7103 research outputs found
Sort by
A novel safety analysis method for marine cyber-physical systems
Get PDFCyber-Physical Systems (CPSs) represent a systems category expected to enhance the safety and improve the efficiency of maritime operations. However, new challenges due to the CPSs complexity are also anticipated leading to an unpredictable system behaviour, thus jeopardising safety.This thesis aims at developing a novel safety analysis method and system for enhancing the safety of the marine CPSs considering both their design and operation. Based on a comprehensive literature review, the safety-related properties for CPSs are identified. Then, the different hazard identification methods are analysed on their effectiveness to identify scenarios linked to the CPSs safety related properties.;As the existing literature demonstrates, the existing hazard identification methods such as Fault Tree Analysis (FTA), Failure Modes and Effects Analysis and System-Theoretic Process Analysis (STPA) applications to the CPSs have been criticised for not capturing either the software-intensive character of CPSs or not allowing for quantitative safety analysis.;To address these limitations, a novel Combinatorial Approach for Safety Analysis (CASA) is developed by integrating STPA, Events Sequence Identification (ESI) method and FTA. The method initiates with STPA, then employs ESI using input from STPA to identify the different scenarios and develops a Fault Tree based on ESI results. This Fault Tree is populated with STPA results, further refined, and enriched with the FTA results. The final Fault Tree can be used for estimation of the top-event failure rate and frequency, importance measures estimation and uncertainty analysis.;The novel method is applied for estimating the failure rate and importance measures estimation of two types of marine CPSs: exhaust gas open-loop scrubber system and a reference cruise ship Diesel-Electric Propulsion (DEP). Failure rate for 12 DEP system alternatives blackout is also estimated. The derived results for the scrubber system and DEP system demonstrate that the developed Fault Tree is much richer than for the previous studies. Moreover, it is demonstrated that the increase of the DEP system reliability/availability does not always result in DEP system blackout frequency reduction, as other system parameters have significant influence on blackout.;Based on the CASA method results for the DEP reference system, a novel automated blackout monitoring concept for the DEP system is proposed. This concept is used to estimate the blackout probability variation in time in a virtual environment for the reference DEP system by integrating a number of measured system parameters, historical data and the CASA developed Fault Tree by providing a functional alarm to the crew and allowing better system monitoring and control.The novel CASA method is expected to support the system safety analysis and enhancement during the system design, whilst the proposed blackout monitoring concept is expected to enhance the safety of the DEP system operations.Cyber-Physical Systems (CPSs) represent a systems category expected to enhance the safety and improve the efficiency of maritime operations. However, new challenges due to the CPSs complexity are also anticipated leading to an unpredictable system behaviour, thus jeopardising safety.This thesis aims at developing a novel safety analysis method and system for enhancing the safety of the marine CPSs considering both their design and operation. Based on a comprehensive literature review, the safety-related properties for CPSs are identified. Then, the different hazard identification methods are analysed on their effectiveness to identify scenarios linked to the CPSs safety related properties.;As the existing literature demonstrates, the existing hazard identification methods such as Fault Tree Analysis (FTA), Failure Modes and Effects Analysis and System-Theoretic Process Analysis (STPA) applications to the CPSs have been criticised for not capturing either the software-intensive character of CPSs or not allowing for quantitative safety analysis.;To address these limitations, a novel Combinatorial Approach for Safety Analysis (CASA) is developed by integrating STPA, Events Sequence Identification (ESI) method and FTA. The method initiates with STPA, then employs ESI using input from STPA to identify the different scenarios and develops a Fault Tree based on ESI results. This Fault Tree is populated with STPA results, further refined, and enriched with the FTA results. The final Fault Tree can be used for estimation of the top-event failure rate and frequency, importance measures estimation and uncertainty analysis.;The novel method is applied for estimating the failure rate and importance measures estimation of two types of marine CPSs: exhaust gas open-loop scrubber system and a reference cruise ship Diesel-Electric Propulsion (DEP). Failure rate for 12 DEP system alternatives blackout is also estimated. The derived results for the scrubber system and DEP system demonstrate that the developed Fault Tree is much richer than for the previous studies. Moreover, it is demonstrated that the increase of the DEP system reliability/availability does not always result in DEP system blackout frequency reduction, as other system parameters have significant influence on blackout.;Based on the CASA method results for the DEP reference system, a novel automated blackout monitoring concept for the DEP system is proposed. This concept is used to estimate the blackout probability variation in time in a virtual environment for the reference DEP system by integrating a number of measured system parameters, historical data and the CASA developed Fault Tree by providing a functional alarm to the crew and allowing better system monitoring and control.The novel CASA method is expected to support the system safety analysis and enhancement during the system design, whilst the proposed blackout monitoring concept is expected to enhance the safety of the DEP system operations
Chlorine dioxide impacts on microcystis aeruginosa cell stress, growth and microcystin production
