163 research outputs found
Application of Deep Learning Methods in Monitoring and Optimization of Electric Power Systems
This PhD thesis thoroughly examines the utilization of deep learning
techniques as a means to advance the algorithms employed in the monitoring and
optimization of electric power systems. The first major contribution of this
thesis involves the application of graph neural networks to enhance power
system state estimation. The second key aspect of this thesis focuses on
utilizing reinforcement learning for dynamic distribution network
reconfiguration. The effectiveness of the proposed methods is affirmed through
extensive experimentation and simulations.Comment: PhD thesi
Performance Analysis Of Data-Driven Algorithms In Detecting Intrusions On Smart Grid
The traditional power grid is no longer a practical solution for power delivery due to several shortcomings, including chronic blackouts, energy storage issues, high cost of assets, and high carbon emissions. Therefore, there is a serious need for better, cheaper, and cleaner power grid technology that addresses the limitations of traditional power grids. A smart grid is a holistic solution to these issues that consists of a variety of operations and energy measures. This technology can deliver energy to end-users through a two-way flow of communication. It is expected to generate reliable, efficient, and clean power by integrating multiple technologies. It promises reliability, improved functionality, and economical means of power transmission and distribution. This technology also decreases greenhouse emissions by transferring clean, affordable, and efficient energy to users. Smart grid provides several benefits, such as increasing grid resilience, self-healing, and improving system performance. Despite these benefits, this network has been the target of a number of cyber-attacks that violate the availability, integrity, confidentiality, and accountability of the network. For instance, in 2021, a cyber-attack targeted a U.S. power system that shut down the power grid, leaving approximately 100,000 people without power. Another threat on U.S. Smart Grids happened in March 2018 which targeted multiple nuclear power plants and water equipment. These instances represent the obvious reasons why a high level of security approaches is needed in Smart Grids to detect and mitigate sophisticated cyber-attacks. For this purpose, the US National Electric Sector Cybersecurity Organization and the Department of Energy have joined their efforts with other federal agencies, including the Cybersecurity for Energy Delivery Systems and the Federal Energy Regulatory Commission, to investigate the security risks of smart grid networks. Their investigation shows that smart grid requires reliable solutions to defend and prevent cyber-attacks and vulnerability issues. This investigation also shows that with the emerging technologies, including 5G and 6G, smart grid may become more vulnerable to multistage cyber-attacks. A number of studies have been done to identify, detect, and investigate the vulnerabilities of smart grid networks. However, the existing techniques have fundamental limitations, such as low detection rates, high rates of false positives, high rates of misdetection, data poisoning, data quality and processing, lack of scalability, and issues regarding handling huge volumes of data. Therefore, these techniques cannot ensure safe, efficient, and dependable communication for smart grid networks. Therefore, the goal of this dissertation is to investigate the efficiency of machine learning in detecting cyber-attacks on smart grids. The proposed methods are based on supervised, unsupervised machine and deep learning, reinforcement learning, and online learning models. These models have to be trained, tested, and validated, using a reliable dataset. In this dissertation, CICDDoS 2019 was used to train, test, and validate the efficiency of the proposed models. The results show that, for supervised machine learning models, the ensemble models outperform other traditional models. Among the deep learning models, densely neural network family provides satisfactory results for detecting and classifying intrusions on smart grid. Among unsupervised models, variational auto-encoder, provides the highest performance compared to the other unsupervised models. In reinforcement learning, the proposed Capsule Q-learning provides higher detection and lower misdetection rates, compared to the other model in literature. In online learning, the Online Sequential Euclidean Distance Routing Capsule Network model provides significantly better results in detecting intrusion attacks on smart grid, compared to the other deep online models
Modelling of the Electric Vehicle Charging Infrastructure as Cyber Physical Power Systems: A Review on Components, Standards, Vulnerabilities and Attacks
The increasing number of electric vehicles (EVs) has led to the growing need
to establish EV charging infrastructures (EVCIs) with fast charging
capabilities to reduce congestion at the EV charging stations (EVCS) and also
provide alternative solutions for EV owners without residential charging
facilities. The EV charging stations are broadly classified based on i) where
the charging equipment is located - on-board and off-board charging stations,
and ii) the type of current and power levels - AC and DC charging stations. The
DC charging stations are further classified into fast and extreme fast charging
stations. This article focuses mainly on several components that model the EVCI
as a cyberphysical system (CPS)
The Electromagnetic Threat to the US: Resilience Strategy Recommendations
The article of record as published may be located at https://doi.org/10.18278/jcip.3.2.10https://www.jcip1.org/the-electromagnetic-threat-to-the-us-recommendations-for-resilience-strategies.htmlThis work is based on a master’s thesis completed by the lead author: Samuel Averitt, “The Electromagnetic Threat To The United States: Recommendations For Consequence Management” (Monterey, CA, Naval Postgraduate School, 2021), https://calhoun.nps.edu/handle/10945/68695This article analyzes the threat of both electromagnetic pulse (EMP) and geomagnetic disturbances (GMD) to various federal agencies and the civilian population of the United States. EMP/GMD events are classified as low-probability/high-impact events that have potential for catastrophic consequences to all levels of government as well as the country's civilian population. By reviewing current literature and conducting two thought experiments, we determined that specific critical infrastructure sectors and modern society are at substantial risk from the effects of these events. Some of the most serious consequences of a large-scale EMP/GMD include longterm power loss to large geographic regions, loss of modern medical services, and severe communication blackouts that could make recovery from these events extremely difficult. In an attempt to counteract and mitigate the risks of EMP/GMD events, resilience engineering concepts prescribe several recommendations that could be utilized by policymakers to mitigate the effects of EMP or GMD. Some of the recommendations include utilizing hardened micro-grid systems, fast tracking available black start options, and various changes to government agency organizations that would provide additional resilience and recovery to American critical infrastructure systems in the post-EMP/GMD environment
Improvise, Adapt, Overcome: Dynamic Resiliency Against Unknown Attack Vectors in Microgrid Cybersecurity Games
Cyber-physical microgrids are vulnerable to rootkit attacks that manipulate
system dynamics to create instabilities in the network. Rootkits tend to hide
their access level within microgrid system components to launch sudden attacks
that prey on the slow response time of defenders to manipulate system
trajectory. This problem can be formulated as a multi-stage, non-cooperative,
zero-sum game with the attacker and the defender modeled as opposing players.
