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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
ARTIFICIAL INTELLIGENCE-BASED SMART POWER SYSTEMS Authoritative resource describing 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. It covers many new topics such as distribution phasor measurement units, 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 editors include many learning resources throughout the text, including MATLAB, 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 HVDC/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 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.
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Veröffentlichungsjahr: 2022
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Table of Contents
Cover
Title Page
Copyright
Editor Biography
List of Contributors
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
2 Modeling and Analysis of Smart Power System
2.1 Introduction
2.2 Modeling of Equipment's for Steady-State Analysis
2.3 Modeling of Equipments for Dynamic and Stability Analysis
2.4 Dynamic Analysis
2.5 Voltage Stability
2.6 Case Studies
2.7 Conclusion
References
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
3.4 Simulation and Hardware Results
3.5 Prominent Structures of Estimated DC–DC Converter with Prevailing Converter
3.6 Power Electronic Converters for Renewable Energy Sources (Applications of MLCB)
3.7 Modes of Operation
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
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
4.3 Classification of DC-Link Topologies
4.4 Briefing on DC-Link Topologies
4.5 Comparison on DC-Link Topologies
4.6 Future and Research Gaps in DC-Link Topologies with Balancing Techniques
4.7 Conclusion
References
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
5.5 Types of Energy Storage Systems
5.6 Energy Storage Systems for Other Applications
5.7 Conclusion
References
6 Real-Time Implementation and Performance Analysis of Supercapacitor for Energy Storage
6.1 Introduction
6.2 Structure of Supercapacitor
6.3 Bidirectional Buck–Boost Converter
6.4 Experimental Results
6.5 Conclusion
References
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
7.4 Fuzzy Logic Command for the MPPT of the PMSG
7.5 Results and Discussions
7.6 Conclusion
References
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
8.4 Results
8.5 Conclusion
Acknowledgment
References
9 Comparative Analysis of Machine Learning Approaches in Enhancing Power System Stability
9.1 Introduction
9.2 Power System Models
9.3 Methods
9.4 Data Preparation and Model Development
9.5 Results and Discussions
9.6 Conclusions
References
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
10.4 Wind Power Generation Plant
10.5 Pitch Angle Control Techniques
10.6 Proposed DVRs Topology
10.7 Proposed Controlling Technique of DVR
10.8 Results of the Proposed Topologies
10.9 Conclusion
References
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
11.3 Applications in Power Systems
11.4 Mathematical Formulation of Objective Function
11.5 Interior-point Technique & KKT Condition
11.6 Test Results and Discussion
11.7 Comparative Analysis with Other Methods
11.8 Conclusion
11.9 Assignment
Acknowledgment
References
12 Power Quality Conditioners in Smart Power System
12.1 Introduction
12.2 Power Quality Conditioners
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
12.11 Simulation Results and Discussion
12.12 Conclusion
References
13 The Role of Internet of Things in Smart Homes
13.1 Introduction
13.2 Internet of Things Technology
13.3 Different Parts of Smart Home
13.4 Proposed Architecture
13.5 Controller Components
13.6 Proposed Architectural Layers
13.7 Services
13.8 Applications
13.9 Conclusion
References
14 Electric Vehicles and IoT in Smart Cities
14.1 Introduction
14.2 Smart City
14.3 The Concept of Smart Electric Networks
14.4 IoT Outlook
14.5 Intelligent Transportation and Transportation
14.6 Information Management
14.7 Electric Vehicles
14.8 Proposed Model of Electric Vehicle
14.9 Prediction Using LSTM Time Series
14.10 Conclusion
Exercise
References
15 Modeling and Simulation of Smart Power Systems Using HIL
15.1 Introduction
15.2 Why HIL Is Important?
15.3 HIL for Renewable Energy Systems (RES)
15.4 HIL for HVDC and FACTS
15.5 HIL for Electric Vehicles
15.6 HIL for Other Applications
15.7 Conclusion
References
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
16.7 Applications of PMUs in Distribution Systems
16.8 Conclusion
References
17 Blockchain Technologies for Smart Power Systems
17.1 Introduction
17.2 Fundamentals of Blockchain Technologies
17.3 Blockchain Technologies for Smart Power Systems
17.4 Blockchain for Smart Contracts
17.5 Digitize and Decentralization Using Blockchain
17.6 Challenges in Implementing Blockchain Techniques
17.7 Solutions and Future Scope
17.8 Application of Blockchain for Flexible Services
17.9 Conclusion
References
18 Power and Energy Management in Smart Power Systems
18.1 Introduction
18.2 Definition and Constituents of Smart Power Systems
18.3 Challenges Faced by Utilities and Their Way Towards Becoming Smart
18.4 Ways towards Smart Transition of the Energy Sector
18.5 Conclusion
References
Index
End User License Agreement
List of Tables
Chapter 2
Table 2.1 Known and unknown quantities of various buses.
Table 2.2 Data of wind power generation, reactive power, and power loss.
Table 2.3 Transmission power flow in the vicinity of wind farm.
Table 2.4 Transmission line parameters.
Table 2.5 Voltage ratings of different transmission lines under normal and e...
Table 2.6 Power flow from the windfarm to grid substations without interconn...
Table 2.7 Power flow from the windfarm to grid substations with interconnect...
Table 2.8 With 50 MW generation line details on 66 and 220 kV side of substa...
Table 2.9 Transmission line power flows in the vicinity of wind farm.
Table 2.10 With 60 MW generation line details on 66 and 220 kV side of subst...
Chapter 3
Table 3.1 Difference between conventional inverter and MLI.
Table 3.2 Components table for MCBC.
Table 3.3 Assessment of proposed MCBC with appropriate converter.
Table 3.4 Level circuit operation of MCBC.
Table 3.5 Switching table for multilevel AC output voltage.
Chapter 4
Table 4.1 Classification of separate auxiliary active DC Links with literatu...
Table 4.2 Comparison of filter type passive DC Links.
Table 4.3 Comparison of filter type passive DC Links with control.
Table 4.4 Comparison of filter type passive DC Links with interleaved type....
Table 4.5 Comparison of separate auxiliary circuit active DC Links (H-bridge...
Table 4.6 Comparison of separate auxiliary circuit active DC Links (generic ...
Table 4.7 Comparison of separate auxiliary circuit active DC Links (Flyback ...
Table 4.8 Comparison of integrated circuit active DC Link.
Chapter 5
Table 5.1 Comparison of various energy storage technologies.
Chapter 6
Table 6.1 Comparison of storage devices under different parameters.
Table 6.2 Parameters of supercapacitor.
Table 6.3 Parameters of buck–boost converter.
Table 6.4 Performance of supercapacitor during charging.
Table 6.5 Performance of supercapacitor during discharging.
