Artificial Intelligence-based Smart Power Systems

Name: Artificial Intelligence-based Smart Power Systems
Brand: Wiley
Price: 115.99 EUR
Availability: OnlineOnly

P. Sanjeevikumar Sivaraman Palanisamy Sharmeela Chenniappan Jens Bo Holm-Nielsen(Autor*in)

Wiley (Verlag)

1. Auflage

Erschienen am 2. Dezember 2022

400 Seiten

E-Book

PDF mit Adobe-DRM

Systemvoraussetzungen

978-1-119-89397-4 (ISBN)

115,99 €inkl. 7% MwSt.

Systemvoraussetzungen

für PDF mit Adobe-DRM

E-Book Einzellizenz

Als Download verfügbar

Beschreibung

Weitere Details

Weitere Ausgaben

Personen

Inhalt

Cover
Title Page
Copyright
Contents
Editor Biography
List of Contributors
Chapter 1 Introduction to Smart Power Systems
1.1 Problems in Conventional Power Systems
1.2 Distributed Generation (DG)
1.3 Wide Area Monitoring and Control
1.4 Automatic Metering Infrastructure
1.5 Phasor Measurement Unit
1.6 Power Quality Conditioners
1.7 Energy Storage Systems
1.8 Smart Distribution Systems
1.9 Electric Vehicle Charging Infrastructure
1.10 Cyber Security
1.11 Conclusion
References
Chapter 2 Modeling and Analysis of Smart Power System
2.1 Introduction
2.2 Modeling of Equipment's for Steady-State Analysis
2.2.1 Load Flow Analysis
2.2.1.1 Gauss Seidel Method
2.2.1.2 Newton Raphson Method
2.2.1.3 Decoupled Load Flow Method
2.2.2 Short Circuit Analysis
2.2.2.1 Symmetrical Faults
2.2.2.2 Unsymmetrical Faults
2.2.3 Harmonic Analysis
2.3 Modeling of Equipments for Dynamic and Stability Analysis
2.4 Dynamic Analysis
2.4.1 Frequency Control
2.4.2 Fault Ride Through
2.5 Voltage Stability
2.6 Case Studies
2.6.1 Case Study 1
2.6.2 Case Study 2
2.6.2.1 Existing and Proposed Generation Details in the Vicinity of Wind Farm
2.6.2.2 Power Evacuation Study for 50?MW Generation
2.6.2.3 Without Interconnection of the Proposed 50?MW Generation from Wind Farm on 66?kV Level of 220/66?kV Substation
2.6.2.4 Observations Made from Table
2.6.2.5 With the Interconnection of Proposed 50?MW Generation from Wind Farm on 66?kV level of 220/66?kV Substation
2.6.2.6 Normal Condition without Considering Contingency
2.6.2.7 Contingency Analysis
2.6.2.8 With the Interconnection of Proposed 60?MW Generation from Wind Farm on 66?kV Level of 220/66?kV Substation
2.7 Conclusion
References
Chapter 3 Multilevel Cascaded Boost Converter Fed Multilevel Inverter for Renewable Energy Applications
3.1 Introduction
3.2 Multilevel Cascaded Boost Converter
3.3 Modes of Operation of MCBC
3.3.1 Mode-1 Switch SA Is ON
3.3.2 Mode-2 Switch SA Is ON
3.3.3 Mode-3-Operation - Switch SA Is ON
3.3.4 Mode-4-Operation - Switch SA Is ON
3.3.5 Mode-5-Operation - Switch SA Is ON
3.3.6 Mode-6-Operation - Switch SA Is OFF
3.3.7 Mode-7-Operation - Switch SA Is OFF
3.3.8 Mode-8-Operation - Switch SA Is OFF
3.3.9 Mode-9-Operation - Switch SA Is OFF
3.3.10 Mode 10-Operation - Switch SA is OFF
3.4 Simulation and Hardware Results
3.5 Prominent Structures of Estimated DC-DC Converter with Prevailing Converter
3.5.1 Voltage Gain and Power Handling Capability
3.5.2 Voltage Stress
3.5.3 Switch Count and Geometric Structure
3.5.4 Current Stress
3.5.5 Duty Cycle Versus Voltage Gain
3.5.6 Number of Levels in the Planned Converter
3.6 Power Electronic Converters for Renewable Energy Sources (Applications of MLCB)
3.6.1 MCBC Connected with PV Panel
3.6.2 Output Response of PV Fed MCBC
3.6.3 H-Bridge Inverter
3.7 Modes of Operation
3.7.1 Mode 1
3.7.2 Mode 2
3.7.3 Mode 3
3.7.4 Mode 4
3.7.5 Mode 5