Get PDFCyanobacteria and their toxins severely contaminate water sources and pose a danger to the health of humans and animals. A way to disinfect water is needed to create safe drinking water without creating additional hazards. The use of disinfectants was hypothesised to create cellular stress. Cellular stress was hypothesized to trigger increased production of cyanotoxins.;Here, chlorine dioxide (ClO2) was tested to inhibit a toxin-producing strain of Microcystis aeruginosa. Through a series of experiments, ClO2 was exposed to M. aeruginosa, and the production of microcystin was monitored. The first experiment, batches of M. aeruginosa were exposed to ClO2 (0-10 mg/L) for 30 minites. Chlorophyll a and microcystin concentrations were lowered at higher levels of disinfection, suggesting that ClO2 can treat both M. aeruginosa and its toxins.;The second investigation focused on the effective treatment of ClO2 to viability of cells, stress and microcystin production. M. aeruginosa, during four stages of population growth, were exposed to 0-5 mg/L ClO2 for various contact times (3 hours to 3 days) Population growth was measured by chlorophyll a and optical density. Viability was assayed by MTT (3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide) and electrolyte leakage (conductivity). Cellular stress was measured by malondialdehyde (MDA) lipid peroxidation, catalase (CAT), superoxide dismutase (SOD), and carotenoids (CARs) levels.;Microcystin was quantified by enzyme-linked immunosorbent assay (ELISA). ClO2 affected the viability of cells. In addition, increased electrolyte leakage illustrated the membrane damage that directly related to the deterioration of cells in every growth phase. Early populations (or early blooms) were more responsive to ClO2 inhibition than late-growth populations. In term of stress, ClO2 treatments at 1.5 mg/L induced oxidative stress. SOD activity increased in all three quartiles of population growth. Carotenoids and chlorophyll a became oxidized. The residual carotenoids may react to quench stress induced by ClO2. However, catalase activity increased to scavenge reactive oxygen species.;The experiment suggests that crucial parameters for evaluation should include the viability of cells and metabolic activity. The study suggests relationships between ClO2 treatment and stress as well as microcystins production exist. However, no relationship was observed between stress and microcystins production. Further, low population densities exhibited a greater response to the disinfection, and had the greatest ClO2 related cyanotoxin release. These findings have implications that the timing and dosage of disinfection are very important in treating water with cyanobacteria to avoid the additional release of cyanotoxins and additional risk to human and animal health.Cyanobacteria and their toxins severely contaminate water sources and pose a danger to the health of humans and animals. A way to disinfect water is needed to create safe drinking water without creating additional hazards. The use of disinfectants was hypothesised to create cellular stress. Cellular stress was hypothesized to trigger increased production of cyanotoxins.;Here, chlorine dioxide (ClO2) was tested to inhibit a toxin-producing strain of Microcystis aeruginosa. Through a series of experiments, ClO2 was exposed to M. aeruginosa, and the production of microcystin was monitored. The first experiment, batches of M. aeruginosa were exposed to ClO2 (0-10 mg/L) for 30 minites. Chlorophyll a and microcystin concentrations were lowered at higher levels of disinfection, suggesting that ClO2 can treat both M. aeruginosa and its toxins.;The second investigation focused on the effective treatment of ClO2 to viability of cells, stress and microcystin production. M. aeruginosa, during four stages of population growth, were exposed to 0-5 mg/L ClO2 for various contact times (3 hours to 3 days) Population growth was measured by chlorophyll a and optical density. Viability was assayed by MTT (3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide) and electrolyte leakage (conductivity). Cellular stress was measured by malondialdehyde (MDA) lipid peroxidation, catalase (CAT), superoxide dismutase (SOD), and carotenoids (CARs) levels.;Microcystin was quantified by enzyme-linked immunosorbent assay (ELISA). ClO2 affected the viability of cells. In addition, increased electrolyte leakage illustrated the membrane damage that directly related to the deterioration of cells in every growth phase. Early populations (or early blooms) were more responsive to ClO2 inhibition than late-growth populations. In term of stress, ClO2 treatments at 1.5 mg/L induced oxidative stress. SOD activity increased in all three quartiles of population growth. Carotenoids and chlorophyll a became oxidized. The residual carotenoids may react to quench stress induced by ClO2. However, catalase activity increased to scavenge reactive oxygen species.;The experiment suggests that crucial parameters for evaluation should include the viability of cells and metabolic activity. The study suggests relationships between ClO2 treatment and stress as well as microcystins production exist. However, no relationship was observed between stress and microcystins production. Further, low population densities exhibited a greater response to the disinfection, and had the greatest ClO2 related cyanotoxin release. These findings have implications that the timing and dosage of disinfection are very important in treating water with cyanobacteria to avoid the additional release of cyanotoxins and additional risk to human and animal health
Experimental investigation of the combined effects of corrosion and mean stress on fatigue strength of low carbon steel
Get PDFThis thesis presents an experimental investigation of the combined effects of corrosion and mean stress on the fatigue strength of a low carbon steel.;Corrosion fatigue is a large problem in many engineering fields, which also affects the mining and oil and gas industries, hence the interest from Weir Group. This phenomenon can significantly reduce the fatigue strength of materials by damaging surface structures and accelerating fatigue crack growth. Positive displacement GEHO diaphragm pumps are widely used in mining industries because of the advantage of separating the power end components from the pumped fluid. However, the Fluid End components are operated in harsh environments and are subjected to fatigue and mean stress effects. Understanding the fatigue behaviour of Fluid End components material exposed to freshwater environments and the effects of positive mean stress on the corrosion fatigue strength is of paramount importance to ensure a fatigue life of 10^9 cycles, equals to the design life of GEHO pumps.;The scope of this research work is to evaluate the effect of freshwater environments on the fatigue behaviour of low carbon steel S355J2, the material used to manufacture Fluid End components, and to develop a model to