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Efficient water-based electricity strategies to reduce the number of switching operations in a smart grid
No full textThe increasing complexity of modern power systems, driven by the integration of renewable energy sources and the need for enhanced operational efficiency, has led to the widespread adoption of Transmission Switching (TS) as a cost-reduction strategy. TS optimizes the configuration of the transmission network by selectively switching transmission lines, thereby reducing the overall operational costs. However, this approach comes with significant drawbacks. The frequent switching operations required can degrade critical system components, particularly circuit breakers (CBs), leading to a shorter lifespan, higher maintenance and repair costs, increased likelihood of line outages, and a greater probability of load shedding. Moreover, these issues can collectively undermine the reliability of the entire power system.
To address these challenges, this thesis presents a novel congestion management framework integrated within the Security-Constrained Unit Commitment (SCUC) problem. The primary objective of the proposed framework is to minimize the number of TS operations necessary to manage congestion, thereby mitigating the adverse effects on CBs and enhancing the overall reliability of the power grid. The framework introduces a grid-connected water-power system that leverages a fuel cell-based renewable energy source, coupled with a hydrogen storage tank, to provide additional flexibility in managing grid congestion. By utilizing this water-power system, the framework reduces the need for frequent TS operations, thus alleviating the associated strain on the transmission network.
Additionally, the thesis addresses the inherent uncertainties in grid operations, particularly those related to fluctuating renewable energy output and unpredictable demand. To this end, an uncertainty-based Unscented Transform (UT) function is incorporated into the SCUC framework. This function enhances the robustness of the proposed methodology, ensuring that it remains effective under a wide range of operational scenarios and uncertainties.
The proposed framework is validated through comprehensive simulations conducted on two standard test systems: a 6-bus and a 118-bus IEEE grid. These simulations are performed using Bender’s decomposition method in GAMS software, a widely recognized tool for large-scale optimization problems in power systems. The results from these simulations demonstrate that the proposed strategy significantly reduces line congestion and the number of TS operations required. Specifically, the framework achieves a 77% reduction in switching operations for the 6-bus system and a 45% reduction for the 118-bus system. These
reductions not only extend the lifespan of CBs but also lead to substantial decreases in operational costs, thereby offering a more sustainable and cost-effective solution for modern power systems.
The findings of this research contribute to the ongoing development of more resilient, efficient, and sustainable power systems, particularly in light of the increasing reliance on renewable energy sources. The proposed framework offers a viable path forward for grid operators seeking to balance cost efficiency with system reliability, all while integrating more renewable energy into the power grid.Includes bibliographical references (pages 118-132
Cruise #: FKt231024
No full textPublishedThe overarching goal of this project was to study seafloor hydrothermal processes along the Galapagos Spreading Centre. The project was designed around a multidisciplinary approach, combining the fields of geology, geophysics, biology, and oceanography to address a range of inter-related scientific questions related to occurrence, evolution, and ecological significance of high-temperature hydrothermal systems along the Galapagos Rift. Specific project goals included:
1. Evaluate the utility of SAS surveys as an effective tool for documenting/characterizing the seafloor environment (including geological and biological features), especially in regions of significant seafloor topography.
2. Characterize the mineralogical, geochemical, and biological evolution of hydrothermal deposits at inactive vent fields and inactive deposits within active vent fields.
3. Survey, explore, document, and sample previously unexplored vent fields.
4. Document the benthic soundscape at hydrothermal vents and measure CTD and sound velocity (i.e. bulk compressibility) to inform the equation of state at relatively high temperatures and salinities.
5. Refine the methodologies for integrating multi-resolution datasets to classify ecological and geological features on the seafloor and develop approaches for the use of these techniques for marine spatial planning and conservation
Understanding cancer survivors' perceptions of medical assistance in dying
No full textObjectives: Medical assistance in dying (MAID) is a medical intervention that allows medical practitioners to prescribe or administer a substance to end a patient's life. People diagnosed with cancer largely represent those who receive MAID. This exploratory analysis investigated perceptions of MAID among early-stage cancer survivors to identify considerations that may influence future willingness to consider MAID.
Method: Cancer survivors (N = 16) were recruited from across Canada via social media. Participants participated in virtual semi-structured interviews aimed at understanding participants' knowledge, familiarity, opinions, and willingness to consider MAID. Interviews were transcribed and analyzed using reflexive thematic analysis and checked by a code reviewer.
