 2013.
[2] Y. Zou, M. Elbuluk, and Y. Sozer, “A novel maximum power pointstracking operation of doubly-fed induction generator wind power system,” in Proc. IEEE Int. Conf. Ind. Appi. Soc. Ann. Meeting, Las Vegas, NV, USA, pp. 1–6, Oct. 2012.
[3] M. Jannati , S.H. Hosseinian, B. Vahidi , and G. J. Li, “A survey on energy storage resources configurations in order to propose an optimum configuration for smoothing fluctuations of future large wind power plants,” Renewable and Sustainable Energy Reviews, vol. 29, pp. 158–172, Jan. 2014.
[4] Y. Zhang, W. Hu, Z. Chen, and M. Cheng, “Individual Pitch Control for Mitigation of Power Fluctuation of Variable Speed Wind Turbines,” in Proc. IEEE Int. Conf. Power and Energy Conf., Ho Chi Minh City,Vietnam, pp. 638–643, Dec. 2012.
[5] E. Iyasere, M. Salah, D. Dawson, J. Wagner, and E. Tatlicioglu, “Optimum seeking-based non-linear controller to maximise energy capture in a variable speed wind turbine,” IET. Control Theory A, pp. 526–532, Mar. 2012.
[6] Z. Ming, H. Lixin, Y. Fan, and J. Danwei, “Research of the problems of renewable energy orderly combined to the grid in smart grid,” in Proc. IEEE Int. Conf. Power Engineering and Optimization Conf., Chengdu, China, pp. 1–4, Mar. 2010.
[7] M. Liserre, T. Sauter, and J. Y. Hung, “Future energy systems: integrating renewable energy sources into the smart power grid through industrial electronics,” IEEE Ind. Electron. Mag., vol. 4, no. 1, pp. 18–37, Mar. 2010.
[8] J. Zhao, K. Graves, C. Wang, G. Liao, and C. P. YehA, “Hybrid Electric/Hydro Storage Solution for Standalone Photovoltaic Applications in Remote Areas,” in Proc. IEEE Int. Conf. Power and Energy Soc. General Meeting, pp. 1–6, Jul. 2012.
[9] N. S. Hasan, M. Y. Hassan, M. S. Majid, and H. A. Rahman, “Mathematical Model of Compressed Air Energy Storage in Smoothing 2MW Wind Turbine,” in Proc. IEEE Int. Conf. Power Eng. and Optimization Conf., Melaka, Malaysia, pp. 339–343, Jun. 2012.
[10] F. Islam, H. Hasanien, A. Al-Durra, and S. M. Muyeen, “A New Control Strategy for Smoothing of Wind Farm Output using Short-Term Ahead Wind Speed Prediction and Flywheel Energy Storage System,” in Proc. IEEE Int. Conf. American Control Conf., Montreal, QC, Canada, pp. 3026–3031, Jun. 2012.
[11] T. Ise, M. Kita, and A. Taguchi, “A Hybrid Energy Storage With a SMES and Secondary Battery,” IEEE Trans. Appl. Supercond., vol. 15, no. 2, pp. 1915–1918 , Jun. 2005.
[12] S. Nomura, Y. Ohata, T. Hagita, H. Tsutsui, S. Tsuji-Iio, and R. Shimada, “Wind Farms Linked by SMES Systems,” IEEE Trans. Appl. Supercond., pp. 1951–1954, Jun. 2005.
[13] T. Kinjo, T. Senjyu, N. Urasaki, and H. Fujita, “Output Levelling of Renewable Energy by Electric Double-Layer Capacitor Applied for Energy Storage System,” IEEE Trans. Energy Convers., pp. 221–227, Mar. 2006.
[14] X. Li, C. Hu , C. Liu, and D. Xu, “Modeling and Control of Aggregated Super-capacitor Energy Storage System for Wind Power Generation,” in Proc. IEEE Int. Conf. Ind. Electron., Orlando, FL, USA, pp. 3370–3375, Nov. 2008.
