UVicSpaceThe UVicSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material.https://dspace.library.uvic.ca:84432017-04-25T18:03:14Z2017-04-25T18:03:14ZOnline intrusion detection design and implementation for SCADA networksWang, Hongruihttp://hdl.handle.net/1828/79842017-04-25T16:56:08Z2017-04-25T00:00:00ZOnline intrusion detection design and implementation for SCADA networks
Wang, Hongrui
The standardization and interconnection of supervisory control and data acquisition
(SCADA) systems has exposed the systems to cyber attacks. To improve the security of the SCADA systems, intrusion detection system (IDS) design is an effective method. However, traditional IDS design in the industrial networks mainly exploits the prede fined rules, which needs to be complemented and developed to adapt to the big data scenario. Therefore, this thesis aims to design an anomaly-based novel hierarchical online intrusion detection system (HOIDS) for SCADA networks based on machine learning algorithms theoretically and implement the theoretical idea of the anomaly-based intrusion detection on a testbed. The theoretical design of HOIDS by utilizing the server-client topology while keeping clients distributed for global protection, high detection rate is achieved with minimum network impact. We implement accurate models of normal-abnormal binary detection and multi-attack identification based on logistic regression and quasi-Newton optimization algorithm using the Broyden-Fletcher-Goldfarb-Shanno approach. The detection system is capable of accelerating detection by information gain based feature selection or principle component analysis based dimension reduction. By evaluating our system using the KDD99 dataset and the industrial control system datasets, we demonstrate that our design is highly scalable, e fficient and cost effective for securing SCADA infrastructures. Besides the theoretical IDS design, a testbed is modi ed and implemented for SCADA network security research. It simulates the working environment of SCADA systems with the functions of data collection and analysis for intrusion detection. The testbed is implemented to be more flexible and extensible compared to the existing related work on the testbeds. In the testbed, Bro network analyzer is introduced to support the research of anomaly-based intrusion detection. The procedures of both signature-based intrusion detection and anomaly-based intrusion detection using Bro analyzer are also presented. Besides, a generic Linux-based host is used as the container of different network functions and a human machine interface (HMI) together
with the supervising network is set up to simulate the control center. The testbed does not implement a large number of traffic generation methods, but still provides useful examples of generating normal and abnormal traffic. Besides, the testbed can be modi ed or expanded in the future work about SCADA network security.
2017-04-25T00:00:00ZNucleosynthesis in stellar models across initial masses and metallicities and implications for chemical evolutionRitter, Christian Heikohttp://hdl.handle.net/1828/79832017-04-25T14:27:19Z2017-04-25T00:00:00ZNucleosynthesis in stellar models across initial masses and metallicities and implications for chemical evolution
Ritter, Christian Heiko
Tracing the element enrichment in the Universe requires to understand the element production in stellar models which is not well understood, in particular at low metallicity. In this thesis a variety of nucleosynthesis processes in stellar models across initial masses and metallicities is investigated and their relevance for chemical evolution explored.
Stellar nucleosynthesis is investigated in asymptotic giant branch (AGB) models and massive star models with initial masses between 1 M⊙ and 25 M⊙ for metal fractions of Z = 0.02, 0.01, 0.006, 0.001, 0.0001. A yield grid with elements from H to Bi is calculated. It serves as an input for chemical evolution simulations. AGB models are computed towards the end of the AGB phase and massive star models are calculated until core collapse followed by explosive core-collapse nucleosynthesis. The simulations include convective boundary mixing in all AGB star models and feature efficient hot-bottom burning and hot dredge-up in AGB models as well the predictions of both heavy elements and CNO species under hot-bottom burning conditions. H-ingestion events in the low-mass low-Z AGB model with initial mass of 1M⊙ at Z = 0.0001 result in the production of large amounts of heavy elements. In super-AGB models H ingestion could potentially lead to the intermediate neutron-capture process.
