由於風力渦輪發電機存在著非線性的特性，其最大功率點運轉位置將隨著風力狀況的改變而有所不同。為了使風力發電系統在任何風速下皆可操作在最大功率點，並避免實際運轉時使用轉速計，可從控制系統方面來操作到最大功率點。影響風力發電主要有三個因素，風速、風力機的功\率係數和葉片半徑，風力機的功率係數是隨著葉片旋角和葉尖速率比而變化。
本文主要的目的是發展一智慧型控制風力發電系統，並結合交流/直流/交流功率轉換器來達成獨立供電之應用。為了使風力發電系統皆可操作在最大功率點及系統實功率達到一個快速又穩定的響應，故提出之智慧型控制器包含模糊類神經網路、遞迴式模糊類神經網路、徑向基底函數網路和改良型遞迴式類神經網路。此外，所提出之模糊類神經網路、遞迴式模糊類神經網路、徑向基底函數網路和改良型遞迴式類神經網路皆利用倒傳遞法則來線上訓練網路之參數。在網路的學習速率選取方面，通常採用嘗試錯誤法來尋找適當之學習速率，然而此方法過於耗時。因此本文採用改良型粒子群尋優法來線上搜尋網路之最佳學習速率，以提升類神經網路的學習能力。接著提出滑動模式和模型參考適應系統理論為基礎之速度估測器，而且無轉速感測理論應用於市電並聯之風力感應發電機上。另一方面，提出以徑向基底函數網路為基礎之爬坡法則來完成永磁同步發電機之最大功\率追蹤。最後，以模擬驗證上述所提出之智慧型控制風力發電系統的有效性。

The wind turbine generation system (WTGS) exhibits a nonlinear characteristic and its maximum power point varies with changing atmospheric conditions. In order to operate the WTGS at maximum power output under various wind speeds and to avoid using speed encoder in practical applications, it is necessary to improve the controller system to operate the maximum power points in the WTGS. There are three factors to influence wind generator, the wind speed, power coefficient and the radius of blade. The power coefficient depends on the blade pitch angle and tip speed ratio (TSR).
The objective of the dissertation is to develop an intelligent controlled wind energy conversion system (WECS) using AC/DC and DC/AC power converters for grid-connected power application. To achieve a fast and stable response for the real power control, an intelligent controller was proposed, which consists of a fuzzy neural network (FNN), a recurrent fuzzy neural network (RFNN), a wilcoxcon radial basis function network (WRBFN) and a improved Elman neural network (IENN) for MPPT. Furthermore, the parameter of the developed FNN, RFNN, WRBFN and IENN are trained on-line using back-propagation learning algorithm. However, the learning rates in the FNN, RFNN, WRBFN, and IENN are usually selected by trial and error method, which is time-consuming. Therefore, modified particle swarm optimization (MPSO) method is adopted to adjust the learning rates to improve the learning capability of the developed RFNN, WRBFN and IENN. Moreover, presents the estimation of the rotor speed is based on the sliding mode and model reference adaptive system (MRAS) speed observer theory. Furthermore, a sensorless vector-control strategy for an induction generator (IG) operating in a grid-connected variable speed wind energy conversion system can be achieved. On the other hand, a WRBFN based with hill-climb searching (HCS) maximum-power-point-tracking (MPPT) strategy is proposed for permanent magnet synchronous generator (PMSG) with a variable speed wind turbine. Finally, many simulation results are provided to show the effectiveness of the proposed intelligent control wind generation systems.