3.7.6 Mode 6
3.7.7 Mode 7
3.7.8 Mode 8
3.7.9 Mode 9
3.7.10 Mode 10
3.8 Simulation Results of MCBC Fed Inverter
3.9 Power Electronic Converter for E-Vehicles
3.10 Power Electronic Converter for HVDC/Facts
3.11 Conclusion
References
Chapter 4 Recent Advancements in Power Electronics for Modern Power Systems-Comprehensive Review on DC-Link Capacitors Concerning Power Density Maximization in Power Converters
4.1 Introduction
4.2 Applications of Power Electronic Converters
4.2.1 Power Electronic Converters in Electric Vehicle Ecosystem
4.2.2 Power Electronic Converters in Renewable Energy Resources
4.3 Classification of DC-Link Topologies
4.4 Briefing on DC-Link Topologies
4.4.1 Passive Capacitive DC Link
4.4.1.1 Filter Type Passive Capacitive DC Links
4.4.1.2 Filter Type Passive Capacitive DC Links with Control
4.4.1.3 Interleaved Type Passive Capacitive DC Links
4.4.2 Active Balancing in Capacitive DC Link
4.4.2.1 Separate Auxiliary Active Capacitive DC Links
4.4.2.2 Integrated Auxiliary Active Capacitive DC Links
4.5 Comparison on DC-Link Topologies
4.5.1 Comparison of Passive Capacitive DC Links
4.5.2 Comparison of Active Capacitive DC Links
4.5.3 Comparison of DC Link Based on Power Density, Efficiency, and Ripple Attenuation
4.6 Future and Research Gaps in DC-Link Topologies with Balancing Techniques
4.7 Conclusion
References
Chapter 5 Energy Storage Systems for Smart Power Systems
5.1 Introduction
5.2 Energy Storage System for Low Voltage Distribution System
5.3 Energy Storage System Connected to Medium and High Voltage
5.4 Energy Storage System for Renewable Power Plants
5.4.1 Renewable Power Evacuation Curtailment
5.5 Types of Energy Storage Systems
5.5.1 Battery Energy Storage System
5.5.2 Thermal Energy Storage System
5.5.3 Mechanical Energy Storage System
5.5.4 Pumped Hydro
5.5.5 Hydrogen Storage
5.6 Energy Storage Systems for Other Applications
5.6.1 Shift in Energy Time
5.6.2 Voltage Support
5.6.3 Frequency Regulation (Primary, Secondary, and Tertiary)
5.6.4 Congestion Management
5.6.5 Black Start
5.7 Conclusion
References
Chapter 6 Real-Time Implementation and Performance Analysis of Supercapacitor for Energy Storage
6.1 Introduction
6.2 Structure of Supercapacitor
6.2.1 Mathematical Modeling of Supercapacitor
6.3 Bidirectional Buck-Boost Converter
6.3.1 FPGA Controller
6.4 Experimental Results
6.5 Conclusion
References
Chapter 7 Adaptive Fuzzy Logic Controller for MPPT Control in PMSG Wind Turbine Generator
7.1 Introduction
7.2 Proposed MPPT Control Algorithm
7.3 Wind Energy Conversion System
7.3.1 Wind Turbine Characteristics
7.3.2 Model of PMSG
7.4 Fuzzy Logic Command for the MPPT of the PMSG
7.4.1 Fuzzification
7.4.2 Fuzzy Logic Rules
7.4.3 Defuzzification
7.5 Results and Discussions
7.6 Conclusion
References
Chapter 8 A Novel Nearest Neighbor Searching-Based Fault Distance Location Method for HVDC Transmission Lines
8.1 Introduction
8.2 Nearest Neighbor Searching
8.3 Proposed Method
8.3.1 Power System Network Under Study
8.3.2 Proposed Fault Location Method
8.4 Results
8.4.1 Performance Varying Nearest Neighbor
8.4.2 Performance Varying Distance Matrices
8.4.3 Near Boundary Faults
8.4.4 Far Boundary Faults
8.4.5 Performance During High Resistance Faults
8.4.6 Single Pole to Ground Faults
8.4.7 Performance During Double Pole to Ground Faults
8.4.8 Performance During Pole to Pole Faults
8.4.9 Error Analysis
8.4.10 Comparison with Other Schemes
8.4.11 Advantages of the Scheme