To model the chemical enrichment and feedback of simple stellar populations in hydrodynamic simulations and semi-analytic models of galaxy formation the SYGMA module is created and its functionality is verified through a comparison with a widely adopted code. A comparison of ejecta of simple stellar populations based on yields of this work with a commonly adopted yield set shows up to a factor of 3.5 and 4.8 less C and N enrichment from AGB stars at low metallicity which is attributed to complete stellar models, the modeling of the AGB stage and hot-bottom burning in super- AGB stars. Analysis of two different core-collapse supernova fallback prescriptions show that the total amount of Fe enrichment by massive stars differs by up to two at Z = 0.02.
Insights into the chemical evolution at very low metallicity as motivated by the observations of extremely metal poor stars require to understand the H-ingestion events common in stellar models of low metallicity. The occurrence of H ingestion events in super-AGB stars is investigated and identified as a possible site for the production of heavy elements through the intermediate neutron capture process. The peculiar abundance of some C-Enhanced Metal Poor stars are explained with simple models of the intermediate neutron capture process. Initial efforts to model this heavy element production in 3D hydrodynamic simulations are presented.
For the first time the nucleosynthesis of interacting convective O and C shells in massive star models is investigated in detail. 1D calculations based on input from 3D hydrodynamic simulations of the O shell show that such interactions can boost the production of odd-Z elements P, Cl, K and Sc if large entrainment rates associated with O-C shell merger are assumed. Such shell merger lead in stellar evolution models to overproduction factors beyond 1 dex and p-process overproduction factors above 1 dex for 130,132Ba and heavier isotopes. Chemical evolution models are able to reproduce the Galactic abundance trends of these odd-Z elements if O-C shell merger occur in more than 50% of all massive stars.
2017-04-25T00:00:00ZMarket-based demand response integration in super-smart grids in the presence of variable renewable generationBehboodi Kalhori, Sahandhttp://hdl.handle.net/1828/79822017-04-25T14:24:43Z2017-04-25T00:00:00ZMarket-based demand response integration in super-smart grids in the presence of variable renewable generation
Behboodi Kalhori, Sahand
Variable generator output levels from renewable energies is an important technical obstacle to the transition from fossil fuels to renewable resources. Super grids and smart grids are among the most effective solutions to mitigate generation variability. In a super grid, electric utilities within an interconnected system can share generation and reserve units so that they can produce electricity at a lower overall cost. Smart grids, in particular demand response programs, enable flexible loads such as plug-in electric vehicles and HVAC systems to consume electricity preferntially in a grid-friendly way that assists the grid operator to maintain the power balance. These solutions, in conjunction with energy storage systems, can facilitate renewable integration.
This study aims to provide an understanding of the achievable benefits from integrating demand response into wholesale and retail electricity markets, in particular in the presence of significant amounts of variable generation. Among the options for control methods for demand response, market-based approaches provide a relatively efficient use of load flexibility, without restricting consumers' autonomy or invading their privacy. In this regard, a model of demand response integration into bulk electric grids is presented to study the interaction between variable renewables and demand response in the double auction environment, on an hourly basis. The cost benefit analysis shows that there exists an upper limit of renewable integration, and that additional solutions such as super grids and/or energy storage systems are required to go beyond this threshold.
The idea of operating an interconnection in an unified (centralized) manner is also explored. The traditional approach to the unit commitment problem is to determine the dispatch schedule of generation units to minimize the operation cost. However, in the presence of price-sensitive loads (market-based demand response), the maximization of economic surplus is a preferred objective to the minimization of cost. Accordingly, a surplus-maximizing hour-ahead scheduling problem is formulated, and is then tested on a system that represents a 20-area reduced model of the North America Western Interconnection for the planning year 2024. The simulation results show that the proposed scheduling method reduces the total operational costs substantially, taking advantage of renewable generation diversity.
The value of demand response is more pronounced when ancillary services (e.g. real-time power balancing and voltage/frequency regulation) are also included along with basic temporal load shifting. Relating to this, a smart charging strategy for plug-in electric vehicles is developed that enables them to participate in a 5-minute retail electricity market. The cost reduction associated with implementation of this charging strategy is compared to uncontrolled charging. In addition, an optimal operation method for thermostatically controlled loads is developed that reduces energy costs and prevents grid congestion, while maintaining the room temperature in the comfort range set by the consumer. The proposed model also includes loads in the energy imbalance market.