evaluate the effect of mean stresses on the corrosion fatigue strength at elevated fatigue cycles to be applied in the design of GEHO pumps.;This is achieved by developing and performing an experimental programme consisting of corrosion tests on unloaded specimens, uniaxial fatigue tests in air and in the freshwater corrosive environment at different stress ratio conditions. Stress-based experimental results show a decrease in fatigue strength due to the corrosive environment and a continuous decrease of the corrosion fatigue strength with increasing fatigue life. The mean stress effects on corrosion fatigue lives are evaluated by the construction of Haigh diagrams based on experimental results. The sensitivity of S355J2 to mean stress in the corrosive environment is higher at high stress ratios, compared to trends in air. A modified FKM approach is proposed, based on the definition of mean stress sensitivity factor parameters, to predict the allowable stress up to 10^9 fatigue cycles in the freshwater environment.;Industry scale specimens were designed to reproduce critical conditions of Fluid End components of GEHO pumps. Modifications of a test rig available in Weir Minerals Venlo was implemented and two industry scale tests were completed under cyclic pressure loading at different nominal load ratio conditions and results were in good agreement with the proposed predictive model.This thesis presents an experimental investigation of the combined effects of corrosion and mean stress on the fatigue strength of a low carbon steel.;Corrosion fatigue is a large problem in many engineering fields, which also affects the mining and oil and gas industries, hence the interest from Weir Group. This phenomenon can significantly reduce the fatigue strength of materials by damaging surface structures and accelerating fatigue crack growth. Positive displacement GEHO diaphragm pumps are widely used in mining industries because of the advantage of separating the power end components from the pumped fluid. However, the Fluid End components are operated in harsh environments and are subjected to fatigue and mean stress effects. Understanding the fatigue behaviour of Fluid End components material exposed to freshwater environments and the effects of positive mean stress on the corrosion fatigue strength is of paramount importance to ensure a fatigue life of 10^9 cycles, equals to the design life of GEHO pumps.;The scope of this research work is to evaluate the effect of freshwater environments on the fatigue behaviour of low carbon steel S355J2, the material used to manufacture Fluid End components, and to develop a model to evaluate the effect of mean stresses on the corrosion fatigue strength at elevated fatigue cycles to be applied in the design of GEHO pumps.;This is achieved by developing and performing an experimental programme consisting of corrosion tests on unloaded specimens, uniaxial fatigue tests in air and in the freshwater corrosive environment at different stress ratio conditions. Stress-based experimental results show a decrease in fatigue strength due to the corrosive environment and a continuous decrease of the corrosion fatigue strength with increasing fatigue life. The mean stress effects on corrosion fatigue lives are evaluated by the construction of Haigh diagrams based on experimental results. The sensitivity of S355J2 to mean stress in the corrosive environment is higher at high stress ratios, compared to trends in air. A modified FKM approach is proposed, based on the definition of mean stress sensitivity factor parameters, to predict the allowable stress up to 10^9 fatigue cycles in the freshwater environment.;Industry scale specimens were designed to reproduce critical conditions of Fluid End components of GEHO pumps. Modifications of a test rig available in Weir Minerals Venlo was implemented and two industry scale tests were completed under cyclic pressure loading at different nominal load ratio conditions and results were in good agreement with the proposed predictive model
Towards an improved representation of building occupant's thermal interaction, integrating detailed occupant thermal models within building simulation
Get PDFThis thesis is concerned with advancing the modelling of the building occupant thermal interaction in building simulation tools. A detailed multi-segment human thermal model has been implemented within the building simulation tool ESP-rand its integrated computational fluid dynamic CFD module. The improvement of ESP-r's building occupant representation in building simulation has been done in three stages. With the complexity of the integration increasing in each stage. In the first stage, a responsive occupant heat load model has been developed and implemented in ESP-r. In this model, the sensible and latent heat loads are regression equations derived from the literature and are a function of operative temperature and metabolic rate. In the second stage, a two-node thermo-physiology model has been developed and implemented that dynamically simulate with the thermal building model.;This ensures that occupant thermal models are responsive to the prevailing conditions and secondly, improves the resolution modelling of occupants and their environment. In addition, clothing adaptation has been considered by implementing a dynamic clothing algorithm. The third stage involved implementing a multi-segment human thermal model within ESP-r and its integrated CFD module. The integration of all three levels of occupant model has been validated with published experimental data. Moreover, each of the three approaches has been demonstrated using example applications. It is hoped that these fully-integrated models of occupant thermo-physiology help advance the modelling of the indoor environment, occupant thermal comfort and building performance prediction within a whole-building simulation.This thesis is concerned with advancing the modelling of the building occupant thermal interaction in building simulation tools. A detailed multi-segment human thermal model has been implemented within the building simulation tool ESP-rand its integrated computational fluid dynamic CFD module. The improvement of ESP-r's building occupant representation in building simulation has been done in three stages. With the complexity of the integration increasing in each stage. In the first stage, a responsive occupant heat load model has been developed and implemented in ESP-r. In this model, the sensible and latent heat loads are regression equations derived from the literature and are a function of