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El. knyga: Artificial Intelligence-based Smart Power Systems [Wiley Online]

(Anna University, Chennai, India), (Leading Engineering Organisation, Chennai, India), (Aalborg University), (University of South-Eastern Norway, Norway)
  • Formatas: 400 pages
  • Išleidimo metai: 19-Dec-2022
  • Leidėjas: Wiley-IEEE Press
  • ISBN-10: 1119893992
  • ISBN-13: 9781119893998
Kitos knygos pagal šią temą:
  • Wiley Online
  • Kaina: 158,59 €*
  • * this price gives unlimited concurrent access for unlimited time
Artificial Intelligence-based Smart Power SystemsDidesnis paveikslėlis
  • Formatas: 400 pages
  • Išleidimo metai: 19-Dec-2022
  • Leidėjas: Wiley-IEEE Press
  • ISBN-10: 1119893992
  • ISBN-13: 9781119893998
Kitos knygos pagal šią temą:
Wiley Online E-booksMore info about Wiley Online
Partnerių interneto svetainė: https://onlinelibrary.wiley.com/doi/book/10.1002/9781119893998

Authoritative resource describing the artificial intelligence and advanced technologies in smart power systems with simulation examples and case studies

Artificial Intelligence-based Smart Power Systems presents advanced technologies used in various aspects of smart power systems, especially grid-connected and industrial evolution, covering many new topics such as distribution Phasor management, blockchain technologies for smart power systems, the application of deep learning and reinforced learning, and artificial intelligence techniques. The text also explores the potential consequences of artificial intelligence and advanced technologies in smart power systems in the forthcoming years.

To enhance and reinforce learning, the highly qualified editors include many learning resources throughout the text, including MATLAB and HIL codes, end-of-chapter problems, end-of-book solutions, practical examples, and case studies.

Artificial Intelligence-based Smart Power Systems includes specific information on topics such as:

  • Modeling and analysis of smart power systems, covering steady state analysis, dynamic analysis, voltage stability, and more
  • Recent advancement in power electronics for smart power systems, covering power electronic converters for renewable energy sources, electric vehicles, and HDVC/FACTs
  • Distribution Phasor Measurement Units (PMU) in smart power systems, covering the need for PMU in distribution and automation of system reconfigurations
  • Power and energy management systems for microgrids

Engineering colleges and universities, along with industry research centers, can use the in-depth subject coverage and the extensive supplementary learning resources found in Artificial Intelligence-based Smart Power Systems to gain a holistic understanding of the subject and be able to harness that knowledge within a myriad of practical applications.

Editor Biography xv
List of Contributors
xvii
1 Introduction to Smart Power Systems
1(14)
Sivaraman Palanisamy
Zahira Rahiman
Sharmeeta Chenniappan
1.1 Problems in Conventional Power Systems
1(1)
1.2 Distributed Generation (DG)
1(1)
1.3 Wide Area Monitoring and Control
2(2)