Results: Most of participants' considerations toward MAID were based on physical, psychological, or concerns for the well-being of loved ones. However, many considerations within and beyond these themes added additional nuance and context to these concerns, their impacts, and relevance to making decisions on end-of-life care.
Conclusion/Implications: MAID offers an end-of-life option that some view as preferred, particularly those who would like to avoid suffering or a prolonged death, though not all individuals desire this type of death. Individual experience and values often dictate an individual's willingness to consider MAID for themselves
Machine learning for early detection of distillation column flooding
No full textDistillation column significantly affects the overall energy efficiency of a process plant.
Poor performance of a column can result from faults, such as reflux failure, change in
tray efficiency, change in feed temperature, etc. Flooding is one of the severe consequences
of the faults attributed to a distillation column. During
flooding, products go
off-specification, and there is a tendency for a complete shutdown of the production
process. In recent years, machine learning (ML) methods have been widely employed
in process engineering for their ability to discover important patterns in data. One
of the applications of ML is in predicting distillation column
flooding. The supervised
ML methods can predict
flooding by forecasting the pressure drop across the
column. The challenges of applying supervised ML methods for predicting
flooding
in distillation columns include a lack of large volumes of
flooding data, the potential
for overfitting, and long training time in some cases. A large amount of
flooding data
combined with normal operation data is needed to train the supervised ML algorithms
for
flooding detection. Therefore, it is important to identify
flooding data sets from
the operational data. However,
flooding data sets are rare compared to normal data
sets, which leads to an imbalanced data set. In this research, we address the data
scarcity issue surrounding the application of supervised ML for
flooding prediction
by utilizing time-series generative adversarial networks, a framework that uses deep
learning algorithms to generate synthetic data by preserving the temporal order in
the original data. Additional
flooding data sets are generated using this framework.
Supervised ML algorithms are trained and tested to forecast the pressure drop of the
column. Classification of the column data (i.e.,
flooding or not
flooding) is done using
clustering. This method is compared with predicting
flooding using popular unsupervised
ML methods such as principal component analysis (PCA) and autoencoders;
these are unaffected by the data imbalance. Results show that by applying supervised
ML algorithms to the sensor data of the distillation column,
flooding conditions can
be detected 19 minutes in advance and up to 60 minutes before it fully develops.
This outperforms the PCA and autoencoders, which are popular unsupervised ML
methods.Includes bibliographical references (pages 122-135
Learning-based approaches for channel estimation in 6G RIS-NOMA systems: from classical to quantum machine learning
No full textWireless communication systems continue to evolve rapidly to meet growing demands
for higher data rates, increased connectivity, and improved reliability. As research
advances toward sixth-generation (6G) networks, technologies such as reconfigurable
intelligent surfaces (RIS) and non-orthogonal multiple access (NOMA) have emerged
as promising solutions to enhance spectral efficiency, energy efficiency, and network
coverage. RIS technology enables dynamic manipulation of the propagation environment
through programmable reflecting elements, while NOMA allows multiple users to share
the same time-frequency resources through power-domain multiplexing. However, the
integration of these technologies introduces significant challenges for channel estimation
due to complex cascaded channels, particularly when considering practical limitations
such as hardware impairments and user mobility.
This thesis investigates learning-based approaches for channel estimation in 6G
RIS-NOMA systems. The first contribution, presented in Chapter 2, is a classical
machine learning (ML) approach using convolutional neural network (CNN)-long shortterm
memory (LSTM) architecture for channel estimation in RIS-NOMA systems
with hardware impairments. This model aims to perform accurate channel estimation
despite the presence of non-ideal hardware components, such as phase noise in the
RIS elements and distortion noise at transceivers. A dataset generation algorithm is
developed to create realistic training data incorporating various impairment scenarios. Performance analysis shows that the model achieves its best performance with a
root mean square error (RMSE) of 0.00186, mean absolute error (MAE) of 0.00148,
and mean absolute percentage error (MAPE) of 0.06501 under 20 time steps and
4-bit quantisation. The model also demonstrates resilience to increased hardware
impairment levels, maintaining acceptable estimation accuracy even under challenging
conditions.
The second contribution, described in Chapter 3, explores a quantum machine
learning (QML) solution through a hybrid quantum-classical neural network model
combining CNN with quantum LSTM (QLSTM) for RIS-aided NOMA systems.
This novel architecture leverages quantum computing principles through variational
quantum circuits (VQCs) to enhance the processing of temporal dependencies in the
sequential input data. Evaluation results demonstrate that the quantum-enhanced
approach outperforms the classical counterpart, achieving RMSE values of 0.006 and
0.005 for two users respectively, compared to 0.008 and 0.011 for the classical model.