operative temperature and metabolic rate. In the second stage, a two-node thermo-physiology model has been developed and implemented that dynamically simulate with the thermal building model.;This ensures that occupant thermal models are responsive to the prevailing conditions and secondly, improves the resolution modelling of occupants and their environment. In addition, clothing adaptation has been considered by implementing a dynamic clothing algorithm. The third stage involved implementing a multi-segment human thermal model within ESP-r and its integrated CFD module. The integration of all three levels of occupant model has been validated with published experimental data. Moreover, each of the three approaches has been demonstrated using example applications. It is hoped that these fully-integrated models of occupant thermo-physiology help advance the modelling of the indoor environment, occupant thermal comfort and building performance prediction within a whole-building simulation
General video game playing using ensemble decision systems
No full textThis thesis explores the application of Ensemble Decision Systems as an Artificial Intelligence (AI) agent for playing video games on the General Video Game Artificia lIntelligence (GVGAI) platform. The GVGAI offers a platform for research into developing General Video Gameplaying AI agents. Significant progress has been made over the years in the area of game playing AI agents, but it is often a trivial task for designers to propose new problems that the agents are unable to solve. Humans are typically able to solve these additional problems, making them an ideal model for an excellent general video game player and a benchmark for AI agents. One of the objectives of this thesis has been the introduction of Deceptive Games to the GVGAI, which are a class of games that are designed to deliberately deceive AI agents. Ensemble Decision Systems make use of multiple AI algorithms to make their decisions, which may make them more robust to the problems that can deceive singular AIagents.;A wide variety of Ensemble Decision Systems were developed and compared with agents from the GVGAI competition, with the aim of developing an indication of the current level of performance that agents can reach. The Ensemble Decision Systems show improved generality, being able to complete a wider range of games than other agents, at a cost of win rate in specific games. This thesis presents an Ensemble Decision System for GVGP and a suite of Deceptive Games. The Ensemble Decision System detailed in this thesis manages to out perform comparison agents in the Deceptive Games suite, with the top three positions being taken by Ensemble agents for win rate, and a wider range of games.This thesis explores the application of Ensemble Decision Systems as an Artificial Intelligence (AI) agent for playing video games on the General Video Game Artificia lIntelligence (GVGAI) platform. The GVGAI offers a platform for research into developing General Video Gameplaying AI agents. Significant progress has been made over the years in the area of game playing AI agents, but it is often a trivial task for designers to propose new problems that the agents are unable to solve. Humans are typically able to solve these additional problems, making them an ideal model for an excellent general video game player and a benchmark for AI agents. One of the objectives of this thesis has been the introduction of Deceptive Games to the GVGAI, which are a class of games that are designed to deliberately deceive AI agents. Ensemble Decision Systems make use of multiple AI algorithms to make their decisions, which may make them more robust to the problems that can deceive singular AIagents.;A wide variety of Ensemble Decision Systems were developed and compared with agents from the GVGAI competition, with the aim of developing an indication of the current level of performance that agents can reach. The Ensemble Decision Systems show improved generality, being able to complete a wider range of games than other agents, at a cost of win rate in specific games. This thesis presents an Ensemble Decision System for GVGP and a suite of Deceptive Games. The Ensemble Decision System detailed in this thesis manages to out perform comparison agents in the Deceptive Games suite, with the top three positions being taken by Ensemble agents for win rate, and a wider range of games
Development of robust modelling methods for a quick evaluation of biomass pyrolysis performance and its transient behaviour at a particle level
Get PDFBiomass is organic matter that can be used as an energy resource by means of conversion technologies, such as pyrolysis. To assess pyrolysis performance of various biomass materials under different operating conditions and improve the process efficiency of biomass pyrolysis, the development of accurate, fast and robust modelling methods is desirable.;The main objective of this thesis is to develop and assess modelling methods to obtain a deeper insight into the physico-chemical processes affecting a biomass subjected to pyrolysis, more specifically its (1) kinetics, and (2) mass and (3) heat transfer at a particle level.The kinetics of biomass pyrolysis have been studied for biomass in the thermally thin regime, and a novel method to obtain the kinetics parameters has been developed. This method can successfully provide a rapid and accurate estimation of the relative contributions of cellulose, hemicellulose, and lignin to the volatile yield, as well as their kinetic parameters.;The method offers a simple way to obtain the kinetics parameters directly from thermogravimetric data and saves computing time by providing sensible initial values and bounds to the parameters. Wheat straw pellets have been used to study biomass pyrolysis in the thermally thick regime. The heat and mass transfer mechanisms that take place inside the pellet during pyrolysis have been analized using a single particle model.;As a result, the changes inside a biomass pellet can be predicted at any given inner position and time during pyrolysis, and the inner gradients can be observed. It is concluded that the inner temperature distribution of the pellet depends mainly on the properties of the solid phase, but the final product distribution is also linked to the flux of the vapour phase, due to potential additional pyrolysis reactions that could take place if the volatiles generated are not quickly removed from the particle. All the models implemented are validated against experimental data.Biomass is organic matter that can