1.4 Automatic Metering Infrastructure
4(2)
1.5 Phasor Measurement Unit
6(2)
1.6 Power Quality Conditioners
8(1)
1.7 Energy Storage Systems
8(1)
1.8 Smart Distribution Systems
9(1)
1.9 Electric Vehicle Charging Infrastructure
10(1)
1.10 Cyber Security
11(1)
1.11 Conclusion
11(4)
References
11(4)
2 Modeling and Analysis of Smart Power System
15(22)
Madhu Palati
Sagar Singh Prathap
Nagesh Hatasahalli Nagaraju
2.1 Introduction
15(1)
2.2 Modeling of Equipment's for Steady-State Analysis
16(6)
2.2.1 Load Flow Analysis
16(2)
2.2.1.1 Gauss Seidel Method
18(1)
2.2.1.2 Newton Raphson Method
18(1)
2.2.1.3 Decoupled Load Flow Method
18(1)
2.2.2 Short Circuit Analysis
19(1)
2.2.2.1 Symmetrical Faults
19(1)
2.2.2.2 Unsymmetrical Faults
20(1)
2.2.3 Harmonic Analysis
20(2)
2.3 Modeling of Equipments for Dynamic and Stability Analysis
22(2)
2.4 Dynamic Analysis
24(2)
2.4.1 Frequency Control
24(2)
2.4.2 Fault Ride Through
26(1)
2.5 Voltage Stability
26(1)
2.6 Case Studies
27(7)
2.6.1 Case Study 1
27(1)
2.6.2 Case Study 2
28(1)
2.6.2.1 Existing and Proposed Generation Details in the Vicinity of Wind Farm
29(1)
2.6.2.2 Power Evacuation Study for 50 MW Generation
30(1)
2.6.2.3 Without Interconnection of the Proposed 50 MW Generation from Wind Farm on 66 kV Level of 220/66 kV Substation
31(1)
2.6.2.4 Observations Made from Table 2.6
31(1)
2.6.2.5 With the Interconnection of Proposed 50 MW Generation from Wind Farm on 66 kV level of 220/66 kV Substation
31(1)
2.6.2.6 Normal Condition without Considering Contingency
32(1)
2.6.2.7 Contingency Analysis
32(1)
2.6.2.8 With the Interconnection of Proposed 60 MW Generation from Wind Farm on 66 kV Level of 220/66 kV Substation
33(1)
2.7 Conclusion
34(3)
References
34(3)
3 Multilevel Cascaded Boost Converter Fed Multilevel Inverter for Renewable Energy Applications
37(28)
Marimuthu Marikannu
Vijayalakshmi Subramanian
Paranthagan Balasubramanian
Jayakumar Narayanasamy
Nisha C. Rani
Devi Vigneshwari Balasubramanian
3.1 Introduction
37(3)
3.2 Multilevel Cascaded Boost Converter
40(2)
3.3 Modes of Operation of MCBC
42(3)
3.3.1 Mode-1 Switch SA Is ON
42(1)
3.3.2 Mode-2 Switch SA Is ON
42(1)
3.3.3 Mode-3-Operation - Switch SA Is ON
42(1)
3.3.4 Mode-4-Operation - Switch SA Is ON
42(1)
3.3.5 Mode-5-Operation - Switch SA Is ON
42(1)
3.3.6 Mode-6-Operation - Switch SA Is OFF
42(1)
3.3.7 Mode-7-Operation - Switch SA Is OFF
42(1)
3.3.8 Mode-8-Operation - Switch SA Is OFF
43(1)
3.3.9 Mode-9-Operation - Switch SA Is OFF
44(1)
3.3.10 Mode 10-Operation - Switch SA is OFF
45(1)
3.4 Simulation and Hardware Results
45(4)
3.5 Prominent Structures of Estimated DC-DC Converter with Prevailing Converter
49(5)
3.5.1 Voltage Gain and Power Handling Capability
49(1)
3.5.2 Voltage Stress
49(1)
3.5.3 Switch Count and Geometric Structure
49(3)
3.5.4 Current Stress
52(1)
3.5.5 Duty Cycle Versus Voltage Gain
52(1)
3.5.6 Number of Levels in the Planned Converter
52(2)
3.6 Power Electronic Converters for Renewable Energy Sources (Applications of MLCB)
54(1)
3.6.1 MCBC Connected with PV Panel
54(1)
3.6.2 Output Response of PV Fed MCBC
54(1)
3.6.3 H-Bridge Inverter
54(1)