The CNN-QLSTM model also exhibits faster convergence during training, indicating
potential benefits in learning efficiency.
Both approaches effectively track time-varying channels resulting from user mobility
and show strong generalisation to unseen channel conditions. This research contributes
to the advancement of 6G technologies by providing practical solutions for accurate
channel estimation in complex RIS-NOMA environments, while also exploring the
potential applications of quantum computing in wireless communications. The findings
suggest that learning-based methods offer promising alternatives to conventional
approaches, particularly in scenarios with hardware limitations and dynamic channel
conditions
Bringing the b'ys (back) to the Bay: mediating rural renewal in Codroy Valley, Newfoundland using a critical regionalist, ecomuseological praxis
No full textThis thesis considers how tradition, broadly construed, and narrative shift through time, place,
and context, and might be mobilized in a community-building project that seeks to avert
sociocultural, economic, and ecological crisis in Codroy Valley of southwest Newfoundland. It
focuses on residual desires and grievances of a consistently forestalled utopia by sketching a
palimpsest of structures of feeling across time and space. Guided by the malleable form of the
ecomuseum, a community-led "museum without walls," framed by critical regionalism, public
folklore, and autoethnography, I underscore how custom and narrative may draw attention to
commons, queerness, and posthumanism as antidotes to heteronormative, xenophobic, and
ecophobic discourses and practices that devalue and endanger sense of place and belonging. I
suggest that renewed attention to place in this framework may encourage a diversity of "b'ys,"
from past residents to newcomers, to build a desirable life in rural Newfoundland, or "the bay."
Where this work enhances previous studies of rural renewal in Newfoundland and
Labrador is in its 1. critical regionalist approach, connecting local material and discursive
processes to larger-than-local narratives and phenomena; 2. use of autoethnography with critical
theory, providing a convenient vantage point from which to analyze discourse critically and
affectively; and 3. application of findings in a multimodal, public folklore project that builds
community as participants negotiate identity, heritage, and wellbeing. Desirable outcomes
include the revival and strengthening of ecologically/economically mindful narratives, customs,
and art. Caveats related to objectification of culture and identity in the ecomuseum are tempered
through phenomenological practices surrounding in situ interpretation, third space dialogue, and
collaborative ethnography.
My methodology lays interpretations out transparently to facilitate open dialogue that
garners disparate and marginalized perspectives, builds grassroots praxis, encourages research,
and, ultimately, decolonizes violent discourses. I urge ecomuseum practitioners to use this
methodology in Codroy Valley and elsewhere. One year after I pitched the ecomuseum idea to
residents in 2023, Codroy Valley Ecomuseum became a member of the Museum Association of
Newfoundland and Labrador and the Association of Newfoundland and Labrador Archives.
Today, it is managed by dozens of volunteers collecting, interpreting, and displaying their
multifaceted heritage. This thesis is an addendum to their work
Inflammasomes, cell explosions, and pathogenesis: exploring the role of RNA virus-induced programmed cell death as a mechanism of protection or of viral pathogenesis
No full textProgrammed cell death (PCD), namely apoptosis, pyroptosis, and necroptosis, have been reported in the context of many viral infections. However, the role that PCD plays during infection, whether it acts as a mechanism of pathogenesis or as an innate immune response to aid in viral clearance, is unclear. Apoptosis is generally thought of as a non-inflammatory form of PCD while pyroptosis and necroptosis are considered inflammatory. Apoptosis, mediated by caspase-3, results in chromatin condensation and cell blebbing. Pyroptosis, mediated by caspase-1 and gasdermin-D, and necroptosis, mediated by RIPK3 and pMLKL, both result in pore formation in the cell membrane and subsequent lysis of the cell.
RNA viruses have been the cause of well-known large-scale virus outbreaks over the last century including HIV, Ebola, Influenza A viruses (IAVs), and SARS-CoV-2. RNA viruses are of particular interest due to their high mutation rates and association with increased likelihood of pandemics. Hepatitis C virus (HCV) and IAVs are two types of RNA viruses with substantial public health implications. HCV is a blood-borne virus that, if left untreated, will lead to chronic infection in ~80% of individuals. Despite curative drug treatment for HCV, some individuals still develop liver disease, even in the absence of virus infection, necessitating research to understand the cause of ongoing liver disease and inflammation. IAVs have been the causative agent of several pandemics in the last century and are constantly a concern for public health agencies. While seasonal human IAVs have been studied extensively in the context of cell
death, IAVs from non-human hosts, such as birds and swine, have not been studied in this context despite their pandemic potential.