論文審定書..............................................................................i
Acknowledgement................................................................ii
Chinese Abstract.................................................................iii
Abstract..................................................................................iv
Content..................................................................................vi
List of Figures........................................................................x
List of Tables.......................................................................xiv

Chapter 1 Introduction.........................................................1
1.1 Motivation and Background.........................................1
1.2 Literature Review..........................................................3
1.3 Purpose and Contributions........................................4
1.4 Organization of the Dissertation................................6

Chapter 2 Analysis of Wind Generation System ............8
2.1 Composition of Wind Generation System...............8
2.2 Wind Turbine Characteristics....................................8
2.3 Dynamic Models of Generator.................................11
2.3.1 IG Model.....................................................................11
2.3.2 DFIG Model................................................................11
2.3.3 PMSG Model..............................................................12
2.4 Conclusion..................................................................12

Chapter 3 Output Maximization Control for Sensorless
Wind-Driven IG System Based on FNN........................13
3.1 Introduction.................................................................13
3.2 Design of the Control System.................................14
3.2.1 Overall Structure......................................................14
3.2.2 Sliding Mode Flux Observer...................................14
3.2.3 Rotation Speed Observer.......................................17
3.3 Fuzzy Neural Network Controller.............................18
3.3.1 Basic Nodes Operation..........................................19
3.3.2 Supervised Learning and Training Process......22
3.4 Conclusion..................................................................24

Chapter 4 MRAS-Based Sensorless Maximum Wind
Energy Control for Wind Generation System using
RFNN…...............................................................................25
4.1 Introduction.................................................................25
4.2 Design of the Control System.................................26
4.2.1 Overall Structure......................................................26
4.2.2 MRAS Based Speed Observer.............................27
4.3 Recurrent Fuzzy Neural Network with MPSO
Algorithm.............................................................................29
4.3.1 Basic Nodes Operation..........................................31
4.3.2 Supervised Learning and Training Process......33
4.3.3 Modified Particle Swarm Optimization…………35
4.4 Conclusion.................................................................38

Chapter 5 Intelligent Approach to MPPT Control
Strategy for PMSG Wind Turbine Generation
System................................................................................39
5.1 Introduction.................................................................39
5.2 Hill-Climb Searching Control Method....................40
5.2.1 System Configuration.............................................40
5.2.2 Optimal DC-Link Voltage Search.........................41
5.3 The Proposed Intelligent MPPT Control
Algorithm.............................................................................43
5.3.1 Wilcoxon Radial Basis Function Network..........43
5.3.2 The Network Training and Learning Process...44
5.4 WRBFN Learning Rates Adjustment using
MPSO..................................................................................45
5.5 Conclusion.................................................................48

Chapter 6 IENN-Based Control Algorithm for
Adjustable-Pitch Variable Speed DFIG System..........49
6.1 Introduction.................................................................49
6.2 Analysis of DFIG System..........................................50
6.3 Design of MPPT Algorithm Based on IENN
Optimized with MPSO.......................................................51
6.3.1 Improved Elman Neural Network Controlle.......52
6.3.2 Online Supervised Learning and Training
Process...............................................................................53
6.3.3 Learning Rates Adjustment using MPSO..........55
6.4 Conclusion.................................................................57

Chapter 7 Simulation Results and Discussion..........59
7.1 Introduction.................................................................59
7.2 Simulation Tests of the Proposed FNN................59
7.2.1 Simulation of a Wind Step Change.....................60
7.2.2 Simulation of Variable Wind Speed.....................61
7.2.3 Simulation of the Maximum Power Tracking.....63
7.3 Simulation Tests of the Proposed RFNN.............64
7.3.1 Simulation of Variable Wind Speed.....................65
7.3.2 Simulation of the Maximum Power Tracking.....68
7.3.3 Comparison of Performance................................69
7.4 Simulation Tests of the Proposed WRBFN..........70
7.4.1 PI Algorithm for Vdc Control..................................71
7.4.2 Fuzzy-Based Algorithm for Vdc Control...............72
7.4.3 WRBFN with MPSO Algorithm for Vdc Control...73
7.5 Simulation Tests of the Proposed IENN...............75

Chapter 8 Conclusion and Future Work......................84
8.1 Conclusion................................................................84
8.2 Future Work...............................................................85
References.......................................................................88
Appendix A .......................................................................95
List of Publications and Projects..................................97

[1] R. Cardenas, R. Pena, J. Proboste, G. Asher, and J. Clare, “MRAS observer for sensorless control of standalone doubly fed induction generators,” IEEE Trans. Energy Convers., vol. 20, no.4, pp. 710-718, Dec. 2005.