3.7 Modes of Operation
55(5)
3.7.1 Mode 1
55(1)
3.7.2 Mode 2
55(1)
3.7.3 Mode 3
56(1)
3.7.4 Mode 4
56(1)
3.7.5 Mode 5
56(1)
3.7.6 Mode 6
56(2)
3.7.7 Mode 7
58(1)
3.7.8 Mode 8
58(1)
3.7.9 Mode 9
59(1)
3.7.10 Mode 10
59(1)
3.8 Simulation Results of MCBC Fed Inverter
60(1)
3.9 Power Electronic Converter for E-Vehicles
61(1)
3.10 Power Electronic Converter for HVDC/Facts
62(1)
3.11 Conclusion
63(2)
References
63(2)
4 Recent Advancements in Power Electronics for Modern Power Systems-Comprehensive Review on DC-Link Capacitors Concerning Power Density Maximization in Power Converters
65(34)
Naveenkumar Marati
Shariq Ahammed
Kathirvel Karuppazaghi
Bdiraj Vaithilingam
Gyan R. Biswal
Phaneendra B. Bobba
Sanjeevikumar Padmanaban
Sharmeela Chenniappan
4.1 Introduction
65(1)
4.2 Applications of Power Electronic Converters
66(2)
4.2.1 Power Electronic Converters in Electric Vehicle Ecosystem
66(1)
4.2.2 Power Electronic Converters in Renewable Energy Resources
67(1)
4.3 Classification of DC-Link Topologies
68(1)
4.4 Briefing on DC-Link Topologies
69(13)
4.4.1 Passive Capacitive DC Link
69(1)
4.4.1.1 Filter Type Passive Capacitive DC Links
70(2)
4.4.1.2 Filter Type Passive Capacitive DC Links with Control
72(2)
4.4.1.3 Interleaved Type Passive Capacitive DC Links
74(1)
4.4.2 Active Balancing in Capacitive DC Link
75(1)
4.4.2.1 Separate Auxiliary Active Capacitive DC Links
76(2)
4.4.2.2 Integrated Auxiliary Active Capacitive DC Links
78(4)
4.5 Comparison on DC-Link Topologies
82(12)
4.5.1 Comparison of Passive Capacitive DC Links
82(1)
4.5.2 Comparison of Active Capacitive DC Links
83(3)
4.5.3 Comparison of DC Link Based on Power Density, Efficiency, and Ripple Attenuation
86(8)
4.6 Future and Research Gaps in DC-Link Topologies with Balancing Techniques
94(1)
4.7 Conclusion
95(4)
References
95(4)
5 Energy Storage Systems for Smart Power Systems
99(16)
Sivaraman Patanisamy
Logeshkumar Shanmugasundaram
Sharmeela Chenniappan
5.1 Introduction
99(1)
5.2 Energy Storage System for Low Voltage Distribution System
100(1)
5.3 Energy Storage System Connected to Medium and High Voltage
101(3)
5.4 Energy Storage System for Renewable Power Plants
104(5)
5.4.1 Renewable Power Evacuation Curtailment
106(3)
5.5 Types of Energy Storage Systems
109(2)
5.5.1 Battery Energy Storage System
109(1)
5.5.2 Thermal Energy Storage System
110(1)
5.5.3 Mechanical Energy Storage System
110(1)
5.5.4 Pumped Hydro
110(1)
5.5.5 Hydrogen Storage
110(1)
5.6 Energy Storage Systems for Other Applications
111(1)
5.6.1 Shift in Energy Time
111(1)
5.6.2 Voltage Support
111(1)
5.6.3 Frequency Regulation (Primary, Secondary, and Tertiary)
112(1)
5.6.4 Congestion Management
112(1)
5.6.5 Black Start
112(1)
5.7 Conclusion
112(3)
References
113(2)
6 Real-Time Implementation and Performance Analysis of Supercapacitor for Energy Storage
115(14)
Thamatapu Eswararao
Sundaram Elango
Umashankar Subramanian
Krishnamohan Tatikonda
Garika Gantaiahswamy
Sharmeela Chenniappan
6.1 Introduction J
25(92)
6.2 Structure of Supercapacitor
117(1)
6.2.1 Mathematical Modeling of Supercapacitor
117(1)
6.3 Bidirectional Buck-Boost Converter
