False data injection in power smart grid and identification of the most vulnerable bus; a case study 14 IEEE bus network

Zhong, Xuefei; Li, Guang xin; Zhng, Chuyan

doi:10.1016/j.egyr.2021.08.029

Cited by 8 publications

(2 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Its simplicity and representation of real-world scenarios make it an ideal platform for evaluating different integration scenarios. Zhong et al (2021) employed the IEEE 14-Bus System to study false data injection in power smart grids, showcasing its relevance in contemporary power system research.…”

Section: Literature Review Theoretical Reviewmentioning

confidence: 99%

Navigating Power Paths: Assessing Line Losses in the IEEE 14-Bus System amidst Electric Vehicle and Renewable Energy Integration

Gül

2024

EJT

View full text Add to dashboard Cite

Purpose: This study thoroughly investigates the impacts of Electric Vehicles (EVs) and Renewable Energy Sources (RES) on line losses within the IEEE 14-Bus System, with a particular emphasis on addressing challenges and optimizing power system planning. The primary purpose of this study is to assess and understand the intricate relationships between EVs, RES, and line losses within the IEEE 14-Bus System. The research aims to provide insights that contribute to the development of tailored strategies in power system planning, addressing challenges posed by EV adoption and RES integration. By exploring the impact on grid efficiency, sustainability, and reliability, the study aims to guide future power system planners in optimizing the integration of EVs and RES. Materials and Methods: The study utilizes load flow analysis, specifically the Newton-Raphson method, to model and simulate scenarios reflecting EV charging and discharging dynamics alongside intermittent RES integration within the IEEE 14-Bus System. A 24-hour dynamic load flow analysis is conducted to capture the diverse and dynamic impacts under varying load conditions. This comprehensive approach allows for a detailed assessment of line losses in the presence of EVs and RES. Findings: The study reveals nuanced impacts, indicating higher EV adoption and increased RES integration result in notable escalations in line losses. This highlights challenges associated with grid efficiency, emphasizing the need for tailored strategies in power system planning. Implications to Theory, Practice and Policy: The study advances theoretical understanding of dynamic interactions between EVs, RES, and power systems. In practice, it underscores the importance of adaptive control mechanisms and optimized strategies for accommodating EVs and RES while minimizing line losses. On a policy level, the findings suggest the need for regulatory frameworks incentivizing sustainable energy practices and technological research to mitigate challenges posed by EV adoption and RES integration.

show abstract

Section: Literature Review Theoretical Reviewmentioning

confidence: 99%

Navigating Power Paths: Assessing Line Losses in the IEEE 14-Bus System amidst Electric Vehicle and Renewable Energy Integration

Gül

2024

EJT

View full text Add to dashboard Cite

show abstract

“…By artificially injecting faults of varying intensity into sensor data, we can quantify the performance of the anomaly/fault detection model. FDIAs are commonly used in power systems to detect abnormalities in power measurements [18][19][20][21].…”

Section: Background and Related Workmentioning

confidence: 99%

Anomaly Detection in a Smart Microgrid System Using Cyber-Analytics: A Case Study

Thulasiraman,

Hackett,

Musgrave

et al. 2023

Energies

View full text Add to dashboard Cite

Smart microgrids are being increasingly deployed within the Department of Defense. The microgrid at Marine Corps Air Station (MCAS) Miramar is one such deployment that has fostered the integration of different technologies, including 5G and Advanced Metering Infrastructure (AMI). The objective of this paper is to develop an anomaly detection framework for the smart microgrid system at MCAS Miramar to enhance its cyber-resilience. We implement predictive analytics using machine learning to deal with cyber-uncertainties and threats within the microgrid environment. An autoencoder neural network is implemented to classify and identify specific cyber-attacks against this infrastructure. Both network traffic in the form of packet captures (PCAP) and time series data (from the AMI sensors) are considered. We train the autoencoder model on three traffic data sets: (1) Modbus TCP/IP PCAP data from the hardwired network apparatus of the smart microgrid, (2) experimentally generated 5G PCAP data that mimic traffic on the smart microgrid and (3) AMI smart meter sensor data provided by the Naval Facilities (NAVFAC) Engineering Systems Command. Distributed denial-of-service (DDoS) and false data injection attacks (FDIA) are synthetically generated. We show the effectiveness of the autoencoder on detecting and classifying these types of attacks in terms of accuracy, precision, recall, and F-scores.

show abstract

Guest Editorial: Special Issue on Cyber–Physical Security

2023

View full text Add to dashboard Cite

False data injection in power smart grid and identification of the most vulnerable bus; a case study 14 IEEE bus network

Cited by 8 publications

References 51 publications

Navigating Power Paths: Assessing Line Losses in the IEEE 14-Bus System amidst Electric Vehicle and Renewable Energy Integration

Navigating Power Paths: Assessing Line Losses in the IEEE 14-Bus System amidst Electric Vehicle and Renewable Energy Integration

Anomaly Detection in a Smart Microgrid System Using Cyber-Analytics: A Case Study

Guest Editorial: Special Issue on Cyber–Physical Security

Contact Info

Product

Resources

About