[2] T. Senjyu, R. Sakamoto, N. Urasaki, and T. Funabashi, and H. Sekine, “Output power leveling of wind farm using pitch angle control with fuzzy neural network,” in Proc. IEEE Power Engineering Sociey General Metting, June 2006.
[3] R. Sakamoto, T. Senjyu, R. Sakamoto, T. Kaneko, N. Urasaki, and T. Takagi, S. Sugimoto, and H. Sekine, “Output power leveling of wind turbine generator by pitch angle control using Η∞ control,” in Proc. IEEE PSCE Conf., pp. 2044-2049, 2006.
[4] G. Ramtharan, J. B. Ekanayake, and N. Jenkins, “Frequency support from doubly fed induction generator wind turbines,” IET Renew. Power Gener., vol. 1, no.1, pp. 3-9, 2007.
[5] J. Matas, M. Castilla, J. M. Guerrero, L. G. de Vicuna, and J. Miret, “Feedback linearization of direct-drive synchronous wind-turbines via a sliding mode approach,” IEEE Trans. Power Electron., vol. 23, no. 3, pp. 1093-1103, May 2008.
[6] Z. Chen, J. M. Guerrero, and F. Blaabjerg, “A review of the state of the art of power electronics for wind turbines,” IEEE Trans. Power Electron., vol. 24, no. 8, pp. 1859-1875, Aug. 2009.
[7] S. Morimoto, H. Nakayama, M. Sanada, and Y. Takeda, “Sensorless output maximization control for variable-speed wind generation system using IPMSG,” IEEE Trans. Ind. Appl., vol. 41, no. 1, pp. 60-67, Jan/Feb 2005.
[8] R. J. Wai, C. Y. Lin, and Y. R. Chang, “Novel maximum-power-extraction algorithm for PMSG wind generation system,” IET Electr. Power Appl., vol. 1, no. 2, pp. 275-283, 2007.
[9] K. Tan, and S. Islam, “Optimum control strategies in energy conversion of PMSG wind turbine system without mechanical sensors,” IEEE Trans. Energy Convers., vol. 19, no.2, pp. 392-399, June 2004.
[10] M. Chinchilla, S. Arnaltes, and J. C. Burgos, “Control of permanent-magnet generators applied to variable-speed wind-energy systems connected to the Grid,” IEEE Trans. Energy Convers., vol. 21, no.1, pp. 130-135, 2006.
[11] M. G. Simoes, B. K. Bose, and R. J. Spiegel, “Fuzzy logic-based intelligent control of a variable speed cage machine wind generation system,” IEEE Trans. Power Electron., vol. 12, no. 1, pp.87-95, Jan 1997.
[12] Q. Wang, and L. Chang, “An intelligent maximum power extraction algorithm for inverter-based variable speed wind turbine systems,” IEEE Trans. Energy Convers., vol. 19, no.5, pp. 1242-1249, 2004.
[13] M. Karrari, W. Rosehart, and O. P. Malik, “Comprehensive control strategy for a variable Speed Cage Machine Wind Generation Unit,” IEEE Trans. Energy Convers., vo1. 2, no.2, pp.415-423, 2005.
[14] Hui Li, K. L. Shi, and P. G. McLaren, “Neural-network-based Sensorless maximum wind energy capture with compensated power coefficient,” IEEE Trans. Ind. Appl., vol. 41, no.6, pp.1548-1556, 2005.
[15] E. Muljadi, and C. P. Butterfield, “Pitch-controlled variable-speed wind turbine generation,” IEEE Trans. Ind. Appl. vol. 37, no. 1, pp. 240-246, 2001.