8.5 Conclusion
Acknowledgment
References
Chapter 9 Comparative Analysis of Machine Learning Approaches in Enhancing Power System Stability
9.1 Introduction
9.2 Power System Models
9.2.1 PSS Integrated Single Machine Infinite Bus Power Network
9.2.2 PSS-UPFC Integrated Single Machine Infinite Bus Power Network
9.3 Methods
9.3.1 Group Method Data Handling Model
9.3.2 Extreme Learning Machine Model
9.3.3 Neurogenetic Model
9.3.4 Multigene Genetic Programming Model
9.4 Data Preparation and Model Development
9.4.1 Data Production and Processing
9.4.2 Machine Learning Model Development
9.5 Results and Discussions
9.5.1 Eigenvalues and Minimum Damping Ratio Comparison
9.5.2 Time-Domain Simulation Results Comparison
9.5.2.1 Rotor Angle Variation Under Disturbance
9.5.2.2 Rotor Angular Frequency Variation Under Disturbance
9.5.2.3 DC-Link Voltage Variation Under Disturbance
9.6 Conclusions
References
Chapter 10 Augmentation of PV-Wind Hybrid Technology with Adroit Neural Network, ANFIS, and PI Controllers Indeed Precocious DVR System
10.1 Introduction
10.2 PV-Wind Hybrid Power Generation Configuration
10.3 Proposed Systems Topologies
10.3.1 Structure of PV System
10.3.2 The MPPTs Technique
10.3.3 NN Predictive Controller Technique
10.3.4 ANFIS Technique
10.3.5 Training Data
10.4 Wind Power Generation Plant
10.5 Pitch Angle Control Techniques
10.5.1 PI Controller
10.5.2 NARMA-L2 Controller
10.5.3 Fuzzy Logic Controller Technique
10.6 Proposed DVRs Topology
10.7 Proposed Controlling Technique of DVR
10.7.1 ANFIS and PI Controlling Technique
10.8 Results of the Proposed Topologies
10.8.1 PV System Outputs (MPPT Techniques Results)
10.8.2 Main PV System outputs
10.8.3 Wind Turbine System Outputs (Pitch Angle Control Technique Result)
10.8.4 Proposed PMSG Wind Turbine System Output
10.8.5 Performance of DVR (Controlling Technique Results)
10.8.6 DVRs Performance
10.9 Conclusion
References
Chapter 11 Deep Reinforcement Learning and Energy Price Prediction
11.1 Introduction
11.2 Deep and Reinforcement Learning for Decision-Making Problems in Smart Power Systems
11.2.1 Reinforcement Learning
11.2.1.1 Markov Decision Process (MDP)
11.2.1.2 Value Function and Optimal Policy
11.2.2 Reinforcement Learnings to Deep Reinforcement Learnings
11.2.3 Deep Reinforcement Learning Algorithms
11.3 Applications in Power Systems
11.3.1 Energy Management
11.3.2 Power Systems' Demand Response (DR)
11.3.3 Electricity Market
11.3.4 Operations and Controls
11.4 Mathematical Formulation of Objective Function
11.4.1 Locational Marginal Prices (LMPs) Representation
11.4.2 Relative Strength Index (RSI)
11.4.2.1 Autoregressive Integrated Moving Average (ARIMA)
11.5 Interior-point Technique & KKT Condition
11.5.1 Explanation of Karush-Kuhn-Tucker Conditions
11.5.2 Algorithm for Finding a Solution
11.6 Test Results and Discussion
11.6.1 Illustrative Example
11.7 Comparative Analysis with Other Methods
11.8 Conclusion
11.9 Assignment
Acknowledgment
References
Chapter 12 Power Quality Conditioners in Smart Power System
12.1 Introduction
12.1.1 Voltage Sag
12.1.2 Voltage Swell
12.1.3 Interruption
12.1.4 Under Voltage
12.1.5 Overvoltage
12.1.6 Voltage Fluctuations
12.1.7 Transients
12.1.8 Impulsive Transients
12.1.9 Oscillatory Transients
12.1.10 Harmonics
12.2 Power Quality Conditioners
12.2.1 STATCOM
12.2.2 SVC
12.2.3 Harmonic Filters
12.2.3.1 Active Filter
12.2.4 UPS Systems
12.2.5 Dynamic Voltage Restorer (DVR)
