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Modeling, Control, Estimation, and Optimization for Microgrids: A Fuzzy-Model-Based Method [Kõva köide]

  • Formaat: Hardback, 379 pages, kõrgus x laius: 234x156 mm, kaal: 900 g, 4 Tables, black and white; 42 Illustrations, black and white
  • Ilmumisaeg: 20-Nov-2019
  • Kirjastus: CRC Press
  • ISBN-10: 1138491659
  • ISBN-13: 9781138491656
Teised raamatud teemal:
Modeling, Control, Estimation, and Optimization for Microgrids: A Fuzzy-Model-Based MethodSuurem pilt
  • Formaat: Hardback, 379 pages, kõrgus x laius: 234x156 mm, kaal: 900 g, 4 Tables, black and white; 42 Illustrations, black and white
  • Ilmumisaeg: 20-Nov-2019
  • Kirjastus: CRC Press
  • ISBN-10: 1138491659
  • ISBN-13: 9781138491656
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781138491656.html
Märksõnad:

Due to increasing economic and environmental pressures, small-scale grids have received increasing attention in the last fifteen years. These renewable sources, such as solar PVs, wind turbines, and fuel cells, integrated with grid, have changed the way we live our lives. This book describes microgrid dynamics modeling and nonlinear control issues from introductory to the advanced steps. The book addresses the most relevant challenges in microgrid protection and control including modeling, uncertainty, stability issues, local control, coordination control, power quality, and economic dispatch.

List of Figures xiii
List of Tables xv
Preface xvii
Part I Fuzzy Modeling and Local Control for Microgrid Components
Chapter 1 Fuzzy Modeling and Control of Photovoltaic (PV) Power
5(32)
1.1 Modeling of PV Power
6(4)
1.1.1 Modeling of PV Power with DC Load
6(2)
1.1.2 Modeling of PV Power with AC Load
8(2)
1.2 Control of PV Power
10(2)
1.2.1 Stability Analysis of PV Power
10(1)
1:2.2 Control Synthesis of PV Power
11(1)
1.3 MPPT Fuzzy Control of PV Power
12(5)
1.3.1 Modeling of MPPT of PV Power with DC Load
12(2)
1.3.2 Modeling of MPPT of PV Power with AC Load
14(1)
1.3.3 MPPT Controller Design
15(2)
1.4 Robust MPPT Fuzzy Observer-Based Control
17(6)
1.4.1 Modelling of Uncertain PV Power
18(2)
1.4.2 Design of Observer-Based Controller
20(3)
1.5 Finite-Time MPPT via Sliding Mode Control
23(7)
1.5.1 Design of FSMC Law for PV Power with MPPT
24(2)
1.5.2 Reaching Phase in FTB for PV Power with FSMC Law
26(4)
1.5.3 Design Procedure for MPPT Algorithm
30(1)
1.6 Simulation Studies
30(4)
1.6.1 Solar PV Power with DC-DC Boost Converter
30(1)
1.6.2 Solar PV Power with DC-DC Buck Converter
31(1)
1.6.3 Solar PV Power with MPPT Control
32(2)
1.7 References
34(3)
Chapter 2 Fuzzy Modeling and Control of Wind Power
37(22)
2.1 Modeling of Wind Power
38(8)
2.1.1 Modeling of Variable Speed Wind Power
38(3)
2.1.2 Modeling of Wind Power with DC Load
41(3)
2.1.3 Modeling of Wind Power with AC Load
44(2)
2.2 Control of Wind Power with PMSG
46(2)
2.2.1 Stability Analysis of Wind Power
46(1)
2.2.2 Design of Wind Power with MPPT Control
46(2)
2.3 Finite-Time MPPT of Wind Power via Sliding Mode Control
48(6)
2.3.1 Design of Wind Power with FSMC Law
49(2)
2.3.2 Reaching Phase in FTB of Wind Power
51(3)
2.3.3 Design Procedure for MPPT Algorithm
54(1)
2.4 Simulation Studies
54(3)
2.4.1 MPPT Control of Wind Power with PMSG
54(1)