[16] D. Andrea, and D. Lorenzo, “Estimator based adaptive fuzzy logic control technique for a wind turbine-generator system,” Energy Conversion and Management, vo1. 44, no. 1, pp. 135-153, 2003.
[17] B. Boukhezzar, and H. Siguerdidjane, “Nonlinear control with wind estimation of a DFIG variable speed wind turbine for power capture optimization,” Energy Conversion and Management, vo1. 50, no. 4, pp. 885-892, 2009.
[18] A. G. Abo-Khalil, and D. C. Lee, “MPPT control of wind generation systems Based on estimated wind speed using SVR,” IEEE Trans. Industrial Electron., vo1. 55, no. 3, pp. 1489-1490, 2008.
[19] B. K. Bose, Power electronics and AC drive. Prentice-Hall, Englewood Cliffs, New York : John Wiley, 1986.
[20] J. O. Pinto, B. K. Bose, L. E. B. Silva, and M P. Kazmierkowski, “A neural-network-based space-vector PWM controller for voltage-fed inverter induction motor drive,” IEEE Trans. Ind. Appl. vol. 36, no. 6, pp.1628-1635, 2000.
[21] M. A. Yurdusev, R. Ata, and N. S. Cetin, “Assessment of optimum tip speed ratio in wind turbines using artificial neural networks,” Energy, vo1. 31, no. 12, pp.2153-2161, 2006.
[22] T. Senjyu, R. Sakamoto, N. Urasaki, and T. Funabashi, H. Fujita, and H. Sekine, “Output power leveling of wind turbine generator for all operating regions by pitch angle control,” Trans. Energy Convers., vol. 21, no.2, pp. 467-475, 2006.
[23] C. T. Lin, and C. S. George Lee, Neural Fuzzy Systems, Prentice-Hall, Inc., 1996.
[24] G. Abad, M. A. Rodriguez, G. Iwanski, and J. Poza, “Direct power control of doubly-fed-induction-generator-based wind turbine under unbalanced grid voltage,” IEEE Trans. Power Electron., vol. 25, no. 2, pp. 442-452, Feb. 2010.
[25] R. Cardenas, and R. Pena, “Sensorless Vector control of induction machines for variable-speed wind energy applications,” IEEE Trans. Energy Convers., vol. 19, no.1, pp. 196-205, 2004.
[26] Y. Y. Hong, S. D. Lu, and C. S. Chiou, “MPPT for PM wind generator using gradient approximation,” Energy Conversion and Management, vol. 50, no. 1, pp. 82-89, 2009.
[27] T. Senjyu, S. Tamaki, N. Urasaki, and K. Uezato, H. Higa, T. Funabashi, H. Fujita, and H. Sekine, “Wind velocity and rotor position sensorless maximum power point tracking control for wind generation system,” 35th Annual IEEE Power Electron. Specialists Conf., pp. 2023-2028, 2004..
[28] M. Rashed, K. B. Goh, M. W. Dunnigan, P. F. A. MacConnell, A. F. Stronach, and B. W. Williams, “Sensorless second-order sliding-mode speed control of a voltage-fed induction-motor drive using nonlinear state feedback,” IEE Proc-Electr. Power Appl., vol. 152, no.5, pp.1127-1136, 2005.
[29] L. M. Fernandez, C. A. Garcia, and F. Jurado, “Comparative study on the performance of control systems for doubly fed induction generator (DFIG) wind turbines operating with power regulation,” Energy, vo1. 33, no.9, pp. 1438-1452, 2008.
[30] T. Furuhashi, S. Sangwongwanich, and S. Okuma, “A position and velocity sensorless control for brushless DC motors using an adaptive sliding mode observer,” IEEE Trans. Ind. Electron., vo1. 39, no.2, pp.89-95, 1992.