The goal of the research for this thesis was a better understanding of pathogenesis, the role of PCD, and the mechanisms by which PCD is induced by RNA virus infections. Chapter 1 introduces these topics. Chapter 2 focuses on our investigation of apoptosis and pyroptosis induction by HCV. We found significant crosstalk between the apoptosis and pyroptosis pathways during HCV infection and showed that PCD was necessary for efficient virus propagation. Chapter 3 traces our investigation of the trigger of HCV-induced pyroptosis in hepatocytes, finding that fully infectious virus production was necessary to trigger pyroptosis in HCV-infected Huh-7.5 cells. We also attempted to identify mechanisms by which bystander pyroptosis could be occurring and investigated the role of immune cells in bystander pyroptosis, finding that THP-1 cells are susceptible to bystander pyroptosis induced by HCV. Chapter 4 outlines our investigation of PCD induced by two IAVs of avian-origin and two of swine-origin. We found that the two avian viruses preferentially induced different forms of PCD and that one of these viruses was unexpectedly able to infect human lung cells while the other abortively infected the lung cells. The two swine viruses both induced all three forms of PCD albeit to different extents.
Given the importance of RNA viruses to public health and the substantial number of disease outbreaks caused by them, understanding how these viruses cause disease is an important academic and globally relevant pursuit. The clearer understanding of the mechanisms of cell
death contributing to viral pathogenesis or innate immune clearance presented here may aid in drug development and help prioritize research on viruses most likely to cause future outbreaks. Overall, the work presented in this thesis adds to the growing body of research that aims to understand the role of PCD during virus infection.Includes bibliographical references (pages 220-268
Exploring barriers in promoting circular economy practices: insights on consumer durable goods in remote and Indigenous communities In Newfoundland and Labrador
No full textConsumer durable goods (CDGs), including appliances and electronics, are a major driver of global waste and create challenges for remote and rural communities—both Indigenous and non- Indigenous—due to limited waste management infrastructure. While urban areas have well- established recycling and disposal systems, rural and Indigenous communities lack these resources, accumulating waste that could otherwise be repurposed or recycled. This issue is exacerbated by restricted access to repair services, high transportation costs, and the widespread impact of planned obsolescence. This research examines opportunities and limitations to the adoption of circular economy (CE) principles for CDGs in two Newfoundland and Labrador (NL) communities: Harbour Main, a remote non-Indigenous community, and Conne River, a Mi'kmaq First Nations community. Using a qualitative research approach, semi-structured interviews were conducted with community members, garbage collectors, and band council members to understand their waste disposal behaviors, repair and reuse practices, and the economic and cultural factors influencing product lifespan decisions. Using grounded theory, themes were developed through the analysis of primary research data using ATLAS.Ti software. The findings revealed three major categories of barriers: logistical, attitudinal, and cultural, resulting in seven key themes. These include the inaccessibility of repair services, the high costs associated with transportation and repairs, the impact of planned obsolescence, the affordability of new goods compared to repairs, and the decline of TEK in
managing waste. Participants also highlighted the need for community-driven initiatives and policy support to improve waste management practices in these regions.
By analyzing broader consumption trends at the community-level impacts, this research identifies if there is an opportunity to integrate Traditional Ecological Knowledge (TEK) with CE strategies, adapting resource efficiency, waste reduction, and environmental sustainability. The findings emphasize the need for targeted infrastructure investments and inclusive policies supporting remote and Indigenous communities adopting CE practices. Addressing these challenges is essential for promoting equitable participation in sustainability efforts and enhancing environmental and economic resilience in rural and remote regions.Includes bibliographical references (pages 102-107
(Re)kindling kinship knowledges: a poetic remembering of place
No full textThis thesis investigates the primary research question: How can heritage folklore and embodied, place-based knowledges enable me to better understand myself in relation to the environment?
By utilising an array of creative and embodied research methods, I, the author, unpack how the
environmental, socio-cultural, political, and personal realms of my existence inform my
worldview. Drawing from fieldwork in L'Anse aux Meadows, The Boreal Poetry Garden, and
Aotearoa, New Zealand, alongside an engagement with the abstract heritage archives of Norse
kennings, rock art, and Land, I use poetry as an interrogative medium. Further, at the heart of this
research is an argument for the legitimacy of engaging creative-practice research within a
folkloric framework while challenging established understandings of what it means to know
something