To solve the game, this paper proposes a deep reinforcement learning-based
strategy that dynamically identifies rootkit access levels and isolates
incoming manipulations by incorporating changes in the defense plan. A major
advantage of the proposed strategy is its ability to establish resiliency
without altering the physical transmission/distribution network topology,
thereby diminishing potential instability issues. The paper also presents
several simulation results and case studies to demonstrate the operating
mechanism and robustness of the proposed strategy
Resilience assessment and planning in power distribution systems:Past and future considerations
Over the past decade, extreme weather events have significantly increased
worldwide, leading to widespread power outages and blackouts. As these threats
continue to challenge power distribution systems, the importance of mitigating
the impacts of extreme weather events has become paramount. Consequently,
resilience has become crucial for designing and operating power distribution
systems. This work comprehensively explores the current landscape of resilience
evaluation and metrics within the power distribution system domain, reviewing
existing methods and identifying key attributes that define effective
resilience metrics. The challenges encountered during the formulation,
development, and calculation of these metrics are also addressed. Additionally,
this review acknowledges the intricate interdependencies between power
distribution systems and critical infrastructures, including information and
communication technology, transportation, water distribution, and natural gas
networks. It is important to understand these interdependencies and their
impact on power distribution system resilience. Moreover, this work provides an
in-depth analysis of existing research on planning solutions to enhance
distribution system resilience and support power distribution system operators
and planners in developing effective mitigation strategies. These strategies
are crucial for minimizing the adverse impacts of extreme weather events and
fostering overall resilience within power distribution systems.Comment: 27 pages, 7 figures, submitted for review to Renewable and
Sustainable Energy Review
Challenges and pathways of low-carbon oriented energy transition and power system planning strategy: a review
This paper provides an overview of the challenges and pathways involved in achieving a low-carbon-oriented energy transition roadmap and power system planning strategy. The transition towards low-carbon energy sources is crucial in mitigating the global climate change crisis. However, this transition presents several technical, economic, and political challenges. The paper emphasizes the importance of an integrated approach to power system planning that considers the entire energy system (including both physical and information systems and market mechanisms) and not just individual technologies. To achieve this goal, the paper discusses various pathways toward low-carbon energy transition, including the integration of renewable energy sources into current energy systems, energy efficiency measures, and market-based and regulatory strategies encompassing the implementation of regulations, standards, and policies. Furthermore, the paper underscores the need for a comprehensive and coordinated approach to energy planning, taking into account the socio-economic and political dimensions of the transition process. In addition, the paper reviews the methodologies used in modeling low-carbon-oriented power system planning, including both model-based methods and advanced machine learning-assisted solutions. Overall, the paper concludes that achieving a low-carbon-oriented energy transition roadmap and power system planning strategy requires a multi-dimensional approach that considers technical, economic, political, and social factors
WE ARE ALL GONNA DIE: HOW THE WEAK POINTS OF THE POWER GRID LEAVE THE UNITED STATES WITH AN UNACCEPTABLE RISK
Federal regulations aim to ensure grid reliability and harden it against outages; however, widespread outages continue. This thesis examines the spectrum of regulations to evaluate them. It outlines their structure, the regulations’ intent, and weighs them against evolving cyber and physical threats and natural disaster risks. Currently, the regulatory structure is incapable of providing uniform security. Federal standards protect only the transmission portion of the grid, leaving the distribution section vulnerable to attack due to varying regulations from state to state, or county to county. The regulations cannot adapt quickly enough to meet dynamic threats, rendering them less effective. Cyber threats can be so agile that protectors are unaware of vulnerabilities, and patching requirements are too lengthy, which increases the risk exposure. No current weather mitigation or standard is capable of protecting the grid despite regular natural disasters that cause power shutdowns. The thesis concludes that bridging these gaps requires not increasing protection standards, but redundancy. Redundancy, mirrored after the UK's infrastructure policy, is more likely to reduce failure risk through layered components and systems. Microgrids are proven effective in disasters to successfully deliver such redundancy and should be implemented across all critical infrastructure sectors.Civilian, Department of Homeland SecurityApproved for public release. Distribution is unlimited
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