2.4.2 FTB of SMC of Wind Power with PMSG
55(2)
2.5 References
57(2)
Chapter 3 Fuzzy Modeling and Control Energy Storage Systems
59(26)
3.1 Modeling and Control of Lead-Acid Batteries
60(6)
3.1.1 Modeling of Lead-Acid Batteries
60(1)
3.1.2 Charge Modeling
61(2)
3.1.3 Discharge Modeling
63(1)
3.1.4 Switching Charge and Discharge Operations
64(1)
3.1.5 SOC Estimation of Switching Operations
65(1)
3.2 Modeling and Control of Li-Ion Batteries
66(11)
3.2.1 Li-Ion Batteries Based on Single Particle Model (SPM)
67(3)
3.2.2 Li-Ion Batteries Based on Circuit Model
70(2)
3.2.3 Stability Analysis of SOC Estimation System
72(2)
3.2.4 Design of Observer-Based Fuzzy Controller
74(3)
3.3 Modeling of Supercapacitors
77(1)
3.4 Simulation Studies
78(1)
3.5 References
79(6)
Part II Coordinated Fuzzy Control for Microgrids
Chapter 4 Centralized Fuzzy Control
85(34)
4.1 Modeling of Multi-PV Generators
85(6)
4.1.1 Modeling of Multi-PVs with DC Load
85(3)
4.1.2 Modeling of Multi-Photovoltaic System with AC Load
88(3)
4.2 Modeling of Multi-Machine Wind Generators
91(4)
4.2.1 Modeling of Multi-Wind Systems with DC Loads
91(2)
4.2.2 Modeling of Multi-Wind Generator With AC Load
93(2)
4.3 Centralized Control of Tracking Synchronization
95(21)
4.3.1 Centralized Fuzzy Control
95(1)
4.3.2 Design of Stabilization Controller
96(1)
4.3.3 Centralized Sampled-Data Controller with Event-Triggered ZOH
97(5)
4.3.4 Centralized Sampled-Data Controller Design with Time-Triggered ZOH
102(6)
4.3.5 Centralized Sampled-Date Control with Time Delay
108(8)
4.4 Simulation Studies
116(1)
4.5 References
117(2)
Chapter 5 Decentralized Fuzzy Control
119(26)
5.1 Modeling of Multi-PV Generators
119(2)
5.1.1 Modeling of Multi-PV Power with DC Load
119(1)
5.1.2 Modeling of Multi-PV Generators with AC Load
120(1)
5.2 Modeling of Multi-Machine Wind Generator
121(2)
5.2.1 Modeling of Multi-Machine Wind with DC Load
121(1)
5.2.2 Modeling of Multi-Machine Wind Generator with AC Load
122(1)
5.3 Decentralized Control of Tracking Synchronization
123(18)
5.3.1 Decentralized Fuzzy Control
123(4)
5.3.2 Decentralized Sampled-Data Control with Event-Driven ZOH
127(7)
5.3.3 Decentralized Sampled-Data Control with Time-Driven ZOH
134(7)
5.4 Simulation Studies
141(3)
5.5 References
144(1)
Chapter 6 Distributed Fuzzy Control
145(32)
6.1 Distributed Control of Tracking Synchronization
145(22)
6.1.1 Design of Distributed Fuzzy Controller
145(4)
6.1.2 Design of Distributed Sampled-Data Controller
149(9)
6.1.3 Distributed Sampled-Data Control with Time-Driven ZOH
158(9)
6.2 Simulation Studies
167(4)
6.3 References
171(6)
Part III Energy Management for Microgrids
Chapter 7 Operation of Microgrids
177(54)
7.1 Photovoltaic System for DC Load
177(6)
7.1.1 Operation Modes
177(1)
7.1.2 Dynamic Modeling
178(5)
7.2 Photovoltaic System for AC Load
183(7)
7.2.1 Operation Modes
183(2)
7.2.2 Dynamic Modeling
185(5)
7.3 PMSG System for DC Load
190(7)
7.3.1 Operation Modes
190(2)
7.3.2 Dynamic Modeling
192(5)
7.4 PMSG System for AC Load
197(9)
7.4.1 Operation Modes
199(1)
7.4.2 Dynamic Modeling
199(7)
7.5 PV system and PMSG system for DC load
206(10)
7.5.1 Operation Modes
208(1)
7.5.2 Dynamic Modeling
208(8)
7.6 PMSG system and PV system for AC load
216(13)
7.6.1 Operation Modes
217(1)
7.6.2 Dynamic Modeling
217(12)
7.7 References
229(2)