be used as an energy resource by means of conversion technologies, such as pyrolysis. To assess pyrolysis performance of various biomass materials under different operating conditions and improve the process efficiency of biomass pyrolysis, the development of accurate, fast and robust modelling methods is desirable.;The main objective of this thesis is to develop and assess modelling methods to obtain a deeper insight into the physico-chemical processes affecting a biomass subjected to pyrolysis, more specifically its (1) kinetics, and (2) mass and (3) heat transfer at a particle level.The kinetics of biomass pyrolysis have been studied for biomass in the thermally thin regime, and a novel method to obtain the kinetics parameters has been developed. This method can successfully provide a rapid and accurate estimation of the relative contributions of cellulose, hemicellulose, and lignin to the volatile yield, as well as their kinetic parameters.;The method offers a simple way to obtain the kinetics parameters directly from thermogravimetric data and saves computing time by providing sensible initial values and bounds to the parameters. Wheat straw pellets have been used to study biomass pyrolysis in the thermally thick regime. The heat and mass transfer mechanisms that take place inside the pellet during pyrolysis have been analized using a single particle model.;As a result, the changes inside a biomass pellet can be predicted at any given inner position and time during pyrolysis, and the inner gradients can be observed. It is concluded that the inner temperature distribution of the pellet depends mainly on the properties of the solid phase, but the final product distribution is also linked to the flux of the vapour phase, due to potential additional pyrolysis reactions that could take place if the volatiles generated are not quickly removed from the particle. All the models implemented are validated against experimental data
Fault anticipation in distribution networks
Get PDFThis thesis is concerned with the topic of Fault Anticipation in Distribution Networks, focusing on the rapidly changing operational nature of distribution networks and outlining the anticipated data-related challenges that result from these changes, encountered in practice and from related literature. With the challenge of limited data availability in mind, a data analysis methodology for Distribution Network Operators (DNOs) is presented and demonstrated through a number of short and more detailed case studies.;The short case studies are illustrative examples of how the proposed methodology would be used by a DNO. In these, the identification of solar PV operation, phase imbalance and the detection of unusual network operation using dimensionality reduction are examined. The more detailed case studies form the main part of the thesis and focus on the following two areas: (i) Prediction of weather-related faults on minimally observed distribution networks and (ii) Impact of substation loading on the occurrence of power quality disturbances. More specifically, on the topic of weather-related fault prediction, the impact of weather conditions alone on the occurrence distribution network faults is explored, with the case study looking separately into the HV level (mainly 11kV -20kV) and LV level (0.4kV) of the distribution network.;The relationship of power quality events, mainly overcurrent and voltage swell events, with the load behaviour as observed at the LV side of secondary transformers is explored in the second detailed case study.;The contribution of the work presented in this thesis is twofold. First, the fact that distribution networks are currently minimally monitored or access to operational data is restricted for various reasons is acknowledged. This thesis attempts to overcome this challenge by exploring the potential of machine learning techniques to extract valuable information from distribution networks with minimal observation. When required, the available network information is jointly analysed with data coming from different sources that can be easily obtained, such as weather observations.;As mentioned above, this research has mainly focused on two areas which form the basis for the two more detailed case studies presented in this thesis. The weather-related fault prediction case study demonstrated that DNOs can predict the occurrence of weather-related faults in their distribution networks, using only weather observations from a nearby weather station and historic fault records. The other detailed case study which addressed the impact of distribution substation loading on power quality event occurrence identified a relation between representative load profiles and the transitions between them with the occurrence of power quality events.;Both research subjects were selected with a common final goal in mind, which was to utilise machine learning in order to develop a methodology towards the prediction of distribution network disturbances in the absence of extensive monitoring. For the second part of the contribution, the data challenges associated with the changing state of distribution networks are assessed and suggestions to deal with these issues are made. As a result of the work presented in this thesis, an overall data analysis methodology for DNOs is proposed. The main purpose of this methodology is to identify operational or environmental factors that are more likely to lead to the occurrence of certain types of disturbances and establish relations between these factors and fault occurrence, which can then be used to predict these events.;The specific case studies presented in this thesis identify relations between environmental conditions and power system faults as well as substation loading and power quality events. However, the methodology can be applied to different operating conditions and types of faults as well. Being able to establish such relations would be beneficial for DNOs as it would lead to an increased understanding of their network and allow them to act proactively in order to prevent, or minimise the impact of impending events.This thesis is concerned with the topic of Fault Anticipation in Distribution Networks, focusing on the rapidly changing operational nature of distribution networks and outlining the anticipated data-related challenges that result from these changes, encountered in practice and from related literature. With the challenge of limited data availability in mind, a data analysis methodology for Distribution Network Operators (DNOs) is presented and demonstrated through a