118(2)
6.3.1 FPGA Controller
119(1)
6.4 Experimental Results
120(3)
6.5 Conclusion
123(6)
References
125(4)
7 Adaptive Fuzzy Logic Controller for MPPT Control in PMSG Wind Turbine Generator
129(12)
Rania Moutchou
Ahmed Abbou
Bouazza Jabri
Salah E. Rhaili
Khalid Chigane
7.1 Introduction
129(1)
7.2 Proposed MPPT Control Algorithm
130(1)
7.3 Wind Energy Conversion System
131(2)
7.3.1 Wind Turbine Characteristics
131(1)
7.3.2 Model of PMSG
132(1)
7.4 Fuzzy Logic Command for the MPPT of the PMSG
133
7.4.1 Fuzzification
134(1)
7.4.2 Fuzzy Logic Rules
134(1)
7.4.3 Denazification
134(1)
7.5 Results and Discussions
135(4)
7.6 Conclusion
139(2)
References
139(2)
8 A Novel Nearest Neighbor Searching-Based Fault Distance Location Method for HVDC Transmission Lines
141(16)
Aleena Swetapadma
Shobha Agarwal
Satarupa Chakrabarti
Soham Chakrabarti
8.1 Introduction
141(1)
8.2 Nearest Neighbor Searching
142(2)
8.3 Proposed Method
144(2)
8.3.1 Power System Network Under Study
144(1)
8.3.2 Proposed Fault Location Method
145(1)
8.4 Results
146(8)
8.4.1 Performance Varying Nearest Neighbor
147(1)
8.4.2 Performance Varying Distance Matrices
147(1)
8.4.3 Near Boundary Faults
148(1)
8.4.4 Far Boundary Faults
149(1)
8.4.5 Performance During High Resistance Faults
149(1)
8.4.6 Single Pole to Ground Faults
150(1)
8.4.7 Performance During Double Pole to Ground Faults
151(1)
8.4.8 Performance During Pole to Pole Faults
151(1)
8.4.9 Error Analysis
152(1)
8.4.10 Comparison with Other Schemes
153(1)
8.4.11 Advantages of the Scheme
154(1)
8.5 Conclusion
154(3)
Acknowledgment
154(1)
References
154(3)
9 Comparative Analysis of Machine Learning Approaches in Enhancing Power System Stability
157(22)
Md. I. H. Pathan
Mohammad S. Shahriar
Mohammad M. Rahman
Md. Sanwar Hossain
Nadia Awatif
Md. Shafiullah
9.1 Introduction
157(2)
9.2 Power System Models
159(2)
9.2.1 PSS Integrated Single Machine Infinite Bus Power Network
159(1)
9.2.2 PSS-UPFC Integrated Single Machine Infinite Bus Power Network
160(1)
9.3 Methods
161(4)
9.3.1 Group Method Data Handling Model
161(1)
9.3.2 Extreme Learning Machine Model
162(1)
9.3.3 Neurogenetic Model
162(1)
9.3.4 Multigene Genetic Programming Model
163(2)
9.4 Data Preparation and Model Development
165(1)
9.4.1 Data Production and Processing
165(1)
9.4.2 Machine Learning Model Development
165(1)
9.5 Results and Discussions
166(7)
9.5.1 Eigenvalues and Minimum Damping Ratio Comparison
166(4)
9.5.2 Time-Domain Simulation Results Comparison
170(1)
9.5.2.1 Rotor Angle Variation Under Disturbance
170(1)
9.5.2.2 Rotor Angular Frequency Variation Under Disturbance
171(2)
9.5.2.3 DC-Link Voltage Variation Under Disturbance
173(1)
9.6 Conclusions
173(6)
References
174(5)
10 Augmentation of PV-Wind Hybrid Technology with Adroit Neural Network, ANFIS, and PI Controllers Indeed Precocious DVR System
179(28)
Jyoti Shukla
Basanta K. Panigrahi
Monika Vardia
10.1 Introduction
179(1)
10.2 PV-Wind Hybrid Power Generation Configuration
180(1)
10.3 Proposed Systems Topologies
181(6)
10.3.1 Structure of PV System
181(2)
10.3.2 The MPPTs Technique
183(1)
10.3.3 NN Predictive Controller Technique