[31] R. Blasco-Gimenoz, G. M. Asher, M. Sumner, and K. J. Brdley, “Dynamic performance limitations for MRAS based sensorless induction motor drives,” IET Electr. Power Appl., vol. 143, no. 2, pp. 113-121, 1996.
[32] V. I. Utkin, “Sliding mode control design principles and applications to electric drives,” IEEE Trans. Ind. Electron., vol. 40, no. 1, pp. 23-36, 1993.
[33] F. J. Lin, R. F. Fung, H. H. Lin, and C. M. Hong, “A supervisory fuzzy neural network for slider-crank mechanism,” Mechatronics, vo1. 11, no. 2, pp. 227-250, 2001.
[34] G. C. Liao, and T. P. Tsao, ”Application of a fuzzy neural network combined with a chaos genetic algorithm and simulated annealing to short-term load forecasting,” IEEE Trans. Evolutionary Computation, vol.10, no.3, pp.330-340, 2006.
[35] Y. C. Chen, and C. C. Teng, “A model reference control structure using a fuzzy neural network,” Fuzzy Sets and systems, vo1.73, pp.291-312, 1995.
[36] W. M. Lin, C. M. Hong, and F. S. Cheng, “Fuzzy neural network output maximization control for sensorless wind energy conversion system,” Energy, vol. 35, no. 2, pp.592-601, 2010.
[37] F. J. Lin, K. K. Shyu, and R. J. Wai, “Recurrent-fuzzy-neural-network sliding-mode Controlled motor-toggle servomechanism,” IEEE-ASME Trans. Mechatronics, vo1. 6, no. 4, pp. 453-466, 2001.
[38] S. Kamel, T. Mohammad, and A. S. Mahdi, “Recurrent fuzzy neural network by using feedback error learning approaches for LFC in interconnected power system,” Energy Conversion and Management, vol. 50, no. 4, pp. 938-946, 2009.
[39] C. Schauder, “Adaptive speed identification for vector control of induction motors without rotational transducers,” IEEE Trans. Ind. Appl., vo1. 28, no. 5, pp. 1054-1061, 1992.
[40] H. Tajima, and Y. Hori, “Speed sensorless field orientation control of the induction machine,” IEEE Trans. Ind. Appl., vo1. 29, no. 1, pp. 175-180, 1993.
[41] G. Yang, and T. H. Chin, “Adaptive-speed identification scheme for a vector-controlled speed sensorless inverter-induction motor drive,” IEEE Trans. Ind. Appl., vo1. 29, no. 4, pp. 820-825, 1993.
[42] F. Z. Peng , and T. Fufao, “Robust speed identification for speed-sensorless vector control of induction motors,” IEEE Trans. Ind. Appl., vo1. 30, no. 5, pp. 1234-1240, 1994.
[43] Y. D. Landau, Adaptive Control – The Model Reference Approach, Marcel Dekker, New York : John Wiley, 1979.
[44] W. M. Lin, C. M. Hong, and F. S. Cheng, “Design of intelligent controllers for wind generation system with sensorless maximum wind energy control,” Energy Conversion and Management, vol. 52, no. 2, pp. 1086-1096, 2011.
[45] A. A. Esmin, G. L. Torres, and C. Z. Souza, “A hybrid particle swarm optimization applied to loss power minimization,” IEEE Trans. Power Systems vo1. 20, no. 2, pp. 859-866, 2005.
[46] T. Li, C. Wei, and W. Pei, “PSO with sharing for multimodal function optimization,” IEEE Int. Conf. Neural Networks and Signal Process., vol. 1, pp. 450-453, 2003.
[47] T. Thiringer, and J. Linders, “Control by variable rotor speed of a fixed-pitch wind turbine operating in a wide speed range,” IEEE Trans. Energy Convers. vol. EC-8, pp. 520-526, 1993.