Chapter 7
Table 7.1 Wind parameters.
Table 7.2 PMSG parameters.
Table 7.3 Rule base of fuzzy logic controller.
Chapter 8
Table 8.1 Performance varying nearest neighbor.
Table 8.2 Performance varying distance.
Table 8.3 Performance of near boundary P1G faults.
Table 8.4 Performance of far boundary P2G faults.
Table 8.5 Performance during high resistance faults.
Table 8.6 Performance during single pole to ground fault.
Table 8.7 Performance during double pole to ground fault.
Table 8.8 Performance during P1P2 fault.
Table 8.9 Comparison of the proposed approach with previous methods.
Chapter 9
Table 9.1 Operating ranges in per unit (pu) for both electric networks.
Table 9.2 Loading conditions (LC) of two electric networks.
Table 9.3 Eigenvalues of first electric network for LC #1.
Table 9.4 Eigenvalues of first electric network for LC #2.
Table 9.5 Eigenvalues of first electric network for LC #3.
Table 9.6 Eigenvalues of second electric network for LLC.
Table 9.7 Eigenvalues of second electric network for NLC.
Table 9.8 Eigenvalues of second electric network for HLC.
Table 9.9 Online estimated PSS parameters for randomly selected three loadin...
Chapter 10
Table 10.1 Obtained parameters from
PV
array.
Table 10.2 Data used for training the controller.
Table 10.3 Ratings of the proposed wind turbine system.
Chapter 11
Table 11.1 Estimation results of ARIMA (Gaussian model) for LMP.
Table 11.2 Estimation results of ARIMA (Gaussian model) for RSI.
Table 11.3 Description of electricity forecasting methods in recent years.
Chapter 12
Table 12.1 Power quality conditioner for power quality enhancement.
Table 12.2 Comparison of THD levels with and without controller.
Chapter 14
Table 14.1 Factors associated with an intelligent network.
Chapter 18
Table 18.1 Application recovery tolerated delay for various communicating de...
List of Illustrations
Chapter 1
Figure 1.1 Single line diagram of a rooftop solar PV system connected to the...
Figure 1.2 Single line diagram of a rooftop solar PV system connected to the...
Figure 1.3 Block diagram of wide-area monitoring and control.
Figure 1.4 Basic building blocks of AMI.
Figure 1.5 Transmission line data.
Figure 1.6 Typical arrangement of PMU in substation and PDC in the load disp...
Chapter 2
Figure 2.1 Single-line diagram of power system network.
Figure 2.2 Fundamental sinusoidal waveform and distorted waveform.
Figure 2.3 Power system model developed in ETAP software and the simulation ...
Figure 2.4 Power angle versus time for stable and unstable system.
Figure 2.5 Block diagram of automatic generation control.
Figure 2.6 Interconnection of areas through tie lines.
Figure 2.7
PV
and
QV
characteristics.
Figure 2.8 Single-line diagram of wind farm in Tirunelveli region.
Figure 2.9 Percentage wind generation versus reactive power and real power l...
Figure 2.10 Schematic diagram of wind power connected to 220/66 kV substatio...
Chapter 3
Figure 3.1 Requirement of high gain converters.
Figure 3.2 Multilevel inverter scheme with one DC source.
Figure 3.3 Multilevel inverter scheme with multiple DC sources.
Figure 3.4 Multi boost converter fed MLI scheme with single DC source.
Figure 3.5 Generalized cascaded boost converters with
K
-multilevel output vo...
Figure 3.6 MCBC with five level structure.
Figure 3.7 (a) Mode-1 switch S
A
is closed, (b) Mode-2 switch S
A
is closed, (...
Figure 3.8 Voltage obtained across
R
(load) for the suggested five level MCB...
Figure 3.9 Output voltage obtained from CBC, MLBC, and MCBC.
Figure 3.10 Current through and voltage across L
1
, L
2
, C
A
, and switch of MCB...
Figure 3.11 (a) Prototype of cascaded multilevel boost converter. (b) Input ...
Figure 3.12 Output voltage assessment of suggested converter with latest con...
Figure 3.13 Comparison of MCBC converter with latest converters voltage gain...
Figure 3.14 Voltage stress assessment of suggested converter with latest con...
Figure 3.15 Switch count assessment of suggested converter with latest conve...
Figure 3.16 Assessment of current stress with various converter.
Figure 3.17 Duty cycle
V
S
voltage gain assessment of the recommended MCBC co...
Figure 3.18 Output voltage
V
s
number of levels in proposed MCBC.
Figure 3.19 Simulation diagram of MCBC with PV panel.
Figure 3.20 (a) Input supply voltage obtained from PV panel. (b) Five level ...
Figure 3.21 H-bridge inverter.
Figure 3.22 (a) Mode 1, (b) Mode 2, (c) Mode 3, (d) Mode 4, (e) Mode 5, (f) ...
Figure 3.23 Simulated output of MCBC fed inverter.
Figure 3.24 THD spectrum for MCBC fed inverter.
Chapter 4
Figure 4.1 Typical power train architecture of hybrid electric vehicle with ...
Figure 4.2 Typical power train architecture of electric vehicle with power e...
Figure 4.3 Block diagram for soar PV integration to the grid.
Figure 4.4 Classification of capacitive DC Links.
Figure 4.5 (a) DC series for DC output, (b) DC parallel for DC output, (c) D...
Figure 4.6 Resistive balancing for capacitive DC Link (passive method).
Figure 4.7 (a) Smoothing transformer filter capacitor choke proposed in [11]...
Figure 4.8 (a) Improved six switch high power density AC/DC/AC power module,...
Figure 4.9 Single phase rectifier unit with control block scheme for partial...
Figure 4.10 Power decoupling with H-bridge and split capacitor [15].
Figure 4.11 (a) High power density transformer-less PV inverter [16], (b) Hi...
Figure 4.12 Dual DC–DC converter with reduced DC-Link capacitor [18].
Figure 4.13 Four capacitor based transformer-less PV inverter [19].
Figure 4.14 Differential converter arrangement for fuel cell drawn current s...
Figure 4.15 Differential converter for double line frequency elimination [21...
Figure 4.16 Differential converter on modified H-bridge [22].
Figure 4.17 Flyback/PFC-based auxiliary support topologies. (a) Harmonic red...
Figure 4.18 H-bridge based auxiliary support. (a) An active low-frequency ri...
Figure 4.19 (a) Four-switch three-port DC/DC/AC converter [35], (b) Active p...
Figure 4.20 Radar chart for trade-off comparison between efficiency, power d...
Chapter 5
Figure 5.1 SLD of UPS system.
Figure 5.2 SLD of ESS used in a manufacturing plant.