number of short and more detailed case studies.;The short case studies are illustrative examples of how the proposed methodology would be used by a DNO. In these, the identification of solar PV operation, phase imbalance and the detection of unusual network operation using dimensionality reduction are examined. The more detailed case studies form the main part of the thesis and focus on the following two areas: (i) Prediction of weather-related faults on minimally observed distribution networks and (ii) Impact of substation loading on the occurrence of power quality disturbances. More specifically, on the topic of weather-related fault prediction, the impact of weather conditions alone on the occurrence distribution network faults is explored, with the case study looking separately into the HV level (mainly 11kV -20kV) and LV level (0.4kV) of the distribution network.;The relationship of power quality events, mainly overcurrent and voltage swell events, with the load behaviour as observed at the LV side of secondary transformers is explored in the second detailed case study.;The contribution of the work presented in this thesis is twofold. First, the fact that distribution networks are currently minimally monitored or access to operational data is restricted for various reasons is acknowledged. This thesis attempts to overcome this challenge by exploring the potential of machine learning techniques to extract valuable information from distribution networks with minimal observation. When required, the available network information is jointly analysed with data coming from different sources that can be easily obtained, such as weather observations.;As mentioned above, this research has mainly focused on two areas which form the basis for the two more detailed case studies presented in this thesis. The weather-related fault prediction case study demonstrated that DNOs can predict the occurrence of weather-related faults in their distribution networks, using only weather observations from a nearby weather station and historic fault records. The other detailed case study which addressed the impact of distribution substation loading on power quality event occurrence identified a relation between representative load profiles and the transitions between them with the occurrence of power quality events.;Both research subjects were selected with a common final goal in mind, which was to utilise machine learning in order to develop a methodology towards the prediction of distribution network disturbances in the absence of extensive monitoring. For the second part of the contribution, the data challenges associated with the changing state of distribution networks are assessed and suggestions to deal with these issues are made. As a result of the work presented in this thesis, an overall data analysis methodology for DNOs is proposed. The main purpose of this methodology is to identify operational or environmental factors that are more likely to lead to the occurrence of certain types of disturbances and establish relations between these factors and fault occurrence, which can then be used to predict these events.;The specific case studies presented in this thesis identify relations between environmental conditions and power system faults as well as substation loading and power quality events. However, the methodology can be applied to different operating conditions and types of faults as well. Being able to establish such relations would be beneficial for DNOs as it would lead to an increased understanding of their network and allow them to act proactively in order to prevent, or minimise the impact of impending events
'Doctor's orders', type 1 diabetes and the consultative relationship, 1948-2002
Get PDFSince 1922, insulin has saved the lives of countless individuals diagnosed with what we now know as Type 1 Diabetes. It is, however, not a cure. Insulin therapy is a lifelong commitment that involves regular self-medication. This treatment can be complex, as dose titration and timing must be balanced with carbohydrate intake to prevent dangerous long-and short-term complications. In Britain, for much of the twentieth century, the medical profession sought to achieve this via the imposition of carefully prescribed, and usually highly restrictive, treatment regimens that precisely outlined a timetable of diet and insulin, deviation from which was strongly discouraged. By the mid-2000s, however, orthodox management tended to eschew such an approach, encouraging a more autonomous framework in which the individual was taught to determine personal therapeutic requirements according to their own diet and lifestyle, while healthcare professionals were reconceptualised as remote sources of support should advice or assistance be required. This thesis analyses the process by which this transition occurred, arguing that from the late 1970s a confluence of factors both within and without diabetology provided the practical, scientific, and political rationale for the cautious enlistment of the patient as a medical auxiliary, and that, moreover, due to the material conditions of insulin therapy, this development inadvertently rendered laypeople a distinct political and moral force in their own right, able not only to exert influence over the framework of care but also over the construction of value within it, and in doing so often directly challenged the fundamental assumptions of professional practice. Twenty-first century 'patient-led' approaches to care, it concludes, reflect an imperfect compromise that attempts, but often fails, to reconcile orthodox medical power structures to an increasingly alienated patient-body with which it often has profound ideological differences, and upon which it struggles to impose its traditional authority.Since 1922, insulin has saved the lives of countless individuals diagnosed with what we now know as Type 1 Diabetes. It is, however, not a cure. Insulin therapy is a lifelong commitment that involves regular self-medication. This treatment can be complex, as dose titration and timing must be balanced with carbohydrate intake to prevent dangerous long-and short-term complications. In Britain, for much of the twentieth century, the medical profession sought to achieve this via the imposition of carefully prescribed, and usually highly restrictive, treatment regimens that precisely outlined a timetable of diet and insulin, deviation from which was strongly discouraged. By the mid-2000s, however, orthodox management tended to eschew such an approach, encouraging a more autonomous framework in which