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.
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| Інші автори: | , , |
| Формат: | eКнига |
| Мова: | Англійська |
| Опубліковано: |
San Diego :
Elsevier,
2024.
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| Предмети: | |
| Онлайн доступ: | ScienceDirect |
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| 100 | 1 | |a Sood, Vijay K. | |
| 245 | 1 | 0 | |a Smart Metering : |b Infrastructure, Methodologies, Applications, and Challenges. |
| 260 | |a San Diego : |b Elsevier, |c 2024. | ||
| 300 | |a 1 online resource (290 p.) | ||
| 341 | 0 | |b Unknown accessibility |2 onix | |
| 500 | |a Description based upon print version of record. | ||
| 505 | 0 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 520 | |a 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. | ||
| 650 | 0 | |a Digital multimeters. | |
| 650 | 6 | |a Multimètres numériques. |0 (CaQQLa)201-0307985 | |
| 650 | 7 | |a SCIENCE / Energy. |2 bisacsh | |
| 650 | 7 | |a TECHNOLOGY & ENGINEERING / Power Resources / General. |2 bisacsh | |
| 650 | 7 | |a Digital multimeters |2 fast |0 (OCoLC)fst00893719 | |
| 655 | 0 | |a Electronic books. | |
| 700 | 1 | |a Biswal, Monalisa. | |
| 700 | 1 | |a sarangi, saumendra. | |
| 700 | 1 | |a Alhelou, Hassan Haes. | |
| 776 | 0 | 8 | |i Print version: |a Sood, Vijay K. |t Smart Metering |d San Diego : Elsevier,c2024 |z 9780443153174 |
| 856 | 4 | 0 | |3 ScienceDirect |u https://www.sciencedirect.com/science/book/9780443153174 |