12.2.6 Enhancement of Voltage Sag
12.2.7 Interruption Mitigation
12.2.8 Mitigation of Harmonics
12.3 Standards of Power Quality
12.4 Solution for Power Quality Issues
12.5 Sustainable Energy Solutions
12.6 Need for Smart Grid
12.7 What Is a Smart Grid?
12.8 Smart Grid: The "Energy Internet"
12.9 Standardization
12.10 Smart Grid Network
12.10.1 Distributed Energy Resources (DERs)
12.10.2 Optimization Techniques in Power Quality Management
12.10.3 Conventional Algorithm
12.10.4 Intelligent Algorithm
12.10.4.1 Firefly Algorithm (FA)
12.10.4.2 Spider Monkey Optimization (SMO)
12.11 Simulation Results and Discussion
12.12 Conclusion
References
Chapter 13 The Role of Internet of Things in Smart Homes
13.1 Introduction
13.2 Internet of Things Technology
13.2.1 Smart House
13.3 Different Parts of Smart Home
13.4 Proposed Architecture
13.5 Controller Components
13.6 Proposed Architectural Layers
13.6.1 Infrastructure Layer
13.6.1.1 Information Technology
13.6.1.2 Information and Communication Technology
13.6.1.3 Electronics
13.6.2 Collecting Data
13.6.3 Data Management and Processing
13.6.3.1 Service Quality Management
13.6.3.2 Resource Management
13.6.3.3 Device Management
13.6.3.4 Security
13.7 Services
13.8 Applications
13.9 Conclusion
References
Chapter 14 Electric Vehicles and IoT in Smart Cities
14.1 Introduction
14.2 Smart City
14.2.1 Internet of Things and Smart City
14.3 The Concept of Smart Electric Networks
14.4 IoT Outlook
14.4.1 IoT Three-layer Architecture
14.4.2 View Layer
14.4.3 Network Layer
14.4.4 Application Layer
14.5 Intelligent Transportation and Transportation
14.6 Information Management
14.6.1 Artificial Intelligence
14.6.2 Machine Learning
14.6.3 Artificial Neural Network
14.6.4 Deep Learning
14.7 Electric Vehicles
14.7.1 Definition of Vehicle-to-Network System
14.7.2 Electric Cars and the Electricity Market
14.7.3 The Role of Electric Vehicles in the Network
14.7.4 V2G Applications in Power System
14.7.5 Provide Baseload Power
14.7.6 Courier Supply
14.7.7 Extra Service
14.7.8 Power Adjustment
14.7.9 Rotating Reservation
14.7.10 The Connection between the Electric Vehicle and the Power Grid
14.8 Proposed Model of Electric Vehicle
14.9 Prediction Using LSTM Time Series
14.9.1 LSTM Time Series
14.9.2 Predicting the Behavior of Electric Vehicles Using the LSTM Method
14.10 Conclusion
Exercise
References
Chapter 15 Modeling and Simulation of Smart Power Systems Using HIL
15.1 Introduction
15.1.1 Classification of Hardware in the Loop
15.1.1.1 Signal HIL Model
15.1.1.2 Power HIL Model
15.1.1.3 Reduced-Scaled HIL Model
15.1.2 Points to Be Considered While Performing HIL Simulation
15.1.3 Applications of HIL
15.2 Why HIL Is Important?
15.2.1 Hardware-In-The-Loop Simulation
15.2.2 Simulation Verification and Validation
15.2.3 Simulation Computer Hardware
15.2.4 Benefits of Using Hardware-In-The-Loop Simulation
15.3 HIL for Renewable Energy Systems (RES)
15.3.1 Introduction
15.3.2 Hardware in the Loop
15.3.2.1 Electrical Hardware in the Loop
15.3.2.2 Mechanical Hardware in the Loop
15.4 HIL for HVDC and FACTS
15.4.1 Introduction
15.4.2 Modular Multi Level Converter
15.5 HIL for Electric Vehicles
15.5.1 Introduction
15.5.2 EV Simulation Using MATLAB, Simulink
15.5.2.1 Model-Based System Engineering (MBSE)
15.5.2.2 Model Batteries and Develop BMS
15.5.2.3 Model Fuel Cell Systems (FCS) and Develop Fuel Cell Control Systems (FCCS)
15.5.2.4 Model Inverters, Traction Motors, and Develop Motor Control Software
