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Smart Metering : Infrastructure, Methodologies, Applications, and Challenges.

Smart Metering: Infrastructure, Methodologies, Applications and Challenges combines the fundamentals of smart meters in smart grids with the latest advances and technologies in advanced smart infrastructure.

Bibliographic Details
Main Author: Sood, Vijay K.
Corporate Author: ScienceDirect (Online service)
Other Authors: Biswal, Monalisa, sarangi, saumendra, Alhelou, Hassan Haes
Format: eBook
Language:English
Published: San Diego : Elsevier, 2024.
Subjects:
Digital multimeters.
Multimètres numériques.
SCIENCE / Energy.
TECHNOLOGY & ENGINEERING / Power Resources / General.
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • Intro
  • Smart Metering: Infrastructure, Methodologies, Applications, and Challenges
  • Copyright
  • Contents
  • Contributors
  • About the editors
  • Preface
  • Chapter 1: Smart meters in smart grid
  • 1. Introduction
  • 1.1. Earlier works
  • 1.2. Chapter contribution and organization
  • 2. Infrastructure
  • 2.1. Advanced metering architecture
  • 2.1.1. Smart meters (SMs)
  • 2.1.2. Measurement unit
  • 2.1.3. Calculation unit
  • 2.1.4. Communication unit
  • 2.1.5. Data concentrator
  • 2.2. Communication architecture
  • 2.2.1. Home area network(HAN)
  • 2.2.2. Neighbor area network (NAN)
  • 2.2.3. Field area network (FAN)
  • 2.3. Wide area network (WAN)
  • 3. Smart meter data analytics
  • 3.1. Demand side management (DSM)
  • 3.2. Theft detection
  • 3.3. Load forecasting and price forecasting
  • 3.4. Outage management
  • 3.5. Distribution automation for protection of high-impedance fault
  • 4. Key challenges
  • 4.1. Technical challenges
  • 4.1.1. Inappropriate infrastructure
  • 4.1.2. Big data management
  • 4.1.3. Huge dependency on technology
  • 4.1.4. Communication network
  • 4.1.5. Interoperability challenge
  • 4.1.6. Security challenges
  • 4.1.7. Catastrophic failure
  • 4.1.8. Integration of renewable and DER
  • 4.1.9. Issues related to storage technology and power quality
  • 4.2. Nontechnical challenges
  • 4.2.1. Policies and framework
  • 4.2.2. Financial challenges
  • 4.2.3. Inadequacy of expertise
  • 4.2.4. Environmental challenges
  • 5. Security
  • 5.1. Objectives of cybersecurity
  • 5.1.1. Integrity
  • 5.1.2. Confidentiality
  • 5.1.3. Availability
  • 5.1.4. Accountability
  • 5.2. Threats in SG metering network
  • 5.2.1. Threats using radio signals
  • 5.2.2. Infiltration of malware at nodes
  • 5.2.3. Attack at the back office compromise
  • 5.2.4. Denial of service (DoS)
  • 6. Conclusion
  • References.
  • Chapter 2: Multifunctional IOT-based smart energy meter
  • 1. Introduction
  • 2. Block diagram of SEM
  • 2.1. Voltage sensor
  • 2.2. Current sensor
  • 2.3. Liquid crystal display
  • 2.4. Wi-Fi module (ESP8266)
  • 2.5. DC voltage power supply
  • 3. Result and observation
  • 4. Conclusion
  • Acknowledgment
  • References
  • Chapter 3: Smart load forecasting methodologies
  • 1. Introduction
  • 1.1. Related literature
  • 1.2. Highlighted points of the chapter
  • 2. Basics of load forecasting
  • 2.1. Frequently used terminologies/definitions
  • 2.2. Types of consumers
  • 2.3. Classification of load forecasting based on the forecast horizon
  • 2.4. Factors affecting load forecasting
  • 3. Overview of forecasting methodologies
  • 3.1. Statistical methods
  • 3.2. Data-driven method
  • 3.2.1. Supervised learning
  • 3.2.2. Unsupervised learning
  • 3.2.3. Reinforcement learning
  • 3.2.4. Deep learning
  • 4. Smart meter data for load forecasting
  • 5. Numerical example
  • 5.1. Dataset
  • 5.2. Data preprocessing
  • 5.3. Forecasting model details
  • 5.4. Evaluation metric
  • 5.5. Results
  • 6. Discussion and future direction
  • Acknowledgment
  • References
  • Chapter 4: Cyber security challenges and solutions in protective relaying
  • 1. Introduction
  • 2. Structure of CPPS
  • 3. Cyber threats in CPPS
  • 3.1. False data injection (FDI) attack
  • 3.2. Replay attack
  • 3.3. Spoofing attack
  • 3.4. Man-in-the-middle attack
  • 3.5. Distributed denial of service attack
  • 3.6. Malware attack
  • 4. Simulation of cyberattack in power system
  • 4.1. Simulation of cyberattack in overcurrent relay
  • 4.2. Simulation of cyberattack in the distance relay
  • 5. Defensive strategies against cyberattacks in CPPS
  • 6. Machine learning for cybersecurity
  • 6.1. Elaborate network modeling is not required
  • 6.2. Big data in power systems.