Figure 5.3 Direction of power flow during normal operating conditions.
Figure 5.4 Direction of power flow during emergency conditions.
Figure 5.5 Direction of power flow during emergency conditions.
Figure 5.6 The typical SLD of the grid network shows the locations of ESS.
Figure 5.7 The typical structure of a battery energy storage system connecte...
Figure 5.8 The typical step-up transformer-based BESS system connected to me...
Figure 5.9 The parallel combination of multiple BESS systems.
Figure 5.10 SLD of 15 MW solar PV power plant.
Figure 5.11 Output power from the 15 MW solar PV power plant – day 1.
Figure 5.12 Output power from the 15 MW solar PV power plant – day 2.
Figure 5.13 SLD of 10 MW solar PV power plant.
Figure 5.14 Output power from the 10 MW solar PV power plant – day 1.
Figure 5.15 Output power from the 10 MW solar PV power plant – day 2.
Figure 5.16 Output power from the 10 MW solar PV power plant – day 3.
Figure 5.17 Typical power generation of the day.
Figure 5.18 Power generation at reduced capacity due to curtailment.
Figure 5.19 Peak shaving example.
Figure 5.20 Voltage variation out of standard range.
Chapter 6
Figure 6.1 Classification of supercapacitors.
Figure 6.2 Proposed structure of supercapacitor.
Figure 6.3 Equivalent circuit of supercapacitor.
Figure 6.4 Bidirectional buck–boost converter.
Figure 6.5 SPARTAN6 FPGA controller.
Figure 6.6 Proposed system line diagram.
Figure 6.7 Supercapacitor charging under different duty cycles at constant i...
Figure 6.8 Supercapacitor discharging under different duty cycles at constan...
Figure 6.9 Supercapacitor total charging time at constant input voltage of 7...
Figure 6.10 Supercapacitor total discharging time at constant load of 250 W ...
Figure 6.11 Representation of total charging and discharging time of superca...
Figure 6.12 Experimental setup of the proposed system, DSO = digital storage...
Chapter 7
Figure 7.1 System block diagram.
Figure 7.2 Scheme of the fuzzy logic controller.
Figure 7.3 Basic structure of fuzzy logic controller.
Figure 7.4 Structure membership functions of (
e
), membership functions of (d
Figure 7.5 Defuzzification.
Figure 7.6 Control surface of fuzzy logic controller.
Figure 7.7 Wind speed profile.
Figure 7.8 Tip speed ratio (
λ
).
Figure 7.9 Power coefficient (
C
p
) of the turbine.
Figure 7.10 Mechanical power.
Figure 7.11 Reference and measured rotor speed.
Figure 7.12 The electromagnetic torque.
Chapter 8
Figure 8.1 NNS method for prediction.
Figure 8.2 Flowchart of the planned fault location estimation approach.
Figure 8.3 LCC–HVDC transmission lines.
Figure 8.4 Input feature obtained during P1G fault varying different fault l...
Figure 8.5 Error obtained varying the nearest neighbor. (a) Error for LG fau...
Figure 8.6 Error obtained varying distance matrix.
Figure 8.7 Performance during single pole to ground P1P2G fault. (a) Fault l...
Figure 8.8 Error analysis.
Chapter 9
Figure 9.1 Structure of first electric networks – (a) PSS integrated SMIB te...
Figure 9.2 Structure of second electric networks – (a) UPFC-PSS integrated S...
Figure 9.3 Single hidden-layer feedforward architecture for extreme learning...
Figure 9.4 The functional flow diagram for neurogenetic system.
Figure 9.5 Structure of trees for MGGP algorithm to predict target
Y
with re...
Figure 9.6 MDR value comparison for – (a) the first electric network and (b)...
Figure 9.7 Time-domain responses with the initiation of external disturbance...
Figure 9.8 Time-domain responses with incitation of external disturbances – ...
Figure 9.9 Time-domain responses with incitation of external disturbances – ...
Chapter 10
Figure 10.1 Single-line diagram of the proposed hybrid system.
Figure 10.2 Proposed solar
PV
cell equivalent circuit.
Figure 10.3 Electrical characteristics of the
PV
array.
Figure 10.4 Simplified presentation of boost converter circuit.
Figure 10.5 Schematic circuit of applied technique to MPPT.
Figure 10.6 NN predictive controllers identification of system.
Figure 10.7 Predictive control operational representation.
Figure 10.8 NN predictive controllers performance obtained.
Figure 10.9 Equivalent proposed architecture of ANFIS.
Figure 10.10 Sugeno type FIS for MPPT.
Figure 10.11 (a) The characteristics of PMSG-based wind turbine. (b) Represe...
Figure 10.12 Response plot obtained after tuning the controller.
Figure 10.13 Schematic of NARMA-L2 controller.
Figure 10.14 NN controllers training performance.
Figure 10.15 The proposed fuzzy logic controller designer view.
Figure 10.16 Rules formation of proposed controller.
Figure 10.17 General structure of the proposed DVR to the system.
Figure 10.18 Complete representation of controlling techniques proposed in t...
Figure 10.19 FIS of the ANFIS controller.
Figure 10.20 After training the network of NN Predictive Controller obtained...
Figure 10.21 Validating data of controller.
Figure 10.22 Regression plot obtained after training the controller.
Figure 10.23 ANFIS structure of MPPT technique.
Figure 10.24 The Active Power without the DVRs contribution.
Figure 10.25 The Reactive Power without DVRs contribution.
Figure 10.26 The Active Power with the DVRs contribution.
Figure 10.27 The Reactive Power with DVRs contribution.
Figure 10.28 Testing data obtained after training the controller.
Figure 10.29 Regression plot obtained after training the controller.
Figure 10.30 Performance of the controlling techniques applied to system.
Figure 10.31 The electromagnetic torque obtained of turbine.
Figure 10.32 The three-phase voltage obtained of the system.
Figure 10.33 The active power of the system without the DVRs contribution.
Figure 10.34 The reactive power of the system without DVRs contribution.
Figure 10.35 The active power obtained with the DVR contribution.
Figure 10.36 The reactive power obtained with the DVRs contribution.
Figure 10.37 ANFIS structure obtained of DVR.
Figure 10.38 The training performance from ANFIS controller.
Figure 10.39 Surface viewer of the proposed ANFIS controller in system.
Figure 10.40 Voltage to the system without DVR.
Figure 10.41 Voltage to the system with DVR.
Chapter 11
Figure 11.1 Deep reinforcement learning (DRL) structure.
Figure 11.2 Illustration of Markov decision process (MDP).
Figure 11.3 Electricity Market Participants.
Figure 11.4 Load Profiles at nodes of modified IEEE-9 bus system.
Figure 11.5 PV Generation Profiles in modified IEEE-9 bus System.