15.5.2.5 Deploy, Integrate, and Test Control Algorithms
15.5.2.6 Data-Driven Workflows and AI in EV Development
15.6 HIL for Other Applications
15.6.1 Electrical Motor Faults
15.7 Conclusion
References
Chapter 16 Distribution Phasor Measurement Units (PMUs) in Smart Power Systems
16.1 Introduction
16.2 Comparison of PMUs and SCADA
16.3 Basic Structure of Phasor Measurement Units
16.4 PMU Deployment in Distribution Networks
16.5 PMU Placement Algorithms
16.6 Need/Significance of PMUs in Distribution System
16.6.1 Significance of PMUs - Concerning Power System Stability
16.6.2 Significance of PMUs in Terms of Expenditure
16.6.3 Significance of PMUs in Wide Area Monitoring Applications
16.7 Applications of PMUs in Distribution Systems
16.7.1 System Reconfiguration Automation to Manage Power Restoration
16.7.1.1 Case Study
16.7.2 Planning for High DER Interconnection (Penetration)
16.7.2.1 Case Study
16.7.3 Voltage Fluctuations and Voltage Ride-Through Related to DER
16.7.4 Operation of Islanded Distribution Systems
16.7.5 Fault-Induced Delayed Voltage Recovery (FIDVR) Detection
16.8 Conclusion
References
Chapter 17 Blockchain Technologies for Smart Power Systems
17.1 Introduction
17.2 Fundamentals of Blockchain Technologies
17.2.1 Terminology
17.2.2 Process of Operation
17.2.2.1 Proof of Work (PoW)
17.2.2.2 Proof of Stake (PoS)
17.2.2.3 Proof of Authority (PoA)
17.2.2.4 Practical Byzantine Fault Tolerance (PBFT)
17.2.3 Unique Features of Blockchain
17.2.4 Energy with Blockchain Projects
17.2.4.1 Bitcoin Cryptocurrency
17.2.4.2 Dubai: Blockchain Strategy
17.2.4.3 Humanitarian Aid Utilization of Blockchain
17.3 Blockchain Technologies for Smart Power Systems
17.3.1 Blockchain as a Cyber Layer
17.3.2 Agent/Aggregator Based Microgrid Architecture
17.3.3 Limitations and Drawbacks
17.3.4 Peer to Peer Energy Trading
17.3.5 Blockchain for Transactive Energy
17.4 Blockchain for Smart Contracts
17.4.1 The Platform for Smart Contracts
17.4.2 The Architecture of Smart Contracting for Energy Applications
17.4.3 Smart Contract Applications
17.5 Digitize and Decentralization Using Blockchain
17.6 Challenges in Implementing Blockchain Techniques
17.6.1 Network Management
17.6.2 Data Management
17.6.3 Consensus Management
17.6.4 Identity Management
17.6.5 Automation Management
17.6.6 Lack of Suitable Implementation Platforms
17.7 Solutions and Future Scope
17.8 Application of Blockchain for Flexible Services
17.9 Conclusion
References
Chapter 18 Power and Energy Management in Smart Power Systems
18.1 Introduction
18.1.1 Geopolitical Situation
18.1.2 Covid-19 Impacts
18.1.3 Climate Challenges
18.2 Definition and Constituents of Smart Power Systems
18.2.1 Applicable Industries
18.2.2 Evolution of Power Electronics-Based Solutions
18.2.3 Operation of the Power System
18.3 Challenges Faced by Utilities and Their Way Towards Becoming Smart
18.3.1 Digitalization of Power Industry
18.3.2 Storage Possibilities and Integration into Grid
18.3.3 Addressing Power Quality Concerns and Their Mitigation
18.3.4 A Path Forward Towards Holistic Condition Monitoring
18.4 Ways towards Smart Transition of the Energy Sector
18.4.1 Creating an All-Inclusive Ecosystem
18.4.1.1 Example of Sensor-Based Ecosystem
18.4.1.2 Utilizing the Sensor Data for Effective Analytics
18.4.2 Modular Energy System Architecture
18.5 Conclusion
References
Index
EULA

Systemvoraussetzungen

Als PDF speichern Als Link merken