  • 6.3. Effective against new kind of cyberattacks
  • 6.4. Development of new algorithms
  • 7. Intelligent protective relays (IPR)
  • 7.1. Choosing appropriate machine learning/deep learning model
  • 7.2. Collecting appropriate training data
  • 7.3. Preprocessing the data
  • 7.4. Training the model
  • 7.5. Calculating the threshold error for online applications
  • 8. Results
  • 8.1. Normal nonfault scenario
  • 8.2. Normal fault scenario
  • 8.3. Cyberattacked fault scenarios
  • 9. Conclusion
  • References
  • Chapter 5: Cyberattack issues on smart metering infrastructure
  • 1. Overview
  • 1.1. Advantages of smart meter
  • 1.2. Liability of SMI
  • 1.3. Vulnerability of smart meter
  • 1.4. The impact of a cyberattack on smart meters
  • 2. Smart Meter Infrastructure (SMI)
  • 2.1. The blocks of SMI
  • 2.1.1. Smart meters
  • 2.1.2. Communication networks
  • 2.1.3. Data Acquisition System (DAS)
  • 2.1.4. Data management system
  • 2.2. Different types of smart meters
  • 2.2.1. Artificial Intelligent Meter (AIM)
  • 2.2.2. Prepaid energy metering system module
  • 2.2.3. OpenZmeter
  • 2.3. Different technologies used in smart meter
  • 3. Security concerns in smart meter
  • 3.1. Cyber security issues in smart meter
  • 3.2. Frequently used intrusion detection
  • 3.2.1. Signature-based intrusion detection
  • 3.2.2. Anomaly-based intrusion detection
  • 4. Conclusion
  • References
  • Chapter 6: Benefits and challenges of an advanced metering infrastructure to detect and locate energy theft
  • 1. Introduction
  • 1.1. Nontechnical losses worldwide
  • 1.2. Classification of nontechnical losses
  • 1.3. Nontechnical losses in the context of AMI
  • 2. Energy theft detection and location methods
  • 2.1. Data-driven methods
  • 2.2. Pattern recognition of load curves and energy consumption
  • 2.3. Socioeconomic data
  • 2.4. Model-driven methods.
  • 2.5. Methods based on voltage and/or current measurements
  • 2.6. Load flow-based methods
  • 2.7. State estimation-based methods
  • 2.8. Challenges associated with energy theft detection methods using AMI
  • 3. Distribution system state estimation for nontechnical losses identification
  • 3.1. Distribution system state estimation modeling
  • 3.2. Principles of bad data treatment for nontechnical loss identification
  • 3.2.1. Multiple energy theft location
  • 3.2.2. Successive correction with normalized residuals
  • 3.2.3. Location exploring the nature of nontechnical losses
  • 3.3. Challenge of low redundancy
  • 3.4. Other challenges
  • 3.5. Alternative formulations for distribution system state estimation
  • 4. Conclusions
  • Acknowledgments
  • References
  • Chapter 7: Fault location identifications in distribution system using smart meter data
  • 1. Introduction
  • 2. Issues in computation of fault location
  • 3. Fault location using smart meter information
  • 4. Different methods to compute fault location in DS
  • 4.1. Impedance based methods
  • 4.1.1. Reasons for error in impedance-based method
  • 4.1.2. Issues due to multiple laterals
  • 4.1.3. Error due to the addition of the DGs
  • 4.2. Traveling wave based method
  • 4.3. Data feature extraction methods
  • 4.4. Intelligent approaches
  • 5. Application of smart meter to locate the fault
  • 5.1. Fault location in a 4-bus and IEEE 33-bus distribution system using smart meter data
  • 5.1.1. Fault location using only smart meter current
  • 5.1.2. Results and discussions
  • 5.1.3. Fault location using only smart meter voltage and current
  • 6. Future scope
  • References
  • Chapter 8: Strategic deployment of advanced measuring instruments to enhance robustness of state estimation in smart grid ...
  • 1. Introduction
  • 1.1. Literature survey.
  • 2. Hybrid state estimation (HSE) and bad data detection (BDD)
  • 3. Cyberattack in a smart grid network
  • 3.1. False data injection attack
  • 4. Development of a strategic sensor placement scheme for imparting robustness to state estimation
  • 4.1. Formulation of objective function
  • 4.2. Binary bat algorithm (BBA)
  • 5. Results and discussion
  • 5.1. IEEE 14 bus system
  • 5.2. IEEE 30 bus system
  • 6. Conclusion
  • References
  • Chapter 9: Outage management in distribution system using smart meter data
  • 1. Introduction
  • 2. Impact of outage in DS
  • 3. Need for effective outage management system
  • 4. Outage management system for DS
  • 5. Existing techniques for OMS
  • 5.1. Reactive approaches: Detection, fault isolation, and restoration
  • 5.2. Proactive approaches: Outage prevention and mitigation strategies
  • 6. Limitation of existing outage management system
  • 7. Advanced technologies for outage management
  • 7.1. Real-time monitoring
  • 7.2. Data analytics and machine learning tools
  • 8. Intelligent outage management system
  • 8.1. Fault isolation and service restoration
  • 8.1.1. Automated fault isolation techniques
  • 8.1.2. Service restoration strategies for minimizing downtime
  • 8.2. Optimal resource allocation
  • 8.2.1. Efficient techniques deployment of restoration crews
  • 8.3. Crew scheduling and dispatching
  • 8.3.1. Optimization models for crew scheduling and dispatching during restoration
  • 8.3.2. Communication and coordination strategies among crews and control centers
  • 8.4. Coordination with stakeholders and customers
  • 8.4.1. Strategies for effective communication and coordination with customers and stakeholders
  • 8.4.2. Providing outage status updates and estimated restoration times
  • 8.4.3. Outage status updates
  • 8.4.3.1. Estimated restoration times
  • 9. Conclusion and future scope
  • References.