the individual was taught to determine personal therapeutic requirements according to their own diet and lifestyle, while healthcare professionals were reconceptualised as remote sources of support should advice or assistance be required. This thesis analyses the process by which this transition occurred, arguing that from the late 1970s a confluence of factors both within and without diabetology provided the practical, scientific, and political rationale for the cautious enlistment of the patient as a medical auxiliary, and that, moreover, due to the material conditions of insulin therapy, this development inadvertently rendered laypeople a distinct political and moral force in their own right, able not only to exert influence over the framework of care but also over the construction of value within it, and in doing so often directly challenged the fundamental assumptions of professional practice. Twenty-first century 'patient-led' approaches to care, it concludes, reflect an imperfect compromise that attempts, but often fails, to reconcile orthodox medical power structures to an increasingly alienated patient-body with which it often has profound ideological differences, and upon which it struggles to impose its traditional authority
The development of microfluidic assays for functional neural network communication studies & CNS drug discovery
Get PDFThe study of neuroscience and the research tools used to perform this research is fundamental to the understanding of the human brain under physiological and pathophysiological conditions. Indeed, the prevalence of CNS disorders in the global population has sharply increased over the last two decades, and new techniques are required to dissect the complexities of the underlying mechanisms.;Whilst in vivo research presents the closest means of replicating human CNS disorders, there are significant translational challenges in replicating these diseases using animal models. Breaking down the complexities present within the brain to assess single cellular mechanisms sequentially may be facilitated by in vitro methodologies, which provide invaluable information on neural network function under controlled conditions.;A surge in microfluidic technology over the last 15 years has enabled considerable advances in the development of new in vitro research tools for neuroscientific research, offering greater control over experimental conditions including neural network patterning and fluid handling. Microfluidic devices are often transparent and thus can be readily interfaced with microscopy for optical imaging assays following chemical stimulation of neuronal cultures.;This thesis explores novel avenues for microfluidic assay development using dual chamber microfluidic devices containing environmentally isolated, but synaptically connected neural networks. First, voltage imaging assays are considered as an alternative to Ca2+ imaging assays to improve upon the temporal resolutions of standard optical imaging, whilst maintaining a higher data throughput when compared to electrophysiological whole cell patch clamp recordings. Then, the need for manual drug applications are resolved by means of developing a microfluidic perfusion system for early CNS drug discovery.;Finally, chemogenetic assays are employed in combination with Ca2+ imaging and microfluidic perfusion to selectively stimulate a sub-population of transfected neurons whilst monitoring the subsequent cascade of activity in the surrounding neural network. In conclusion, the microfluidic assays developed can be used for studying neurophysiological mechanisms of synaptic communication, are capable of screening CNS acting drugs, and lay the groundwork for alternative methods to manipulate the activity of neural networks.The study of neuroscience and the research tools used to perform this research is fundamental to the understanding of the human brain under physiological and pathophysiological conditions. Indeed, the prevalence of CNS disorders in the global population has sharply increased over the last two decades, and new techniques are required to dissect the complexities of the underlying mechanisms.;Whilst in vivo research presents the closest means of replicating human CNS disorders, there are significant translational challenges in replicating these diseases using animal models. Breaking down the complexities present within the brain to assess single cellular mechanisms sequentially may be facilitated by in vitro methodologies, which provide invaluable information on neural network function under controlled conditions.;A surge in microfluidic technology over the last 15 years has enabled considerable advances in the development of new in vitro research tools for neuroscientific research, offering greater control over experimental conditions including neural network patterning and fluid handling. Microfluidic devices are often transparent and thus can be readily interfaced with microscopy for optical imaging assays following chemical stimulation of neuronal cultures.;This thesis explores novel avenues for microfluidic assay development using dual chamber microfluidic devices containing environmentally isolated, but synaptically connected neural networks. First, voltage imaging assays are considered as an alternative to Ca2+ imaging assays to improve upon the temporal resolutions of standard optical imaging, whilst maintaining a higher data throughput when compared to electrophysiological whole cell patch clamp recordings. Then, the need for manual drug applications are resolved by means of developing a microfluidic perfusion system for early CNS drug discovery.;Finally, chemogenetic assays are employed in combination with Ca2+ imaging and microfluidic perfusion to selectively stimulate a sub-population of transfected neurons whilst monitoring the subsequent cascade of activity in the surrounding neural network. In conclusion, the microfluidic assays developed can be used for studying neurophysiological mechanisms of synaptic communication, are capable of screening CNS acting drugs, and lay the groundwork for alternative methods to manipulate the activity of neural networks
Changes to system inertia and the impact on frequency response requirements