Figure 11.6 LMP and RSI Time series plot.
Figure 11.7 Forecasted values of LMPs with respective time series.
Figure 11.8 Forecasted RSI values using ARIMA.
Chapter 12
Figure 12.1 Simplified single line diagram.
Figure 12.2 Voltage sag in R & Y phase.
Figure 12.3 Instantaneous voltage waveform at 11 kV.
Figure 12.4 Voltage in RMS trend at 11 kV.
Figure 12.5 STATCOM installation at 11 kV bus.
Figure 12.6 UPS system powering the critical loads.
Figure 12.7 Power flow direction during normal operation.
Figure 12.8 Power flow direction during interruption.
Figure 12.9 Voltage waveform at 22 kV.
Figure 12.10 Current waveform at 22 kV.
Figure 12.11 Voltage harmonic spectrum in %.
Figure 12.12 Current harmonic spectrum in %.
Figure 12.13 Model of shunt active filter.
Figure 12.14 Smart grid environment.
Figure 12.15 Network of smart grid.
Figure 12.16 Entire smart grid network.
Figure 12.17 Firefly movement.
Figure 12.18 Distance between two fireflies.
Figure 12.19 SMO in a single group.
Figure 12.20 SM in two group.
Figure 12.21 SMO in three group.
Figure 12.22 SM with min size group.
Figure 12.23 THD of the system with PI controller.
Figure 12.24 THD of the system with PI tuned by firefly algorithm.
Figure 12.25 THD of the system with PI tuned SMO.
Figure 12.26 Comparison of THD reduction by intelligent algorithms.
Figure 12.27 PI-controller chart of various controllers.
Chapter 13
Figure 13.1 Smart home structure.
Figure 13.2 A sample of smart home architecture.
Figure 13.3 Architecture of smart home applications based on the Internet of...
Figure 13.4 Proposed architecture.
Figure 13.5 Components of architectural layers.
Figure 13.6 Environment and application.
Chapter 14
Figure 14.1 IoT architecture.
Figure 14.2 The structure of artificial intelligence.
Figure 14.3 The structure of the human nerve cell.
Figure 14.4 An artificial neural network layer.
Figure 14.5 Fill the valleys and remove the peaks with electric vehicles.
Figure 14.6 Schematic view of the connection between the electric vehicle an...
Figure 14.7 Forecast of the day ahead for electric vehicles.
Figure 14.8 The concept of the presence of electric vehicles in the context ...
Figure 14.9 Information extraction steps and one day forecast for the networ...
Figure 14.10 Standard LSTM model.
Chapter 15
Figure 15.1 Components of HIL.
Figure 15.2 Signal HIL model.
Figure 15.3 Power and reduced scale HIL model.
Figure 15.4 Block diagram for HIL simulation.
Figure 15.5 Power hardware in the loop setup.
Figure 15.6 Example of power hardware in the loop setup.
Figure 15.7 Real-world environment and HIL test environment interaction stru...
Figure 15.8 An interaction exists between the device under test, the compute...
Figure 15.9 Three-phase MMC-HVDC topology.
Figure 15.10 Submodule topology.
Figure 15.11 Block diagram of MMC-HVDC topology.
Figure 15.12 Battery management system.
Figure 15.13 Model fuel cell systems (FCS) and fuel cell control systems (FC...
Figure 15.14 Model inverters, traction motors, and motor control software.
Figure 15.15 Deploy, integrate, and test control algorithms.
Figure 15.16 Data-driven workflows and AI in EV development.
Figure 15.17 HIL structure for fault testing.
Figure 15.18 HIL implementation blocks.
Chapter 16
Figure 16.1 Comparison of SCADA and PMU devices.
Figure 16.2 Block diagram of phasor measurement units. ROCOF, Rate Of Change...
Figure 16.3 Applications of wide area monitoring systems (WAMS). WA-PSS, Wid...
Figure 16.4 Typical MV/LV distribution system.
Figure 16.5 System configuration in project “RegModHarz.”
Figure 16.6 Island mode detection principle.
Chapter 17
Figure 17.1 Conceptual diagram of Blockchain.
Figure 17.2 Blockchain transaction steps.
Figure 17.3 Smart Grid—New cyber layer using Blockchain.
Figure 17.4 Blockchain technology-based microgrid automation [31].
Figure 17.5 Blockchain-based distributed management, control, and validation...
Figure 17.6 Six layer architecture of smart contracting for energy applicati...
Figure 17.7 Design of transactive energy systems.
Figure 17.8 Working on smart contract system.
Figure 17.9 Six layer architecture of smart contracting for energy applicati...
Chapter 18
Figure 18.1 Evolution of power electronics in all spectrums of electricity u...
Figure 18.2 Critical components of a TCSC device. OVT = optical voltage tran...
Figure 18.3 Illustration of mean power compensation of transmitted power fol...
Figure 18.4 An illustration of a multilevel VSC converter technology.
Figure 18.5 RoCoF illustration of ERCOT network on the 15 February 2021.
Figure 18.6 Risk, cost, and performance optimization challenges of utility g...
Figure 18.7 Sympathetic inrush event experienced by transformers in the neig...
Figure 18.8 Illustration of sympathetic behavior in the grid for an existing...
Figure 18.9 Time scale of different responses in a power systems world.
Figure 18.10 Schematic representation of various components in a digital sub...
Figure 18.11 Digital substation current and future roadmaps for Icelandic sy...
Figure 18.12 Typical example of various forms of fast frequency inertia beyo...
Figure 18.13 An illustration of voltage deviation passed from MV upstream to...
Figure 18.14 Geospatial representation PQ data obtained from different meter...
Figure 18.15 Classification of different events presented in a time scale.
Figure 18.16 Evolution of decision-making ability on an asset or a substatio...
Figure 18.17 Division of responsibility in OT and IT domain in a utility env...
Figure 18.18 A concept of holistic condition monitoring: an essential step t...
Figure 18.19 Modern power ecosystem.
Figure 18.20 SNR and path gain for in isolated 5G network scenario.
Figure 18.21 SNR in presence of eMBB at different network interference level...
Figure 18.22 Illustration of training data and result for different supervis...
Figure 18.23 Representation of DR-based classification of HVDC data.
Figure 18.24 Comparison of predicted versus actual in random forest model.
Figure 18.25 e-Mesh architecture for grid edge solutions from Hitachi energy...