183(1)
10.3.4 ANFIS Technique
184(2)
10.3.5 Training Data
186(1)
10.4 Wind Power Generation Plant
187(2)
10.5 Pitch Angle Control Techniques
189(3)
10.5.1 PI Controller
189(1)
10.5.2 NARMA-L2 Controller
190(2)
10.5.3 Fuzzy Logic Controller Technique
192(1)
10.6 Proposed DVRs Topology
192(1)
10.7 Proposed Controlling Technique of DVR
193(3)
10.7.1 ANFIS and PI Controlling Technique
193(3)
10.8 Results of the Proposed Topologies
196(8)
10.8.1 PV System Outputs (MPPT Techniques Results)
196(1)
10.8.2 Main PV System outputs
196(2)
10.8.3 Wind Turbine System Outputs (Pitch Angle Control Technique Result)
198(1)
10.8.4 Proposed PMSG Wind Turbine System Output
199(4)
10.8.5 Performance of DVR (Controlling Technique Results)
203(1)
10.8.6 DVRs Performance
203(1)
10.9 Conclusion
204(3)
References
204(3)
11 Deep Reinforcement Learning and Energy Price Prediction
207(26)
Deepak Yadav
Saad Mekhilef
Brijesh Singh
Muhyaddin Rawa
Abbreviations
207(1)
11.1 Introduction
208(2)
11.2 Deep and Reinforcement Learning for Decision-Making Problems in Smart Power Systems
210(3)
11.2.1 Reinforcement Learning
210(1)
11.2.1.1 Markov Decision Process (MDP)
210(1)
11.2.1.2 Value Function and Optimal Policy
211(1)
11.2.2 Reinforcement Learnings to Deep Reinforcement Learnings
212(1)
11.2.3 Deep Reinforcement Learning Algorithms
212(1)
11.3 Applications in Power Systems
213(5)
11.3.1 Energy Management
213(2)
11.3.2 Power Systems' Demand Response (DR)
215(1)
11.3.3 Electricity Market
216(1)
11.3.4 Operations and Controls
217(1)
11.4 Mathematical Formulation of Objective Function
218(2)
11.4.1 Locational Marginal Prices (LMPs) Representation
219(1)
11.4.2 Relative Strength Index (RSI)
219(1)
11.4.2.1 Autoregressive Integrated Moving Average (ARIMA)
219(1)
11.5 Interior-point Technique & KKT Condition
220(1)
11.5.1 Explanation of Karush-Kuhn-Tucker Conditions
220(1)
11.5.2 Algorithm for Finding a Solution
221(1)
11.6 Test Results and Discussion
221(2)
11.6.1 Illustrative Example
221(2)
11.7 Comparative Analysis with Other Methods
223(1)
11.8 Conclusion
224(1)
11.9 Assignment
224(9)
Acknowledgment
225(1)
References
225(8)
12 Power Quality Conditioners in Smart Power System
233(26)
Zahira Rahiman
Lakshmi Dhandapani
Ravi Chengalvarayan Natarajan
Pramila Vallikannan
Sivaraman Palanisamy
Sharmeela Chenniappan
12.1 Introduction
233(2)
12.1.1 Voltage Sag
234(1)
12.1.2 Voltage Swell
234(1)
12.1.3 Interruption
234(1)
12.1.4 Under Voltage
234(1)
12.1.5 Overvoltage
234(1)
12.1.6 Voltage Fluctuations
234(1)
12.1.7 Transients
235(1)
12.1.8 Impulsive Transients
235(1)
12.1.9 Oscillatory Transients
235(1)
12.1.10 Harmonics
235(1)
12.2 Power Quality Conditioners
235(9)
12.2.1 STATCOM
235(1)
12.2.2 SVC
235(1)
12.2.3 Harmonic Filters
236(1)
12.2.3.1 Active Filter
236(1)
12.2.4 UPS Systems
236(1)
12.2.5 Dynamic Voltage Restorer (DVR)
236(1)
12.2.6 Enhancement of Voltage Sag
236(1)
12.2.7 Interruption Mitigation
237(4)
12.2.8 Mitigation of Harmonics
241(3)
12.3 Standards of Power Quality
244(1)
12.4 Solution for Power Quality Issues
244(1)
12.5 Sustainable Energy Solutions
245(1)
12.6 Need for Smart Grid
245(1)