[48] R. Chedid, F. Mrad, and M. Basma, “Intelligent control of a class of wind energy conversion systems,” IEEE Trans. Energy Convers., vol. EC-14, pp.1597-1604, 1999
[49] T. Tanaka, and T. Toumiya, “Output control by hill-climbing method for a small wind power generating system,” Renewable Energy, vo1. 12, no. 4, pp. 387-400, 1997.
[50] E. Koutroulis, and K. Kalaitzakis, “Design of a maximum power tracking system for wind-energy-conversion applications,” IEEE Trans. Ind. Electron., vo1. 53, no. 2, pp. 486-494, 2006.
[51] C. T. Pan, and Y. L. Juan, “A novel sensorless MPPT controller for a high efficiency micro-scale wind power generation system,” IEEE Trans. Energy Convers., vol. 25, no. 1, pp. 207-216, 2010.
[52] Y. F. Chen, H. T. Yang, J. T. Liao, and Z. S. Zeng, “Design and implementation of grid-connected wind power conversion system with three-phase PFC and MPPT Functions,” Proceedings of the 31st Symposium on Electrical Power Engineering, pp. 1819-1824, Dec. 2010.
[53] J. S. R. Jang, and C. T. Sun, Neuro-Fuzzy and Soft Computing: A computational approach to learning and machine intelligence. Upper Saddle River, NJ: Prentice-Hall, 1997.
[54] J. S. R. Jang, and C. T. Sun, “Function equivalence between radial basis function networks and fuzzy inference systems,” IEEE Trans. Neural Networks, vol. 4, no. 1, pp. 156-159,1993.
[55] S. Seshagiri, and H. K. Khail, “Output feedback control of nonlinear systems using RBF Function equivalence between radial basis function neural networks,” IEEE Trans. Neural Networks, vol.11, no. 1, pp. 69-79, 2000.
[56] R. V. Hogg, J. W. McKean, and A. T. Craig, Introduction to Mathematical Statistics. Sixth Edition, New Jersey: Prentice-Hall, 2005.
[57] G. W. Chang, C. I Chen, and Y. F. Teng, “Radial basis function-based neural network for power system harmonics detection,” IEEE Trans. Ind. Electron., vol. 57, no. 6, pp. 2171-2179, June 2010.
[58] W. M. Lin, and C. M. Hong, “Intelligent approach to maximum power point tracking control strategy for variable-speed wind turbine generation system,” Energy, vol. 35, no. 6, pp. 2440-2447, 2010.
[59] J. L. Elman, “Finding structure in time,” Cognitive Science, vo1. 14, no.2, pp.179-211,1990.
[60] H. Liu, S. Wang, and P. Ouyang, ”Fault diagnosis based on improved Elman neural network for a hydraulic servo system,” in Proc. Int. Conf. Robotics, Automation and Mechatronics, pp. 1-6, 2006.
[61] X. Li, G. Chen, Z. Chen, and Z. Yuan, “Chaotifying linear Elman networks,” IEEE Trans. Neural Network, vol. 13, no.5, pp. 1193-1199, 2002.
[62] F. J. Lin, and Y. C. Hung, “FPGA based Elman neural network control system for linear ultrasonic motor,” IEEE Trans. Ferr. Ultras. And Freq. Control., vol. 56, no.1, pp. 101-113, 2009.
[63] R. Jacob, and R. S. Yahya, “Particle swarm optimization in electromagnetics,” IEEE Trans. Antennas and Propag., vol. 52, no.2, pp. 397-407, 2004.
[64] Q. Wei, Z. Wei, J. M. Aller, and R. G. Harley, “Wind speed estimation based sensorless output maximization control for a wind turbine driving a DFIG,” IEEE Trans. Power Electron., vol. 23, no. 3, pp.1156-1169, May 2008.
[65] B. Shen, B. Mwinyiwiwa, Y. Zhang, and B. T. Ooi, “Sensorless maximum power point tracking of wind by DFIG using rotor position phase lock loop (PLL),” IEEE Trans. Power Electron., vol. 24, no. 4, pp. 942-951, April 2009.