Chapter 8 Optimization of Microgrids
231(32)
8.1 Power Management Strategy
232(1)
8.2 Transient Performance Analysis
232(13)
8.2.1 MPPT Optimal Algorithm for Single Generator
232(4)
8.2.2 MPPT Optimal Algorithm for Multi-Machine Generators
236(6)
8.2.3 Optimal Algorithm for Multi-Mode Operation
242(3)
8.3 Steady-State Performance Analysis
245(10)
8.3.1 MPPT Optimal Algorithm for Single Generator
245(3)
8.3.2 MPPT Optimal Algorithm for Multi-Machine Generators
248(4)
8.3.3 Optimal Algorithm for Multi-Mode Operation
252(3)
8.4 Simulation Studies
255(1)
8.5 References
256(7)
Part IV Cyber-Physical Control Framework for Microgrids
Chapter 9 Fuzzy Control with Network-Induced Delay
263(40)
9.1 Network-Induced Delays in Local Subsystems
263(16)
9.1.1 Decentralized Control Problems
263(3)
9.1.2 Model Transformation
266(2)
9.1.3 Design of Decentralized Dynamic Output Feedback Control
268(11)
9.2 Network-Induced Delay in Interconnected Systems
279(19)
9.2.1 Model Transformation
282(2)
9.2.2 Design of Decentralized Control of Reachable Set
284(14)
9.3 Simulation Studies
298(2)
9.4 References
300(3)
Chapter 10 Event-Triggered Fuzzy Control
303(46)
10.1 Centralized Event-Triggered Fuzzy Control
303(12)
10.1.1 Problem Formulation
303(4)
10.1.2 Design of Centralized Event-Triggered Control
307(2)
10.1.3 Relaxing Design of Centralized Event-Triggered Control
309(6)
10.2 Decentralized Event-Triggered Fuzzy Control
315(14)
10.2.1 Problem Formulation
315(4)
10.2.2 Co-Design of Decentralized Event-Triggered Control
319(10)
10.3 Distributed Event-Triggered Fuzzy Control
329(11)
10.3.1 Design of Distributed Event-Triggered Controller
329(11)
10.4 Simulation Studies
340(6)
10.5 References
346(3)
Chapter 11 Estimation and Compensation for TDS Attacks
349(22)
11.1 TDS Attack of Local Components
350(7)
11.1.1 Reachable Set Estimation for Tracking Control
351(1)
11.1.2 Observer Design for System State and Delay Perturbation
351(4)
11.1.3 Compensation Mechanism for the Perturbation of TDS Attack
355(2)
11.1.4 Design Procedure for Reachable Set Estimation
357(1)
11.2 TDS Attack of Power Networks
357(9)
11.2.1 Fuzzy Modeling of Power Networks
357(2)
11.2.2 TDS Attacks
359(1)
11.2.3 Observer Design for TDS Attacks
360(4)
11.2.4 Compensation Control for TDS Attacks
364(2)
11.2.5 Design Procedure for Attenuating TDS Attacks
366(1)
11.3 Simulation Studies
366(3)
11.4 References
369(2)
Index 371
Zhixiong Zhong received a B.S. degree in Mechatronics from Fuzhou University, Fuzhou, China, in 2008 and a M.S. degree in control theory and control engineering from Fuzhou University, Fuzhou, China, in 2012. In 2013 he was a visiting student with the Department of Mechanical Engineering, University of Victoria, Canada. In 2015 he received the Ph.D. degree in the Research Institute of Intelligent Control and Systems, Harbin Institute of Technology, Harbin, China and jointed in the School of Electrical Engineering and Automation, Xiamen University of Technology, Xiamen, China. He has published a number of international journal and conference papers and authored the book Large-Scale Fuzzy Interconnected Control Systems Design and Analysis (IGI Global, 2017). He is a Reviewer of several journals. His research interests include fuzzy control, robust filtering and microgrid control.