12.7 What Is a Smart Grid?
245(1)
12.8 Smart Grid: The "Energy Internet"
245(1)
12.9 Standardization
246(1)
12.10 Smart Grid Network
247(7)
12.10.1 Distributed Energy Resources (DERs)
247(1)
12.10.2 Optimization Techniques in Power Quality Management
247(1)
12.10.3 Conventional Algorithm
248(1)
12.10.4 Intelligent Algorithm
248(1)
12.10.4.1 Firefly Algorithm (FA)
248(2)
12.10.4.2 Spider Monkey Optimization (SMO)
250(4)
12.11 Simulation Results and Discussion
254(3)
12.12 Conclusion
257(2)
References
257(2)
13 The Role of Internet of Things in Smart Homes
259(14)
Sanjeevikumar Padmanaban
Mostafa Azimi Nasab
Mohammad Ebrahim Shiri
Hamid Haj Seyyed Javadi
Morteza Azimi Nasab
Mohammad Zand
Tina Samavat
13.1 Introduction
259(1)
13.2 Internet of Things Technology
260(2)
13.2.1 Smart House
261(1)
13.3 Different Parts of Smart Home
262(2)
13.4 Proposed Architecture
264(1)
13.5 Controller Components
265(1)
13.6 Proposed Architectural Layers
266(1)
13.6.1 Infrastructure Layer
266(1)
13.6.1.1 Information Technology
266(1)
13.6.1.2 Information and Communication Technology
266(1)
13.6.1.3 Electronics
266(1)
13.6.2 Collecting Data
267(1)
13.6.3 Data Management and Processing
267(1)
13.6.3.1 Service Quality Management
267(1)
13.6.3.2 Resource Management
267(1)
13.6.3.3 Device Management
267(1)
13.6.3.4 Security
267(1)
13.7 Services
267(1)
13.8 Applications
268(1)
13.9 Conclusion
269(4)
References
269(4)
14 Electric Vehicles and loT in Smart Cities
273(18)
Sanjeevikumar Padmanaban
Tina Samavat
Mostafa Azimi Nasab
Morteza Azimi Nasab
Mohammad Zand
Fatemeh Nikokar
14.1 Introduction
273(2)
14.2 Smart City
275(1)
14.2.1 Internet of Things and Smart City
275(1)
14.3 The Concept of Smart Electric Networks
275(1)
14.4 IoT Outlook
276(2)
14.4.1 IoT Three-layer Architecture
276(1)
14.4.2 View Layer
276(1)
14.4.3 Network Layer
277(1)
14.4.4 Application Layer
278(1)
14.5 Intelligent Transportation and Transportation
278(1)
14.6 Information Management
278(3)
14.6.1 Artificial Intelligence
278(1)
14.6.2 Machine Learning
279(1)
14.6.3 Artificial Neural Network
279(1)
14.6.4 Deep Learning
280(1)
14.7 Electric Vehicles
281(3)
14.7.1 Definition of Vehicle-to-Network System
281(1)
14.7.2 Electric Cars and the Electricity Market
281(1)
14.7.3 The Role of Electric Vehicles in the Network
282(1)
14.7.4 V2G Applications in Power System
282(1)
14.7.5 Provide Baseload Power
283(1)
14.7.6 Courier Supply
283(1)
14.7.7 Extra Service
283(1)
14.7.8 Power Adjustment
283(1)
14.7.9 Rotating Reservation
284(1)
14.7.10 The Connection between the Electric Vehicle and the Power Grid
284(1)
14.8 Proposed Model of Electric Vehicle
284(1)
14.9 Prediction Using LSTM Time Series
285(2)
14.9.1 LSTM Time Series 2S6
14.9.2 Predicting the Behavior of Electric Vehicles Using the LSTM Method
287(1)
14.10 Conclusion
287(4)
Exercise
288(1)
References
288(3)
15 Modeling and Simulation of Smart Power Systems Using HIL
291(20)
Gunapriya Devarajan
Puspalatha Naveen Kumar
Muniraj Chinnusamy
Sabareeshwaran Kanagaraj
Sharmeela Chenniappan
15.1 Introduction
291(2)
15.1.1 Classification of Hardware in the Loop
291(1)
15.1.1.1 Signal HIL Model
291(1)
15.1.1.2 Power HIL Model
292(1)
15.1.1.3 Reduced-Scaled HIL Model
292(1)
15.1.2 Points to Be Considered While Performing HIL Simulation
293(1)
15.1.3 Applications of HIL
293(1)