Guide
Cover
Title Page
Copyright
Editor Biography
List of Contributors
Table of Contents
Begin Reading
Index
End User License Agreement
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IEEE Press445 Hoes LanePiscataway, NJ 08854
IEEE Press Editorial BoardSarah Spurgeon, Editor in Chief
Jón Atli Benediktsson Anjan BoseAdam DrobotPeter (Yong) Lian
Andreas MolischSaeid NahavandiJeffrey ReedThomas Robertazzi
Diomidis SpinellisAhmet Murat Tekalp
Artificial Intelligence-based Smart Power Systems
Edited by
Sanjeevikumar Padmanaban
Department of Electrical Engineering, Information Technology, and Cybernetics, University of South-Eastern Norway, Porsgrunn, Norway
Sivaraman Palanisamy
World Resources Institute (WRI) India, Bengaluru, India
Sharmeela Chenniappan
Department of Electrical and Electronics Engineering, Anna University, Chennai, India
Jens Bo Holm-Nielsen
Department of Energy Technology, Aalborg University, Aalborg, Denmark
Copyright © 2023 by The Institute of Electrical and Electronics Engineers, Inc. All rights reserved.
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Editor Biography
Sanjeevikumar Padmanaban received his PhD degree in electrical engineering from the University of Bologna, Bologna, Italy, 2012. He was an Associate Professor at VIT University from 2012 to 2013. In 2013, he joined the National Institute of Technology, India, as a faculty member. Then, he served as an Associate Professor with the Department of Electrical and Electronics Engineering, University of Johannesburg, South Africa, from 2016 to 2018. From March 2018 to February 2021, he was an Assistant Professor with the Department of Energy Technology, Aalborg University, Esbjerg, Denmark. He continued his activities from March 2021 as an Associate Professor with the CTIF Global Capsule (CGC) Laboratory, Aarhus University, Herning, Denmark. Presently, he is a Full Professor in Electrical Power Engineering with the Department of Electrical Engineering, Information Technology, and Cybernetics, University of South-Eastern Norway, Norway. He is a fellow of the Institution of Engineers, India, the Institution of Electronics and Telecommunication Engineers, India, and the Institution of Engineering and Technology, UK. He is listed among the world's top two scientists (from 2019) by Stanford University, USA.
Sivaraman Palanisamy was born in Vellalur, Madurai District, Tamil Nadu, India. He completed schooling in Govt. Higher Secondary School, Vellalur, received his BE in Electrical and Electronics Engineering and ME in Power Systems Engineering from Anna University, Chennai, India, in 2012 and 2014, respectively. He has more than eight years of industrial experience in the field of power system analysis, grid code compliance studies, electric vehicle charging infrastructure, solar PV system, wind power plant, power quality studies, and harmonic assessments. Presently he is working as a Program Manager – EV charging infrastructure at WRI India (major contribution done before joining this position). He is an expert in power system simulation software such as ETAP, PSCAD, DIGSILENT POWER FACTORY, PSSE, and MATLAB. He is a working group member of various IEEE standards such as P2800.2, P2418.5, P1854, and P3001.9. He is an IEEE Senior Member, a Member of CIGRE, Life Member of the Institution of Engineers (India), and The European Energy Center (EEC). He is also a speaker who is well versed on both national and international standards.
Sharmeela Chenniappan holds a BE in Electrical and Electronics Engineering, ME in Power Systems Engineering from Annamalai University, Chidambaram, India, and a PhD in Electrical Engineering from Anna University, Chennai, India, in 1999, 2000, and 2009, respectively. She received her PG Diploma in Electrical Energy Management and Energy Audit from Annamalai University, Chidambaram in 2010. At present, she holds the post of Professor in the Department of EEE, CEG campus, Anna University, Chennai, India. She has more than 21 years of teaching/research experience and has taught various subjects to undergraduate and postgraduate students. She did a number of research projects and consultancy work in renewable energy, Electric Vehicle Charging Infrastructure, power quality, and design of PQ compensators for various industries. She is an IEEE Senior Member, a Fellow of the Institution of Engineers (India), and a Life Member of CBIP, ISTE, and SSI.
Jens Bo Holm-Nielsen currently works as Head of the Esbjerg Energy Section at the Department of Energy Technology, Aalborg University. Through his research, he helped establish the Center for Bioenergy and Green Engineering in 2009 and served as the head of the research group. He has vast experience in the field of biorefinery concepts and biogas production, in particular anaerobic digestion. He has implemented bio-energy systems projects in various provinces in Denmark and European states. He served as the technical advisor for many industries in this field. He has executed many large-scale European Union and United Nation projects in research aspects of bioenergy, biorefinery processes, the full biogas chain, and green engineering. He was a member on invitation with various capacities in committees for over 250 various international conferences and organizer of international conferences, workshops, and training programs in Europe, Central Asia, and China. His focus areas are renewable energy, sustainability, and green jobs for all.
List of Contributors
Ahmed AbbouDepartment of Electrical EngineeringMohammed V University in RabatMohammadia School of EngineersRabatMorocco
Shobha AgarwalDepartment of Higher Technical Education and Skill DevelopmentJharkhand UniversityRanchiIndia
Shariq AhammedPESValeo India Private LimitedChennaiIndia
Nadia AwatifDepartment of Electrical, Electronic and Communication EngineeringMilitary Institute of Science and TechnologyDhakaBangladesh
Gyan R. BiswalDepartment of Electrical and Electronics EngineeringVeer Surendra Sai University of Technology (VSSUT)BurlaOdishaIndia
Phaneendra B. BobbaDepartment of Electrical and Electronics EngineeringGokaraju Rangaraju Institute of Engineering and Technology (GRIET)HyderabadIndia
Satarupa ChakrabartiSchool of Computer EngineeringKIIT UniversityBhubaneswarIndia
Soham ChakrabartiSchool of Computer EngineeringKIIT UniversityBhubaneswarIndia
Ravi Chengalvarayan NatarajanDepartment of Electrical and Electronics EngineeringVidya Jyothi Institute of TechnologyHyderabadTelanganaIndia
Sharmeela ChenniappanDepartment of Electrical and Electronics EngineeringAnna UniversityChennaiIndia
Khalid ChiganeDepartment of Electrical EngineeringMohammed V University in RabatMohammadia School of EngineersRabatMorocco
Muniraj ChinnusamyDepartment of EEEKnowledge Institute of TechnologySalemIndia
Gunapriya DevarajanDepartment of EEESri Eshwar College of EngineeringCoimbatoreIndia
Lakshmi DhandapaniDepartment of Electrical and Electronics EngineeringAcademy of Maritime Education and Training (AMET)ChennaiIndia
Sundaram ElangoDepartment of Electrical and Electronics EngineeringCoimbatore Institute of TechnologyCoimbatoreIndia
Thamatapu EswararaoDepartment of Electrical and Electronics EngineeringCoimbatore Institute of TechnologyCoimbatoreIndia
Garika GantaiahswamyDepartment of Electrical and Electronics EngineeringJNTU KakinadaAndhra Loyola Institute of Engineering and TechnologyVijayawadaAndhra PradeshIndia
A. GayathriDepartment of EEESri Krishna College of TechnologyCoimbatoreTamil NaduIndia