[15] R. Saiju, S. Heier, “Performance Analysis of Lead Acid Battery Model for Hybrid Power System,” in Proc. IEEE Int. Conf. Transmission and Distribution Conf. and Expo., Chicago, IL, USA, pp. 1–6, Apr. 2008.
[16] X. Han , F. Chen , X. Cui , Y. Li, and X. Li, “A Power Smoothing Control Strategy and Optimized Allocation of Battery Capacity Based on Hybrid Storage Energy Technology,” Energies, pp. 1593-1612, May 2015.
[17] Q. Jiang, H. Wang, “Two-Time-Scale Coordination Control for a Battery Energy Storage System to Mitigate Wind Power Fluctuations,” IEEE Trans. Energy Convers., pp. 52–61, Mar. 2013.
[18] Q. Jiang, Y. Gong, and H. Wang, “A Battery Energy Storage System Dual-Layer Control Strategy for Mitigating Wind Farm Fluctuations,” IEEE Trans. Power Syst., pp. 3263–3273, Mar. 2013.
[19] M. R. I. Sheikh, S. M. Muyeen, R. Takahashi, T. Murata and J. Tamura, “Minimization of Fluctuations of Output Power and Terminal Voltage of Wind Generator by Using STATCOM/SMES,” in Proc. IEEE Int. Conf. Power Tech Conf., Bucharest, pp. 1–6, Jun. 2009.
[20] W. Wang , C. Mao, J. Lu, and D. Wang, “An Energy Storage System Sizing Method for Wind Power Integration,” Energies, pp. 3392-3404, Jul. 2013.
[21] X. Li, “Fuzzy adaptive Kalman filter for wind power output smoothing with battery energy storage system,” IET Renew. Power Gener., vol. 6, no. 5, pp. 340–347, Sep. 2012.
[22] M. Jannati, S. H. Hosseinian, B. Vahidi, and G. J. Li, “Mitigation of windfarm power fluctuation by adaptive linear neuron-based power tracking method with flexible learning rate,” IET Renew. Power Gener., vol. 8, no. 6, pp. 659–669, Aug. 2014.
[23] L. X. Wang, “A Course in Fuzzy Systems and Control,” Prentice-Hall International, Inc.
[24] C. C. Chuang, S. F. Su, and S. S. Chen, “Robust TSK Fuzzy Modeling for Function Approximation With Outliers,” IEEE Trans. Fuzzy Syst., vol. 9, no. 6, pp. 810–821, Dec. 2001.
[25] S. Cong and Y. Liang, “PID-Like Neural Network Nonlinear Adaptive Control for Uncertain Multivariable Motion Control Systems,” IEEE Trans. Industrial Electron., vol. 56, no. 10, pp. 3872–3879, Oct. 2009.
[26] T. Orlowska-Kowalska, M. Dybkowski, and K. Szabat, “Adaptive Sliding-Mode Neuro-Fuzzy Control of the Two-Mass Induction Motor Drive Without Mechanical Sensors,” IEEE Trans. Industrial Electron., vol. 57, no. 2, pp. 553–564, Feb. 2010.
[27] J. Sun, “Dynamics and Control of Switched Electronic Systems,” Springer, ch. 2, sec. 3, pp. 48-53, 2012.
[28] M. N. Kokate and P. V. Kapoor, “Comparison of Simulation Results Three Level and Five Level H-bridge Inverter and hardware implementation of Single Leg H-Bridge Three Level Inverter, ” Int. J. Innov. Res. Stud., vol. 2, no. 4, pp. 389–403, Apr. 2013.
[29] R. Teodorescu and F. Blaabjerg, “Flexible control of small wind turbines with grid failure detection operating in stand-alone and grid-connected mode,” IEEE Trans. Power Electron., vol. 19, no. 5, pp. 1323–1332, Sept. 2004.
[30] HX-15P Application Note, LEM Co.

指導教授

林法正(Faa-Jeng Lin)

審核日期

2015-8-24

推文