15.2 Why HIL Is Important?
293(3)
15.2.1 Hardware-In-The-Loop Simulation
294(1)
15.2.2 Simulation Verification and Validation
295(1)
15.2.3 Simulation Computer Hardware
295(1)
15.2.4 Benefits of Using Hardware-In-The-Loop Simulation
296(1)
15.3 HIL for Renewable Energy Systems (RES)
296(3)
15.3.1 Introduction
296(1)
15.3.2 Hardware in the Loop
297(1)
15.3.2.1 Electrical Hardware in the Loop
297(1)
15.3.2.2 Mechanical Hardware in the Loop
297(2)
15.4 HIL for HVDC and FACTS
299(2)
15.4.1 Introduction
299(1)
15.4.2 Modular Multi Level Converter
300(1)
15.5 HIL for Electric Vehicles
301(5)
15.5.1 Introduction
301(1)
15.5.2 EV Simulation Using MATLAB, Simulink
302(1)
15.5.2.1 Model-Based System Engineering (MBSE)
302(1)
15.5.2.2 Model Batteries and Develop BMS
302(1)
15.5.2.3 Model Fuel Cell Systems (FCS) and Develop Fuel Cell Control Systems (FCCS)
303(1)
15.5.2.4 Model Inverters, Traction Motors, and Develop Motor Control Software
304(1)
15.5.2.5 Deploy, Integrate, and Test Control Algorithms
304(1)
15.5.2.6 Data-Driven Workflows and AI in EV Development
305(1)
15.6 HIL for Other Applications
306(1)
15.6.1 Electrical Motor Faults
306(1)
15.7 Conclusion
307(4)
References
308(3)
16 Distribution Phasor Measurement Units (PMUs) in Smart Power Systems
311(16)
Ceethanjali Muthiah
Meenakshi Devi Manivannan
Hemavathi Ramadoss
Sharmeela Chenniappan
16.1 Introduction 31J
16.2 Comparison of PMUs and SCADA
312(1)
16.3 Basic Structure of Phasor Measurement Units
313(1)
16.4 PMU Deployment in Distribution Networks
314(1)
16.5 PMU Placement Algorithms
315(1)
16.6 Need/Significance of PMUs in Distribution System
315(2)
16.6.1 Significance of PMUs - Concerning Power System Stability
316(1)
16.6.2 Significance of PMUs in Terms of Expenditure
316(1)
16.6.3 Significance of PMUs in Wide Area Monitoring Applications
316(1)
16.7 Applications of PMUs in Distribution Systems
317(5)
16.7.1 System Reconfiguration Automation to Manage Power Restoration
317(1)
16.7.1.1 Case Study
317(2)
16.7.2 Planning for High DER Interconnection (Penetration)
319(1)
16.7.2.1 Case Study
319(1)
16.7.3 Voltage Fluctuations and Voltage Ride-Through Related to DER
320(1)
16.7.4 Operation of Islanded Distribution Systems
320(2)
16.7.5 Fault-Induced Delayed Voltage Recovery (FIDVR) Detection
322(1)
16.8 Conclusion
322(5)
References
323(4)
17 Blockchain Technologies for Smart Power Systems
327(9)
A. Gayathri
S. Saravanan
P. Pandiyan
V. Rukkumani
17.1 Introduction
327(1)
17.2 Fundamentals of Blockchain Technologies
328(3)
17.2.1 Terminology
328(1)
17.2.2 Process of Operation
329(1)
17.2.2.1 Proof of Work (PoW)
329(1)
17.2.2.2 Proof of Stake (PoS)
329(1)
17.2.2.3 Proof of Authority (PoA)
330(1)
17.2.2.4 Practical Byzantine Fault Tolerance (PBFT)
330(1)
17.2.3 Unique Features of Blockchain
330(1)
17.2.4 Energy with Blockchain Projects
330(1)
17.2.4.1 Bitcoin Cryptocurrency
331(1)
17.2.4.2 Dubai: Blockchain Strategy
331(1)
17.2.4.3 Humanitarian Aid Utilization of Blockchain
331(1)
17.3 Blockchain Technologies for Smart Power Systems
331(5)
17.3.1 Blockchain as a Cyber Layer
331(1)
17.3.2 Agent/Aggregator Based Microgrid Architecture
332(1)
17.3.3 Limitations and Drawbacks
332(1)