Md. Sanwar HossainDepartment of Electrical and Electronic EngineeringBangladesh University of Business and TechnologyDhakaBangladesh
Bouazza JabriDepartment of PhysicalLCS LaboratoryFaculty of SciencesMohammed V University in RabatRabatMorocco
Hamid Haj Seyyed JavadiDepartment of Mathematics and Computer ScienceShahed UniversityTehranIran
Jayakumar NarayanasamyDepartment of EEEThe Oxford College of EngineeringBommanahalliBangaloreIndia
Sabareeshwaran KanagarajDepartment of EEEKarpagam Institute of TechnologyCoimbatoreIndia
Kathirvel KaruppazaghiPESValeo India Private LimitedChennaiIndia
Meenakshi Devi ManivannanDepartment of Electrical and Electronics EngineeringThiagarajar College of EngineeringMaduraiIndia
Saad MekhilefPower Electronics and Renewable Energy Research LaboratoryDepartment of Electrical EngineeringUniversity of MalayaKuala LumpurMalaysia
and
School of Science, Computing and Engineering TechnologiesSwinburne University of TechnologyHawthornVicAustralia
and
Smart Grids Research GroupCenter of Research Excellence in Renewable Energy and Power SystemsKing Abdulaziz UniversityJeddahSaudi Arabia
Naveenkumar MaratiPESValeo India Private LimitedChennaiIndia
Marimuthu MarikannuDepartment of EEESaranathan College of EngineeringTrichyIndia
Rania MoutchouDepartment of Electrical EngineeringMohammed V University in RabatMohammadia School of EngineersRabatMorocco
Geethanjali MuthiahDepartment of Electrical and Electronics EngineeringThiagarajar College of EngineeringMaduraiIndia
Nagesh Halasahalli NagarajuPower System StudiesPower Research & Development Consultants Pvt LtdBengaluruIndia
Morteza Azimi NasabCTIF Global CapsuleDepartment of Business Development and TechnologyAarhus UniversityHerningDenmark
Mostafa Azimi NasabCTIF Global CapsuleDepartment of Business Development and TechnologyAarhus UniversityHerningDenmark
and
Department of Electrical and Computer EngineeringBoroujerd BranchIslamic Azad UniversityBoroujerdIran
Puspalatha Naveen KumarDepartment of EEESri Eshwar College of EngineeringCoimbatoreIndia
Fatemeh NikokarDepartment of Business Development and TechnologyCTIF Global CapsuleAarhus UniversityHerningDenmark
Sanjeevikumar PadmanabanDepartment of Electrical Engineering, Information Technology, and CyberneticsUniversity of South-Eastern NorwayPorsgrunnNorway
Sivaraman PalanisamyWorld Resources Institute (WRI) IndiaBengaluruIndia
Madhu PalatiDepartment of Electrical and Electronics EngineeringBMS Institute of Technology and ManagementAffiliated to Visvesvaraya Technological UniversityDoddaballapur Main Road, AvalahalliYelahankaBengaluruIndia
P. PandiyanDepartment of EEEKPR Institute of Engineering and TechnologyCoimbatoreTamil NaduIndia
Basanta K. PanigrahiDepartment of Electrical EngineeringInstitute of Technical Education & ResearchSOA UniversityBhubaneswarIndia
Paranthagan BalasubramanianDepartment of EEESaranathan College of EngineeringTrichyIndia
Md. I. H. PathanDepartment of Electrical and Electronic EngineeringHajee Mohammad Danesh Science and Technology UniversityDinajpurBangladesh
Sagar Singh PrathapEnergy and Power SectorCenter for Study of Science Technology and PolicyBengaluruIndia
Zahira RahimanDepartment of Electrical and Electronics EngineeringB.S. Abdur Rahman Crescent Institute of Science & TechnologyChennaiIndia
Mohammad M. RahmanInformation and Computing Technology DivisionHamad Bin Khalifa UniversityCollege of Science and EngineeringDohaQatar
Hemavathi RamadossDepartment of Electrical and Electronics EngineeringThiagarajar College of EngineeringMaduraiIndia
Nisha C. RaniDepartment of EEEThe Oxford College of EngineeringBommanahalliBangaloreIndia
Muhyaddin RawaSmart Grids Research GroupCenter of Research Excellence in Renewable Energy and Power SystemsKing Abdulaziz UniversityJeddahSaudi Arabia
and
Department of Electrical and Computer EngineeringFaculty of EngineeringK.A. CARE Energy Research and Innovation CenterKing Abdulaziz UniversityJeddahSaudi Arabia
Salah E. RhailiDepartment of Electrical EngineeringMohammed V University in RabatMohammadia School of EngineersRabatMorocco
V. RukkumaniDepartment of EIESri Ramakrishna Engineering CollegeCoimbatoreTamil NaduIndia
Subrat SahooHitachi Energy ResearchVasterasSweden
Tina SamavatCTIF Global CapsuleDepartment of Business Development and TechnologyAarhus UniversityHerningDenmark
S. SaravananDepartment of EEESri Krishna College of TechnologyCoimbatoreTamil NaduIndia
Md. ShafiullahKing Fahd University of Petroleum & MineralsInterdisciplinary Research Center for Renewable Energy and Power SystemsDhahranSaudi Arabia
Mohammad S. ShahriarDepartment of Electrical EngineeringUniversity of Hafr Al-BatinHafr Al BatinSaudi Arabia
Logeshkumar ShanmugasundaramDepartment of Electronics and Communication EngineeringChrist the King Engineering CollegeCoimbatoreIndia
Mohammad Ebrahim ShiriMathematics and Computer Science DepartmentAmirkabir University of TechnologyTehranIran
Jyoti ShuklaDepartment of Electrical EngineeringPoornima College of EngineeringRTUJaipurIndia
Brijesh SinghDepartment of Electrical and Electronics EngineeringKIET Group of InstitutionsGhaziabadIndia
Umashankar SubramanianRenewable Energy LaboratoryDepartment of Communications and NetworksPrince Sultan UniversityCollege of EngineeringRiyadhSaudi Arabia
Aleena SwetapadmaSchool of Computer EngineeringKIIT UniversityBhubaneswarIndia
and
Renewable Energy LaboratoryDepartment of Communications and NetworksPrince Sultan UniversityCollege of EngineeringRiyadhSaudi Arabia
Krishnamohan TatikondaDepartment of Electrical and Electronics EngineeringJNTU KakinadaAndhra Loyola Institute of Engineering and TechnologyVijayawadaAndhra PradeshIndia
Balraj VaithilingamPESValeo India Private LimitedChennaiIndia
Pramila VallikannanDepartment of Electrical and Electronics EngineeringB.S. Abdur Rahman Crescent Institute of Science & TechnologyChennaiIndia
Monika VardiaDepartment of Electrical EngineeringPoornima College of EngineeringRTUJaipurIndia
Devi Vigneshwari BalasubramanianDepartment of EEEThe Oxford College of EngineeringBommanahalliBangaloreIndia
Vijayalakshmi SubramanianDepartment of EEESaranathan College of EngineeringTrichyIndia
Deepak YadavPower Electronics and Renewable Energy Research LaboratoryDepartment of Electrical EngineeringUniversity of MalayaKuala LumpurMalaysia
Mohammad ZandCTIF Global CapsuleDepartment of Business Development and TechnologyAarhus UniversityHerningDenmark
1Introduction to Smart Power Systems
Sivaraman Palanisamy1, Zahira Rahiman2, and Sharmeela Chenniappan3
1World Resources Institute (WRI) India, Bengaluru, India
2Department of Electrical and Electronics Engineering, B.S. Abdur Rahman Crescent Institute of Science & Technology, Chennai, India
3Department of Electrical and Electronics Engineering, Anna University, Chennai, India
1.1 Problems in Conventional Power Systems
The conventional power system is generally classified as power generation, power transmission, and power distribution systems. The power is generated from thermal plants, nuclear plants, or hydroplants at remote locations and this is transmitted to the load center through a power transmission system [1]. The distribution system is used to distribute the electric power to various end-users. It has limited control and visibility of power flows from generation to the end user's load. Some of the problems associated with conventional systems are limited visibility in power flows, limited control, delay in measurement and control, higher energy losses in transmission and distribution systems, poor power quality, etc. [2].
