Implementation of self-tuning control for turbine generators

PhD ThesisThis thesis documents the work that has been done towards the development of a 'practical' self-tuning controller for turbine generator plant. It has been shown by simulation studies and practical investigations using a micro-alternator system that a significant enhancement in the overall performance in terms of control and stability can be achieved by improving the primary controls of a turbine generator using self-tuning control. The self-tuning AVR is based on the Generalised Predictive Control strategy. The design of the controller has been done using standard off-the-shelf microprocessor hardware and structured software design techniques. The proposed design is thus flexible, cost-effective, and readily applicable to 'real' generating plant. Several practical issues have been tackled during the design of the self-tuning controller and techniques to improve the robustness of the measurement system, controller, and parameter estimator have been proposed and evaluated. A simple and robust measurement system for plant variables based on software techniques has been developed and its suitability for use in the self-tuning controller has been practically verified. The convergence, adaptability, and robustness aspects of the parameter estimator have been evaluated and shown to be suitable for long-term operation in 'real' self-tuning controllers. The self-tuning AVR has been extensively evaluated under normal and fault conditions of the turbine generator. It has been shown that this new controller is superior in performance when compared with a conventional lag-lead type of fixed-parameter digital AVR. The use of electrical power as a supplementary feedback signal in the new AVR is shown to further improve the dynamic stability of the system. The self-tuning AVR has been extended to a multivariable integrated self-tuning controller which combines the AVR and EHG functions. The flexibility of the new AVR to enable its expansion for more complex control applications has thus been demonstrated. Simple techniques to incorporate constraints on control inputs without upsetting the loop decoupling property of the multivariable controller have been proposed and evaluated. It is shown that a further improvement in control performance and stability can be achieved by the integrated controller.Parsons Turbine Generators Ltd

A review of instrumental variable estimators for Mendelian randomization.

Instrumental variable analysis is an approach for obtaining causal inferences on the effect of an exposure (risk factor) on an outcome from observational data. It has gained in popularity over the past decade with the use of genetic variants as instrumental variables, known as Mendelian randomization. An instrumental variable is associated with the exposure, but not associated with any confounder of the exposure-outcome association, nor is there any causal pathway from the instrumental variable to the outcome other than via the exposure. Under the assumption that a single instrumental variable or a set of instrumental variables for the exposure is available, the causal effect of the exposure on the outcome can be estimated. There are several methods available for instrumental variable estimation; we consider the ratio method, two-stage methods, likelihood-based methods, and semi-parametric methods. Techniques for obtaining statistical inferences and confidence intervals are presented. The statistical properties of estimates from these methods are compared, and practical advice is given about choosing a suitable analysis method. In particular, bias and coverage properties of estimators are considered, especially with weak instruments. Settings particularly relevant to Mendelian randomization are prioritized in the paper, notably the scenario of a continuous exposure and a continuous or binary outcome.Stephen Burgess is supported by the Wellcome Trust (grant number 100114). Dylan Small was supported by a grant from the US National Science Foundation Measurement, Methodology and Statistics program. Simon G. Thompson is supported by the British Heart Foundation (grant number CH/12/2/29428).This is the final version of the article. It was first available from SAGE via http://dx.doi.org/10.1177/096228021559757

Adjusting the effect of integrating antiretroviral therapy and tuberculosis treatment on mortality for non-compliance : an instrumental variables analysis using a time-varying exposure.

Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg.In South Africa and elsewhere, research has shown that the integration of antiretroviral therapy (ART) and tuberculosis (TB) treatment saves lives. The randomised controlled trials (RCTs) which provided this compelling evidence used intent-to-treat (ITT) strategy as part of their primary analysis. As much as ITT is protected against selection bias caused by both measured and unmeasured confounders, but it is capable of drawing results towards the null and underestimate the e ectiveness of treatment if there is too much non-compliance. To adjust for non-compliance, \as-treated"and \per-protocol"comparisons are commonly made. These contrast study participants according to their received treatment, regardless of the treatment arm to which they were assigned, or limit the analysis to participants who followed the protocol. Such analyses are generally biased because the subgroups which they compare often lack comparability. In view of the shortcomings of the \as-treated"and \per-protocol"analyses, our objective was to account for non-compliance by using instrumental variables (IV) analysis to estimate the e ect of ART initiation during TB treatment on mortality. Furthermore, to capture the full complexity of compliance behaviour outside the TB treatment duration, we developed a novel IV-methodology for a time-varying measure of compliance to ART. This is an important contribution to the IV literature since IV-methodology for the e ect of a time-varying exposure on a time-to-event endpoint is currently lacking. In RCTs, IV analysis enable us to make use of the comparability o ered by randomisation and thereby have the capability of adjusting for unmeasured and measured confounders; they have the further advantage of yielding results that are less sensitive to random measurement error in the exposure. In order to carry out IV analysis, one needs to identify a variable called an instrument, which needs to satisfy three important assumptions. To apply the IV methodology, we used data from Starting Antiretroviral Therapy at Three Points in Tuberculosis (SAPiT) trial which was conducted by the Centre for the AIDS Programme of Research in South Africa. This trial enrolled HIV and TB co-infected patients who were assigned to start ART either early or late during TB treatment or after TB treatment completion. The results from IV analysis demonstrate that survival bene t of fully integrating TB treatment and ART is even higher than what has been reported in the ITT analysis since non-compliance has been accounted for