[66] W. M. Lin, C. M. Hong, “A new Elman neural network-based control algorithm for adjustable-pitch variable speed wind energy conversion systems,” IEEE Trans. Power Electron., vol. 26, no. 2, pp. 473-481, 2011.
[67] W. M. Lin, C. M. Hong, T. C. Ou, and T. M. Chiu, “Hybrid intelligent control of PMSG wind generation system using pitch angle control with RBFN,” Energy Conversion and Management, vol. 52, no. 2, pp. 1244-1251, 2011.
[68] W. M. Lin, C. M. Hong, and F. S. Cheng “On-line designed hybrid controller with adaptive observer for variable-speed wind generation system,” Energy, vol. 35, no. 7, pp. 3022-3030, 2010.
[69] Z. Chen, S. A. Gomez, M. McCormick, “A fuzzy logic controlled power electronic system for variable speed wind energy conversion systems,” in Proc. 8th Int. Conf. on Power Electron. and Variable Speed Drives. pp. 114-119, 2000.
[70] D. Santos-Martin, J. L. Rodriguez-Amenedo, S. Arnaltes, “Providing ride-through capability to a doubly fed induction generator under unbalanced voltage dips,” IEEE Trans. Power Electron., vol. 24, no. 7, pp. 1747-1757, July 2009.
[71] G. Abad, M. A. Rodriguez, G. Iwanski, and J. Poza, “Direct power control of doubly-fed-induction-generator-based wind turbine under unbalanced grid voltage,” IEEE Trans. Power Electron., vol. 25, no. 2, pp. 442-452, Feb. 2010.
[72] P. S. Flannery, and G. Venkataramanan, “Unbalance voltage sag ride-through of doubly fed Induction generator wind turbine with series grid-side converter,” IEEE Trans. Ind. Appl., vol. 45, no. 5, pp. 1879-1887, Sept./Oct. 2010.
[73] S. Heier, and R. Waddington, Grid Integration of Wind Energy Conversion Systems, Chichester, New York : John Wiley & Sons, 1998.
[74] Marco Liserre, “Grid requirements to connect DPGS based on RES,” Available: www.tf.uni-kiel.de/etit/LEA/dl-open/vl.../liserre_lecture_3.pps.
[75] “Eltra Specifications, Wind Farms Connected to the Grid With Voltages Over 100 KV. Technical Regulations for the Properties and Control of Wind Turbines,” Tech. Rep., Eltra Corp., 2004.
[76] “Regeneration energy,” Tech. Rules, Taiwan power company, Available: http://www.taipower.com.twRegeneration_energy.htm.
[77] Y. A-R. Mohamed, and E. F. El-Saadany, “Adaptive decentralized droop control to preserve power sharing stability of paralleled inverters in distributed genrtation microgrids,” IEEE Trans. Power Electron., vol. 23, no. 6, pp. 2806-2816, Nov. 2008.
[78] N. Hatziargyrious, H. Asano, R. Iravani, and C. Marnay, “Microgrids,” IEEE Power Enery Mag.,, vol. 5, no. 4, pp. 78-94, Jul./Aug. 2007.
[79] F. S. Pai, and S. J. Huang, “Design and operation of power converter for micro-turbine powered distributed generator with capacity-expansion capability,” IEEE Trans. on Energy Convers., vol. 23, no. 1, pp. 110-118, 2008.
[80] Y. M. Cheng, Y. C. Liu, S. C. Hung, and C. S. Cheng, “Multi-input inverter for grid-connected hybrid PV/wind power system,” IEEE Trans. Power Electron., vol. 22, no. 3, pp. 1070–1076, May 2007.
[81] A. Payman, S. Pierfederici, and F. Meibody-Tabar, “Energy management in a fuel cell/supercapacitor multisource/multiload electrical hybrid system,” IEEE Trans. Power Electron., vol.24, no.12, pp.2681-2691, Dec. 2009.