1.2 Distributed Generation (DG)
The distributed generation (DG) is used to produce the electric power closer to the load center or end-user loads to reduce the energy loss in the transmission as well as distribution system and improve the voltage profile. The sources of DG can be both renewable energy sources (like solar, wind, and fuel cells), and nonrenewable energy sources (like diesel generators). These sources as simply called distributed energy resources (DERs) [3]. Generally, these DGs are interconnected with the primary or secondary distribution systems based on their rating. Figure 1.1 shows the single-line diagram of a 100 kW rooftop solar PV system as DG connected to the 415 V, 50 Hz secondary distribution system.
Figure 1.2 shows the single-line diagram of a 1 MW rooftop solar PV system as DG connected to the 11 kV, 50 Hz primary distribution system.
The intermittency is one of the major challenges of using renewable energy sources such as solar PV and wind energy conversion systems as DG. Due to intermittence, the output power from the solar PV system and wind energy conversion system also varies throughout the operation resulting in power balance and stability issues [4]. The impact of intermittency can be reduced to a certain extent by using a complex software program/tool to predict the energy output based on various historical data.
Figure 1.1 Single line diagram of a rooftop solar PV system connected to the secondary distribution system.
1.3 Wide Area Monitoring and Control
Power grids are the most complicated and essential systems in today's life. The risk of experiencing a wide variety of faults and failures is increasing [5]. The unpredictable and cascaded events of faults lead to a blackout, and they have an impact on a large range of consumers. Many grid codes allow the frequency within the specified tolerance limits. Hence, flexibility in frequency leads to under drawl or over drawl of real power, as well as under generation or over a generation by the utilities. This results in the overloading of transmission lines and under voltage or over voltage of the grid. Also, unpredictability, intermittency, and variability of renewable energy integration pose challenges in grid operation. Conventional Supervisory Control and Data Acquisition (SCADA) systems are limited to steady-state measurements and cannot be used for observing the system dynamics behavior. To overcome the drawbacks of a conventional system, one of the most recent advancements in modern power grids is wide-area monitoring (WAM). With the developments of WAM, power system dynamic behavior is monitored closely in real-time. So that the faults in the power grid can be identified and protected in a wider range [6].
The overall goal of using WAM is to improve protection and to develop new protection concepts that will make blackouts less probable and much less severe even if they do occur. The following are the key areas where WAM can help to protect power systems.
Dealing with large-scale interruptions
Taking the appropriate precautions to mitigate the impact of failed systems
Ignoring relay settings that are incompatible with the current system configuration
Achieving a reasonable balance between security and dependability
Figure 1.2 Single line diagram of a rooftop solar PV system connected to the primary distribution system.
The purpose of protection is to safeguard specific elements of the power system as well as the security of the power system as a whole.
In the case of main equipment protection, WAM plays a significant role. This is due to the fact that primary protection must consistently offer a very fast response to any failure on the element that it safeguards. WAM, on the other hand, can be a beneficial tool for increasing system performance due to the slower response time necessary for backup protection and the fact that it protects a zone of the system. Wide-area measurements have the potential to enable the development of supervisory methods for backup protection, more complex types of system protection, and altogether new protection concepts. Examples of these protection functions are
Dynamic relays adjust their parameters in response to changes in the system condition.
Multiterminal line protection has been improved.
Predictive end-of-line protection, which monitors the distant location breaker and replaces the under-reaching Zone 1 with an instantaneous characteristic if it is open.
Modify relay settings temporarily to prevent malfunction during cold load pickup.
Employ the capability of modern relays to self-monitor to find hidden faults and use the IEC 61850 hot-swap capabilities to eliminate them.
Artificial controlled microgrids provide an adaptive controlled divergence to prevent an uncontrolled system separation.
WAM gathers data from remote places throughout the power grid and integrates them in real-time into a single snapshot of the power system for a given time. Synchronized measurement technology (SMT) is a crucial component of WAM because it allows measurements to be correctly timestamped, typically using global positioning system (GPS) timing signals. The data may be simply merged with these timestamps, and phase angle measurements can be made with a common reference [7]. Figure 1.3 shows the generic WAMS architecture based on phasor measurement units (PMUs). PMUs, phasor data concentrators (PDCs), communication networks, data storage, and application software are the primary components of WAM. The number of substation PDCs is determined by the power system requirements. Voltage, current, and frequency are measured by PMUs placed in substations. These readings are routed straight to the central PDC or a substation PDC.
Figure 1.3 Block diagram of wide-area monitoring and control.
The following functions are available at the PDC substation:
Synchronization of date and time
Gathers info from PMUs
Analyzes collected data
Data is sent to the central PDC
Communicates data with the regional SCADA
Data is archived locally
Carries out local data analysis and security actions
1.4 Automatic Metering Infrastructure
The name Advanced Metering Infrastructure or simply AMI refers to the entire infrastructure, which includes everything from smart meters to two-way communication networks to control center equipment, as well as all the applications that allow for the gathering and transfer of energy usage data in real-time. The backbone of the smart grid [8] is AMI, which enables two-way connectivity with customers. Error-free meter reading from remote, network problem and its diagnosis, load profile/patterns, energy audits/consumptions, and partial load curtailment in place of load shedding are all potential objectives of AMI. The typical building blocks of AMI are shown in Figure 1.4.