Patterns, Influences and Genetic Underpinnings of the Development of ADHD

Background Attention-Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder characterised by age-inappropriate, disruptive and pervasive manifestations of inattention and/or hyperactivity/impulsivity. ADHD symptoms typically emerge in childhood and persist into later stages of life. ADHD also frequently co-occurs with a number of psychiatric disorders and medical conditions, thereby bringing a tremendous burden to affected individuals as well as society. In addition to symptom severity and chronicity, the development of ADHD also plays a determinant role in disease outcomes. However, few studies have systematically investigated different predictive factors and underlying aetiologies associated with the development of ADHD. Aims This thesis aims to examine patterns, influences and genetic underpinnings of the development of ADHD from childhood to adolescence. The first study investigates childhood factors that differentiate late-onset ADHD from childhood-onset ADHD and differences in adolescent outcomes. The second study examines genetic and environmental contributions underlying the effects of the development of inattention on academic performance. The third and the fourth studies investigate the developmental relationships between ADHD and BMI through triangulation of evidence from longitudinal statistical analyses and genetically informed causal inference approaches. Methods All of the studies adopt a development-sensitive design using data from the “Twin Early Development Study” (TEDS), a longitudinal cohort in the UK. A pluralistic statistical approach is employed for different study objectives. To strengthen causal inference, this thesis compares and contrasts findings from longitudinal statistical approaches and different genetically informed methods under a triangulation framework. Results Findings of this thesis suggest that 1) late-onset ADHD is more likely to be found in males and children who exhibit increased conduct problems and experience more childhood family adversity. Moreover, low socioeconomic status specifically predicts de novo late-onset ADHD, while additional factors predict subthreshold late-onset ADHD; 2) both the baseline level and the developmental course of inattention influence academic performance. Genetic contributions to the development of inattention also affect academic performance; 3) longitudinal statistical analyses identify unidirectional effects from ADHD symptoms to subsequent BMI, while genetic methods suggest a bidirectional causal relationship. Triangulation of evidence shows that multiple sources of confounding are involved in the relationships between ADHD and BMI, including unmeasured confounding and dynastic effects. Conclusions This thesis identifies specific childhood risk factors and genetic underpinnings associated with different developmental patterns of ADHD. Influences of the developmental course of ADHD on psychological and functional outcomes can be attributable to direct causal relationships, genetic and environmental confounding, or a combination of both. Altogether, these findings contribute to a more complete and systematic understanding of different developmental aspects of ADHD. To disentangle aetiological pathways between the development of ADHD and associated conditions, a pluralistic statistical approach to triangulate evidence regarding causal mechanisms is necessary

Using physician’s prescribing preference as an instrumental variable in comparative effectiveness research

Background: Comparative effectiveness research (CER) studies using non-randomised study designs sometimes employ instrumental variables (IVs) to address the problem of unmeasured confounding. Physician’s prescribing preference (PPP) is a commonly used IV in this context and had been shown to have utility in many CERs. However, these IVs are generally used as a supplementary method rather than the main analytical strategy. In this thesis, I aim to test the validity of PPP IVs, including an evaluation of the different ways they can be constructed to help promote their more widespread use in CER. Methods: This thesis consists of a range of underpinning methodological approaches, including a literature review summarising applied and simulation studies between 2005 and 2020 that use PPP IV in CER; applied CERs using PPP IV in studies utilising routinely-collected health datasets; target trial emulation approaches based on benchmarking from a randomised clinical trial; and simulation studies to test the performance of PPP IV in multiple CER settings. Results: My literature review provides guidance on the further use of physician’s prescribing preference as instrumental variables in comparative effectiveness research. It highlighted that practical use of PPP needs to consider the findings from simulation studies in the area. In my empirical chapters, I provide strong evidence that PPP is a valid IV approach for conducting CERs using non-randomised study designs. I found that constructing PPP using longer prescription histories generally produces stronger instruments, which in turn leads to greater precision in estimation of treatment effects. In practice, validation of assumptions is crucial for the utility of IVs in CER. In my applied research, I found strong real-world evidence that supports diazepam is associated with lower risk of rehospitalisation and mortality due to the alcohol intoxication and harmful than chlordiazepoxide; that disulfiram is superior to acamprosate in terms of preventing alcohol dependence-related hospitalisations; and that sulfonylureas (SU) performs better than dipeptidyl peptidase-4 inhibitor (DPP-4 inhibitor) in reducing HbA1c levels as the second-line treatment for Type-2 diabetes patients. In my simulation studies, I found PPP IV, when unmeasured confounding exists, can produce less biased estimates of treatment effects than conventional multivariable regressions that only adjust for measured confounding variables, albeit with lower statistical power. The simulations also show PPP IV has potential in alleviating noncollapsibility in non-linear IV approaches. Implications: Findings from this thesis indicate that PPP IVs can be valid IVs and reduce unmeasured confounding in observational CER studies. However, I have found that there is room for improvement in the application of PPP IV in CER studies; researchers need to pay more attention on validating IV assumptions and carefully consider how different formulations of PPP IVs can be applied in order to improve the quality of statistical inference. Future applied PPP IV research should consider findings from relevant simulation studies to inform study designs and analysis plans. Conversely, one also needs information on PPP IVs from empirical studies to inform future simulation study design and to gain further knowledge from triangulation between applied and simulation findings. Many of my thesis findings can be generalised to the use of non-PPP IV approaches in CER