Get PDFPreviously held under moratorium from 23 June 2020 until 23 June 2022The changing power landscape and associated reduction of inertia in the power system,introduces concerns about the assurance of frequency stability and the adequacy ofdynamic frequency responses at low inertia. In islanded power systems, like those ofIreland and Great Britain (GB), understanding the issues posed and deploying effectivesolutions benefit from an investigation concerning the changing demand for frequencycontainment reserve and containment limits following a credible loss risk.This thesis reviews frequency management in GB in the context of the changingenergy landscape towards a lower inertia power system, identifying steps already takenby National Grid the GB Electricity System Operator (ESO) to address the issue,towards managing system frequency in a future lower inertia GB power system,without increasing the risk of system instability. A model and tools are developed tofacilitate the studies presented in this thesis, and it is shown that methods can beemployed to understand and define the factors influencing frequency behaviour, whichcan facilitate improved management of frequency and loss risk containment. Inaddition, an exchange rate method is proposed to convert the amount of reserve heldbetween different frequency containment services, allowing one service to becompared and equated to another. In particular, a relationship is presented forconverting response reserves from Primary to Enhanced response as they are definedin GB.This work provides insight into the need and provision of future frequency responseservices in GB. It is shown that at low-demand and low-inertia existing dynamicfrequency containment services alone are insufficient to manage a credible loss risk,highlighting the changing need for dynamic frequency containment reserve and theneed for, and value of, faster dynamic frequency response services. In addition, it isestimated that in GB the demand for Primary response will exceed Secondary responsefor at least 41% of the year by 2025/26, compared to at least 21% in 2016/17,reinforcing the growing need for additional frequency containment to supplementexisting services. In GB, at present, there exists no dynamic restoration only product,as the services are bundled and the plants that deliver dynamic frequency containmentalso deliver dynamic frequency restoration as an extension of dynamic frequencycontainment, based on the operation of thermal plants. These services are procured asa bundle with demand for dynamic frequency restoration driving tenders in thecommercial frequency response market. In order to meet the increasing demand forcontainment reserve, new frequency containment services are required, and theseshould be unbundled from frequency restoration services. A concept for a suitableframework of frequency containment services is presented that shows that deployingsupplementary reserves as unbundled service manages frequency stability aseffectively as the bundled services, while the inclusion of a rate of change of frequencymanagement service improves performance at extremely low-inertia. In addition, tofacilitate improved market participation and the competitive provision of containmentservices, it is argued that a shift in gate closure for the procurement of frequencycontainment services from month-ahead to day-ahead or even closer to real-time isrequired.The changing power landscape and associated reduction of inertia in the power system,introduces concerns about the assurance of frequency stability and the adequacy ofdynamic frequency responses at low inertia. In islanded power systems, like those ofIreland and Great Britain (GB), understanding the issues posed and deploying effectivesolutions benefit from an investigation concerning the changing demand for frequencycontainment reserve and containment limits following a credible loss risk.This thesis reviews frequency management in GB in the context of the changingenergy landscape towards a lower inertia power system, identifying steps already takenby National Grid the GB Electricity System Operator (ESO) to address the issue,towards managing system frequency in a future lower inertia GB power system,without increasing the risk of system instability. A model and tools are developed tofacilitate the studies presented in this thesis, and it is shown that methods can beemployed to understand and define the factors influencing frequency behaviour, whichcan facilitate improved management of frequency and loss risk containment. Inaddition, an exchange rate method is proposed to convert the amount of reserve heldbetween different frequency containment services, allowing one service to becompared and equated to another. In particular, a relationship is presented forconverting response reserves from Primary to Enhanced response as they are definedin GB.This work provides insight into the need and provision of future frequency responseservices in GB. It is shown that at low-demand and low-inertia existing dynamicfrequency containment services alone are insufficient to manage a credible loss risk,highlighting the changing need for dynamic frequency containment reserve and theneed for, and value of, faster dynamic frequency response services. In addition, it isestimated that in GB the demand for Primary response will exceed Secondary responsefor at least 41% of the year by 2025/26, compared to at least 21% in 2016/17,reinforcing the growing need for additional frequency containment to supplementexisting services. In GB, at present, there exists no dynamic restoration only product,as the services are bundled and the plants that deliver dynamic frequency containmentalso deliver dynamic frequency restoration as an extension of dynamic frequencycontainment, based on the operation of thermal plants. These services are procured asa bundle with demand for dynamic frequency restoration driving tenders in thecommercial frequency response market. In order to meet the increasing demand forcontainment reserve, new frequency containment services are required, and theseshould be unbundled from frequency restoration services. A concept for a suitableframework of frequency containment services is presented that shows that deployingsupplementary reserves as unbundled service manages frequency stability aseffectively as the bundled services, while the inclusion of a rate of change of frequencymanagement service improves performance at extremely low-inertia. In addition, tofacilitate improved market participation and the competitive provision of containmentservices, it is argued that a shift in gate closure for the procurement of frequencycontainment services from month-ahead to day-ahead or even closer to real-time isrequired
Access Repository Dashboard
Do you manage STAX (Strathclyde Repository)? Access insider analytics, issue reports and manage access to outputs from your repository in the CORE Repository Dashboard!