AMI is made up of several hardware and software components that all work together to measure energy consumption and send data about it to utility companies and customers [8]. The key technological components of AMI are,
Smart Meters
: Advanced meter devices that could gather data of electrical parameters at various intervals and transfer the data to the utility via fixed communication networks, as well as receiving information from the utility such as pricing signals and relaying it to the consumer
[9]
.
Figure 1.4 Basic building blocks of AMI.
Communication Network
: Smart meters can provide data to utility companies and vice versa. The advanced communication networks allow two-way communication between smart meters and utility companies. For these applications, networks like
Broadband over Powerline
(
BPL
),
Power Line Communications
(
PLC
), Fiber Optic Communication, Fixed
Radio Frequency
(
RF
), or public networks (e.g. landline, cellular, paging) are used
[10]
.
Meter Data Acquisition System
: Data is collected from smart meters over a communication network and sent to the
meter data management system
(
MDMS
) using software applications on the Control Centre hardware and
DCU
s (
Data Concentrator Unit
s).
MDMS Metering: receives the information, stores it, and analyzed it by the host system.
Home Area Network
(
HAN
)
: It can be a consumer-side extension of AMI, allowing for easier communication between household appliances and AMI, and thus better load control by both the utility and the consumer
[11]
.
The benefits of AMI are multifold and can be generally categorized as follows:
Operational Benefits
: The entire system benefits from AMI since it improves meter reading accuracy, detects energy theft, and responds to power outages while removing the need for an on-site meter reading.
Financial Benefits
: Utility companies financially benefit from AMI because it lowers equipment and maintenance costs, enables faster restoration of electric service during outages, and streamlines the billing process.
Customer Benefits
: Electric customers benefit from AMI because it detects meter faults early, allows for speedier service restoration, and improves billing accuracy and flexibility. AMI also offers time-based tariff choices, which can help consumers save money and better manage their energy usage.
Security Benefits
: AMI technology allows for better monitoring of system resources, reducing the risk of cyber-terrorist networks posing a threat to the grid.
In spite of various advantages, AMI deployment faces three significant challenges: higher capital costs or investments, connection or interoperability with other grid systems, and standardization.
High Capital Costs
: A full-scale implementation of AMI necessitates investments in all hardware and software components, including smart meters, network infrastructures, and network management software, as well as costs associated with meter installation and maintenance.
Integration:
Customer Information System
s (
CIS
s),
Geographical Information System
s (
GIS
s),
Outage Management System
s (
OMS
s),
Work Management System
(
WMS
),
Mobile Workforce Management
(
MWM
), SCADA/DMS,
Distribution Automation System
(
DAS
), and other utilities' information technology systems essentially integrated with AMI.
Standardization
: Compatibility standards must be created, as they are the keys to properly connecting and sustaining an AMI-based grid system. They set universal requirements for AMI technology, deployment, and general operations.
Investing in AMI to modernize the power grid system will alleviate several grid stresses caused by the rising power demands. AMI will improve three critical aspects of power grid infrastructure such as system reliability, energy cost, and electricity theft.
System Reliability
: AMI technology increases electricity distribution and overall dependability by allowing electricity distributors to identify and respond to electric demand automatically, reducing power outages.
Energy Costs
: Increased stability and functionality, as well as fewer power outages and streamlined billing operations, will greatly reduce the expenses involved with providing and maintaining the grid, resulting in significantly cheaper electricity bills.
Electricity Theft
: Electricity theft is a prevalent problem in Society. AMI systems that track energy usage will aid in monitoring power in real-time, resulting in enhanced system transparency.
1.5 Phasor Measurement Unit
A phasor measurement unit or simply PMU is a crucial measurement tool that is used on electric power systems to improve grid operators' visibility on the huge power grid network/system [12]. It measures the parameter called a phasor and it provides the information/data of magnitude and phase angle of voltage or current at a particular location [13]. This information/data shall be used to find the operating frequency at a particular time instant and examine the condition of the system as shown in Figure 1.5.
A PMU may provide up to 60 measurements per second. As compared with a typical SCADA-based system, the measurements per second are higher in PMU. A typical SCADA-based system will provide the data (one measurement data in two to four seconds time interval) [14]. The main advantage of using PMU over conventional SCADA system is PMU can collect the data of all PMU at a particular time through GPS. This means, that collected data across the power grid are time-synchronized. Because of this reason, PMUs are also called synchro phasors [15].
The information collected from the PMU conveys to the system operator whether the main electrical parameters such as voltage, current, and frequency are within the specified limit with tolerance or not. The capability of the PMU is as follows,
Line congestion: prediction, analysis, and manage
Analyzing the event after the disturbance or fault (post fault analysis)
Instability and stress detection
Inefficiencies detection
In this decade, several thousands of PMUs are successfully installed and commissioned in transmission and/or distribution grids across the globe. A PMU can be integrated with smart controllers, and this will reduce the manual operations required by the SCADA system in decision making and control. Due to this feature, the grid becomes robust and efficient, it allows the more integration of renewable powers, DERs, and microgrids.
The report on Unified Real-Time Dynamic State Measurement (URTDSM) by Power Grid Corporation of India Ltd. (PGCIL) shows the importance of PMU data (data from various lines at time-stamped) is useful for prediction and post fault event analysis. PGCIL followed the philosophy stated below for installing the PMUs across India, installation of PMUs on substations at 400 kV level above, all generating stations at 220 kV level and above, HVDC terminals, important inter-regional connection points, inter-national connection points, etc. Also, the provision of PDC at all State Load Dispatch Centers (SLDCs), Regional Load Dispatch Centers (RLDCs), and National Load Dispatch Center (NLDC) [7].
The PMU is used to measure the magnitude and phase angle of bus voltage and line current phasor. PMU takes the bus PT input for voltage and line CT input for current at the substation as well as GPS time signal. The PMU presently available in the market can measure one set of bus voltage (three-phase) and two sets of line current (three-phase). The typical arrangement of PMU in substation and Main Phasor Data Concentrator (MPDC)/Sub Phasor Data Concentrator (SPDC) in load dispatch center is shown in Figure 1.6[7].
Figure 1.5 Transmission line data.
Figure 1.6 Typical arrangement of PMU in substation and PDC in the load dispatch center.
The PMU output from the substation is communicated to PDC through a Local Area Network (LAN