The simulation results show that market-based demand response can contribute to a significant cost saving at the sub-hourly level (e.g. HVAC optimal operation), but not at the super-hourly level. Therefore, we conclude that demand response programs and super grids are complementary approaches to overcoming renewable generation variation across a range of temporal and spatial scales.
2017-04-25T00:00:00ZStochastic methods for unsteady aerodynamic analysis of wings and wind turbine bladesFluck, Manuelhttp://hdl.handle.net/1828/79812017-04-25T14:21:56Z2017-04-25T00:00:00ZStochastic methods for unsteady aerodynamic analysis of wings and wind turbine blades
Fluck, Manuel
Advancing towards `better' wind turbine designs engineers face two central challenges: first, current aerodynamic models (based on Blade Element Momentum theory) are inherently limited to comparatively simple designs of flat rotors with straight blades. However, such designs present only a subset of possible designs. Better concepts could be coning rotors, swept or kinked blades, or blade tip modifications. To be able to extend future turbine optimization to these new concepts a different kind of aerodynamic model is needed. Second, it is difficult to include long term loads (life time extreme and fatigue loads) directly into the wind turbine design optimization. This is because with current methods the assessment of long term loads is computationally very expensive -- often too expensive for optimization. This denies the optimizer the possibility to fully explore the effects of design changes on important life time loads, and one might settle with a sub-optimal design.
In this dissertation we present work addressing these two challenges, looking at wing aerodynamics in general and focusing on wind turbine loads in particular. We adopt a Lagrangian vortex model to analyze bird wings. Equipped with distinct tip feathers, these wings present very complex lifting surfaces with winglets, stacked in sweep and dihedral. Very good agreement between experimental and numerical results is found, and thus we confirm that a vortex model is actually capable of analyzing complex new wing and rotor blade geometries.
Next stochastic methods are derived to deal with the time and space coupled unsteady aerodynamic equations. In contrast to deterministic models, which repeatedly analyze the loads for different input samples to eventually estimate life time load statistics, the new stochastic models provide a continuous process to assess life time loads in a stochastic context -- starting from a stochastic wind field input through to a stochastic solution for the load output. Hence, these new models allow obtaining life time loads much faster than from the deterministic approach, which will eventually make life time loads accessible to a future stochastic wind turbine optimization algorithm. While common stochastic techniques are concerned with random parameters or boundary conditions (constant in time), a stochastic treatment of turbulent wind inflow requires a technique capable to handle a random field. The step from a random parameter to a random field is not trivial, and hence the new stochastic methods are introduced in three stages.
First the bird wing model from above is simplified to a one element wing/ blade model, and the previously deterministic solution is substituted with a stochastic solution for a one-point wind speed time series (a random process).
Second, the wind inflow is extended to an $n$-point correlated random wind field and the aerodynamic model is extended accordingly. To complete this step a new kind of wind model is introduced, requiring significantly fewer random variables than previous models.
Finally, the stochastic method is applied to wind turbine aerodynamics (for now based on Blade Element Momentum theory) to analyze rotor thrust, torque, and power.
Throughout all these steps the stochastic results are compared to result statistics obtained via Monte Carlo analysis from unsteady reference models solved in the conventional deterministic framework. Thus it is verified that the stochastic results actually reproduce the deterministic benchmark. Moreover, a considerable speed-up of the calculations is found (for example by a factor 20 for calculating blade thrust load probability distributions).
Results from this research provide a means to much more quickly analyze life time loads and an aerodynamic model to be used a new wind turbine optimization framework, capable of analyzing new geometries, and actually optimizing wind turbine blades with life time loads in mind. However, to limit the scope of this work, we only present the aerodynamic models here and will not proceed to turbine optimization itself, which is left for future work.
2017-04-25T00:00:00Z