11,504 research outputs found
Probabilistic Graphs for Sensor Data-driven Modelling of Power Systems at Scale
The growing complexity of the power grid, driven by increasing share of
distributed energy resources and by massive deployment of intelligent
internet-connected devices, requires new modelling tools for planning and
operation. Physics-based state estimation models currently used for data
filtering, prediction and anomaly detection are hard to maintain and adapt to
the ever-changing complex dynamics of the power system. A data-driven approach
based on probabilistic graphs is proposed, where custom non-linear, localised
models of the joint density of subset of system variables can be combined to
model arbitrarily large and complex systems. The graphical model allows to
naturally embed domain knowledge in the form of variables dependency structure
or local quantitative relationships. A specific instance where neural-network
models are used to represent the local joint densities is proposed, although
the methodology generalises to other model classes. Accuracy and scalability
are evaluated on a large-scale data set representative of the European
transmission grid
Formal analysis techniques for gossiping protocols
We give a survey of formal verification techniques that can be used to corroborate existing experimental results for gossiping protocols in a rigorous manner. We present properties of interest for gossiping protocols and discuss how various formal evaluation techniques can be employed to predict them
AI Modelling and Time-series Forecasting Systems for Trading Energy Flexibility in Distribution Grids
We demonstrate progress on the deployment of two sets of technologies to
support distribution grid operators integrating high shares of renewable energy
sources, based on a market for trading local energy flexibilities. An
artificial-intelligence (AI) grid modelling tool, based on probabilistic
graphs, predicts congestions and estimates the amount and location of energy
flexibility required to avoid such events. A scalable time-series forecasting
system delivers large numbers of short-term predictions of distributed energy
demand and generation. We discuss the deployment of the technologies at three
trial demonstration sites across Europe, in the context of a research project
carried out in a consortium with energy utilities, technology providers and
research institutions
Assessing and augmenting SCADA cyber security: a survey of techniques
SCADA systems monitor and control critical infrastructures of national importance such as power generation and distribution, water supply, transportation networks, and manufacturing facilities. The pervasiveness, miniaturisations and declining costs of internet connectivity have transformed these systems from strictly isolated to highly interconnected networks. The connectivity provides immense benefits such as reliability, scalability and remote connectivity, but at the same time exposes an otherwise isolated and secure system, to global cyber security threats. This inevitable transformation to highly connected systems thus necessitates effective security safeguards to be in place as any compromise or downtime of SCADA systems can have severe economic, safety and security ramifications. One way to ensure vital asset protection is to adopt a viewpoint similar to an attacker to determine weaknesses and loopholes in defences. Such mind sets help to identify and fix potential breaches before their exploitation. This paper surveys tools and techniques to uncover SCADA system vulnerabilities. A comprehensive review of the selected approaches is provided along with their applicability
Scalable Approach to Uncertainty Quantification and Robust Design of Interconnected Dynamical Systems
Development of robust dynamical systems and networks such as autonomous
aircraft systems capable of accomplishing complex missions faces challenges due
to the dynamically evolving uncertainties coming from model uncertainties,
necessity to operate in a hostile cluttered urban environment, and the
distributed and dynamic nature of the communication and computation resources.
Model-based robust design is difficult because of the complexity of the hybrid
dynamic models including continuous vehicle dynamics, the discrete models of
computations and communications, and the size of the problem. We will overview
recent advances in methodology and tools to model, analyze, and design robust
autonomous aerospace systems operating in uncertain environment, with stress on
efficient uncertainty quantification and robust design using the case studies
of the mission including model-based target tracking and search, and trajectory
planning in uncertain urban environment. To show that the methodology is
generally applicable to uncertain dynamical systems, we will also show examples
of application of the new methods to efficient uncertainty quantification of
energy usage in buildings, and stability assessment of interconnected power
networks
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Modelling the Spread of Botnet Malware in IoT-Based Wireless Sensor Networks
The propagation approach of a botnet largely dictates its formation, establishing a foundation of bots for future exploitation. The chosen propagation method determines the attack surface, and consequently, the degree of network penetration, as well as the overall size and the eventual attack potency. It is therefore essential to understand propagation behaviours and influential factors in order to better secure vulnerable systems. Whilst botnet propagation is generally well-studied, newer technologies like IoT have unique characteristics which are yet to be thoroughly explored. In this paper, we apply the principles of epidemic modelling to IoT networks consisting of wireless sensor nodes. We build IoT-SIS, a novel propagation model which considers the impact of IoT-specific characteristics like limited processing power, energy restrictions, and node density on the formation of a botnet. Focusing on worm-based propagation, this model is used to explore the dynamics of spread using numerical simulations and the Monte Carlo method, and to discuss the real-life implications of our findings
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