17.3.4 Peer to Peer Energy Trading
333(2)
17.3.5 Blockchain for Transactive Energy
335(1)
17 A Blockchain for Smart Contracts
336(13)
17.4.1 The Platform for Smart Contracts
337(1)
17.4.2 The Architecture of Smart Contracting for Energy Applications
338(1)
17.4.3 Smart Contract Applications
339(1)
17.5 Digitize and Decentralization Using Blockchain
340(1)
17.6 Challenges in Implementing Blockchain Techniques
340(2)
17.6.1 Network Management
341(1)
17.6.2 Data Management
341(1)
17.6.3 Consensus Management
341(1)
17.6.4 Identity Management
341(1)
17.6.5 Automation Management
342(1)
17.6.6 Lack of Suitable Implementation Platforms
342(1)
17.7 Solutions and Future Scope
342(1)
17.8 Application of Blockchain for Flexible Services
343(1)
17.9 Conclusion
343(6)
References
344(5)
18 Power and Energy Management in Smart Power Systems
349(24)
Subrat Sahoo
18.1 Introduction
349(2)
18.1.1 Geopolitical Situation
349(1)
18.1.2 Covid-19 Impacts
350(1)
18.1.3 Climate Challenges
350(1)
18.2 Definition and Constituents of Smart Power Systems
351(5)
18.2.1 Applicable Industries
352(1)
18.2.2 Evolution of Power Electronics-Based Solutions
353(2)
18.2.3 Operation of the Power System
355(1)
18.3 Challenges Faced by Utilities and Their Way Towards Becoming Smart
356(10)
18.3.1 Digitalization of Power Industry
359(1)
18.3.2 Storage Possibilities and Integration into Grid
360(2)
18.3.3 Addressing Power Quality Concerns and Their Mitigation
362(1)
18.3.4 A Path Forward Towards Holistic Condition Monitoring
363(3)
18.4 Ways towards Smart Transition of the Energy Sector
366(5)
18.4.1 Creating an All-Inclusive Ecosystem
366(1)
18.4.1.1 Example of Sensor-Based Ecosystem
367(1)
18.4.1.2 Utilizing the Sensor Data for Effective Analytics
368(2)
18.4.2 Modular Energy System Architecture
370(1)
18.5 Conclusion
371(2)
References 373(4)
Index 377
Sanjeevikumar Padmanaban, PhD, is a Full Professor with the Department of Electrical Engineering, IT and Cybernetics, at the University of South-Eastern Norway, Porsgrunn, Norway. He serves as an Editor/Associate Editor/Editorial Board Member of many refereed journals, in particular, the IEEE Systems Journal, the IEEE Access Journal, IEEE Transactions on Industry Applications, the Deputy Editor/Subject Editor of IET Renewable Power Generation, and IET Generation, Transmission and Distribution Journal, Subject Editor of FACETS and Energies MDPI Journal.

Sivaraman Palanisamy is a Program Manager - EV Charging Infrastructure in WRI India. He is an IEEE Senior Member, a Member of CIGRE, and Life Member of the Institution of Engineers (India). He is an active participant in the IEEE Standards Association.

Sharmeela Chenniappan, PhD, is a Professor in the Department of EEE, CEG campus, Anna University, Chennai, India. She is an IEEE Senior Member, a Life Member of CBIP, and Member of the Institution of Engineers (India), ISTE, and SSI.

Jens Bo Holm-Nielsen, PhD, is the Head of the Esbjerg Energy Section with the Department of Energy Technology at Aalborg University. He has been an organizer of various international conferences, workshops, and training programs.
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